THE CASE FOR BIG DATA

Building an effective business case for a Big Data project involves identifying several key elements that can be tied directly to a business process and are easy to understand as well as quantify. These elements are knowledge discovery, actionable information, short-term and long-term benefits, the resolution of pain points, and several others that are aligned with making a business process better by providing insight.

In most instances, Big Data is a disruptive element when introduced into an enterprise, and this disruption includes issues of scale, storage, and data center design. The disruption normally involves costs associated with hardware, software, staff, and support, all of which affect the bottom line. That means that return on investment (ROI) and total cost of ownership (TCO) are key elements of a Big Data business plan. The trick is to accelerate ROI while reducing TCO. The simplest way to do this is to associate a Big Data business plan with other IT projects driven by business needs.

While that might sound like a real challenge, businesses are actually investing in storage technologies and improved processing to meet other business goals, such as compliance, data archiving, cloud initiatives, and continuity planning. These initiatives can provide the foundation for a Big Data project, thanks to the two primary needs of Big Data: storage and processing.

Lately the natural growth of business IT solutions has been focused on processes that take on a distributed nature in which storage and applications are spread out over multiple systems and locations. This also proves to be a natural companion to Big Data, further helping to lay the foundation for Big Analytics.

Building a business case involves using case scenarios and providing supporting information. An extensive supply of examples exists, with several draft business cases, case scenarios, and other collateral, all courtesy of the major vendors involved with Big Data solutions. Notable vendors with massive amounts of collateral include IBM, Oracle, and HP.

While there is no set formula for building a business case, there are some critical elements that can be used to define how a business case should look, which helps to ensure the success of a Big Data project.

A solid business case for Big Data analytics should include the following:

- The complete background of the project. This includes the drivers of the project, how others are using Big Data, what business processes Big Data will align with, and the overall goal of implementing the project.

- Benefits analysis. It is often difficult to quantify the benefits of Big Data as static and tangible. Big Data analytics is all about the interpretation of data and the visualization of patterns, which amounts to a subjective analysis, highly dependent on humans to translate the results. However, that does not prevent a business case from including benefits driven by Big Data in nonsubjective terms (e.g., identifying sales trends, locating possible inventory shrinkage, quantifying shipping delays, or measuring customer satisfaction). The trick is to align the benefits of the project with the needs of a business process or requirement. An example of that would be to identify a business goal, such as 5 percent annual growth, and then show how Big Data analytics can help to achieve that goal.

- Options. There are several paths to take to the destination of Big Data, ranging from in-house big iron solutions (data centers running large mainframe systems) to hosted offerings in the cloud to a hybrid of the two. It is important to research these options and identify how each may work for achieving Big Data analytics, as well as the pros and cons of each. Preferences and benefits should also be highlighted, allowing a financial decision to be tied to a technological decision.

- Scope and costs. Scope is more of a management issue than a physical deployment issue. It all comes down to how the implementation scope affects the resources, especially personnel and staff. Scope questions should identify the who and the when of the project, in which personnel hours and technical expertise are defined, as well as the training and ancillary elements. Costs should also be associated with staffing and training issues, which helps to create the big picture for TCO calculations and provides the basis for accurate ROI calculations.

- Risk analysis. Calculating risk can be a complex endeavor. However, since Big Data analytics is truly a business process that provides BI, risk calculations can include the cost of doing nothing compared to the benefits delivered by the technology. Other risks to consider are security implications (where the data live and who can access it), CPU overhead (whether the analytics will limit the processing power available for a line of business applications), compatibility and integration issues (whether the installation and operation will work with the existing technology), and disruption of business processes (installation creates downtime). All of these elements can be considered risks with a large-scale project and should be accounted for to build a solid business case.

Of course, the most critical theme of a business case is ROI. The return, or benefit, that an organization is likely to receive in relation to the cost of the project is a ratio that can change as more research is done and information is gathered while building a business case. Ideally, the ROI-to-cost ratio improves as more research is done and the business case writers discover additional value from the implementation of a Big Data analytics solution. Nevertheless, ROI is usually the most important factor in determining whether a project will ultimately go forward. The determination of ROI has become one of the primary reasons that companies and nonprofit organizations engage in the business case process in the first place.

Taken from : Big Data Analytics: Turning Big Data into Big Money