World-Class Data & Analytics Platforms: Guiding Principles
Amitabh is an accomplished technology leader with extensive experience in digital transformation and an excellent track record of achievements within the pharmaceutical, manufacturing and aviation/aerospace sectors.
Big Pharma was an early adopter of analytics platforms. Early on, the focus was on how analytics could deliver value in terms of revenue, profit ability, etc. Today’s analytic needs are less about driving volume or speed, and more about driving value, e.g. patient outcomes.
However, there is not much literature available on the strategic thought process behind the development of data and analytics platforms in major American pharmaceuticals. In this article, I elaborate on the answer to the following question: What are the guiding principles that drive the mindset of business executives who are responsible for developing world-class data and analytics platforms? What is important to them? How do they decide on an analytics strategy that would not only provide executives with actionable insights, but also broadly help transform the goals of the business?
Why are the guiding principles important? Because they provide a framework to executives for what questions to ask:(i)what is our vision, and what are our priorities?(ii)what are our data sources?(iii) which cross-functional data linkages should we consider?(iv)which platform product or technologies should we pick?
Although, as technical leaders, our thoughts may be predominantly solution focused or even technology focused, that’s not how business executives think. Business executives don’t care much about the technology so long as the RoI is good and they can solve the problems that are important to business.
Understanding the Guiding Principles is Important
The first guiding principle is that analytics platforms are increasingly being viewed as value drivers. There is a shift from volume to value-based business models. A business executive is more likely to view an analytics platform as a driver of
value, as in patient payer, provider and economic outcomes, as opposed to a driver of volume, such as revenue, profits and market leadership.
The second is that business executives are demanding an increasingly complex commercial model. Data is an important source that feeds into commercial decision making, according to Bain. Today’s commercial model incorporates all the key players physicians,key account managers, field reimbursement managers, service reps, product specialists, medical science liaisons and others and considers their unique contribution to strategy.
Once you’ve agreed on these four guiding principles, you’re ready to start realizing your data and platform vision
The third is that the explosion of data is making it extremely challenging to source, store and manage all the data. There is an explosion of data in Pharma. How do organizations store and manage the data so that actionable insights may be obtained? Executives wish for data to be findable, accessible, interoperable and reusable, in accordance with FAIR Data Principles.
How to make sense of all the data? That is the fourth guiding principle: adopt a use-case approach to the journey of the data from ingestion all the way to consumption. The impact of use cases across
the organization can be enhanced through a focus on cross functional data linkages. Listing use cases across functions e.g. regulatory and drug safety, manufacturing and supply chain, sales and marketing, etc. and investigating synergies allows you to connect the dots and determine which data sets are needed for a use case theme. This is complex, but connecting the dots is important if you want to deliver useful analytics.
It’s important to be able to categorize the function, the number of data sources number of files and use case themes. For example you may say ‘Marketing, 12 sources, 260 files, six use case themes.’
Once you’ve agreed on these four guiding principles, you’re ready to start realizing your data and platform vision. There are three steps there:
Deciding on a base camp: here is where you begin, where you don’t know where your data resides. You first need to gain an understanding of your legacy infrastructure: how long it takes to triage issues, onboard new data sources, load data, etc., how many systems and processes exist, how many monthly business reports get created, etc.
Defining a vision: you’ll then define the broad contours of the data volumes and use cases, for example that in three years you’d like to have 150 TB of data in your data warehouse and cover 600 end-to-end use cases.
Determining a roadmap: here you outline a vision and a timeline of how you go from the base camp to the state where you’re realizing value. You’ll define the different stages e.g. explore, monitor, investigate, perform etc. and a broad guidance on the timeline of when you’ll execute which stage.
Embarking on a data & analytics journey can be exciting and risky if you’re not constantly aware of, and seeking to employ, the guiding principles. At the end of the day, it's not your ability to answer questions but to ask the right questions that’ll determine whether you succeed in your endeavor.