Diving deeper on the particularities about analytics and more specific terms, let’s explore the Analytics concepts. It has been used as an advance of BI discipline, with broader and more precise definition as the combination of:

  • Computerized technology
  • Technology applied to business
  • Statistics.

that goes wider than reading and understanding the past but also identifying ways to support business decisions based on predictive analysis.


From the view of experts and in simple words, Business Intelligence and Business Analytics can be defined as follow:
“BI is needed to run the business while Business Analytics are needed to change the business.” – Pat Roche, Vice President of Engineering at Magnitude Software
“BI is looking in the rearview mirror and using historical data. Business Analytics is looking in front of you to see what is going to happen.” – Mark van Rijmenam, CEO / Founder at BigData-Startups


Let’s move 1 step further, knowing which sub-concept of analytics solves each type of use case! Below you can find a clear view of the three big categories of business analytics.

With this landscape of Business Analytics, we can want to help you defining which type of project we need and what do we expect out of it.


As an example of how can prescriptive analytics be helpful in our real lives, the let’s learn more from Predictive Targeting, the project that took First Place at First-Ever J&J Data Science Summit in Janssen NA!


The Need: The traditional sales volume-based methods for HCP targeting needs to be refined. The expanded focus on specialty products with niche patient populations, multiple line therapies, and increasing formulary complexity are shaping new trends and increased the need for well-timed and precise approaches to better identify and retain clinically-appropriate patients and their associated HCPs. Each therapeutic area in Janssen has invested in building a predictive targeting engine to drive precision targeting, based on patient needs, HCP prescribing propensity and payer dynamics.


The Solution: The combination of human input, big data and machine learning to better understand patient needs, along with the analysis of factors influencing HCP responsiveness, and payer dynamics, helped the teams prioritizing HCPs with the highest potential to help getting our medications to the patients who need them most. Thanks to the application of algorithmic call planning, teams could easily measure how many – and how quickly – the highest potential customers were being served. In addition to that, field teams were provided with customer and local market insights, along with additional foresight into future trends.

This is one good example where prescriptive analytics positively impacted brands results, with an increase of around +15% in sales – for the specific brands when compared to control group - helping on the selection and prioritization of our efforts and better serve our patients.

In the upcoming articles we will be giving you more examples on how these different techniques can be applied in our day to day activities and the next one, you will see when does Artificial Intelligence come to play?