Pros and cons
Such an approach is particularly advanced and brings many advantages, for example in the energy field where companies need to know in advance future fluctuations in production and consumption to optimize distribution through the power grid.
It is also a rather common tool in the arsenal of marketers, particularly in the context of sentiment analysis.
An algorithm can be trained to recognize positive and negative customer comments based on sample texts to autonomously predict the tone of future comments and give insights into overall sentiment.
At the same time, this type of analysis is heavily based on very expensive technologies such as machine learning (ML) and deep learning (DL) and requires a massive availability of high-quality data to function properly.
4. Prescriptive analytics
The purpose of prescriptive analytics, which is certainly the most advanced category on our list, is to suggest a course of action to avoid future problems or to get the maximum benefit from a promising trend.
This type of analysis is widely used, for example, by recommendation engines to determine buying patterns among similar clients and provide them with personalized recommendations based on their browsing activity.
To this end, recommendation systems analyze customer data collected through descriptive analytics (such as previously purchased products) and predictive analytics (e.g. market segmentation) to recommend the most suitable product for a specific customer.
Recommendations should always be tested and validated against the desired result, for example by verifying that consumers have actually clicked on the recommended product or service. Such a feedback mechanism helps improve performance over time.
ML for prescriptive analysis
Prescriptive analytics, as much as predictive analytics, works best when powered by machine learning and deep learning algorithms, with all the pros and cons that come with it and that we’ve already mentioned above.
It is a particularly data-hungry approach and requires both internal and external data sources to be exploited to its full potential.
For this reason, any company that wants to implement prescriptive analysis in its business processes should carefully evaluate the possible costs and compare them to the expected benefits.
The most popular types of data analytics
According to the “2016 Global Data and Analytics Survey”, descriptive analytics was the type on which company executives relied most for their decision making (58%) in the category “Rarely data-driven decision- making”.
However, diagnostic analytics dominated with 34% in the “Rather data-driven” category, while predictive analytics was ranked first in the “highly data-driven” category with 36%.
What does it mean? Well, basically these results tell us that any choice regarding the type of data analytics depends to a large extent on the development phase of a company.
The more a company relies on informed decisions, the more it will be necessary to implement the most advanced types of analysis.
The growing trend of advanced analytics
In the last years, we have seen a shift of attention towards the more advanced types of data analysis, namely predictive and prescriptive analytics.
This trend seems confirmed by more recent surveys. For example, a majority of the “2018 Advanced and Predictive Analytics Market Research” respondents indicated for the first time advanced analytics as “critical” or “very important”.
A majority of the “2018 Advanced and Predictive Analytics Market Research” respondents indicated for the first time advanced analytics as “critical” or “very important”.
Can we afford advanced analytics?
On the other hand, we shouldn’t forget that more advanced data analytics also means increased costs, requirements, and risks.
Some useful insights in this regard come from the 2020 BARC’s study “The Future of Analytics”, which is based on a global survey of more than three hundred professionals working with advanced analytics.
66% of respondents declared to lack the human resources necessary to carry out advanced analytics.
According to the study, in fact, “using advanced analytics across the entire company only works when data access and governance is right and when enough users possess the relevant skills”.
The right data analytics type for your business
As you may have already realized, the different types of data analysis are not mutually exclusive but, rather, they coexist and complement each other.
But what’s the right mix for your business? To understand this, you should take into account your current progress in implementing data analytics and your actual need to dig deeper into the data in order to get better insights and more effective decision making.
Generally, the best way is to gradually move from the simpler and cheaper types of analysis to the more complex, expensive, and challenging to implement.
Just remember that every extra step you take comes at a cost and risks giving disappointing results in terms of ROI. So… choose wisely and don’t overdo it!