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The Business Case for Big Data


A Recommended Approach to Implement Big Data

There’s no singular method to deploy a business intelligence solution to answer unique company questions, but there is an approach to take advantage of Big Data which minimizes risk and increases the likelihood of a successful outcome.

  1. Begin with Stakeholders — No project should begin before identifying your stakeholders and their success criteria, and while the C-suite is part of this crowd, Big Data stakeholders are more so the knowledge workers and decision makers. Hmm … that actually means pretty much everybody in the company, which means you’ll need to categorize stakeholders by role, prioritize their information making value and pursue a sequential and progressive road map.

  2. Consider Culture — For many organizations, better decision making requires a cultural shift which expects data-driven, fact-based decisions, and does not accept unsupported or gut-feel conclusions. Business leaders need to champion an internal emphasis on optimizing business performance through quantitative measurements. “Show me the data” is the mandate when executives or managers are being asked to approve decisions or recommendations.

  3. Find Your Data Stewards — Finding the right people to define data governance and implement the data management processes can be tough. Complex analytics have historically been relegated to statisticians, analysts, data scientists or other highly cerebral thinkers. But such titles are not within the org charts of most companies, and integrating these roles with line of business managers to solve business problems can be a challenge. New technologies and a new breed of data stewards are finding that a mix of technical and business skills, whether from a single person or members of a tightly aligned team, are producing successful results.

    And interestingly, these roles may or may not report to IT. In fact, with increasing frequency, analysts are operating in a more decentralized environment closely aligned with departmental functions. According to Forrester’s James Kobielus, data analytics teams are usually organized by business function or placed directly within a business unit. Kobielus shares that developing, testing and maintaining complex analytical data models requires strong domain and business knowledge, a requirement that doesn‘t easily lend itself to centrally controlled analytics.

  4. Set Clear Goals — Big Data projects are hard, so don’t try to boil the ocean. Instead start small, show a win and grow incrementally. Catalogue use cases and decisions which benefit from Big Data attributes, weight each decision’s impact, and then make the goals use case driven. Scope the entire information management landscape, but pick out the low hanging fruit. And remember that goals aren’t complete until they are SMART (smart, measurable, actionable, realistic and time-bound).

  5. Create the Plan — When developing your plan, link the goals to the constructs that define Big Data (volume, velocity and variety). Also recognize Big Data is a compliment, not a replacement, to your existing analytics such as data warehouses, OLAP, and decision support systems (DSS). And of course no plan is complete without ROI projection, but don’t try to create an overarching “Big Data ROI” forecast. Instead develop ROI forecasts by each of the use cases. For example, if using customer sentiment social analytics permits changes to product offerings or customer support which in turn lowers customer churn by 2% annually, what’s that use case worth to the company?

  6. Establish Metrics — In my experience in helping clients deploy Big Data solutions, I have found it helpful to limit the number of metrics to only a few high priority measures, rather than a more exhaustive list. The two I generally favor out of the gate are Time to Decision and Decision Impact. When flattening the Time to Decision, the entire use case cycle should be measured. Taking this data in motion metric a step further can tap into the velocity of data, or in this case, the complete cycle time from when data is sourced to when it is consumed. Most data has a limited shelf life, and data management isn’t free, so this measurement can aid data management effectiveness — and especially cost — in a big way. There are many ways to calculate Decision Impact, such as reduced risk, increased confidence levels or Quality of Decision, but I prefer to translate Decision Impact results to financial metrics either in the form of cost avoidance or incremental revenues.

  7. Deploy the Technology — There’s a conundrum that Big Data technology can help resolve. Most companies are both drowning in data and starved for information. Most companies have a tough time getting value from the data they already have because that data is unstructured, unclassified or in an otherwise raw form. Further, while data volumes are growing exponentially, companies recognize they don’t have the best sources of data and among the data they do have, they’re ability to process multiple data types is limited at best. And businesses that are unable to manage their data become overwhelmed by it, fail to harness it for any material benefit and are left to make serious business decisions in the dark.

    Big Data by definition applies to information that cannot be leveraged using traditional processes and tools and can resolve many of these all too common challenges. A common technology starting point is the open source Big Data engine, Hadoop. This tool is particularly well suited for loosely structured or unstructured data as well as high volume search and discovery. I should point out that it can also be used with structured data, but in my experience information systems plans seldom use this tool for this purpose. Also in the context of information systems plans, there’s a data management mind shift that often needs to occur. We’ve grown to accept and even demand that in a data warehouse, DSS or structured data analytics tool data must traverse a data management path whereby it is sourced, cleansed, normalized, tagged with metadata and compliant with the Master Data Management (MDM) strategy, which collectively leads to an expensive and time consuming process for information with limited scope and definitive parameters. Big Data is different. It stays in its native object format. Because the unstructured data incurs less processing and the fidelity of the data remains intact, the labor and data management costs of Big Data are lower than traditional structured data analytics. Big Data is available for the questions not yet asked, or the questions that will be posed from successive learning that leads to more questions. Big Data repositories with intelligent search tools permit decision makers to sift through large volumes of data in order to discover the data nuggets that are valuable. Sometimes, once those valued data sources are identified they sync with or migrate into more structured tools such as data warehouses and DSS.

    Due to the exponential rise in data volume, velocity and variety, this approach of managing big data in a relatively low cost model, until such information is gleaned and warrants further processing, makes the best financial sense. A slew of start-ups and large IT vendors now offer tools that compliment and extend Hadoop for various purposes. And while these tools are powerful, they are not required to tap into the benefits of Big Data. In my most recent project, we did a quick and dirty deployment of Radian6 to collect customer sentiment, and then used Hadoop as a data management platform and Cognos Consumer Insights as a rich data visualization engine to discover new data points, data snapshots and trends in order to bring data to what was otherwise guess work. The project achieved a 5 month payback and a very impressive ROI.

  8. Make Big Data Little — Delivering little data in context with business use cases and to decision makers in a way that insights are easily consumed and acted upon represents the last mile in making Big Data useful. To aid the challenge, data insights should be tailored by role and included in the applications, devices and channels where decision makers spend their time, and in way where the insights are aligned or joined with their existing presentation technologies. For example, rather than requiring a separate application for big data presentations, it’s far more effective to include big data insights within existing decision support systems, or within existing business apps such as CRM, ERP and HCM software applications.

  9. Design for Continuous Process Improvement (CPI) — Making better business decisions is not a onetime activity, so integrating a CPI methodology with the plan will achieve learning, improve performance and earn increasingly higher ROI over time. There are plenty of good CPI methods to test, learn and improve over successive iterative loops, however, my experience in deploying Big Data is that simplicity is a big benefit, and for that reason I recommend a CPI such as the Malcolm Baldrige Plan-Do-Check-Act (PDCA). It’s a KISS method that works well in simplifying complex projects to the maximum extent. I’ve spoken with Big Data consultants who opt for Six Sigma, but except for Government, Healthcare and some Financial Services companies, I find this methodology to be more investment than it’s worth.

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The Business Case for Big Data



There’s a conundrum that Big Data can help resolve. Most companies are both drowning in data and starved for information.

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