We live in an age dominated by digital content. The volume of data modern enterprises have to process, interpret, and reconfigure on a regular basis is nothing short of massive. To handle this influx of information, many businesses are turning to business intelligence tools such as diagnostic, descriptive, predictive and prescriptive analytics. This article will dive into the differences between them and explain when each is useful, as well as how to select the right analytics solution for your business.
The Goals of Analytics
With mobile devices and the internet of things (IoT) growing more and more prevalent, the amount of data is rapidly increasing — we generate around 2.5 quintillion bytes per day, and that number is only going up. This is particularly true when it comes to supply chain systems.
Studies show that up to 73% of corporate data never gets used for analytic purposes. That’s a huge waste of resources that could be directly improving your ROI, reducing customer losses, increasing efficiency, or whatever else you’re trying to do by collecting data. If you want your business to have a holistic perspective on the market and its place within it, a watertight analytic setup is essential. It helps businesses shrink operating costs, increase sales, expand their product range, and bring them closer to their customers.
When you look at analytics in this way, it becomes easier to understand why they’re most valuable when implemented as a unified system. When isolated, the narrative is incomplete — numbers are useful, but less so than when they’re presented in intuitive visualizations with predictions or suggestions for how to apply them. You miss out on the insights needed to improve decision-making.
In the next section, we’ll talk a little more about the distinctions between the types of analytics and why they’re important. Analytic tools don’t just ask their own questions; they use different data extraction techniques to find the answers.
What Are Diagnostic Analytics?
Diagnostic analytics are a form of advanced analytics that focus on explaining why something has happened based on data analysis. Like a doctor investigating a patient’s symptoms, they aim to understand the underlying issues and determine why an issue is happening.
Its capabilities allow users to identify anomalies by highlighting areas that could require further study, which are pinpointed when trends or data points raise questions that can’t be answered easily or without digging deeper. Some questions that would have to be addressed with diagnostic analytics include:
- Why did this marketing campaign fail?
- Why have sales increased without any increased marketing attention for a certain region?
- Why did employee performance fall during this month?
As well as other questions that have no obvious answer from a single data source.
Diagnostic analytics offer data discovery, drill-down, data mining and data correlation. Drilling down into the data allows users to identify potential sources for the anomalies discovered in the first step. Analysts can use these capabilities to examine patterns both within and external to the data to draw an informed conclusion. Probability theory, filtering, regression analytics and time-series data analysis are all useful tools related to diagnostic analytics to facilitate this process.
What Are Descriptive Analytics?
The clue is in the name when it comes to descriptive analytics: they describe the state of your business. These solutions process large amounts of data and reconfigure it into easily-interpretable forms such as tables, charts or graphics. This information could be made up of any statistic, event, trend, or specific timeframe from your manufacturing past.
The aim of these types of analytics is to learn from the past. One common example is analyzing seasonal purchasing trends to determine the best time to launch a new product. As consumers are creatures of habit, looking at historical data is an effective way to predict their responses.
Descriptive analytics or statistics can demonstrate everything from total stock inventory to the progress of sales figures over the course of several years. They can show the typical amount customers spend and whether this sum is likely to increase at certain times. If diagnostic analytics are about the why, descriptive analytics explains the what.
What Are Predictive Analytics?
Predictive and descriptive analytics have oppositional objectives, but they’re very closely related. This is because you need accurate information about the past to make predictions for the future. Predictive tools attempt to fill in gaps in the available data. If descriptive analytics answer the question, “what happened in the past,” predictive analytics answer the question, “what might happen in the future?”
Predictive analytics take historical data from CRM, POS, HR, and ERP systems and use it to highlight patterns. Then, algorithms, statistical models and machine learning are employed to capture the correlations between targeted data sets.
The most common commercial example is a credit score. Banks uses historical information to predict whether or not a candidate is likely to keep up with payments. It works in much the same way for manufacturers, except that they’re usually trying to find out if products will sell. Predictive analytics focus on the future of the business.
What Are Prescriptive Analytics?
Of diagnostic, predictive, descriptive, and prescriptive analytics, the latter is the most recent addition to the business intelligence landscape. These tools enable companies to view potential decisions and, based on both current and historical data, follow them through to a likely outcome.
Like predictive analytics, prescriptive analytics won’t be right 100% of the time, because they work with estimates. However, they provide the best way of “seeing into the future” and determining the viability of decisions before they’re made.
The difference between the two is that prescriptive analytics offers opinions as to why a particular outcome is likely. They can then offer recommendations based on this information. To achieve this, they use algorithms, machine learning and computational modeling.
If predictive analytics answers, “What might happen?” then prescriptive analytics answers, “What do we have to do to make it happen?” or “How will this action change the outcome?” Prescriptive deals more with trial and error and has a bit of a hypothesis-testing nature to it.
Summary of the Different Types of Analytics
All these types of analytics provide more efficient ways to extract value from operational information. Through data analysis, they support decision making, streamline customer communications, and can even boost revenue.
Diagnostic analytics ask about the present. They drill down into why something has happened and helps users diagnose issues. Descriptive analytics ask about the past. They want to know what has been happening to the business and how this is likely to affect future sales. Predictive analytics ask about the future. These are concerned with what outcomes can happen and what outcomes are most likely. Finally, prescriptive tools ask about the present’s impact on the future. It wants to know the best course of action for right now in order to positively impact the future. In other words, they’re the decision makers.
What Types of Software Perform Analytics?
Analytical investigation is an integral part of optimizing S&OP (sales operation and planning) strategies. After all, the only way to ensure that manufacturing levels are profitable is to make logical, informed predictions about demand. In short, you need to create a data-driven culture.
However, it isn’t always easy to find the right analytics tools. There are a lot of choices out there, and considering all of the different options, it can be an intimidating process. For small businesses, the recommendation is to split the market into its three main product types.
These are diagnostic, predictive, descriptive, and prescriptive analytics, and not all solutions perform all of these types of analysis. The first thing to understand is that, while they can be used in isolation, the best results come with a cohesive merger of all four. When applied correctly, they have the power not just to cooperate, but also to diversify your data analysis.
Business intelligence is the largest term for solutions that provide analytical capabilities and typically offers all these types of analytics, but occasionally might only provide descriptive and diagnostic. Within the larger umbrella category, business analytics focuses on predictive and prescriptive analytics, big data analytics tackles massive data sets, embedded analytics can be embedded inside other software programs, and enterprise reporting slims down the suite to offer a leaner module of reporting tools.
How to Select Analytics Software
Selecting the right type of analytics software can mean the difference between confident business decisions and continued uncertainty in your choices. This guide will provide a clear path forward as you choose a business intelligence, business analytics, embedded BI, enterprise reporting or big data analytics tool.
To determine which of the many analytical software options is the best fit for you, you should first decide on which requirements you need to utilize. This BI requirements template will help you sort out which requirements you need and which are window dressing so you can make the best choice for your unique business.
Once you’ve identified your key requirements, you’re ready to compare solutions based on how well they deliver those requirements. If you only need to identify the problems, a solution that excels in diagnostic analytics might be the best choice. If you want something that helps you plan for solutions, a platform that performs well in diagnostic and prescriptive may be more fitting.
This comparison report breaks down the industry leaders by score for individual features. We recommend selecting the top five or so that match your needs the most closely.
To get an accurate price quote, demo of the product and maybe even a free trial, now is the time to submit an RFP (request for proposal). This BI RFP guide will walk you through the process step-by-step so you’ll know exactly what to include to make sure you find the best-fit product for your business.
At the end of the day, diagnostic, descriptive, predictive and prescriptive analytics solutions work together to build a story. It’s a story about what your business has, what it needs, and what it could achieve. With this narrative for guidance, you can make decisions that are fully informed by your data.
Which methods of analytics has your business found success with? Do you have any tips for implementing any type over another? Let us know with a comment below.