5 strategic tips for avoiding a big data bust

Failed expectations, increased costs, unnecessary legal risks -- going blind into a big data project doesn’t pay

Big data success tip No. 4: Pool best practices for best results

People are discovering what works and what doesn't when it comes to managing big data and analytics. When they are employed by the same organisation, why not share this knowledge?

One way to do this is by creating a big data COE (centre of excellence), a shared entity that provides leadership, best practices, and in some cases support and training.

Typically, COEs have a dedicated budget and are designed to analyse issues; define initiatives, future state, and standards; train users; execute plans and maintain progress, says Eliot Arnold, co-founder of Massive Data Insight, a consulting firm that specialises in big data and analytics programs. Getting a COE started requires an audit of available resources and a senior executive sponsor, he says.

While a big data COE is a good idea on paper, its effectiveness will be determined by how well it's implemented in practice, DRC's Chabot says.

There are many basic challenges with a COE covering the entire data lifecycle, Chabot says, including authoring and identifying the best practices; vetting them in a nonbiased fashion; properly documenting their applicability; overseeing their adoption; and modernising them over time.

DRC has defined a big data maturity level similar to the CMMI (Capability Maturity Model Integration), a process improvement framework used by organisations. The big data maturity-level models map out relevant best practices. These are divided into four groups: planning/management, project execution, architecture, and deployment/runtime/execution, for organisations to incrementally adopt over time. This avoids the pitfalls of trying to be too sophisticated too quickly, Chabot says.

Big data success tip No. 5: Expertise and collaboration are key

Big data is a business initiative, not just a technology project, so it's vital business and IT leaders are on the same page with planning, execution, and maintenance.

"One of the biggest pitfalls for a program is disconnect between IT and the business on who controls strategy and initiatives," Arnold says. "In less mature organisations there is no documented strategy, a hodgepodge of tools are in production, and decision makers favour intuition for charting strategic direction. These types of firms are mostly unaware of the asset value of data."

Business leaders can ensure their big data project is successful by carefully identifying objectives, needs, and requirements; calculating a return on their investment; mapping analytical capabilities to business/mission needs; and installing a mechanism for continuous feedback, DRC's Chabot says.

"A big data project should be divided into multiple phases, incrementally adding value to the organisation," he says.

But getting IT and business leaders to agree, as well as getting departments to work together on data initiatives is not always easy.

"In my experience, for the major companies this is becoming a real corporate challenge," Venture Development Center's Stryker says. "Does the job responsibility associated with chief data officer rest within the IT department, the marketing department, the risk management department, or do each of these departments have their own big data initiatives and coordinate with each other?"

Companies also need to bring in the necessary expertise to exploit big data technologies such as Hadoop, which has enabled low-cost, computationally efficient management of very large data sets and analysis tasks.

"The paradigm shift to big data introduces a new role in the corporate organisation, the data scientist," Caserta says. "This role requires deep understanding of advanced mathematics, system engineering, data engineering, and [business] expertise." In practice, it's common to use a data science team, where statisticians, technologists, and business subject matter experts collectively solve problems and provide solutions, he says.

Many of the people already working in data analytics will need to prepare for culture shock, Caserta says.

"Before a big data project is launched, a strategic readiness test should be performed to assess the adoption of the new paradigm," he says. Business analysts will need to be retrained or repurposed. The goal of shifting to a big data platform may include changing from reactive analysis (for example, how well a campaign worked), to predictive (what should the next campaign offer), he says, "because now we can proactively influence nonbuyers to follow behaviour patterns of loyal customers; or restimulate active customers when their behaviour pattern begins to look like a lost customer."

What are the risks of not building a strong, cohesive big data strategy? Launching expensive endeavours that fail to deliver on their promise.

"Typically big data projects are multidimensional and complex initiatives," Chabot says. "They require significant upfront planning." Before embarking on a big data project, he says, organisational leaders should ensure alignment between strategic, functionality, data, analytics, and technology road maps. These road maps need to be reflected in a business, system, software, data, and technology architecture.

"Misalignment between any of these road maps can cause the entire project to derail," Chabot says. "The risks of not having a strong, cohesive big data strategy with the proper road maps and architectures are likely to be excessive costs, expectation mismatch, lack of value, and ultimately program failure."

This story, "5 strategic tips for avoiding a big data bust," was originally published at InfoWorld.com.

Follow CMO on Twitter: @CMOAustralia.

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