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How to Capture More Value from Big Data and Analytics


Big data is here and in a big way. According to IDC, big data and analytics sales are forecasted to reach $187 billion by 2019, a whopping 50 percent increase over the last forecast period. But despite those numbers, the real story is that companies are missing out on the big and analytics trend, even if most of those companies are already collecting and storing data on a daily basis, as part of their normal business operations.

It turns out that just having a data trove isn’t the same as being a big data and analytics leader. And with few exceptions, if your company wasn’t a leader in big data at the beginning of the decade, it’s probably fallen further behind in the intervening time.

Why is that? Well, the greatest gains have been distributed among companies in the same sectors. And those largely digitized sectors, location-based services and retail, have home court advantage, so to speak.

Moreover, location-based services and retail have been making the most of the latest big data trends: exponential growth in big data volumes, improving data storage capacity, decreasing data storage costs and enhanced computing power.

As the McKinsey Global Institute (MGI) declares, big data is shaping up to be a “winner-take-most” proposition, where leading companies will press their advantages and laggards are at the mercy of wholesale technological shifts that promise to upend entire industries.

Not convinced? Leading companies are not only leveraging big data and analytics to improve internal processes but they’re also creating alternative business models and new revenue streams. In other words, big data tends to bolster competitive advantages in innovation, personalization and operational efficiency.

So what’s holding businesses back? Is it the notion that the big data trend is over-hyped? As noted, the numbers suggest otherwise. Survey data from the Economist Intelligence Unit shows that almost 60 percent of businesses are already generating revenue from their data and will continue to do. Moreover, 83 percent say data is used to make existing products and services more profitable. That’s not bad at all.

It’s more likely that businesses aren’t effectively using the insights generated from their own data, which could improve everyday process, simply because they’re not set up to do so. Another inhibiting factor MGI calls out is the fact that businesses don’t (or can’t) recruit the right mix of scientific and operational talent to make big data work.

Looking at the numbers, certain industries face more structural challenges to adopting big data than others. While retail in the U.S. is doing well, especially factoring in leaders like Amazon and Walmart, the sector as a whole still suffers from a lack of analytical talent.

Manufacturing stands to gain a lot, but progress so far has been uneven. Data tends to be siloed in legacy systems; and leadership remains hesitant to change.

Across high-income countries, public sectors aren’t doing well either. Generally, the public sector also lacks analytical talent and suffers from a lack of inter-agency communication. Similarly, the healthcare sector in the U.S. is fall behind, as big data initiatives face a major hurdle, in that they need to demonstrate clinical utility in order to gain acceptance.

According to MGI, successful big data and analytics transformation require focus on these five elements to make an impact:

1. Deciding for what data and analytics will be used.
2. Building out your data architecture.
3. Acquiring analytics capabilities so as to garner insights from data.
4. Changing business processes to incorporate data insights into actual workflows.
5. Building the capabilities of executives and mid-managers to understand data-driven insights.

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