What type of analytics seeks to identify the courses of action to achieve the best performance possible?
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Analytics is a broad term covering four different pillars in the modern analytics model. Each plays a role in how your business can better understand what your data reveals and how you can use those insights to drive business objectives.As organizations collect more data, what they use it for and how they analyze and interpret that data becomes more nuanced. Data without analytics doesn’t make much sense, but analytics is a broad term that can mean a lot of different things depending on where you sit on the data analytics maturity model. Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive. How do you know which kind of analytics you should use, when you should use it, and why? Understanding the what, why, when, where, and how of your data analytics helps to drive better decision making and enables your organization to meet its business objectives. In this blog we will discuss what each type of analytics provides to a business, when to use it and why, and how they all play a critical role in your organization’s analytics maturity. Descriptive AnalyticsWhat is Descriptive Analytics?Descriptive analytics answer the question, “What happened?”. This type of analytics is by far the most commonly used by customers, providing reporting and analysis centered on past events. It helps companies understand things such as:
Descriptive analytics is used to understand the overall performance at an aggregate level and is by far the easiest place for a company to start as data tends to be readily available to build reports and applications. It’s extremely important to build core competencies first in descriptive analytics before attempting to advance upward in the data analytics maturity model. Core competencies include things such as:
How Do You Get Started with Descriptive Analytics?It’s likely you’ve adopted some form of descriptive analytics internally, whether that be static P&L statements, PDF reports, or reporting within an analytics tool. For a true descriptive analytics program to be implemented, the concepts of repeatability and automation of tasks must be top of mind. Repeatability in that a data process is standardized and can be regularly applied with minimal effort (think a weekly sales report), and automation in that complex tasks (VLOOKUPS, merging of excel spreadsheets, etc.) are automated—requiring little to no manual intervention. The most effective means to achieve this is to adopt a modern analytics tool which can help standardize and automate those processes on the back end and allow for a consistent reporting framework on the front end for end users. Despite only being the first pillar of analytics, descriptive analytics also tend to be where most organizations stop in the analytics maturity model. While extremely useful in framing historical indicators and trends, descriptive analytics tend to lack a tangible call to action or inference on why something occurred which leads us to the next pillar of analytics: diagnostic analytics.
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