There is no question that data is a valuable business resource. In a previous blog, we discussed Eight Best Practices for Data Management, but today we are going to go a little more in-depth. We will discuss how you can analyze and apply your data to improve your business.
Analyzing data starts with defining what data is and how it’s described. Here are some of the basic terms that help build a framework for talking about data.
Data is defined as facts or information, often numerical, that is used for reference or analysis.
Data Analytics is the process of inspecting, processing, or analyzing data allowing us to find connections, useful information, or to draw conclusions about that data.
Qualitative Data is based on observable things that are not measured. It is descriptive and conceptual.
Quantitive Data can be counted, measured, or expressed with numbers.
Determining if data is qualitative or qualitative is frequently the first step in defining and organizing data. Qualitative and quantitative data certainly have different applications. For example, qualitative survey feedback about your hiring process may help you build a more accommodating process that improves employee satisfaction. Alternatively, quantitative statistics about how many people you hire throughout the year may help you get insight into the seasonality of your business, thus plan better marketing strategies to attract more candidates when you need them. Both types of data are important for getting a comprehensive picture of your business, and neither is more important than the other. Ideally, you will want to be gathering both quantitive and qualitative data across your organization.
Good analytics starts with good data. It doesn’t matter how advanced your analytics are if you’re using incorrect or incomplete information. Some key features of good data include:
Having clear goals for what you want to gain from your data is the first step in any data management strategy. Are you trying to improve sales using customer buying analytics? Instead, maybe you’re interested in tracking health trends to determine the best time to schedule preventative screenings. Knowing what processes you want to improve or what you want to learn from the data (best time of day to send an email, which customers are most engaged etc.) will help you with step two – determining what data you need to collect.
Describing or summarizing data using business intelligence (BI) tools to better understand what is going on or what has happened. Descriptive analytics give you the big picture of what is happening, typically through data visualization.
With a focus on identifying patterns from past or current data, this is the most common form of analytics and includes things such as:
Focus on past performance to define what happened and why. Analytics tools are often used to display this type of data in a BI dashboard. Diagnostic analytics help you understand why something is happening.
Diagnostic analytics often build connections by comparing past data. Examples might include:
Emphasizes defining possible outcomes through statistical models and/or by applying machine learning tools. With predictive analytics, you are trying to forecast outcomes and understand what is likely to happen in the future.
Predictive analytics is a powerful tool for improving outcomes and reducing risk. Some common use cases include:
This type of analytics takes it one step further by using predictive analytics to recommend appropriate actions or strategies. By using advanced algorithms and AI tools, predictive analytics helps you decide what to to do next.
This form of analytics is the most complex, but also the most useful. Prescriptive analytics analyzes predictive analytics to generate a plan of action and can help by:
Processing your data within the framework of these four types of analytics helps you understand how data can be used for decision-making and in what ways. It also offers insight on areas where you may be lacking data. Some businesses are so focused on descriptive analytics that they haven’t explored predictive or prescriptive analytics. This unfortunate oversight ignores how these types of analytics can help businesses plan for the future. When you only rely on descriptive or diagnostic analytics, it’s easy to make missteps by becoming too dependent on your business instincts or past trends.
The field of data analytics is constantly advancing, and new tools are making high-quality analytics available to businesses at every level. Understanding how each type of analytics can inform and improve key metrics at your organization is a powerful first step to harnessing your data. Whether you are trying to save lives and improve patient care, or simply build your brand’s reputation and engagement, data is quickly becoming the future of decision making. Indeed, for many industries, good data is becoming essential to staying competitive and that isn’t likely to change any time soon.
Business intelligence tools are a great way to begin leveraging your data. If you would like to learn more about how you can apply business intelligence and data automation to your workflows, then DOMA is here to answer any questions you might have.
DOMA Technologies (DOMA) was founded in 2000 as a Cloud-based document management company. Today DOMA delivers comprehensive solutions using the latest tools to help you collaborate with enterprise data. DOMA captures and transforms information through digital solutions using hyper-automation. Our data and document solutions pair traditional practices like scanning with advanced cloud technology to extract, convert, and visualize the data trapped in your documents.
These services, along with the DOMA Experience (DX) software platform are designed to help support your organization’s Digital Transformation journey. With a considerable portfolio of government, healthcare, education, and commercial business customers DOMA has the experience and infrastructure to deploy integrated solutions that address your business challenges with innovation. Contact DOMA to digitize your workflow; DOMA makes complex operations simple across a wide range of industries.
What is hyper automation? Find out out about how AI tools can help you get more out of your data.
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