Data is becoming increasingly more available and present in the daily operations of businesses today. With data comes analytics, and in order to drive more efficient decision-making, companies must consider various analytic solutions to discover what will allow them to get the most out of their information.
Understanding where the business is currently positioned, the data sources/sets being collected, and the metrics determined as targets for success are elements that data analysts must factor into their analytics strategy.
The 3 Types of Data Analytics
When strategizing for something as comprehensive as data analytics, including solutions across different facets is necessary. These solutions can be categorized into three main types – Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics. While each must be considered for the individual leverage and value they provide, their relationship to one another is also crucial in order for a business to have a complete perspective of how their past and future are connected.
Let’s look at each solution a little more in-depth.
Descriptive Analytics
“What does our past look like and what does it tell us?”
Descriptive analytics is what businesses commonly use as they assess historical data and try to extract the most important trends, occurrences, and areas for improvement. Doing so allows companies to uncover not only what happened, but also what factor(s) may have influenced it to happen, and how it may then impact another metric down the road. Whether looking from a high-level perspective or diving into one aspect of a data set, the analytics for this solution include the aggregating and statistical operations used by businesses, given the data has already been captured.
Across all departments, descriptive analytics are useful for reporting and communicating what has happened and how it relates to the success of the business as a whole. From operations and financials to human resources and management, and areas that vary from company to company, employees can benefit from summarizing their raw data into meaningful and applicable measurements. The more in tune a business is with its historical data, the more effective it can be in adapting its reporting and future strategies for data optimization.
Predictive Analytics
“What is possible in the future and how can we think proactively?”
The analytics of this next step do what is stated directly in the name – they predict. Using the insights provided by descriptive analytics can guide businesses toward efficient predictive analytics to make more sense of the future. They take control of past trends and data distributions and use them to foresee upcoming results so in turn they can manage expectations, restructure strategies, and set new goals.
Depending on the scope at which a company is making predictions, the future can be defined as near or as far from the present when entered into models and algorithms.
It is important to keep in mind, however, that no analytics will be able to tell you exactly what WILL happen in the future. Predictive analytics put in perspective what MIGHT happen, providing respective probabilities of likelihoods given the variables that are being looked at.
Businesses can also use predictive analytics to open up the conversation to other possibilities and scenarios that current data may not be capturing yet. Instead of just wanting to forecast a certain metric or trend, analysts can fill in the information with predictive analytics to explore hypotheticals with a more holistic lens.
Common directions taken with predictive analytics guide questions such as:
- If this metric changes in this way, what would happen to x,y, and z metrics, and which ones are correlated?
- Why are these results happening at this time – will they only happen then or is this a continuous trend?
- If this happens, then…?
- How likely is…?
- What if nothing changes to current patterns?
- Will this change correct the process, to what extent, and in what way?
Prescriptive Analytics
“What is advised for us to do and what decisions should we make?”
Prescriptive analytics extend beyond the historical insights of descriptive analytics and the possible future outcomes of predictive analytics to provide recommendations for the next steps that should now be taken. Companies can evaluate and decide upon a number of options depending on the outcomes that result from simulating various potential scenarios. It takes the forecasts and likelihoods from predictive analytics one step further by creating advised solutions that will also align with the goals of the company’s key metrics.
Every business wants to be efficient and prescriptive analytics create the opportunity to optimize resources and have an upper hand in effective decisions before they have to be made. It provides further insight into why certain results may happen so related factors can be monitored when new actions are taken.
Although prescriptive analytics is also a form of predicting future situations, it still utilizes quantitative tools to make sense of the data that already exists and the real-time data being collected – such as algorithms, statistics, and machine learning. The data can also go beyond that of the organization’s own sources and include external data that may have an influence on a metric being assessed and impact the business at a departmental or organizational level.
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