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June 2, 2016

Slow But Steady Progress Spotted in Operationalization of Big Data

Alex Woodie

(Rawpixel.com/Shutterstock)

It might be a tough word to get out of your mouth, but “operationalization” remains a central element of the big data opportunity. And according to a new Capgemini and Informatica study titled “The Big Data Payoff: Turning Big Data into Business Value,” companies are making slow but steady progress in the operationalization of big data projects.

To operationalize something in the business world is to make it tangible, to make it real and repeatable and reliable. In the context of big data, to operationalize a big data science project is to take it from the planning stage to proof of concept stage to test stage and finally all the way into full production, with all the qualities that production-grade IT projects require, like SLA monitoring, high availability, governance, security, etc.

Considering that big data is still relatively new compared to existing business intelligence projects, it’s no surprise that many companies are still in the early stages of big data operationalization. The global IT and management consultancy Capgemini and Informatica, a provider of data integration products and services, set out to quantify the current state of big data adoption in its study of 210 senior IT or data decision-makers at large companies in the U.S. and Europe.

According to the study, which was conducted by IDG Research Services, nearly every company (97%) has a big data project in the works in some aspect of their business. But there’s quite a bit of difference in the level of maturity and usefulness of big data projects among the respondents, which included companies in the retail, telecommunications, consumer packaged goods, utilities, and energy/chemical companies.

The study found that 14% of companies are still in the proof of concept (PoC) stage, while another 11% are in the planning or strategy stage. About 13% have already modified existing big data projects to meet changing business needs, the study found, while 16% have no plans to modify their existing projects. Perhaps most importantly, 30% of survey respondents said they are accelerating or expanding their big data initiatives to new departments or locations.Capgemini_1

This represents real progress, says Steve Jones, global SVP of Capgemini’s big data practice. “There is a significant trend towards not just greater maturity of the projects but also of the operations and governance,” Jones tells Datanami. “Three years ago, most efforts were small and PoC based, today companies are putting down strategic plans on how they build a full new data substrate for their business. This switch from isolated use cases to the foundation of a strategy shows a huge leap in how big data technologies are being considered.”

No company will continue a big data project without a positive return on investment (ROI), which can be surprisingly hard to measure, as we’ve written about before. And to that end, there’s a bit of bad news from the study, which found that only 27% of survey respondents reported that their big data projects were profitable. About 12% of projects lost money, while 45% of them broke even.

The study also looked to quantify the importance of management buy-in to big data. The study found that those projects with the highest level of buy-in from the CEO and executive team are more likely to report that their big data projects are profitable (49%) compared to those whose executives teams perceive big data as having little potential (6%).

IT budget constraints was the biggest obstacle to overcome in the operationalization of big data, with 44% of respondents citing that as a key factor, according to the study. Other leading challenges include data security (cited by 36% of respondents), integration (35%), a lack of data science or technical expertise (32%), proliferation of data silos (31%), negative corporate culture (30%), and poor data quality (30%) were other top concerns.

Hadoop logo

Only 4% of survey respondents reported using Hadoop in production, while 57% say they are evaluating it.

Despite the headwinds, CEOs and executive teams remain mostly bullish on the potential of big data. More than three-quarters of survey respondents said their senior team views big data’s potential to drive profits “to a great extent” or “to some extent,” compared to 19 percent of senior leaders who ascribed little potential benefit to big data.

“Clearly the key battleground is in the leadership of initiatives,” says John Brahim, head of Capgemini’s Insights & Data Global Practice. “The study suggests, however, that many organizations have some way to go before they become truly insights-driven.”

Capgemini distilled the insight down into a six-step plan of action for driving success in a big data project:

  1. Define objectives and set a roadmap for using new data assets
  2. Appreciate and understand how value is derived from data
  3. Blend existing data landscape with merging big data platforms
  4. Create powerful and active data governance
  5. Work to create a dynamic data-driven culture
  6. Establish a robust platform that delivers insights “on demand’ to business users.

The key to big data success is control, Jones says. “Pirelli used to have a slogan ‘Power is nothing without control’ and that is what we are seeing with big data,” Jones says. “Just having access to data isn’t enough. Effectively that already happens with spreadsheets today. It’s about being able to see how that data connects to other data sets and therefore to find out new insights that enables the creativity.”

You can download the full 12-page report here.

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