In the fast paced and cost-conscious world of industry, very few companies have the appetite to spend millions of dollars, or face extensive waits to access the data that is all around them. The Life Science industry is no exception, so I recently took the opportunity to interview Chris Ferrara and find out if there really is a prescribed alternative to the ailing data warehouses of old.
David: Let’s start with an introduction! Chris, can you share your background in the Life Science industry and a little about your current role at Qlik?
Chris: I spent the great majority of my career in consulting. I worked for companies like Ernst & Young and ISA Consulting, where I specialized in enterprise intelligence and data warehousing. As a consultant, I worked for several pharmaceutical, biotechnology, and medical device companies. Now at Qlik, I work in our Industry Solutions team where I help Life Sciences customers and prospects to see where the industry is going and understand the “art of the possible” with modern analytics.
David: Before we jump to a diagnosis and prescription, how would you describe the core challenges many Life Sciences businesses face in the data landscape?
Chris: Information is the basis of every decision we make and yet for an industry so dependent upon innovation for its success, we often find ourselves relying on legacy practices to gain access to data. There is no changing the fact that data needs to be collected, integrated, cleansed, and optimized before it can be made available. But it’s a bold new world out there and it’s moving fast! For many, the approach to accomplish those tasks has been transformed and the traditional data warehouse simply cannot keep up. In fact, I would even argue that the concept of an enterprise data warehouse is fading fast.
David: What do you think has led to these changes and the impact companies face when trying to keep up?
Chris: Everything from how and where data is created and stored to how information is consumed has changed dramatically in the past few years. Mergers and acquisitions have left us with multiple diverse systems and repositories of data. Many of our information assets, such as physician notes, clinical records, vendor agreements, and invoices have been digitized, leading to various formats and structures of information. Advancements in sensors and connectivity now allow us to connect directly to internal and external devices to capture massive amounts of data. With the growth of cloud and collaboration with strategic partners, data is now coming from many places that extend far beyond our companies four walls.
The impact of all these changes in the data landscape are far reaching – regardless of the technology we choose, there is no single data repository, no data warehouse, no data lake or data garage that can hold all the data we need to be competitive. To make things worse, the whole landscape is a moving beast; even if a data lake is created, it would only be ‘complete’ until the next merger or new data source came along.
David: Let’s talk about the human impact, how do these changes and advancements affect the way we use data and the expectations we have?
Chris: The way we work and interact with data has fundamentally changed in all industries, not just Life Sciences. It is no longer just data scientists and statisticians that need data to do their jobs, it is all people including executives, sales reps, clinical operations, supply chain analysts, quality engineers, scientists, external partners, and sometimes even machines! Consumers who would have been happy with standard reports are now more advanced and their expectations of data availability have increased. They look for self-service data exploration and visualization capabilities in order to consume and interpret the variety of data that supports their role and the timeframe they need it.
David: Given this backdrop of change, do you have any great tips for Life Sciences companies to succeed in this new world? What is your prescription for success?
Chris: I have seen so much success in adopting new strategies, the prescription could be long and complex! If I were to name my top 10 tips, they would include the following:
Get out of your comfort zone and be innovative! Critically evaluate everything you do with data and consider alternatives to long projects which deliver slow value.
Go directly to the source wherever possible! Modern technologies will insulate the source systems from over-use and facilitate high performance results.
Focus on centralizing master data rather than all data. If you are able to keep your master data current and accurate, it makes it much easier to bring together the various sources of data available to you.
Think business, not technical – a ‘killer question’ is more important than a ‘killer app’! Rather than trying to store all data to address every possible business need, start with a specific use case and find the data that supports it.
Bring data consolidation closer to the business; engage business users early and throughout the development cycle. The market is filled with technology that empowers users to collect, integrate, cleanse, and consume data for analytics.
Move fast! In order to take advantage of opportunities, your business has to make decisions quickly. Decide which data will add value and start with that, expand incrementally and learn to succeed fast and also fail fast – weeks, not months, is the key mantra here.
Data quality is not a destination, it’s a journey. Data quality should be an on-going effort that provides continuous improvement but does not act as a barrier to providing information to the business.
Create consistency through process and behavioral change. Don’t let the idea of multiple applications or replication of data paralyze you! Create consistency without losing your focus on agility by creating lite-touch process with well-defined standards for all aspects of data literacy.
Empower individuals to be self-sufficient data activists and create a culture of sharing, which is easier said than done!
Consider full or hybrid cloud based solutions for data infrastructure or analytics. This could significantly reduce your dependency on internal skills and enable a more agile approach to technology.
David: Thank you for sharing your insights, Chris! It’s clear the prescription is available for those willing to embrace the new data landscape and all that it offers.