Q&A: Successful Strategic Planning in the World of Big Data

Lack of a business intelligence strategy can hamstring your business and prevent BI from delivering on its promise -- and even more so when you're working with big data.

Lack of a business intelligence strategy can hamstring a business, enable silos of information to flourish, and prevent BI from delivering on its promise. HP's Mike Mansur, worldwide competency lead for HP's Global Methods for EIS, explains how developing a BI vision and strategic plan is all-important.

Mansur is a master consultant with HP's Enterprise Information Solutions group. He has worked in BI/DW development since the early 90s where, for seven years, he managed the data warehouse team at the University of California, San Diego. He also managed the BI solutions team at eBay and was with the consulting team at Knightsbridge when it was acquired by HP in 2007. His focus for the past several years has been enterprise BI and data warehousing vision and strategy.

BITW: Does your data show that BI has delivered on its promise in terms of return on investment?

Michael Mansur: Our data, from experience with clients and market research, shows that business intelligence has not delivered promised ROI. In fact, HP conducted research with Coleman Parkes in October 2011 that shows 98 percent of companies cannot deliver the right information at the right time to support the right business outcomes all of the time. Why? That same research study showed that fewer than 50 executives surveyed believe they have an effective information strategy in place. This is the biggest problem.

So the problem is often the lack of a solid BI strategy?

Right. In the absence of an effective business intelligence strategy, we see siloed department-level BI initiatives spring up all over organizations. Too often, these initiatives are driven by IT without sufficient business participation.

We can also point to traditional challenges with information and BI that have plagued organizations for years -- incomplete data, master data inconsistency, poor data governance, and more. These are basic building blocks of successful business intelligence, but because they're challenging, expensive, and time-consuming, many organizations overlook them.

Now, the ground is shifting beneath companies, as new "big data" challenges loom. Traditional earlier generation BI approaches can't provide the speed and agility required to integrate the various data types we are dealing with today, analyze data in real time, and generate the intelligence required by today's face-paced, rapidly changing business environment.

Given all of those challenges, especially big data, what are some solutions that can help make BI effective?

"Big data" refers to a category of data challenges that go beyond traditional data management techniques. Driving toward "big data" solutions, therefore, is a multifaceted challenge, potentially involving many different competencies and technologies, according to the nature of the business need and the targeted solution. In one situation, it may be the sheer overwhelming volume of data that is of concern, or it may be the timeframe in which the data is needed -- or both. In another scenario, it may be the complexity of unstructured, natural-language-based data, combined with a variety of sources (ee-mail, call center recordings, Facebook, or Twitter). Still another time, it might be all of these at once.

Frequently, the first challenge isn't in the data itself, but in simply knowing where to begin to define priorities. This is where a good BI strategy comes into play, by defining a BI implementation road map based on realizing real business value. Once the source of business value is identified, along with a general idea of corresponding readiness (cost) to implement, it becomes clear how to move forward with an appropriate solution aligned to an enterprise BI strategy.

Speaking of an enterprise BI strategy, how important is IT-business alignment in bringing that about?

Successful BI initiatives are those driven by business objectives to achieve measurable business value. They involve participation of all key stakeholders as well as shared ownership by both business and IT. This has always been true and continues to be critical -- especially in the big data world. For example, with big data's social media component, marketers have more interest than ever in how this new information source may be leveraged and how IT might help provide access to otherwise inaccessible business value.

Careful prioritization of short-term project phases that deliver value quickly is critical, while incrementally building a comprehensive, sustainable architecture for the long term that is both scalable and adaptable.

Does big data change that focus for either IT or the business?

In effect, big data simply ups the ante on both sides. It mounts pressure on IT to keep up with the technology alternatives to real solutions for ever-greater data volumes, variety, and complexity. At the same time, it puts increasing pressure on the business to first define the value justification for the needed investments.

How important is social media data when we talk about big data and analytics?

In many ways, social media is driving the data explosion. As a result, it's a key tenet of the conversation. Social media networks, blogs, forums, chat rooms, and consumer comments on retail Web sites are some of the many growing sources of unstructured customer data that can be leveraged to gain insights into customer behavior.

Organizations that choose to ignore these new sources of data do so at their peril. Their competitors will be using this data to surpass them by gaining insights that help improve customer interactions, enhance products, and manage risk.

I'm not saying it's easy to do, but organizations have to get started here. Many have explored using search technologies and social media listening applications as a starting point, but these are only a small component of what HP calls social intelligence. These efforts often remain separate from customer analytics programs. As a result, organizations miss out on the full value of the data, and the enterprise continues to have an incomplete view of its customer base.

Social media data can be characterized by the "three Vs and C" of big data -- volume, velocity, variety, and complexity. This places demands on IT organizations to keep pace in terms of computing power and storage to enable real-time analytics on massive volumes of data.

The other challenges are integrating this data with traditional data types, providing context to the data, and feeding insight back into business processes, where it really starts to make a difference.

Responding to the challenges of big data depends upon new BI approaches that are flexible and timely enough to meet ever-changing business requirements, while also being able to leverage technology that is data-type agnostic (i.e., integrate all data sources). To harness the true potential of social intelligence, companies must deploy a broad information management and analytics program, tightly linked with the overall business strategy.

What are some top problems companies might encounter when trying to establish the enterprise business-driven strategy we've talked about?

This is a good question. In fact, I recently wrote a blog about the problems that stand in the way of organizations developing a business intelligence strategy. You can find that at

What is HP's vision and strategy in this area?

To achieve a comprehensive and business value-driven business intelligence program, we offer the HP Business Intelligence Master Plan. It helps clients scope and plan BI programs that are aligned with enterprise strategy and builds the business sponsorship critical to long-term success so they can:

  • Connect and exploit previously untapped or inaccessible information

  • Realize greater return on IT investments by realigning and leveraging siloed, uncoordinated BI activities

  • Increase business agility with a cohesive, aligned BI strategy, organizations are better positioned to adapt to changing business needs, customer demands, and capitalize on emerging market opportunities

  • Institute a business-driven vision for BI; incorporate a thorough business vision for BI rather than only information technology's perspective of what the business wants

  • Ensure the previous disconnects between the business and IT that hampered success are not repeated

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