It can be difficult to analyze those results, find actionable insights, and present them in a helpful way to stakeholders because businesses find themselves lost between two distinct ways of handling information: exploratory and explanatory data analysis. These two types of analysis  are separate but not contradictory stages within the analytics process. They’re equally vital aspects when it comes to data contextualization and storytelling and if you want to convey data insights as effectively as possible, it’s crucial to know how they differ. 

The Process and Purpose of Analysis

The goal of data analytics is to interpret data and turn it into easy-to-understand knowledge that will drive decision-making. It takes information from disparate sources, including numbers, observations, and facts, and turns it into coherent and actionable insights. The data process typically includes the following steps:

• Data requirements specification: the data needed for analysis is usually based on a question or a hypothesis which determines how the data will be inputted. 
• Data collection: the process of gathering information with an emphasis on accurate and honest collection.
• Data processing: This step means structuring the data as needed for whatever analysis tools are being used. 
• Data cleaning: the process of correcting or preventing errors such as inaccuracies, duplications, or incomplete information.
• Data analysis: the process of understanding, interpreting, and gaining insights.
• Communication: reporting the analysis in a format that will support decisions and further action.

Exploratory and explanatory analysis are both part of the analysis process, but serve distinct purposes in helping stakeholders understand data and the story they’re telling. However, to be an effective data communicator, it’s important to weave data into a story, making it easier for an audience to understand and remember.

What Is Exploratory Data Analysis?

Exploratory data analysis helps users get familiar with the data and make sense of them. This might involve looking at it from different angles to identify any patterns or trends. Tools such as spreadsheets, dashboards, or data visualizations to reveal trends, outliers, anomalies, and other points of interest are typically used for exploratory analysis.

In exploratory data analysis, clues and evidence are used to draw insights from the gathered information. It tries to find meaningful relationships between variables, but its implications may be limited. Although it gives you conclusions or answers to your hypotheses, it does not provide you with actionable insights just yet. For example, if your hypothesis is that customers leave your website after arriving on a certain landing page, you would input bounce rates, time on page, content from that landing page, etc, into a tool that organizes and lays out this information in such a way to allow for further analysis. 

What Is Explanatory Data Analysis?

This phase happens after exploratory analysis when you’ve identified something interesting or insightful from the collected information and are ready to share it with an audience. Using the above website example, this is the stage where you would dig into all of the factors that contributed to the user drop-off to understand why it’s happening and how you might fix it.

From there, you would have an understanding of the actionable insights needed to make any necessary adjustments. And, the most critical part of this stage is communicating those insights to the appropriate stakeholders and decision makers who can hopefully take the necessary actions to make positive change. Again, the important thing to remember here is that you’re weaving data into a larger story. You want your audience to understand and remember what you’re telling them – which is where storytelling comes in – and back it up with the evidence of your data.

Conclusion

Data analytics can be a challenge for many businesses and requires a firm understanding of the required process – from requirements to communication – and the difference between exploratory and explanatory analysis. While they go hand-in-hand, they play different roles in  the analysis process and help you arrive at different conclusions. Exploratory data analysis can help you get familiar with data and identify patterns and trends while explanatory analysis tells you the “why” and gives you the actionable insights needed to drive decisions.

Data analysis is critical to helping businesses move forward and maximize their outcomes but can be difficult for a business to manage on its own. At Gemini Data, we can help your business solve your biggest data challenges, enabling you to understand and share data stories. We can help you connect the dots, contextualize your data, and drive actionable insights. Contact us today to learn more. 

In this article, we’ll dive into just a few of the ways that small businesses can power up their data science usage to optimize their outcomes. 

Utilize Accurate Data for Strategic Business Decisions

Data-based decision making has become almost standard practice for businesses. Understanding critical insights from data can help stakeholders increase sales, better engage with customers, and manage risk. But before you get to insights, you have to be sure you’re using accurate data. The correct data set is critical since it will be the source of the evidence and statistics you use to make decisions. Data comparison is also required to determine the accuracy of the obtained data.

A few methods for checking the validity of your data include collecting survey reports to discover products, services, and features, conducting user surveys to evaluate how well people relate to your product (which is critical when introducing new goods to market and assessing how they may perform), and outlining relevant risks and new business possibilities.

Take Advantage of the Best Big Data Services and Solutions

It’s a huge undertaking to gather and analyze data. Even if you’re able to stay on track and complete all the necessary steps, odds are that time could have been better spent on growing your business and focusing on strategies. It’s also not something that everyone can do. Why spend your time on a task that you’re not experienced in? Lastly, if you perform data collection and analysis manually, you run the risk of inaccurate data inputs and findings. 

Fortunately, there are many tools and platforms available that can help automate data analysis and structuring. Not only does it help you get accurate results, but it also streamlines the data science process so you can come up with action items faster. But remember, not all tools are created equal and you’ll need to do research to ensure you end up with the right tool, or a partner that can handle it all for you.

Get New Leads by Nurturing Current Ones

Nurturing existing leads and customers is a brilliant way of acquiring new ones. Using data analysis to understand the patterns, behaviors, demographics, and more of your existing customers can be a surefire way to tailor effective marketing campaigns for new ones. You can also experiment with referral marketing – leveraging positive word-of-mouth – and target your best and most satisfied customers to spread the word to their friends and networks. Understanding the customer data you have will help you understand which customers might make the best advocate for your business (and have large networks). 

Using big data, you can also better discover your target market and optimize consumer behavior data to provide what they need when they need it. Amazon is a great example of this (think about the curated items they find just for you) and being the top e-commerce platform is proof of their effective use of data to nurture and grow their customer base.

Conclusion

Data science can provide small businesses with the ability to continually identify new trends and growth possibilities. By using accurate data to make smarter decisions, employing the right tools and platforms, and understanding current customers to gain new ones, your business can develop practical solutions that can solidify your small business as a significant player. Running a small business in today’s world may be challenging, but it is possible to boost your small business with data science, and see your small business achieve big things. 

If you’re wondering what your small business’s data is telling you, reach out to Gemini to learn how we can help you understand and share data stories. We can help simplify and speed up your data collection and analysis to get to insights faster.Connect the dots and tell your story.

But with mass volumes of data being collected daily, it can be difficult for businesses to keep up and make sense of it all. One of the first steps in an effective data analytics structure is data ingestion – the transportation of data from multiple sources to a storage medium where it can be accessed, used, and analyzed. Whether through in-house teams specializing in data science or a third-party providing insight services, data ingestion is one of the most effective ways for an organization to manage large influxes of information. 

So how is data ingestion done and how can businesses leverage it to their benefit? Keep reading to learn a few best practices for effective data ingestion. 

Data Ingestion Methods

Data ingestion can be performed in various ways but there are three methods that stand out:

• Batch. In the batch approach, data analysts collect and shift data at certain times. This is a good option for processes that occur regularly at predetermined times such as weekly or daily reports. 
• Real-time. Ingesting in real-time, also known as streaming, is the best way of collecting time-sensitive information. In this process, data gets retrieved, processed, and stored in applications that render visualizations as events happen. This method is what companies use for making high-level decisions.
• Lambda architecture. This is a method that combines real-time and batch procedures. This strategy combines the advantages of the two methods; making use of real-time ingestion to extract information from time-sensitive data and batch ingestion to provide a broad view of recurring data.

Data Ingestion Best Practices

Data is an ever growing asset in the corporate world today, and increasing exponentially on a daily basis. As a result, most businesses have multiple data sources they need to pull from. To maximize all of this data, companies need to make sure they’re ingesting them properly before they’re stored and processed. Otherwise, the company could miss out on crucial insights. 

One way to ensure that businesses ingest data in time is by putting infrastructure in place and adding resources for data management. Using artificial intelligence, anticipating challenges and planning ahead, and leveraging automation are some ways to support a growing enterprise.

• Automated Ingestion

As an organization grows, so too does the amount of data that needs to be processed and stored. At some point, manual techniques that worked for smaller volumes of data will no longer be effective or sustainable. This is where automation comes in. Automating processes along the way of data ingestion can increase productivity, minimize downtime, and save time on processing data. What’s more, automation makes your IT architecture consistent, reduces human error, consolidates information, and keeps data safe.

• Plan, Plan, Plan

The goal of any data analysis is to turn raw information into insights a company can use. When your data volumes increase or you start collecting multiple types of data, your methods for analyzing will grow complex. Data ingestion is one big process that can help you anticipate these types of challenges and plan accordingly to ensure your data processing runs smoothly and efficiently. 

• Leverage Artificial Intelligence

Manual interventions in the ingestion process causing slowdowns, lost time, and redundant work. But employing digital operations and AI using statistical algorithms and machine learning can minimize (and can even eliminate) the need for human interventions during the ingestion process. With AI, you can increase your accuracy levels, save time, and get to actionable insights quicker.

Conclusion

Effective and streamlined data ingestion is a must for any business dealing with large volumes of data. By leveraging this process and employing best practices such as automation, smart planning, and using AI, businesses can effectively manage their data ingestion to make better decisions and optimize outcomes. 

If your business is looking to simplify and improve its data processes, reach out to Gemini today. We can solve the biggest data challenges, enabling you to understand and share data stories. We help organizations construct a connected view of their business by connecting the dots between data from disparate sources. Go from data to insights in no time.

As we discussed in our last blog, big data has transformed the way businesses operate. From healthcare to education to retail and manufacturing, just about every enterprise and organization is leveraging the benefits of big data, particularly to inform data-driven decision making. 

But making sense of big data requires powerful analytics and insights, which can provide many benefits to businesses. In this article, we’ll continue to talk more about the advantages of big data analytics and how it can help your business find more success. 

Know Your Current (and Future) Customers

Big data can provide your business with critical insights into current and potential customers such as geographic location, demographics, and purchasing decisions, to help identify who will best respond to your latest offerings. The information you gather on your consumers, coupled with social media data, can help your business customize content to appeal to a target group, increase conversions, and gain repeat customers.

Through analysis of customer data, you can develop profiles for your ideal customers to better understand your audience and their interests, providing the opportunity for your business to learn more about customer attitudes and behaviors to predict behavior patterns and better target customers. Businesses can also leverage more personalized marketing strategies that are tailored to specific customer needs, pain points, and characteristics, yielding a higher return on investment than other, more general, strategies.

Leverage Complex Supply Chains

For most businesses, the supply chain is critical to keeping things running. The system of organizations, people, activities, information, and resources involved in supplying a product or service to a consumer can be complex and difficult to manage. But with big data, companies can offer supplier networks with greater precision and insights to keep the chain running smoothly. And on the supplier side, the usual constraints can be overcome by applying big data analytics  to use higher levels of contextual intelligence, which is necessary for their success.

Many supply chain executives are leveraging data analytics to disrupt the foundation of supplier networks to include high-level collaboration. This collaboration can allow supply networks to apply new knowledge to existing problems or other scenarios.

Increase Operational Efficiency

Improving processes to keep your business running faster and smoother can be the difference between survival and failure. Finding ways to automate processes, identifying bottlenecks or redundancies, and duplicating what’s working, can lower costs and increase revenues. Big data, and the information gained from them, can offer businesses insights into customer behavior, optimal analysis of industry trends, enhancements in customer service and delivery methods, and much more, leading to better operational efficiency. 

For industries such as manufacturing, an increase in efficient operations, identification of wasteful processes, and fulfillment of objectives at reasonable costs can make all the difference in being successful and beating out the competition. Big data analytics can give businesses real-time, evidence-based insights that take the guesswork out of making smarter decisions. 

Conclusion

Investing in big data provides companies with the unique opportunity of understanding their target audience better and refining their offerings accordingly to become the unique solution to their customers’ problems. By implementing big data analytics, a business can leverage complex supply chains and increase operational efficiencies, thereby securing a competitive advantage, saving money on operations, and acquiring and retaining customers better than before.

Gemini Data provides data management and analytics for businesses, specializing in data availability for AI or machine learning driven analysis and applications. We know how to create a narrative from massive amounts of data, giving you the context and commentary you need to gain critical insights and integrate them into your business strategy. Contact us today to learn more about how we can help you!