Summary: Deep changes are underway in how data science is practiced and successfully deployed to solve Business problems and create strategic advantage. These same changes point to major changes in how data scientists will do their work. Here’s why and how.
There’s a sea change underway in data science. It’s changing how companies embrace data science and it’s changing the way data scientists do their job. The increasing adoption and strategic importance of advanced analytics of all types is the backdrop. There are two parts to this change.
One is what is happening right now as analytic platforms build out to become one-stop shops for data scientists. But the second and more important is what is just beginning but will now take over rapidly. Advanced analytics will become the hidden layer of Systems of Intelligence (SOI) in the new enterprise applications stack.
Both these movements are changing the way data scientists need to do their jobs and how we create value.
Advanced analytic platforms are undergoing several evolutionary steps at once. This is the final buildout in the current competitive strategy being used by advanced analytic platforms to capture as many data science users as possible. These last steps include:
Here are a few examples I’m sure you’ll recognize.
The Whole Strategic Focus of Advanced Analytic Platforms is About to Change
We are in the final stages of large analytics users wanting to assemble different packages in a best of breed strategy. Gartner says users, starting with the largest will increasingly consolidate around a single platform.
These same consolidation forces were at work in ERP systems in the 90s or DW/BI, and CRM systems in the 00s. Give the customer greater efficiency and ease of use with a single vendor solution creating a wide moat of good user experience combined with painful high switching costs.
This is only the end of the last phase and not where advanced analytic platforms are headed over the next two to five years. So far the emphasis has been on internal completeness and self-sufficiency. According to both strategists and Venture Capitalists the next movement will see the advanced analytic platform disappear into an integrated enterprise stack as the critical middle System of Intelligence.
Why the Change in Strategy – and When?
The phrase Systems of Intelligence (SOI) was first used by Microsoft CEO Satya Nadella in early 2015. However it wasn’t until 2017 that the strategy of creating wide moats using SOI was articulated by venture capitalist Jerry Chen at Greylock Partners.
Suddenly Systems of Intelligence is on everyone’s tongue as the next great generational shift in enterprise infrastructure, the great pivot in the ML platform revolution.
Where current Advanced Analytic Platform strategies rely on being the one-stop general-purpose data science platform of choice, those investing and developing the next generation of platforms say that is about to change. That the needs of each industry, or the needs of each major business process like finance, HR, ITSM, supply chain, ecommerce, and others have become so specialized in terms of their data science content that wide moats are best constructed by making the data science disappear as the middle layer between systems of record and systems of engagements.
As Chen states, “Companies that focus too much on technology without putting it in context of a customer problem will be caught between a rock and a hard place”. As an investor he would say that he is unwilling to back a general purpose DS platform for that very reason.
Chen and many others are investing directly on the basis of these thoughts that the future of data science, machine learning, and AI is as the invisible secret sauce middle layer. No one cares exactly how the magic is done, so long as your package arrives on time, or the campaign is successful, or whatever insight the DS has provided proves valuable.
