Why enterprises still struggle with data
Originally published on infoworld.com.
If you spend any time in Silicon Valley these days, it’s easy to believe every business in America is becoming a data analytics powerhouse. We hear so many stories about companies reinventing themselves with data—opening new revenue streams, better targeting customers, slashing costs. It’s enough to make a statistician weep with joy.
The reality on the ground is much different. From the enterprises I speak with, I know that many large companies are only just beginning to use data to really transform their businesses. There are pockets of innovation in every company, but few are truly using data across their organization to uncover game-changing opportunities.
The reason for this state of affairs is not a lack of tools or technology. The market is awash with powerful cloud-based tools for collecting, preparing, integrating, and analyzing data. If there’s one thing Silicon Valley is good at it’s solving technology problems, and VCs poured a record $148 billion into private companies globally last year, many of which are competing fiercely to unlock the power of data.
If lack of technology is not the issue, what is? From my conversations with senior leaders at dozens of large enterprises, I’ve observed three primary obstacles holding companies back.
- Inertia. Most businesses simply don’t recognize the urgency to rethink their business until disruption has hit them in the face, and by then it is often too late. As a leader, I can sympathize with this. Many chief executives are busy keeping the lights on, managing costs, and making what they already have work smoothly. They’re too busy worrying about the head of sales who just quit or the big customer showing signs of unrest to be concerned with sweeping new initiatives.
- Narrow view. What does a new business opportunity look like? Most large companies have a significant proportion of executives who have been there for a decade or more. It’s very hard when you’ve been doing the same thing for ten years to suddenly see things differently. To paraphrase Henry Ford back in the day, if you’d asked the average customers what they wanted, they would have asked for faster horses. They don’t know they need a car because they’ve never seen one.
- Cost avoidance. This is a tricky one because cost comes in many guises. The cost of technology itself is not that high, but public companies are under immense pressure to please investors and meet their quarterly targets. This short-term focus can be the death knell of innovation. Funding what seem like speculative, non-essential projects feels hard to justify, but of course not doing so can cost you dearly in the long term.
There is no silver bullet for any of these problems but I do believe there are steps companies can take to mitigate each of them.
- Appoint a chief data officer. The CDO is not a faddish role; Gartner predicts some 90 percent of large companies will have a CDO in place by 2020, and there are good reasons for that. A talented CDO brought in from outside the company can really address your inertia problem. CDOs should bring a fresh perspective to the opportunities that exist and shake up the culture to encourage data-driven decision making. They need authority to make this happen, which means they should report directly to the C-suite and have their full support.
- Balance investments. Tech companies like Google have pioneered an investment model that reduces the risk of being blindsided by disruption while enabling the core business to operate, and it’s a model other industries should now adopt. We follow a version of this at my company, and so far it has worked well for us. Generally speaking, it looks like this:
- 70 percent of investment goes towards the core challenges that your customers want you to address
- 20 percent goes toward speculative investments to build on those capabilities
- 10 percent goes toward big bets that may pay off enormously
To give an example, five years ago we placed a big bet on machine learning. It was unproven that machine learning would be useful for accelerating enterprise integration projects, but we were fortunate that it helped to greatly improve our product and our customers’ productivity. Allocating investment in this manner allows you to explore new frontiers while keeping the core business stable. It also keeps investors happy, because they can see a regular, predictable pattern to spending.
- Break down the silos. The biggest obstacle to digital transformation is the silos that have formed in large companies over many years. When information is distributed through dozens of applications and databases across a global enterprise, it’s incredibly hard to innovate in a meaningful way. Teams can’t even begin to look for new patterns or combine data in creative ways if that data is dispersed and disconnected. This kills innovation in its tracks.
This is a significant challenge, but again, technology is not the hurdle. My advice to businesses is to identify inefficient parts of their business where innovation would make the biggest impact—be that the supply chain, product development, or marketing—and then identify the sources of data in those areas that are most likely to be part of a solution. They can then begin the process of opening up those silos, making data accessible to those who can make use of it.
This is a gradual process, but businesses need to start somewhere because doing nothing is not an option in today’s climate. You have only to look at how AirBnb disrupted the hospitality industry or how Box disrupted storage to see how quickly change can happen. In May, Netflix became the world’s most valuable media stock, when its market cap surpassed that of Disney. Meanwhile, Amazon acquired Whole Foods just as Toys ’R’ Us—a 50-year-old retailer that never captured the power of data—was going out of business. The data revolution is real, and companies that don’t react to the new reality will not thrive in the future. It is not too late to act, but the time to do so is now.