Types of analytics explained — descriptive, predictive, prescriptive, and more
Most business leaders have a general understanding of data analytics and many companies have departments dedicated to gathering and interpreting information about customers, processes, and markets. But there is more than one kind of analytics — and each tells a different story about your business. Understanding the different types of analytics can help you choose the ones that will benefit your business most and ultimately drive business objectives.
This post will explore the three most common types of data analytics and one less known model. This information will help you gain better insight into what your data says about your business so you can make adjustments to meet your goals.
- Descriptive analytics
- Predictive analytics
- Prescriptive analytics
- Diagnostic analytics
- The future of analytics
Types of business analytics
The process of business analytics is an essential tool for interpreting and applying the vast amount of data your company collects and organizes. From customer behavior and conversion rates to revenue and business processes, the information generated by your company’s operations has to tell a helpful story to benefit you. Business analytics is the process that helps turn those data points into actionable insights.
The four different types of business analytics are descriptive, predictive, prescriptive, and diagnostic. Exploring the distinctions between these models can help you learn how to use each to support your business goals.
Descriptive analytics examines what happened in the past. You’re utilizing descriptive analytics when you examine past data sets for patterns and trends. This is the core of most businesses’ analytics because it answers important questions like how much you sold and if you hit specific goals. It’s easy to understand even for non-data analysts.
Descriptive analytics functions by identifying what metrics you want to measure, collecting that data, and analyzing it. It turns the stream of facts your business has collected into information you can act on, plan around, and measure.
Examples of descriptive analytics include:
- Annual revenue reports
- Survey response summaries
- Year-over-year sales reports
The main difficulty of descriptive analytics is its limitations. It’s a helpful first step for decision makers and managers, but it can’t go beyond analyzing data from past events. Once descriptive analytics is done, it’s up to your team to ask how or why those trends occurred, brainstorm and develop possible responses or solutions, and choose how to move forward.
Predictive analytics is what it sounds like — it aims to predict likely outcomes and make educated forecasts using historical data. Predictive analytics extends trends into the future to see possible outcomes. This is a more complex version of data analytics because it uses probabilities for predictions instead of simply interpreting existing facts.
Use predictive analytics by first identifying what you want to predict and then bringing existing data together to project possibilities to a particular date. Statistical modeling or machine learning are commonly used with predictive analytics. This is how you answer planning questions such as how much you might sell or if you’re on track to hit your Q4 targets.
A business is in a better position to set realistic goals and avoid risks if they use data to create a list of likely outcomes. Predictive analytics can keep your team or the company as a whole aligned on the same strategic vision.
Examples of predictive analytics include:
- Ecommerce businesses that use a customer’s browsing and purchasing history to make product recommendations.
- Financial organizations that need help determining whether a customer is likely to pay their credit card bill on time.
- Marketers who analyze data to determine the likelihood that new customers will respond favorably to a given campaign or product offering.
The primary challenge with predictive analytics is that the insights it generates are limited to the data. First, that means that smaller or incomplete data sets will not yield predictions as accurate as larger data sets might. Getting good business intelligence (BI) from predictive analytics requires sufficient data, but what counts as “sufficient” depends on the industry, business, audience, and the use case.
Additionally, the challenge of predictive analytics being restricted to the data simply means that even the best algorithms with the biggest data sets can’t weigh intangible or distinctly human factors. A sudden economic shift or even a change in the weather can affect spending, but a predictive analytics model can’t account for those variables.
Prescriptive analytics uses the data from a variety of sources — including statistics, machine learning, and data mining — to identify possible future outcomes and show the best option. Prescriptive analytics is the most advanced of the three types because it provides actionable insights instead of raw data. This methodology is how you determine what should happen, not just what could happen.
Using prescriptive analytics enables you to not only envision future outcomes, but to understand why they will happen. Prescriptive analytics also can predict the effect of future decisions, including the ripple effects those decisions can have on different parts of the business. And it does this in whatever order the decisions may occur.
Prescriptive analytics is a complex process that involves many variables and tools like algorithms, machine learning, and big data. Proper data infrastructures need to be established or this type of analytics could be a challenge to manage.
Examples of prescriptive analytics include:
- Calculating client risk in the insurance industry to determine what plans and rates an account should be offered.
- Discovering what features to include in a new product to ensure its success in the market, possibly by analyzing data like customer surveys and market research to identify what features are most desirable for customers and prospects.
- Identifying tactics to optimize patient care in healthcare, like assessing the risk for developing specific health problems in the future and targeting treatment decisions to reduce those risks.
The most common issue with prescriptive analytics is that it requires a lot of data to produce useful results, but a large amount of data isn’t always available. This type of analytics could easily become inaccessible for most.
Though the use of machine learning dramatically reduces the possibility of human error, an additional downside is that it can’t always account for all external variables since it often relies on machine learning algorithms.
Another common type of analytics is diagnostic analytics and it helps explain why things happened the way they did. It’s a more complex version of descriptive analytics, extending beyond what happened to why it happened.
Diagnostics analytics identifies trends or patterns in the past and then goes a step further to explain why the trends occurred the way they did. It’s a logical step after descriptive analytics because it answers questions like why a certain amount was sold or why Q1 targets were hit.
Diagnostic analytics is also a useful tool for businesses that want more confidence to duplicate good outcomes and avoid negative ones. Descriptive analytics can tell you what happened but then it is up to your team to figure out what to do with that data. Diagnostic analytics applies data to figure out why something happened so you can develop better strategies without so much trial and error.
Examples of diagnostic analytics include:
- Why did year-over-year sales go up?
- Why did a certain product perform above expectations?
- Why did we lose customers in Q3?
The main flaw with diagnostic analytics is its limitation of providing actionable observations about the future by focusing on past occurrences. Understanding the causal relationships and sequences may be enough for some businesses, but it may not provide sufficient answers for others. For the latter, managing big data will likely require more advanced analytics solutions and you might have to implement additional tools — venturing into predictive or prescriptive analytics — to find meaningful insights.
The future of analytics
The use of analytics in business is not new, but it is on a steep growth trajectory. Fueled by huge data sets streaming in from the IoT, advancements in AI, and the growth of self-service BI tools, the use of analytics in business has yet to peak.
The US Bureau of Labor Statistics predicts huge growth in the number of research analysts in the coming years, projecting a “must faster than average” growth rate of 19%. Additionally, some of the industry’s top experts in data science and analytics predict the ideal candidate for businesses in the future will be a person who can both understand and speak data.
Even as the need for analytics experts grows, the market for self-service tools continues to escalate as well. A report from Allied Market Research expects the self-service BI market to reach $14.19 billion by 2026, and Gartner cites the growth of business-composed data and analytics, that focuses on people, “shifting from IT to business.”
Implementing more advanced analytics — and for some businesses bringing analytics into business strategy — will continue to become more important for companies of all sizes.
Get started with business analytics
The four types of data analytics give you tools to understand what happened (descriptive), what could happen next (predictive), what should happen in the future (prescriptive), and why something happened in the past (diagnostic). Your ability to make strategic, data-driven decisions for your business depends on the facts you gather and how you use them.
When you’re ready to go further than simple data collection, choose the type of analytics that best fits your business’ needs. Ask yourself what question you need answered or what decision you need to make, and start with the right type of analytics.
Adobe Analytics helps businesses of any size, and in any industry, turn data into business intelligence. Collect and organize data all in one place and put the power of AI to work in analytics that create meaningful, actionable insights.