By Alpesh Doshi, Founder & Managing Partner
There has been a lot of talk and content published around the application of Big Data within enterprises.
In the past 6-7 years that we’ve been involved in this sector, we’ve seen a significant amount of money being spent by corporates to get value from various data technologies. But the missing piece we often see and ask our clients is: Where and how is the wider business involved?
For example, many CIOs have approved the spend to build Hadoop clusters or similar but with little thought into how the wider business will go on to use this technology. Furthermore, the business doesn’t really understand the technology and what it could do for them.
The problem doesn’t get any easier now that we’re seeing a lot of hype around Machine Learning and Artificial Intelligence (AI). The biggest problem I’m seeing today is that there seems to be very little connection between Big Data & Analytics and Machine Learning/AI.
The hype over the last few years has also moved to Digital Transformation and what that means to an industry, and more recently toward the application of blockchain technology (but that’s for another time).
In this post I will focus on how to get started on your Big Data & Analytics journey and connecting the dots between business and IT.
The State of Big Data
We know that the volume of data has grown exponentially and will only continue to grow. Mckinsey’s ‘Age of Analytics’ study published December 2016* describes some of the facts:
- 3 exabytes in 1986,
- 300 exabytes by 2011,
- 2 zettabytes by 2016.
This trend is set to increase and data collection is expected to double every three years. This report comes after the foundation report published in 2011*, which was so optimistic!
Really great, right? Well… Maybe.
Collecting large volumes of data from multiple sources is one thing, but actually making sense and use of it across the business is a challenge that many are still battling. We can see from anecdotal and market evidence that most enterprises are struggling to figure out what to do with all this data. Further than that, the disjoint between business, IT, strategy, digital transformation and external market dynamics (e.g. startups and scale-ups) is pushing enterprises even further behind.
The leaders and new economy companies, like Google, Facebook, Airbnb, Uber and GE have accelerated their growth and separation from most enterprises. Their businesses run on data (or they have a strategy to get there), and because they continuously and effectively use data, they have been pulling far ahead of others that do not. Just look at their valuations!
Challenges of Data Across the Business
The challenges most enterprises face is primarily their operating model and stakeholder management structure. Each department within an enterprise is often responsible for their own data in their own division. It doesn’t help that senior executives are protective of their turf, and understandably so since traditional management structures and reporting lines, performance assessments and incentives are aligned that way.
Some enterprises have now appointed a Chief Data Officer (CDO), to try and move ‘ownership’ of data to a single executive. However, from our experience, we’ve seen that many CDOs are still finding their way in the business. They are busy spending time building trust, nurturing relationships, proving their worth and driving home a collaborative message across the business, rather than getting their “I’m going to own all data in the business now” message across.
The report from Mckinsey Institute (The Age of Analytics, December 2016), an update from their foundational report on data back in 2011, starkly shows that enterprises are still significantly under-utilising their data:
“Most companies are capturing only a fraction of the potential value from data and analytics. Our 2011 report estimated this potential in five domains; revisiting them today shows a great deal of value still on the table… Further, new opportunities have arisen since 2011, making the gap between the leaders and laggards even bigger.” McKinsey Institute Global, 2016.
Where to start on your data journey?
We’ve seen the CIO’s organisation build data platforms and make them available to the rest of the business. We’ve seen enterprises hire CDOs and data scientists, but fundamentally, what should happen next?
To enable use of data across the enterprise and to transform a business model, leveraging that data is foundational.
- How the ‘Data Agenda’ is portrayed across the business and the way in which data can be used to help drive significant business value is critical.
- Being able to put together a robust Data Strategy which aligns with the corporates’ stated business strategy is fundamental to making the use of data prevalent across the business.
- The Data Strategy should be a living breathing document, which must be used to help create an iterative model that brings depth and detail on how data will be used across the entire business. It should demonstrate how to break down silos and integrate use cases and consider customer journeys and operating models across business divisional lines.
Keep in mind that the use of data does not sit in isolation and that there are existing business pressures, as well as new ones (such as Digital Transformation) to consider. We frequently find the two questions asked by a lot of our enterprise clients are:
- Where do we start?
- What do we do?
Figuring out those two most basic questions might be harder than it sounds, but it is the best way to get going. Aligning and getting senior stakeholder buy-in and ownership for those questions also helps to drive the adoption curve.
Connecting the Dots
I’ve attempted to give a few ways to start on your data journey, but there is a lot more to connect together. My next posts will look to bring in more detailed areas around data, analytics, machine learning and AI. Keep a watch out for those!
“The Age of Analytics”, McKinsey Global Institute, 2016
“Big Data: The Next Frontier For Innovation, Competition, and Productivity”, McKinsey Global Institute, 2011