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It’s easy to get tangled in big data. Find your way out of the morass by focusing on three core activities.

Big data seems to have a paralyzing effect on many companies. As much as they may want to exploit it to improve decision-making or uncover ways to monetize it, many organizations’ initial big data efforts flounder and fail to realize desired value.

Richard Starnes, a principal with Deloitte Consulting LLP’s Information Management practice, says companies’ early attempts to extract value from big data break down for two main reasons: They have trouble zeroing in on an appropriate business problem or use case to test in a pilot project, or they get bogged down in tactical questions like: Where do we get the data? How do we store it? What technologies should we use? Who should have access to the data?

To avoid this big data tangle, Starnes and colleagues propose organizations focus on three fundamental activities, and provide specific recommendations for each: identify a few business problems to address, assemble the pilot team, and select the foundational technologies.

Identify Business Problems

Starnes advises companies to initially focus on selecting business problems because those decisions will drive their other choices concerning talent and technology. He further recommends identifying a mix of business problems (possibly four) that can serve as use cases for a pilot. “By choosing a handful of use cases, organizations can increase their odds of demonstrating the benefits of big data. Even if one or two run into roadblocks and can’t be completed, they still have others moving forward,” he says.

Knowing some companies stumble when trying to select use cases, Thaddeus Zaharas, a senior manager with Deloitte Consulting LLP’s Analytics market offering, suggests CIOs ask themselves the following questions to narrow their options:

Do the problems under consideration directly relate to the business strategy? If solved, do those problems have the potential to increase top-line growth or reduce costs?



Which executives have expressed interest in big data and why? Which function may benefit the most from applying unstructured, external data to operational decisions?



Are these long-standing problems the company hasn’t previously been able to address due to the limitations of available technology and data?



Can we measure the financial impact of applying big data to these business problems?

Starnes recommends companies choose at least one customer-focused business problem and at least one focused on operations. He also tells companies not to focus on developing a rigorous business case for the big data pilot. “The danger in making a business case a prerequisite for a pilot is that it may distract the organization from discovering the benefits of big data through an early proof of concept,” he says. “Should an organization decide to expand the big data program following the pilot, the initial use cases and their results can then serve as the foundation for a traditional business case.”

Assemble a Team

Many companies might think they need to hire an army of data scientists to uncover insights hidden in big data, but data scientists represent just one leg of the three-legged big data team. Equally important: subject matter specialists from the business functions or units supplying the use cases who understand the problems being studied, as well as developers and “data jockeys” from IT who know how to model and manipulate data using a mix of traditional SQL and newer big data toolsets.

In cases where the business simply needs access to big data and deep statistical analysis isn’t required, companies may not need a data scientist or analyst, observes Starnes. Instead, business users can consider using software applications like Tableau, Teradata’s Aster, or Birst to represent data and conduct their own exploratory analyses.

Prakul Sharma, a manager with Deloitte Consulting LLP’s Information Management practice, suggests companies appoint an experienced project manager to oversee the big data pilot team and steer members of the team as they move through uncharted territory. He also advises the pilot team to seek support from executive stakeholders in charge of platform management and security since their perspectives will be critical as the big data program grows.

Select the Technology

CIOs tend to overthink technology decisions related to big data, and their lengthy analyses can cause these initiatives to lose steam, observes Starnes.

“CIOs don’t have to lay out a definitive technology road map for a big data pilot as they do with business intelligence or data warehousing,” he says. “Big data doesn’t require the level of investment those other information management technologies require. For a pilot, limit spending, stick with whatever foundational technologies may be required to support the use cases, and consider using the cloud when possible.”

For use cases where large volumes of structured and unstructured data needs to be stored and processed, Starnes advises CIOs to start by picking one of the leading Hadoop providers. Although IT leaders could spend six to 12 months vetting the providers, Starnes says they offer a similar set of core capabilities. “Pick one, begin iterating and learning, then determine whether your selection will provide needed capabilities longer term,” he says. “If your organization decides to expand its big data program following the pilot, then you can think about a technology road map.”

Sharma adds that the business problems a company chooses to solve with big data should drive technology decisions. For example, for users who want interactive responses to queries in near real-time for business critical applications, an in-memory-based platform may work better than Hadoop, notes Sharma. In-memory platforms lend themselves to systems like real-time offer management, which generate deals and promotions for customers as they browse a company’s website, he adds. By contrast, companies wishing to evaluate the effectiveness of online advertising or understand customer behavior on their websites over time may choose Hadoop combined with open source software like Apache Storm, which is designed to promptly ingest and reduce large volumes of Web log data, he says.

Once a CIO has decided on technologies, implementing lightweight processes for handling requests from the business for access to data becomes critical. “Business users shouldn’t have to wait weeks or months to get data, as they typically do with business intelligence,” says Starnes. “Without lean processes for getting data into Hadoop and exposing it to the business teams wishing to manipulate it, the technology is largely useless.”

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To start a big data initiative and maintain momentum for it, companies ultimately need to be pragmatic, decisive, and focused. “Don’t business case or road map it to death,” warns Starnes. “Make some quick, informed decisions about use cases, talent, and technology, and move on. The beauty of big data is that it lends itself to experimentation and learning.”