Insights – Gemini Data

NFL Kickoff Is Here – Let’s Dive Into the Data!

Jenn Snider — Fri, 25 Aug 2023 18:16:14 +0000

Do You Have Your Fantasy League Draft Positions?

Football season is just around the corner, meaning that the popular fantasy leagues are starting up again. Every year, over 10 million players from all over the world participate in fantasy football leagues. Many sports analysts provide their projections on each player’s performance for the year to help others prepare for their upcoming draft. A good draft is the first and most crucial step in claiming the crown for your fantasy leagues.

Why is it hard to succeed in fantasy football leagues?

Relevant data on fantasy football players are stored in different data sources. During the drafting process, participants are only provided with a grand summary of statistics and projections. Participators who go the extra mile by reviewing data sources such as Depth Charts and Injury History may give themselves a competitive advantage. However, this can be a very time-consuming process having to study through multiple spreadsheets.

Typical leagues only give an hour’s notice on position on draft. This makes it difficult to strategize a league-winning team. For online drafts, users have under a minute to decide the best-fitting player for their team. For offline drafts, your friends and colleagues will pressure you to make your selection as fast as possible to minimize the time to their next pick.

Team Schedules/Matchups dictate the game flow and amount of touches for players to accumulate stats. For example, running backs on weaker teams will be less likely to reach double-digit attempts per game if their team is always falling behind early in the game. Younger quarterbacks may also struggle to throw to their star receivers since they lack the experience of facing a professional level of defense.

Trying to find patterns, connections, trends, and outliers in data? Sounds like this would be perfect for Gemini Explore.

How would you like to improve your chances of winning fantasy football?

Using Gemini Explore, we were able to merge and aggregate several relevant data sources into one “canvas” for visualization, exploration, and analysis.

This produced a number of node categories, such as Players from each Position with backups and reserves, Injuries, and Teams. This all came from a total of 16 different data sources.

Selecting any node will provide more specific details, as shown in the profiler panel. Notice for Patrick Mahomes, we can see Durability rankings [1-5], Favorable Schedule [1-5], projected points, and attempts per game that would all be useful to know before drafting.

Searching is easy. Filter on a number of player attributes to narrow down the key players that will potentially be available within your draft turn.

The user-friendly search makes it a breeze to sift through player attributes, helping you pinpoint potential star players right for your next pick.

For a taste, consider the first round’s top contenders: Justin Jefferson, Christian McCaffrey, and Ja’Marr Chase. Our platform reveals Jefferson’s injury risk juxtaposed against his schedule difficulty, compared with the other two. Interesting find? While McCaffrey seems to have a season advantage, he’s also more injury-prone.

Here is Christian McCaffrey:

And Ja’Marr Chase:

Select any of the injuries to open the profiler panel with further details.

Filter by time to see the latest injuries of recent seasons.

Do you have a favorite team?

Start your draft preparation by selecting and isolating your favorite team:

Then by isolating, drill down on your favorite team to view players and the attached ‘projected round’ – shown in green nodes with numbers – that they are predicted to be drafted in.

Add player’s bye weeks – shown by pink nodes near the top – to avoid selecting too many players that will not be playing the same week. The goal is to set yourself up for success every week to give you a fighting chance.

As the NFL Draft Day dawns, arm yourself with the comprehensive insights of Gemini Explore. Dive beyond the surface, understand the connections and relationships, and draft your way to fantasy football dominance.

Happy drafting!

Data sources used: FantasyPros, FantasySharks, ESPN, Yahoo! Sports, and CBS Sports

8 ½ Myths About Generative AI

Jenn Snider — Mon, 21 Aug 2023 10:00:07 +0000

Generative AI and related technologies are in the news more than ever. While these innovations hold immense promise, addressing the prevalent misconceptions and myths that often surround them is essential. Let’s look at some of the top misinterpretations surrounding Generative AI and shed light on the realities shaping this cutting-edge field.

There are several common misconceptions and myths about generative AI that deserve attention:

Generative AI will replace human jobs: While it’s true that generative AI has the potential to automate some tasks across many industries (ask Hollywood right now), it’s unlikely to replace human jobs entirely. Rather, in most cases, generative AI is expected to augment human capabilities, enabling us to accomplish previously impossible or impractical tasks – or simply get more done.
The bigger the AI model, the better: As the titans of tech duke it out to be the LLM to rule them all, you’ll hear a lot of bragging about the size of a systems training data or the many bazillion parameters it uses in its models. And that matters, but it’s not the sole determinant of a model’s performance. The quality of the training data and the training approach employed are equally – if not more – important.
Generative AI models always generate accurate content or “hallucinate”: Language Learning Models (LLMs) are trained on massive text and code datasets, and they sometimes generate text that certainly sounds authoritative and correct but is factually wrong or nonsensical. This highlights the reality that these models are imperfect and can make mistakes – and we need to keep re-reminding ourselves of that.
Generative AI will enable plagiarism and ruin education: Generative AI can indeed generate text and other content similar to human-created work, raising concerns about plagiarism and cheating. However, generative AI can also be used positively in education, such as providing customized content and learning experiences personalized to the ways a student best learns.
Generative AI is a black box: The process of training these models can be complex and opaque (and biased), and so can their results. Right now, there’s a lot of shrugging and saying, “Well, I mean, who really knows?” As more businesses use these technologies, we see a strong call for techniques to make generative AI models more transparent, providing explanations for their outputs.
Generative AI is dangerous: Some fear that generative AI could be used to create fake news or generate harmful content. However, like any tool, generative AI can be used positively and negatively. Its use should be regulated responsibly to ensure it serves constructive purposes. We are already seeing companies and countries start to work this out in the public sphere.
Generative AI can think and create like humans: They generate output based on learned patterns from training data and do not have “thoughts” or “creativity” in the same way that humans do. Whether this approaches the flash of insight or virtuosity of humans remains to be seen.
Generative AI can learn and improve on its own: AI models don’t continue to learn or improve after their training phase without additional data or retraining. Fine-tuning and re-training with new training data is a key part of improving and developing any machine learning system.

And for a halfway myth, let’s look at a switcheroo we see in a lot of tech companies and tech press:

8 ½ General AI is the same as Generative AI. General AI means a system that has the same cognitive functions as a human and can learn just like we do. Think of it as an all-purpose robot that can learn and do any task, sort of like the Terminator. Compare that to a robot built for a particular task in a particular place. General AI is one of those holy grails that most tech companies in the space are working on. But it is not the same as advancements in generative AI, which are still milestones in machine learning – but not the promised land we’ve been promised.

Understanding these myths allows us to appreciate the potential and challenges of generative AI. Despite its powerful capabilities, generative AI is not a magical solution to all problems and must be utilized responsibly, bearing in mind its limitations.

In the realm of Generative AI, separating fact from fiction is crucial for harnessing its potential responsibility. The journey through these misconceptions reveals a more nuanced perspective. While Generative AI has the potential to redefine industries and push the boundaries of creativity, it’s not a silver bullet that replaces human ingenuity or foresight. By acknowledging its limitations and potential pitfalls, we pave the way for a more informed and ethical integration of Generative AI into our lives. As technology continues to evolve, a balanced understanding of its capabilities and constraints empowers us to harness its transformative power while safeguarding against its potential misuse.

Graph Databases: No Code vs Low Code vs Code

Jenn Snider — Wed, 09 Aug 2023 12:07:47 +0000

Gemini Data’s resident data scientist and bioinformatician Sixing Huang recreates the same graph data use case in various architectures with varying levels of technical skill for end users.

"Knowledge graphs are gaining traction quickly. They store information like a human, that is, via subject-verb-object triples. We can transform many existing data into knowledge graphs and then learn a lot by exploring, searching, and analyzing them."

Huang then walks through the major steps in preparing the data to be transformed into a series of subject-verb-object triples in CSV that gets imported into Neo4j. He also imports this data into both Neo4j Desktop and AuraDB and compares and contrasts ease of use and computing power.

Read Sixing Huang’s full tutorial on Medium.

Creating Context-Aware Chatbots with ChatGPT, Knowledge Graphs, Neo4j, and Gemini Explore

Jenn Snider — Thu, 27 Jul 2023 12:31:30 +0000

Gemini Data’s resident data scientist and bioinformatician Sixing Huang dives into creating a dictionary chatbot using ChatGPT, Neo4j, and Gemini Explore:

"In the Japanese language, onomatopoeic words, including “giongo (擬音語)”, “giseigo (擬声語)” and “gitaigo (擬態語),” are unique expressions that vividly depict sounds, actions, and feelings. These words are abundant in Japanese culture and are used in various contexts, including literature, manga, anime, and everyday conversations. But onomatopoeic words are hard for foreigners to learn. You cannot deduce their meanings from the spellings most of the time. For example, the word コツコツ (kotsukotsu) means “laboriously, steadily”, while its look-alike ゴツゴツ (gotsugotsu) means “gnarled, rugged”. And the word ゴホゴホ (gohogoho) represents hacking cough, even though its pronunciation does not sound like coughing at all. It takes time, examples, and lots of practice to internalize even the basic ones. And there are 1,190 of them in the JapanDict."

Huang then outlines how to create a chatbot to help him master the onomatopoeic words in the Japanese language using Neo4j, AuraDB, Gemini Explore, and OpenAI.

Read Sixing Huang’s full tutorial on Medium.

The Key to Fixing Broken Supply Chains

Jenn Snider — Wed, 26 Jul 2023 17:22:31 +0000

Transparency in supply chain processes is more crucial than ever in identifying and addressing risks.

In this TED Talk, Dustin Burke from Boston Consulting Group, highlights the challenges of supply chains and the importance of making them more resilient to crises, such as natural disasters, pandemics, and relatively unexpected shocks. Burke discusses three key ideas that could help make supply chains more resilient: sharing risk, radical transparency, and automated recommendations.

Burke notes that companies need to know not only who their suppliers are but also who their suppliers’ suppliers are, where they get their raw materials, and who else is buying from them. He also argues that better data should lead to better decision-making and that advances in data mining, artificial intelligence, and machine learning can help predict problems before they arise, notify managers, and recommend actions to take.

Burke concludes by urging businesses and governments to be more imaginative in how they use these ideas to make supply chains more resilient.

Binge-watch more of our favorite TED Talks. https://www.geminidata.com/ted-talks/

Clinical Trials as Graphs and Vectors

Jenn Snider — Mon, 24 Jul 2023 14:23:59 +0000

Gemini Data’s resident data scientist and bioinformatician Sixing Huang creates a search engine of clinical trials data with Neo4j, Gemini Explore, and Qdrant.

“As a demonstration, 24 Japanese clinical studies were downloaded from the site. In addition, the English SNOMED CT provides me with the taxonomies for medical conditions and body structures. On the one hand, I used Apache Hop to import the data into the graph database Neo4j. On the other hand, the trial descriptions were embedded and then inserted into the vector database Qdrant. Users can not only search trials semantically on Qdrant, but also learn their details, relationships, and statistics on Neo4j and Gemini Explore. This two-database setup allows users to quickly answer the three example questions above.”

By improving the searchability of clinical trials data providers can ask complex questions of their data sets like, “What trials are targeting leukemia?” and “What trials compare the drugs iberdomide and lenalidomid?”

Read the full walkthrough on Medium.

Infographic: Visualizing Copper’s Global Supply Chain

Jenn Snider — Tue, 18 Jul 2023 10:08:00 +0000

This informative infographic explores copper deposits worldwide and their significance in the global supply chain. It highlights two primary types of copper deposits: porphyry copper deposits and sediment-hosted copper deposits.

The infographic also sheds light on the trade flows of copper, with Chile being a major source for both mined and refined copper, while China dominates in refined copper import and export. It delves into the inherent risks associated with concentrated copper supply, such as declining grades and the tectonic risk faced by mining operations in Chile and Peru. The importance of new copper discoveries is emphasized to meet the growing demand and prevent a potential shortage in the future.

Continue the feast for the eyes and browse the infographics on our site.

Unlocking New Horizons: 4 Perspectives on Leveraging LLMs and Graph Databases

Jenn Snider — Wed, 12 Jul 2023 20:49:10 +0000

In the vast landscape of information technology, Large Language Models (LLMs) and graph databases have emerged as powerful tools revolutionizing how we process and analyze data. LLMs, such as OpenAI’s GPT-X systems powering the wildly popular ChatGPT, have made significant strides in natural language understanding and generation, while graph databases offer a flexible and efficient way to represent and query complex relationships. When these two technologies are combined, they unlock unprecedented possibilities for knowledge extraction and decision-making. Let’s explore four ways of thinking about LLMs and graph databases, shedding light on their potential applications and synergies.

Natural Language Understanding and Generation

LLMs are designed to comprehend and generate human-like text, making them invaluable for natural language understanding (NLU) and natural language generation (NLG) tasks. By leveraging LLMs in conjunction with graph databases, we can enhance the capabilities of traditional query systems. Graph databases, like Gemini Explore, provide a rich representation of connected data with nodes and edges, capturing relationships and context. This enables LLMs to generate more accurate and context-aware responses by taking into account graph-based context.

For example, imagine a customer support chatbot that utilizes a graph database to store information about customer profiles, products, and common support issues. By integrating an LLM, the chatbot can understand and generate responses in a more conversational manner, drawing insights from the graph structure to provide personalized and contextually relevant solutions.

Knowledge Graph Enrichment

Graph databases excel at representing and connecting heterogeneous data sources. LLMs can be employed to enrich knowledge graphs by extracting structured information from unstructured data. We can create powerful knowledge extraction pipelines by training LLMs on domain-specific corpora and integrating them with graph databases.

Consider a healthcare application that stores patient records in a graph database. By applying LLMs to unstructured clinical notes, the application can extract structured information such as diagnoses, medications, and treatment plans. This enriched knowledge graph can then be leveraged for advanced analytics, medical research, and personalized patient care.

Recommendation Systems

Graph databases provide a natural framework for modeling and querying complex relationships, making them an excellent choice for building recommendation systems. With their ability to understand user preferences and generate relevant suggestions, LLMs can enhance the accuracy and personalization of these recommendation systems.

By combining the power of LLMs and graph databases, we can create recommendation engines that consider both explicit and implicit user preferences. For instance, a movie streaming platform can leverage a graph database to model user interactions, such as watched movies, ratings, and social connections. By utilizing an LLM, the platform can generate personalized movie recommendations based on the user’s viewing history, preferences of similar users, and other relevant contextual information from the graph.

Complex Network Analysis

Graph databases serve as a foundation for complex network analysis, enabling us to uncover patterns and insights from interconnected data. When coupled with LLMs, this analysis becomes even more powerful as LLMs can identify complex patterns in large-scale networks.

For example, social media platforms can employ LLMs and graph databases to detect and understand misinformation spread across their networks. LLMs can identify potentially misleading information by analyzing the text content of posts, comments, and shared articles. The graph structure of the social network can be used to track the propagation of such content and identify influential nodes. This integrated approach helps platforms proactively combat misinformation and protect their user base.

Conclusion

The combination of LLMs and graph databases presents a multitude of opportunities across various domains. The potential applications are vast, from improving natural language understanding and generation to enriching knowledge graphs, enhancing recommendation systems, and enabling complex network analysis. By leveraging the strengths of LLMs and graph databases, organizations can unlock new horizons in data analysis, decision-making, and user engagement.

The synergy between LLMs and graph databases enables a deeper understanding of complex relationships and context. It empowers systems to provide more accurate, personalized, and context-aware responses. Whether it’s in customer support, healthcare, recommendation systems, or combating misinformation, integrating LLMs and graph databases offers a paradigm shift in data processing and analysis.

By embracing these technologies and exploring the four perspectives mentioned above – natural language understanding and generation, knowledge graph enrichment, recommendation systems, and complex network analysis – organizations can unlock new insights, improve decision-making, and deliver enhanced user experiences in today’s data-driven world. The journey toward harnessing the full power of LLMs and graph databases has just begun, and it’s an exciting path to be on.

Ready to get started? Gemini Explore integrates with the latest LLM, GPT, and machine learning advancements for enterprises aiming to leverage the potential of generative AI. We offer a quick, secure, and effective way to integrate LLM technology with your enterprise data to deliver actionable insights and recommendations to drive improved business outcomes.

Infographic: Visualizing the Global Semiconductor Supply Chain

Jenn Snider — Thu, 06 Jul 2023 09:45:00 +0000

This insightful infographic provides a visual representation of the intricate and globally interconnected semiconductor supply chain that drives our modern digital society. With the COVID-19 pandemic amplifying the demand for digital goods and technological advancements, the spotlight has been placed on semiconductors. These tiny integrated circuits (chips) are vital in powering various electronic devices, making semiconductors the fourth-most traded product worldwide.

The infographic highlights the significant market applications of semiconductors, including smartphones, personal computing, se centers, and automotive electronics. The complex semiconductor supply chain involves multiple stages, encompassing design, manufacturing, packaging, testing, and end-product integration, with companies spread across different regions. Notable players in the supply chain include fabless semiconductor companies, foundries, outsourced semiconductor assembly and test (OSAT) companies, original equipment manufacturers (OEMs), and integrated device manufacturers (IDMs).

The global nature of the supply chain is demonstrated by the extensive distances traveled by components, exceeding 25,000 miles before final product integration. By unlocking the potential of the digital economy, the semiconductor supply chain enables advancements in 5G, AI, smart factories, automotive technology, and quantum computing, shaping our present and future technological landscape.

Continue the feast for the eyes and browse the infographics on our site.

Book Review: Knowledge Graphs

Jenn Snider — Thu, 29 Jun 2023 11:58:28 +0000

Gemini Data’s resident data scientist and bioinformatician Sixing Huang dives into the new book by Jesús Barrasa, Amy E. Hodler, and Jim Webber, Knowledge Graphs: Data in Context for Responsive Businesses:

"Within merely 79 pages, the authors have covered the key aspects of KG, including the definition, construction, types, and its roles in contextual AI and business digital twins. Both technical and non-technical readers can enjoy the book because it focuses on the core concepts and spares us the programming details. In this article, I would like to share some of my learnings."

Knowledge Graphs has three main sections:

Taxonomy and ontology. The book addresses the potential of enhancing property graph models by adding taxonomy, which helps organize nodes in a broader-narrower hierarchy.
Taking action and making decisions. By classifying knowledge graphs into two types – “actioning,” which is used for data management, and “decisioning” which are used for analytics and data science.
Digital twin. The authors outline the role of knowledge graphs in creating digital twins, virtual models of real-world problems that can be used for analysis and forecasting.

The authors stress the importance of practical experience in mastering knowledge graphs and mention the availability of many practical articles on Medium to guide users in building and evaluating knowledge graphs.

Read Sixing Huang’s full review of Knowledge Graphs on Medium.

Infographic: Visualizing Amazon’s Rising Shipping Costs

Jenn Snider — Mon, 26 Jun 2023 11:00:00 +0000

This revealing infographic delves into the rising shipping costs faced by Amazon, a standout performer during the COVID-19 pandemic. While the company experienced impressive sales growth and market valuation, it also confronted challenges in its shipping and fulfillment department, leading to escalating costs that now constitute a significant portion of revenues.

In 2021 alone, Amazon incurred $151.8 billion in shipping and fulfillment expenses, with shipping costs accounting for $76.7 billion and fulfillment costs amounting to $75.1 billion. These expenditures represent 32% of the company’s revenues, nearly double the figure of 18% in 2011. Despite these rising costs, Amazon’s overall profits remain robust, primarily due to the success of Amazon Web Services (AWS). This high-margin segment that contributes over 50% of the company’s operating profits while representing only 13% of sales.

The financial prowess of AWS has prompted some investors to advocate for its spin-off as a separate entity, as specialized technology companies often command higher equity multiples. Amazon’s shipping and fulfillment expenses are staggering, totaling $288,000 per minute, compared to revenue of $956,000 per minute. To put this into perspective, the infographic highlights that this business expense surpasses the equity value of approximately 90% of companies in the S&P 500.

More data and visualizations are in our treasure trove of infographics on our site.

Infographic: Visualizing Raw Material Inflation in Canada

Jenn Snider — Tue, 13 Jun 2023 22:57:42 +0000

This informative infographic explores the effects of inflation on raw materials used by manufacturers in Canada. As the country experiences climbing inflation rates, the prices of raw materials have surged by an average of 37% year-over-year. Canadian Manufacturers & Exporters (CME) provide valuable insights into the specific materials that have witnessed significant price spikes over the past year.

Crude energy materials, including coal, crude oil, and natural gas, have seen substantial increases due to supply constraints and heightened demand. Wheat inflation has been driven by drought and geopolitical conflicts, impacting food and fuel manufacturers. Lumber prices have risen due to increased housing demand and supply disruptions. Manufacturers face additional challenges, such as elevated shipping costs, supply chain disruptions, global uncertainty, and labor shortages.

Consequently, many have responded by raising prices and implementing strategies such as finding alternative suppliers, increasing inventories, and reducing production. Looking ahead, manufacturers aim to strengthen relationships, diversify suppliers, and localize production. While supply chain issues are anticipated to be resolved in 2023, manufacturers must adapt to the ongoing rise in costs for the time being.

Continue the visual feast and browse the whole section of infographics on our site.

5 Reasons Graph Data Projects Fail

Jenn Snider — Thu, 08 Jun 2023 17:19:14 +0000

Bringing graph data technology to an organization is not for the faint of heart. You are constantly juggling your budget, schedule, and requirements. Expectations collide with the reality of deploying a graph platform inside an organization. We’ve worked with teams worldwide who are bringing the power of graph data to their organizations, and there are a few roadblocks we see over and over again.

Let’s dive in:

#1. Technical Requirements Don’t Match Business Requirements

When implementing graph technology at your company, avoiding a haphazard approach and adopting a more focused strategy to achieve results faster are crucial. Identify specific areas where graph technology will be applied, the problems it will address, and the metrics it aims to improve. Define the expected outcomes and interactions from the perspective of business users or end-users of the graph data application.

Business requirements represent a software project’s high-level goals and objectives, encompassing the necessary features and capabilities. These are typically expressed in non-technical language and concepts, sometimes ambiguous or subject to change.

Once business requirements have been established and agreed upon, they have to be translated into technical specifications. This detail outlines the actual features and functions the application must have to fulfill the business needs. It’s best to use a structured approach by breaking down business questions into data requirements and mapping them to application features.

Effective communication between subject matter experts on both the business and technical sides is crucial to ensure engineers understand end-user needs and that the targeted end-users provide the most straightforward explanations of what they expect to see when the app goes live.

To address this issue:

Clarify business requirements before starting work: Coordinate with stakeholders, subject matter experts, and future end-users to ensure clear and well-defined requirements.
Involve technical experts early: Ensure experts understand requirements and can provide input on the feasibility and limitations of the technology.
Use templates and documentation extensively: Create use cases, process flows, and data models to map out how the application will meet business and technical requirements.
Validate, test, and re-test: Test technical requirements with prototypes or proofs-of-concept to ensure accuracy and feasibility, involving stakeholders and end-users.
Collaborate and communicate: Schedule regular meetings and updates between stakeholders and the technical team to ensure requirements align with business needs and maintain a common repository for documents and data.

#2: Data Quality Is Low, Data is Hard to Access, and Data Modeling Takes Forever

Data sources and quality are critical to a graph data project’s success. Ensuring that applications can connect to the required data sources and maintain high-quality data is essential.

Preparing data for a graph database differs from traditional relational databases. It’s essential to structure, ingest, and process the data correctly and model it to work with the graph. To do this, you must normalize the data and perform ETL (Extract, Transform, Load) processes compatible with the graph database. Often teams need to learn a specialized query language or use third-party tools to ingest the data effectively. Some Configuration may be required to recognize the entities or nodes and relationships in the data and create the connections between them. High-quality data ingestion and modeling are crucial for success.

Key indicators this problem is rearing its ugly head:

Stalling out to get data right: You are six months into the project, and your team is still struggling with data preparation.
Too much time writing connectors: Your team is overwhelmed with writing connectors and parsers for data sources.
Non-normalized data: Storing redundant data can lead to inconsistency and maintenance difficulties.
Poor connectors: Inadequately designed connectors may result in data inconsistencies or loss, such as mishandling data types.
Inadequate ingestion: An improper ingestion process can cause data quality issues.
Clunky data modeling: Incorrect data modeling can result in inefficient queries, redundant data storage, and performance degradation.

How to fix this issue:

Identify entities: Determine the objects or concepts in your data that will be represented as nodes in the graph database.
Identify relationships: Determine the connections between entities that will be represented as edges in the graph database.
Normalize the data: Break down the data into smaller subsets to eliminate redundancies.
Ensure unique identifiers are unique: Create unique identifiers and properties for each entity and relationship.
Test data connectors regularly: Optimize connectors with better hardware, upgrades, or other fine-tuning.

#3: Learning Curve Discourages End Users

Too often, graph implementations are well underway before stakeholders realize that some graph platforms require end users to learn a whole new set of skills or scripts or a coding language to operate the planned application. Many teams don’t incorporate this potentially long learning curve in their project plans and schedules.

Applications that require a lot of training and are difficult to use will not endear end users. End users are busy and want to get going and find what they want.

How you know you have this problem:

A few highly technical users get a lot out of the app, but everyone else has to beg them (or engineering) for help.
End users balk at the learning curve and abandon the app altogether, and this could even be your more “technical” users who don’t want one more thing they have to learn on top of the pile of technologies, tools, and frameworks they’re trying to stay current on.
No one wants to learn a query language like Cypher or Gremlin. This additional learning curve can discourage end users and slow down the adoption of the application.

How to fix this problem:

Invest in front-end development: Customize the interface to meet the specific needs of the business, making it easier for non-technical users to find, enter, and manipulate data. A well-designed front-end can also provide security features that ensure only authorized users access the database, protecting sensitive data.
Keep what’s under the hood, under the hood: Shield end users from the complexity of the database and application, allowing them to focus on solving their business problems. Simplifying user access to data can reduce errors and increase productivity.

#4: End Users Can’t Share Graphs or Collaborate on Analysis

The most incredible graph application in the world can give an end user a startling analysis that could change the direction of the business. But if that analysis can’t be easily shared with others, that limits its reach. Many graph data packages are currently available as desktop or client-based, single-user applications, which can make collaboration and sharing views with colleagues difficult.

When evaluating a solution or application for graph data projects, whether custom-built or from a third party, examining its support for role-based access and its capacity for creating and sharing knowledge is essential. Ideally, users should be able to capture “snapshots” of their work and share them with others through shareable links, similar to Google Drive’s sharing feature. This functionality aligns with users’ expectations of the applications they use daily.

Another crucial aspect to consider is data enrichment. Allowing users to incorporate additional data or context will enhance the graph, enabling faster and more effective problem-solving, and considering these factors when developing or working on graph data projects will lead to a more collaborative and efficient experience for all users.

How to fix this problem:

Prioritize features that facilitate sharing specific projects and views of data sets with shareable links and snapshots, ideally including view/edit roles. While custom coding may be required, this approach will encourage broader app usage, as graph viewers will be more inclined to use the app frequently.
Consider additional features that allow users to add supplementary data sources to a graph for further enrichment and a more comprehensive view.

Check out how Gemini Explore lets you share snapshots of graphs and add new data sources to enrich.

#5: Analysis Takes Too Much Time

We outlined above how there’s a long learning curve for people to get used to using graph technologies due to specialized query languages and the learning hurdles of learning different processes and experiences to learn to get the most out of a graph data application.

With the recent advancements in generative AI like OpenAI’s ChatGPT and Google’s Bard, graph data has become even more accessible to more people. The hurdle in learning a query language has been dissolved. Previously, users had to learn specific query languages and methodologies to use certain tools effectively. Integrating with generative AI allows users to ask questions using human text input and receive graph data output with context as answers without needing to master the tools.

Graph databases are highly effective in managing intricate and interlinked data, making them particularly well-suited for training generative AI. On the other hand, traditional relational databases primarily rely on tables, rows, and columns and may face difficulties when dealing with intricate relationships. In simpler words, graphs provide a versatile, efficient, and easily understandable structure for organizing information, ideal for training AI models to comprehend complex connections. As a result, graph databases serve as an excellent basis for AI projects.

You can see how Gemini Explore solves this problem with our natural language search that uses human data input and responds with graph data output in our blog post, Generative AI, ChatGPT, and the Future of Graph Technology.

Stay Vigilant, Stay Focused

Introducing graph data technology to your organization is a multifaceted endeavor that requires careful planning, clear communication between the technical and business teams, and a thorough understanding of the organization’s data needs and resources. It’s vital to mitigate the challenges posed by the learning curve for end-users, manage data quality and access effectively, and ensure smooth collaboration and information sharing. Incorporating generative AI can drastically reduce the learning curve and open up the potential of graph data to a broader audience. Implementing a graph database, despite its challenges, can unlock tremendous value and insights, enabling organizations to handle complex, interlinked data efficiently and providing powerful, accessible tools for decision-making. The path may be challenging, but with strategic planning and execution, the rewards can be game-changing.

See firsthand how Gemini Explore lets graph data teams leapfrog over the usual stumbling blocks to get broader adoption, faster time-to-value, and increased ROI.

Infographic: The Future of Supply Chain Automation

Jenn Snider — Wed, 07 Jun 2023 22:46:45 +0000

The supply chain management landscape is undergoing a major shift as companies worldwide recognize the need to embrace automation to stay competitive. This infographic from Raconteur explores the current state of automation in global supply chains and offers insights into future investment trends.

Warehouse automation emerges as the top priority for businesses, with 55% of companies investing in this technology to enhance delivery speed and overall profitability. Furthermore, predictive analytics, the Internet of Things (IoT), cloud logistics, and artificial intelligence also garner significant attention as they seamlessly integrate with supply chain automation. While complete automation may take some time, over 70% of warehouse managers have already adopted or plan to incorporate automation within the next five years.

Notably, different aspects of the supply chain, such as order fulfillment, exhibit varying degrees of automation, with considerable room for growth. By harnessing the power of automation and data-driven decision-making, supply chains can become more resilient, mitigating future risks like those experienced during the COVID-19 pandemic.

Dive into our whole trove of infographics on our site.

30+ Ways to Use ChatGPT with Graph Technology

Jenn Snider — Wed, 29 Mar 2023 20:35:51 +0000

Everything’s changing (again). Let’s keep a running list of all the ways you can use ChatGPT, GPT-3, the just-released GPT-4, and related LLMs for knowledge graphs, graph databases, and other graph platforms cases and solutions.

I’ll start with my blog post from a couple of weeks ago, 4 Ways to Use ChatGPT for Graph Databases

Convert text-based, or natural language queries into graph database queries, which we are literally doing in our Gemini Explore product [click here to schedule a call to see it in action]
Data enrichment for more human-readable representation of relationships leading to improved decision-making
Recommendation engines for e-commerce, media, and other applications
Generate synthetic graph data so proprietary or sensitive information isn’t accessed (including sample data)

From Kurt Cagle‘s list (with examples), ChatGPT Tricks for Knowledge Graph Workers

Generating scientific (known) data as an ordered table (periodic table returned as tabular data)
Generating scientific (known) data to a given schema (periodic table returned in Turtle RDF format)
Converting a plain text report into a complex schema (a written police report returned in RDF format)
Generating information graphics from plain text (generate a graph/network diagram from that same report)
Getting supplemental information (DC comics character information returned in specific machine-readable formats)
Entity extraction and content enrichment (extraction from Presidential executive order record into RDF format)
Taxonomy construction (fed an outline, it returns it in RDF format)
Rudimentary reasoning (show all but panthers in a taxonomy list)

Kingsley Uyi Idehen over on Medium has a whole batch of suggestions and amazing (and strangely serene!) videos on YouTube:

Medium: Generate a course description outline from plain text to RDF format
YouTube: Turn a job description into machine-readable format

Mai Văn Khánh on Medium creates diagrams and workflow graphics related to the scenario of “a small software development company wants a tool will allow clients to create projects, assign tasks to team members, and track the progress of the project.” Using the Mermaid.js: (all with in-depth examples)

“Use case diagrams to capture the high-level interactions between the client and the project management tool.
“Class diagrams to model the classes of objects in the system, such as projects, tasks, and team members.
“Sequence diagrams describe the interactions between the client and the system when performing specific tasks, such as creating a new project.
“Activity diagrams to show the flow of activities within the system, such as moving a task from one state to another (e.g., from “in progress” to “completed”). This one is a little iffy.
“Component diagrams that show the physical components of the system and how they relate to each other.
Flowchart diagram showing the order of interactions
State diagram for various objects in the application
Entity relationship diagrams that shows how the various data points are related
A Gantt chart to show the timeline with tasks
Gitgraph showing the “environment developer, staging, and production branching strategy”
User journey diagram (doesn’t really pan out, yet)

And of course our besties at Neo4j are on it with a bonnet:

Konrad Kaliciński details how to turn list of requirements for a graph database into literally the structure with node labels, relationship labels, and property keys
David Stevens walks through generating sample data that their customer success team can use (and reuse) when they can’t get direct access to a prospective customer’s data
Fanghua Yu takes a graph database of movies and actors and turns it into Q&A system

Tomaz Bratanic on Medium:

Create a chatbot with a knowledge graph and ChatGPT

Cobus Greyling from HumanFirst on Medium: (tons of additional writing on LLMs, etc.)

Creating a custom fine-tuned model with GTP-3 language APIs
And a similar process for custom intent classification, in this example, discerning between content about hockey versus baseball

Dean Allemang on Medium:

Using LLMs for narrowing the judgment gap in knowledge graph development and implementation through proposing mappings between an ontology and a data schema

Stay tuned! I’ve got alerts turned on so should be adding to this post pretty frequently!

In the meantime, check out how we’ve integrated OpenAI’s GPT-X APIs with Gemini Explore.

Generative AI, ChatGPT, and the Future of Graph Technology

Jenn Snider — Fri, 10 Mar 2023 13:24:40 +0000

Generative AI has taken over the technology news cycle. Apps like OpenAI’s ChatGPT and Microsoft’s Sydney have enchanted professionals and consumers worldwide. However, it’s been only 3 months since OpenAI released ChatGPT to an unsuspecting public, and there’s already been an avalanche of warnings, condemnations, celebrations, and the like across all industries and verticals. I even used ChatGPT a few weeks ago to generate a list of four ways to use ChatGPT for graph databases.

A Really Powerful Autocomplete

One thing we know is these large language models (LLMs) are here to stay. LLMs like ChatGPT use natural language processing and neural networks to generate text based on user prompts. The best de-mystified description I’ve read so far is veteran entrepreneur and data scientist Steve Wolfram describing it as basically a really powerful autocomplete. The quick adoption of ChatGPT by professionals, consumers, and students indicates how critical text and chat-based interactions will be in bringing more information to more people.

Gemini Data is dedicated to getting graph technology into the hands of as many people as possible. Our no-code Gemini Explore platform is built from the ground up to give even “less technical’ users access to the power and possibilities of graphs. Our search interface was designed to keep all the levers and gears of graph query languages under the hood. With Gemini Explore, end users don’t need to learn Cypher, Gremlin, or GQL to ask questions of their data. We’ve extended this commitment to access and ease of use with our integration with OpenAI’s GPT-3.

Human Text Input, Graph Data Output

GPT-3 is ChatGPT’s big sister, providing similar functionality in natural language processing and understanding. In just under a year, hundreds of companies have added GPT-3 to their applications to make using this advanced technology as easy as just typing or saying what you want to do. Then, the system gives you back exactly what you want. Here’s how it works:

Our first foray into this integration has been to bring GTP-3 into our natural language search feature. End users can simply type what parts of a graph they’d like to see – or get answers to a specific question. Explore captures the query and sends it to GPT-3 for analysis.

GPT-3 is already tuned to know the various actions a user can take with Explore. Commands like LOAD, SELECT, UNSELECT, and REMOVE are well within the purview of GPT-3 to translate. Along with the actual commands of our app, GPT-3 can understand the synonyms for the various verbs, so it knows if the user says “show,” “find,” or “display,” it’s the same as if Explore was issued a LOAD command. GTP-3 also interprets the nouns, verbs, and relationships in the request to determine the types of nodes to display or select.

So if a user tells Explore, “Get all actors that acted in The Mummy” is interpreted by GPT-3 and sent back to Explore as:

Action: LOAD
Method: by relation
Nodes: Actors
Relationship: ACTED_IN
Subject: The Mummy

And those nodes and related relationships are displayed. Without this simplified interface of human text input, the end user would have to know a query language like Cypher and how to construct this command:

MATCH (a:Actors)-[:ACTED_IN]->(m:Movie)
WHERE m.title = 'The Mummy'
RETURN a.name

Properly tuned, GPT-3 will understand that “acted in,” “performed in,” “appeared in,” and “were in” all point to the same ACTED_IN relationships. Further tuning will make sure that similar queries return results for the beloved Brendan Fraser movie in 1999 and Boris Karloff’s legendary star turn 1932, but also the heinous Tom Cruise movie in 2017.

This type of integration can be tuned for general applications but has much more impact with specific graph database use cases. For example, GTP-3 can be tuned to specific graph database configurations, types of nodes, and relationships.

Gen AI is just getting started and we are excited to see where these burgeoning technologies take us next. At Gemini, we are always trying to remove the friction between the end user and getting the most out of their data. GPT-3 is another way we bring more people to harness the capabilities and benefits of the graph.

4 Ways to Use ChatGPT for Graph Databases

Jenn Snider — Thu, 02 Feb 2023 03:30:05 +0000

OpenAI’s ChatGPT is everybody’s favorite new toy, giving anyone the power to generate reams of text about any topic in a matter of seconds. Whether or not the content is correct is another matter entirely. But it sounds human enough. Colleges and schools are already cracking down. Programmers are using it for coding. ChatGPT and other LLMs (large language models) are threatening to put anyone who types or writes anything for a living out of business. And that’s not even counting the artists, designers, and content owners threatened by AI-gen image systems like Stable Diffusion.

But how can we use ChatGPT in the graph database space? I asked in our internal Slack channel, but thought why am I waiting around for a bunch of puny humans to tell me anything when I can just go to the source?

Aha! Instant outline. Now, who knows if this information is correct? It’ll still need a technical review. Let’s tell the system to expand on each of these ideas. You’ll notice a consistent problem-solution-additionally type of structure. I’ve chopped out the over-repetition of some phrasing.

Text-based Queries

Graph databases are a powerful tool for organizing and managing large amounts of interconnected data. However, searching for information within a graph database can be a challenge, especially for users who are not familiar with graph’s query languages. ChatGPT can process natural language queries from users and translate them into graph database queries, making it easier to search for information.

With this method, users can search for information using plain language, making it easier for them to find the information they need. This is particularly useful in industries where complex relationships between data points are common, such as healthcare, finance, and e-commerce. The ability to process natural language queries can make it easier for users to understand the relationships between data points and locate the information they need quickly and efficiently.

ChatGPT can also improve the user experience of text-based queries, helping make more informed decisions by simplifying the process of searching for information. This leads to increased productivity and improved results. Ultimately, the systems ability to process natural language queries can help to make graph databases more accessible and easier to use for a wider range of users.

Data Enrichment

Graph databases are a powerful way to represent complex relationships between data points, but they can also be difficult to interpret and understand. ChatGPT can play a role in data enrichment by adding descriptive text to graph database nodes and edges so users can understand the context and relationships within the data. This can be particularly useful in industries such as finance, where understanding complex relationships between data points is critical.

This also helps users access a more human-readable representation of relationships within the data that makes the data more accessible and easier to understand, leading to improved decision-making. ChatGPT’s ability to generate descriptive text bridges the gap between technical and non-technical users, encouraging more effective collaboration and communication within an organization.

In addition, this data enrichment can improve the accuracy and consistency of the data. Standardizing how data is described ensures it is consistent across all nodes and edges. This improves data quality and reduces errors making it easier to detect and correct any issues that may arise. Ultimately, this ability to add descriptive text can help to make the data more accessible, understandable, and accurate.

Recommendation Engine

The ability to make accurate and relevant recommendations is a key component of many applications, and ChatGPT can generate recommendations for users based on the relationships between nodes in a graph database. This is particularly useful in industries such as online retail, where personalized recommendations can improve customer satisfaction and increase sales.

By using ChatGPT to generate recommendations, organizations can leverage the relationships between nodes to make informed recommendations to each user. By analyzing the relationships between nodes, the system can understand a user’s preferences and behaviors, and generate recommendations tailored to their specific needs.

This capability can also help increase revenue by improving the effectiveness of marketing campaigns. Trends and patterns in consumer behavior can be identified to target marketing campaigns more effectively. This can increase conversions and sales, as users are exposed to recommendations that are relevant to their interests and needs.

Automated Graph Generation

Working with real data in graph databases can come with challenges such as data privacy and data availability. To overcome these challenges, ChatGPT can be used to generate synthetic graph data based on patterns and relationships in the existing graph data. This allows for testing and experimentation without the need to use real data.

With this approach, organizations can experiment with different graph database configurations and algorithms without the risk of compromising actual data. This is particularly useful in industries such as finance and healthcare, which are subject to strict regulations and privacy concerns. This method provides organizations with the flexibility to experiment and test new ideas.

In addition, ChatGPT’s ability to generate synthetic graph data can also be a valuable tool for training and evaluating graph database algorithms. With a synthetic graph data set that is representative of real-world scenarios, organizations can train and evaluate algorithms in a controlled environment. This can improve accuracy and performance since algorithms can be tested and refined in a controlled environment before being applied to real-world data. This provides organizations with a valuable tool for testing and experimenting with graph databases.

What’s next?

And that’s just four quick use cases for ChatGPT in the graph database space. As these LLM technologies become more ubiquitous the possibilities are endless – and the risks. Even with thousands of words of generated content, a human still has to go in and make sure that what the system is saying is factually correct. Whether or not novice readers will know if what they’re reading is accurate is a whole other landmine.

Data Lake or Data Warehouse: Which does your business need?

Jenn Snider — Tue, 11 Jan 2022 14:35:06 +0000

Most businesses collect and store massive amounts of data on a daily basis and are always on the lookout for the best way to store this important information. Data lakes and data warehouses are two of the most common ways to store data and both options support the same goals, but do so in their own way. This may leave you wondering which one is right for your business. Keep reading to understand the differences between a data lake and a data warehouse and which one you should choose to get the most out of your data.

What Is a Data Lake?

A data lake is a large, open repository for all your data. It’s a place where you can store data in any format, without much planning and without too much concern for pre-processing or preparation. The key characteristics of a data lake include:

Data is stored as-is
Data is not standardized or pre-processed
There is no schema or schema evolution
Data has no timestamp
Data is not segmented or aggregated
Data is not processed or cleaned.
Data is stored in any format

Data lakes allow for faster query results using low-cost storage and enable analytics such as machine learning, predictive analytics, data discovery, and profiling. One thing to keep in mind with data lakes is that since data is raw and unstructured, you’ll want to have a strong cataloging procedure in place so that users can more easily find what they’re looking for.

What Is a Data Warehouse?

Unlike a data lake, a data warehouse is organized and structured. It’s a specialized data store that holds metadata and cleans, standardizes, and processes data as it is being stored. The key characteristics of a data warehouse include:

Data is stored in a standardized format
Data is pre-processed, prepared, and cleaned
There is a schema that is enforced during ingestion
There is a data model and metadata
Data is rolled up, segmented, and aggregated
Data has a timestamp
Data is stored in a table structure

Data Lake or Data Warehouse?

Now that you have a better understanding of what differentiates a data lake from a data warehouse, you may still be wondering which one your business should use. But the reality is, you can, and should, use both. In fact, as organizations that use data warehouses see the benefits of data lakes, many of them are evolving their data warehouses to include data lakes to enable diverse query capabilities, data science use-cases, and advanced capabilities.

This means that rather than choosing one over the other, you can store your structured data in a data warehouse and your unstructured data in a data lake. And, you can use a data lake and data warehouse together to accomplish more than one goal. For example, you can use your data warehouse to store data that needs to be rolled up, aggregated, and normalized, and you can analyze it over time. Then, you can use your data lake to analyze untimely data—data that doesn’t need to be rolled up and doesn’t change over time. Ideally, using both a data lake and a data warehouse can help your business get the most out of its data.

The Right Choice

Data is the lifeblood of every organization. With it, you can make smart, informed business decisions. Without it, everything you do will be based on educated guesses or instinct.

Choosing the right data storage solution for your business is key to getting the most out of your data and it’s important that you can store, analyze, and access it easily. Using a combination of a data lake and a data warehouse can enable your business to be agile and flexible in managing all types of data.

If you’re looking to better understand what your data is telling you, reach out to Gemini Data. We can help you solve your biggest data challenges, enabling you to understand and share data stories, and get from data to insights faster.

How Gemini Stream Can Benefit Your Business

Jenn Snider — Thu, 06 Jan 2022 13:22:14 +0000

It’s hard to find a business that isn’t collecting, analyzing, and processing huge amounts of data to inform and influence decision making. Because big data is so critical to how a business operates, it’s equally important to understand how this information is handled and what procedures are being used to manage the lifecycle of data.

This is where data lifecycle management comes into play. Put simply, data lifecycle management refers to a process that helps manage the flow of data from inception to destruction. While there are many interpretations of data lifecycle management depending on the business, we’ll dive into the core principles of the process here.

Creation

Everything starts when data is created or captured. This can come in any form, from a simple image, a PDF file, a document, or even SQL database data. In any organization, a piece of information is created in one of three ways:

Data Acquisition – In this scenario, data already exists somewhere outside of the organization and is only acquired.

Data Entry – Data can also be obtained through manual entry into a system by personnel within the organization.

Data Capture – Capturing data can be done using a variety of tools and devices in a particular process within the organization.

This can be a challenging first step in the life cycle given that information is coming from multiple disparate sources. However, the right platform and tools can make this process much more streamlined. At Gemini, we’ve created one platform that allows you to manage all of your data source nodes in one place, eliminating the need for an army of IT staff and data scientists to help you create your data.

Storage

Once data has been created, you have to find a way to properly store and file it. There are many systems, programs, and software on the market to help with this and it’s just a matter of finding what meets your business’s needs. At this stage, it’s important to ensure that your data is well protected with the appropriate level of security. It’s also recommended to conduct regular data backups and have a recovery process in place in case you need to restore any lost data and to avoid losing any crucial pieces of information that could compromise your customers, clients, or your business.

Usage

Now we’ve come to the fun part of the data lifecycle: using it! Data can be viewed, processed, modified, visualized, and contextualized at this stage. This is the stage where your data starts to work for you and reveal insights that can help with decision making, strategy setting, and reaching goals. At Gemini, we refer to it as “connecting the dots” – the previous stage of creation, along with analysis, allows you to construct a connected view of your business to transform data into stories.

It’s recommended that whatever system you have in place for storing and retrieving data also has an audit trail available for all critical pieces of information. This lets you see who accessed the data, when it was accessed, and how it was used. You can also ensure that all modifications to the data are fully traceable. Depending on the nature of the data, it can also be used by others outside of the organization, such as offshore teams or third-party outsourcing partners.

Archival

Not all pieces of data are needed at all times. That’s why there comes a phase in the data lifecycle where inactive data is moved out of production systems into long-term storage systems. Archived data isn’t mixed with information that’s used in your company’s day-to-day operations. They are stored in an environment where no maintenance or general usage occurs. This keeps your active data and inactive data separate, minimizing confusion or inaccuracies.

Destruction

As more pieces of information are created and captured at an increasing rate, it would be a futile attempt to try and store everything forever, especially if you’re dealing with terabytes of data every minute. Storage cost and compliance issues will hinder you from doing this, which is why destroying data that’s no longer needed is important. However, depending on the industry your business is in, you’ll want to make sure that this phase isn’t carried out until the information has exceeded its required regulatory retention period.

Conclusion

In today’s world, data is king, and understanding the data lifecycle management process is crucial to establishing a systematic way of handling large volumes of data. From creation to storage to usage and beyond, having a clearly defined and documented data lifecycle management process will ensure your business is effectively (and efficiently) handling its data.

Gemini Data is your partner when it comes to solving the biggest data challenges you may face in your organization. When you work with us, you can get instant business context in one platform – making it easy and streamlined to leverage the power of your data. Contact us today to learn more about how you can from data to insights in no time.

What to Look for When Choosing a Data Source

Jenn Snider — Thu, 23 Dec 2021 15:20:02 +0000

While decision-makers at many businesses have realized the power and potential of external data, many are still struggling to figure out how to source their data. As businesses use more and more data to inform decision-making, they’re increasingly leveraging a new kind of specialist – a data hunter who can seek out and identify valid sources of external data. However, building that type of capability is challenging and it can be hard to keep pace as data source lists grow. Additionally, finding good data sources in-house can be a time-consuming and costly endeavor for a business to take on. As a result, many organizations are looking to outsource their data sources. In fact, according to a Forrester report, 66% of decision-makers surveyed said they’re using or planning to use external service providers for data, analytics, and insights.

So how can a business be sure that they’re using quality sources for their external data? Keep reading as we dive into a few of the characteristics to look for when sourcing external data.

High-Quality

One of the first things to look for in an external data source is the quality of the data as this will directly affect multiple outcomes across your business, from informing decision making to influencing marketing strategies. Businesses will want to make sure that the data they’re collecting has comprehensive and high-value text descriptions to make processes like machine learning and natural language processing easier and more accurate. Consider working with a vendor who can help you confirm your data source selections with industry subject matter experts and identify any missing data types or parameters.

Continuously updated

No matter how reliable a data source is, it’s vital that the data is updated regularly, and most importantly, that your business is able to access those updates. Most data sources are regularly updated as parameters, data types, business processes, and more, change. For example, any data pertaining to customer behaviors for a specific type of product are going to be subject to frequent changes as buying patterns fluctuate. Leveraging a platform like Gemini can not only help your business make sense of disparate data sources and their changes, we can also help you move to the next – and more complicated – part of data analysis: connecting the dots and contextualizing the data.

Relevant information

It may seem obvious, but it’s critical that the data source you use is relevant to your business and industry. Gathering and using data that is not germane to your business could throw off your data analysis and lead you down the wrong path. For instance, collecting too much data from a niche brand could introduce bias into your data sets. When reviewing a data source, you want to make sure you’re gathering information from across a spread of markets where there is relevant data. Comprehensive data sources such as Amazon or Walmart often hold vast amounts of data across a range of industries that could be relevant for your business.

Strong online presence

It’s important to make sure that your data sources have a robust presence online to help you make connections among your data. For example, product reviews and online discussions can be critical to making connections between a product and what customers think about it. Sourcing reviews and online forums can provide you with product details, customer sentiment, publication dates, and more, to help you identify exactly how consumers view a product.

Conclusion

The use of external data in decision-making, improving business processes, and more, can help your company improve its performance and achieve its goals, especially if the data is high in quality, updated regularly, relevant to your business, and has a strong online presence. By looking for these characteristics in a data source, you can ensure that you are using the best possible data to get more reliable and effective decision-making results.

At Gemini Data, we’ve simplified the data analysis process, from gathering data from different sources to contextualizing insights to tell a compelling story, Gemini can be your partner in connecting the dots. Schedule a call or start your free trial of Gemini Explore today! to see how we can help your business get from data to insights faster.

How to Make Invisible Analytics Seamless

Jenn Snider — Mon, 20 Dec 2021 16:20:31 +0000

It’s no secret that data analytics is being used in just about every aspect of modern life and that businesses are using it to inform and shape smarter decision making. Despite this, many organizations have found themselves lagging on using analytics to their fullest, with some reporting that their in-house analytics adoption has stalled around 30%. So how can businesses get decision intelligence into the hands of decision makers and truly leverage analytics to stay competitive? It may seem counterintuitive but the solution could just be to make analytics invisible. Intrigued? Keep reading to learn more about how invisible analytics could increase your business’ use of data.

Leverage consumer-style analytics

We may not always be aware of it but we’re swimming in analytics almost every day. Music services like Spotify and Pandora are constantly using algorithms to analyze listeners’ preferences and provide suggestions for new artists, albums, and tracks to enjoy. Navigation systems like Google Maps and Waze analyze traffic patterns and user behavior to suggest shorter routes and warn of accidents or other hazards. These are all examples of invisible analytics experiences, and they’re so seamless that we don’t even realize we’re relying heavily on analytics for day-to-day decisions. This is how businesses should serve up decision intelligence – deliver actionable insights from data seamlessly.

Empower other applications

As you’ve been reading this article, how many alerts, notifications, buzzes, and pings have gone off on your screen? Just think about all of the apps and software your business is using. From Slack to Teams to Asana, people are bombarded by notifications and to-do’s all day long. You certainly don’t want to add another program in the mix to fight for attention (that will probably get ignored). Instead, build in actionable insights on the platforms teams are already using. Serve up the right piece of decision intelligence to the right person, at the right time and place, guiding them to the best next action. Don’t make anyone open up another app or go hunting for what they need next. Companies would be best served to complement their workforces with actionable decision intelligence and choose analytics platforms that allow for simple, personalized, custom analytics inside any program you’re already using (hint, Gemini has all of that).

Get technology out of the way

No one piece of software or technology is a silver bullet to analytics adoption. If it was, we wouldn’t be talking about this. But by infusing workflows and apps with actionable intelligence, teams can focus on the right outcomes instead of finding the right technology. By putting a purpose-driven analytics platform in place, your teams will be able to infuse advanced insights from complex data sources, with AI-driven capabilities, into workflows they’re already using.

Conclusion

The next wave of data analytics is already here, and it’s imperative that businesses leverage their power to stay competitive. The business world needs to approach in-house analytics adoption the way consumer software companies view customers. Teams need seamless actionable insights in the tools and platforms they’re already using and analytics experiences that mirror consumer ones.

If you’re wondering how your business can improve its decision intelligence and produce seamless data insights, reach out to Gemini today. Our full stack system can help you solve your biggest data challenges and get to insights faster.

How Gemini Explore Works

Jenn Snider — Thu, 16 Dec 2021 13:07:17 +0000

At Gemini Data, we say, “People only hear statistics. But they feel stories.” What we mean is that data on its own is powerful, but with a good story, it’s unforgettable. And that’s what Gemini Explore does – it transforms data and analytics by enabling users to easily and intuitively interact with data using contextual storytelling. It’s all about simplifying and making it easier to see, understand, and communicate the complex – so people can learn faster and do their jobs better.

So how do we do it? There are many features of Explore that make it powerful and easy to use. We’ll dive into just a few of them here.

Simple, accessible data onboarding

If you’re using a graph database to do knowledge graph exploration, the standard process for any data engineer or scientist is to physically onboard the data, a process where the required knowledge and technical skill set for most databases is limited to a certain group of people – the data engineers and scientists. Once that data is onboarded, the challenge is ensuring that you have both the right information and enough information without having too much.

But Explore dramatically reduces the complexity of the onboarding process and consolidates it within a single interface where a user can just pop in the data, go through the wizards, create the relationships for the object they need, and produce a graph they can explore.

Conditional Displays

This is a feature we’ve built in that based on the value of a particular set of notes that a user selects, they can choose the size or the coloration of the note. One Explore use case we like is movie databases (you can check it out in our Demo Library). For example, if a movie makes more money over a certain time period, you can set the size of the note to be different – so the lesser amount would be smaller and the larger amount would be bigger. Additionally, this also applies to the edges or the relationship between the notes. A user can adjust the coloration and the thickness of the line to better connect two dots.

Users can also set two separate conditions. For example, you can set sci-fi movies to green and dramas to blue, and so on, allowing users to more clearly distinguish relationships within larger data sets. The user gets to choose the condition they want to match it against.

Unmatched Data Exporting

Once you’ve onboarded your data, you can begin to explore the data on the graph. From there, a user can export the data however they need. Continuing with the movie database example, if a user has an entire movie database ingested into Explore, but they only want the data set that matches a certain genre, they can do that within the platform. A user would choose another condition, let’s say a particular director with gross sales over a certain number, and they can export that using any decision intelligence platform to create a line chart, pie chart, etc. inside that tool. We’re currently working to connect Explore directly with decision intelligence tools and make it possible to suggest the type of visualization tool a person should use.

Powerful Data Model

Explore’s data model is one-of-a-kind and completely no-code. With our process, all you have to know is how to click. That’s it. No data science degree or coding experience required. In fact, no-code or low-code is the focus of Explore (and our whole suite of products) so that anyone who wants to do the job, can do it. We’re aiming to achieve data nirvana, where you have a set of data that you feed in, and you just get the result that you want to see. No need for going through different programming, acquiring a new skill, or learning to write code. Just the results you need simply and quickly.

Better Data Contextualization is Possible with Explore

At the end of the day, we want to enable our users to bring the contextual data analysis and commentary they need to fully appreciate an insight, and do it in one platform without an army of data scientists. Explore can uncover connections using graph techniques in combinations of diverse data at scale; help organizations accelerate capabilities to anticipate, shift, and respond; find relationships between people, places, and things; capture knowledge to make it easier to perform queries and answer questions, and increase understanding around organizing and preparing data.

If you’d like to learn more about how Explore works, check out our demo library across a range of industries, or schedule time to talk with one of our data experts.

How to Leverage Ad Hoc Data Reporting

Jenn Snider — Tue, 14 Dec 2021 12:48:33 +0000

The growing amount of data that is collected and organized in an organization means that leaders and professionals can make better decisions, but it also means they have to trust the data insights they receive. This is only possible if data is accessible, accurate, and up-to-date. Ad hoc reporting and analysis tools can provide organizations with precisely what they need, when they need it, empowering decision makers to answer critical questions in real-time.

So what exactly is ad hoc reporting and how can your business leverage it? Keep reading to find out.

What is Ad Hoc Data Reporting?

Ad hoc reporting is data analyses or reports that are curated and created by users, as and when they need it. It’s a one-time approach to reporting and analysis and is typically initiated based on new information or a need to identify data patterns. Ad hoc reporting in decision intelligence is in complete contrast with the managed reports seen in the early days of business analytics, which relied on templates distributed by IT departments. But today, data in business is constantly changing, with new data being created and others becoming obsolete. Ad hoc data reporting tools enable users to create a new report in real-time with just a few clicks. This type of reporting is also called “developer-driven” or “self-service” reporting.

Why Use Ad Hoc Reporting?

With ad hoc reporting, a user can easily pull together any dataset to answer a specific and real-time question. With the right platform, such as the full stack Gemini system, ad hoc reporting removes the time and cost of incorporating new data into insights, allowing far more freedom in asking the critical questions, without the effort trap of relying on existing analysis.

While ad hoc reporting is helpful for responding to specific questions and needs, it also enables users to create insights and patterns from the data. It allows for the identification of trends and patterns of behavior, giving you a better understanding of how things work and where you can make improvements.

How to Generate the Reports

To use ad hoc reporting in your business, start by considering what information or data you need or what questions need answering. Gemini Data can be a reliable tool to create, execute, and deliver ad hoc reports. You can simplify data contextualization, analysis, and report management, and focus on driving insights and creating actionable business value. You can also view your reports in real-time or schedule reports to run at a specific time.

Through the Gemini Data platform, you can customize each ad hoc report to be what you need and easily view and update data rather than starting from scratch.

The Future of Ad Hoc Reporting

The right analytics and decision intelligence platform can help you infuse actionable intelligence, resulting in faster results and deeper discoveries. Daily tasks can also be incorporated into workflows, nontechnical teams can be empowered to answer new questions as they come up, and data teams can go deep to bring back groundbreaking insights. As business data grows, ad hoc reporting will be even more critical to keeping up with it and accessing the correct data quickly.

Conclusion

Ad hoc reporting and analysis are valuable tools that provide clear, easy-to-read insights that can transform your business and give you the power to make real-time decisions while also providing you with the ability to access information when you need it.

If your business is looking to leverage the power of ad hoc reporting with the right decision intelligence platform, reach out to Gemini. Business users and data scientists alike can leverage all of Gemini’s products – Explore, Central, and Stream – to easily transform and intuitively interact with their data using contextual storytelling. With Gemini’s system, businesses can derive insightful information from data enabling them to accelerate their decision making process and increase analytics team efficiency, as well as capture new opportunities.

How Big Data Analytics Can Benefit Businesses

Jenn Snider — Mon, 06 Dec 2021 21:49:43 +0000

Big data analytics solutions, which use specialized software tools and applications for predictive analytics, data mining, forecasting, and optimization, have become increasingly important for most businesses, no matter what industry they’re in. With these tools, organizations can gather vast amounts of organized and unstructured data, sort and analyze it, and extract patterns and essential business insights. This data has emerged as a significant differentiator in assisting businesses in forecasting and making strategic decisions to stay competitive, boost revenue, mitigate risk, and achieve growth.

Keep reading to learn more about how big data analytics can help your business.

E-commerce & Retail

Many retailers have adopted a business-driven and pragmatic approach to big data. In fact, sixty-two percent of retailers report that the use of information (including big data in retail) and analytics is creating a competitive advantage for their organizations. Some of the most effective strategies for leveraging big data include identifying specific business requirements first and then customizing the infrastructure, such as types of analytics tools, to support those requirements.

In practically every e-commerce and retail selling/buying process stage, big data analytics plays a critical role. It can help predict trends, identify new consumers, optimize pricing models, segment customers based on purchasing behavior, and present tailored, real-time offers based on client preferences. For example, let’s say a retail outlet’s most valued customers “liked” the Food Network on social media and have shopped frequently at Whole Foods. From there, the retailer can then use these insights to target their ads on social media channels for cooking-related shows and organic grocery stores. This will likely result in higher conversion rates and lower costs for customer acquisition strategies.

Manufacturing

The lifeline of the manufacturing industry relies on hundreds – if not thousands – of daily interactions with mechanical equipment, electrical relays, sensors, and more, all of which are coordinated and controlled by complicated systems. To keep things running smoothly, thousands of metrics and signals must be monitored at all times. Many businesses in the manufacturing industry have shifted their attention from traditional monitoring procedures to a more flexible and real-time process using big data analytics tools. This allows them to create tactical insights to significantly boost corporate performance while addressing emergent issues.

There are many use cases for big data analytics in the manufacturing industry, including:

Predictive Quality: Similar to predictive maintenance, this allows manufacturers to track the many variables that can affect product quality and help determine root causes and factors that contributed to lower quality products.
Supply Chain Management: In manufacturing, timing is everything. Big data helps better predict if and when a supplier will deliver and makes it possible to reduce risk by optimizing supply chains.
Improving Throughput and Yield: There are various factors that can negatively (or positively) impact product yield, and by utilizing the insights from big data, manufacturers can identify patterns in various processes to predict product yield and proactively make improvements.

Healthcare

Because the healthcare sector regularly generates large amounts of data (think about the sheer volume of data that flows through insurance companies), big data analytics plays a critical role in keeping this industry moving. In fact, healthcare is one of the most promising sectors for big data utilization. Moreover, it also helps address some of the industry’s most pressing issues, such as patient profiles, genomic analysis, public health monitoring, fraud analysis, and more.

One timely example where big data can help healthcare companies is the prediction of mass outbreaks. Using big data, scientists and doctors can build models of population health and create predictive models of how an outbreak might progress in a certain population. This can help in multiple ways including the development of vaccines, preventing hospital overcrowding, and issuing quarantine recommendations.

Travel

The travel industry generates a large amount of data in the form of bookings, queries, itineraries, fare charts, and consumer feedback, which leads to extensive data trails. The travel sector can provide a significantly better client experience and boost business efficiency by harnessing all this information using big data analytics. Strategic marketing, improved customer experiences, and reputation management can all benefit from the power of big data analytics.

An example of big data analytics in the travel industry is United Airlines’ use of customer data. They analyze over 150 variables in each customer profile to measure everything from past purchases to customer preferences. As a result, they’re able to create compelling and tailor-made offers for their customers. Big data techniques have increased United’s travel industry year-to-year revenue by over 15%.

Another example is improving the customer experience. One of the biggest travel headaches is lost or misplaced luggage. Through Delta Airlines’ app, customers can track their bags using the same technology as the Delta ground staff. Approximately 11 million Delta customers have downloaded the app globally.

Conclusion

The benefits of big data analytics across multiple industries is countless. From healthcare to travel to manufacturing, big data allows businesses to better target their marketing strategies, streamline business processes, and improve the customer experience, just to name a few. With the continued innovation of data management and analysis tools, businesses will be able to continuously improve upon their use of big data analytics to make intelligent choices that help them improve revenue, save expenses, and boost growth.

If you’re wondering what your big data is telling you, reach out to Gemini Data today. Our full stack system simplifies data management and analysis, allowing your business to connect the dots between data from disparate sources and effectively transform data into stories.

How to Build a Business Case through Data Storytelling

Jenn Snider — Tue, 30 Nov 2021 15:17:28 +0000

In a highly competitive and digitized world, data has become a critical source of business information on many fronts – from decision making to improving processes. But understanding what data is saying and deriving useful insights from it can be a taxing endeavor, and it’s critical to leverage the power of data in such a way that decision makers can use it effectively. That’s where data storytelling comes in. If you want your business to succeed and to be the preferred brand for your customers, understanding how data storytelling can inform and influence decisions is key.

Keep reading to learn more about how you can use storytelling to build your own business case for a new idea.

Establish the Context

The first step to a successful business case is sharing the full context of the situation and what you’re trying to solve for. Using data, narratives, and visuals can help decision makers clearly understand what is happening to the customer, their pain points, and how you can solve them. Developing a story around what customers are experiencing (good or bad) builds empathy between you and your audience, making it easier to persuade them to go with your idea. It also helps stakeholders actually experience the pain or challenges that customers are going through.

Explore options

Most businesses collect vast amounts of information about their customers – from their location to buying habits. This data is critical to understanding what customers want (or don’t want) and how your business can meet their needs. You can get direct knowledge from your audience and infuse your business ideas with that information to help explore how you can better serve customers. For example, if you know that your customers want faster resolution to problems, you can explore all the options available to achieve that. This offers a different perspective on the problem and gives you insights that you may not have discovered otherwise. Additionally, including a story around a specific data point (a.k.a. a challenge) will make your idea that much more appealing to decision makers.

Get Good Data

By utilizing all the data your company is collecting, you can build the meat and bones of your proposal. Use analytics, marketing reports, research, and customer surveys to back up all your claims. If you find anything that sounds good on your pitch but you can’t back it up with the data to make it rock-solid, then you probably need to look for other options. Consider using data visualization to present the data and make it easy to digest. No one wants to read tables upon tables and rows of figures that don’t make a lot of sense. You need to paint a clear picture of what you’re proposing by using the data that you have and creating a compelling narrative.

Pitch Your Idea

Now that you have a clear idea of your business case (with solid data to back it up) you can start pitching. The best way to approach this is to explain it in a balanced manner by sharing both the challenges and opportunities your pitch may encounter. Then you bring out the big guns – your data – to project how your new concept solves the customers’ problems. And not only do you use your data to back up your idea, you share a compelling story that clearly demonstrates how your idea will benefit customers.

Emphasize the Business Value

You already have the idea, the context, varying perspectives, and the data. Now, what you need is to justify why the idea is good. Ask yourself the following questions:

What meaningful impact will it have on the business?
What will it take to solve the problem?
How can I measure its success in terms of business outcomes?

These details are crucial to supporting the emotional aspects you raised in the data story. You want to clearly demonstrate – in hard, cold facts – that you have thought about how to put these data insights into action.

Conclusion

A good idea can be easy to come up with, but when it comes to actually making a case for how it can improve an organization, it can get exponentially harder to make it a reality. Thankfully, data storytelling can help. You can establish context, explore options, and make a solid business case using the power of data storytelling. It’s hard to argue with data, and with emotion and compelling stories, you can make your business case even more powerful.

If your organization is looking to leverage the power of data storytelling, contact Gemini Data today. We help organizations construct a view of their business by connecting the dots to tell their story. It’s our goal to enable customers to quickly grasp complex data relationships and increase the pace of human knowledge and advancement with data storytelling.