Types of Analytics
Most big and successful companies today invest in data analytics because as Geoffrey Moore, author of Crossing the Chasm and Inside the Tornado, has once said, “Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.”
In today’s world, data flows in our lives like blood. For a company to succeed, a proper data analytical management system is a must, without which one’s corporation is on the chasm. One needs a proper team, dedicated employees, proper data analytics tools, spearheading skills and a proper business strategy to help the company to take proper decisions and lead in the market.
In this article, we will try to understand the four types of data analytics in precise, clear and simple words.
- Descriptive Analytics
Descriptive Analytics tries to gain insight from the historical data finding an answer for what happened. It is the most common type of analytics that mines through the old and raw data to give a precise summary of the present situation utilizing various mathematical equations and advanced analytics. At least 80% of the business performance reports such as dashboards, scorecards, or summary reports involves the aggregation of past performances making it simpler to recognize and tackle the strengths and weaknesses of the said subject and help strategize the future action more effectively.
Let us consider the past monetary routine to foresee a customer’s financial feat, thus helping the industry categorize buyer’s probable product preferences and sales sequence.
The two important methods of this type are the data aggregation and data mining, helping the analyst foresee the underlying behavior. Thus, although it’s a good way to be a company’s and customer’s prophet, this tool is more like an indicator of future possibilities and behavior. So, various companies which are extremely data-driven prefer more to combine Descriptive Analytics with few other data analytics.
- Diagnostic Analytics
This form of analytics focuses on human decisions through eye on past performance to determine both what happened and why it happened. The result is usually a visual analytics that reflects reasoning. Here, the industry wants to mine deeper into the one particular issue that was earlier found out through Descriptive Analytics, thus making it more complicated stage of analytics. The focus shifts from aggregate to single isolated root-cause of the issue. Business Intelligence (BI) dashboards further integrate one’s study of data over multiple consecutive points in the time and further helping in various data filters.
Example– Healthcare industries have consumer segmentation with numerous filters such as diagnoses, prescript medicines, to measure the risk of hospitalizing the person.
Although, this helps one comprehend decrease or increase series of sales over a specified period of time, this form of analytics has a restricted capability to give an actionable outlook, through methods like attribute importance, principal components, sensitivity, and conjoint analysis. It thus only supplies the company an understanding of underlying bonds and series while stepping back into time.
- Predictive analytics
Predictive Analytics uses a predictive structuring of the data, forecasting what may occur in future, thus predicting future conclusions. It is focused on answering “what most possibly can happen”. This type of analytics uses a range of variable data to establish a relationship, if any, and make predictions based on the relationship. It relies on machine learning and statistics and if properly synced, it can be used to bear multifaceted probabilities in sales, marketing, and many other businesses.
It is necessary to point out here that this form cannot predict if an event will surely take place in the future; it just estimates probabilities of the occurrences of a particular event. It builds itself initially on the descriptive analytics, so as to obtain the possible outcomes.
Example—A telecom company, can classify its subscribers who are most likely to reduce their spending.
- Prescriptive Analytics
Building upon the predictive model, Prescriptive Analytics goes beyond the above-described types of analytics. It puts forward all approving outcomes aligned to a specific action taken, further suggesting various other courses of action to reach an exacting outcome. It is thus not a single action taken but the congregation of multiple actions. It focuses on prescribing “what action to take” and rub away any kind of future trouble. It thus employees a strong feedback structure that persistently studies and informs the link amid the action and the result.
It requires not only on the company owned historical information but also on external information due to the nature of arithmetical algorithms. It uses complicated instruments and technologies, making it tough to apply. It uses tools such as device learning, industry imperatives, and algorithms.
Thus, before going for this form of analytics, a business must think between the necessary efforts and the possible added value.
Example—School time-tables which use this form to avoid class clashes. GPS or any recommendation engines.
So now we know that the earliest form of analytics is the Descriptive analytics that focuses on “what” and dig deeper the historical raw data to tell us the root-causes of the problem; whereas the Diagnostic analytics focuses on what as well as why; thirdly the Predictive analytics, which is the analysis of the likable possibilities of what may happen in future and the last is Prescriptive, which instead of a single action and solutions, summarise several actions and their several outcomes.
With a good comprehension about how to pertain big data to business intelligence, businesses can help itself in getting most from their big data analytics. With different types of analytics, companies can easily and freely opt from these various forms, depending upon their need and how much deeper it is necessary for them to dig in the data analytics so as to gratify their businesses and bring out their best potential by taking the best decision. Where Descriptive and Diagnostic analytics applies a simplified and spontaneous, making the user just back to active, Predictive and Prescriptive analytics makes consumers completely upbeat. Often present developments show how companies today usually end up necessitating the use of advanced data analysis, adopting it out of stagnancy or losses, thus increasing the importance of understanding the different types of analytics rapidly.