AVOIDING WORST PRACTICES

There are many potential reasons that Big Data analytics projects fall short of their goals and expectations, and in some cases it is better to know what not to do rather than knowing what to do. This leads us to the idea of identifying “worst practices,” so that you can avoid making the same mistakes that others have made in the past. It is better to learn from the errors of others than to make your own. Some worst practices to look out for are the following:

- Thinking “If we build it, they will come.” Many organi-zations make the mistake of assuming that simply deploying a data warehousing or BI system will solve critical business problems and deliver value. However, IT as well as BI and analytics program managers get sold on the technology hype and forget that business value is their first priority; data analysis technology is just a tool used to generate that value. Instead of blindly adopting and deploying something, Big Data analytics proponents first need to determine the business purposes that would be served by the technology in order to establish a business case—and only then choose and implement the right analytics tools for the job at hand. Without a solid understanding of business requirements, the danger is that project teams will end up creating a Big Data disk farm that really isn’t worth anything to the organization, earning the teams an unwanted spot in the “data doghouse.”

- Assuming that the software will have all of the answers. Building an analytics system, especially one involving Big Data, is complex and resource-intensive. As a result, many organizations hope the software they deploy will be a magic bullet that instantly does it all for them. People should know better, of course, but they still have hope. Software does help, sometimes dramatically. But Big Data analytics is only as good as the data being analyzed and the analytical skills of those using the tools.

- Not understanding that you need to think differently. Insanity is often defined as repeating a task and expecting different results, and there is some modicum of insanity in the world of analytics. People forget that trying what has worked for them in the past, even when they are confronted with a different situation, leads to failure. In the case of Big Data, some organizations assume that big just means more transactions and large data volumes. It may, but many Big Data analytics initiatives involve unstructured and semistructured information that needs to be managed and analyzed in fundamentally different ways than is the case with the structured data in enterprise applications and data warehouses. As a result, new methods and tools might be required to capture, cleanse, store, integrate, and access at least some of your Big Data.

- Forgetting all of the lessons of the past. Sometimes enterprises go to the other extreme and think that everything is different with Big Data and that they have to start from scratch. This mistake can be even more fatal to a Big Data analytics project’s success than thinking that nothing is different. Just because the data you are looking to analyze are structured differently doesn’t mean the fundamental laws of data management have been rewritten.

- Not having the requisite business and analytical expertise. A corollary to the misconception that the technology can do it all is the belief that all you need are IT staffers to implement Big Data analytics software. First, in keeping with the theme mentioned earlier of generating business value, an effective Big Data analytics program has to incorporate extensive business and industry knowledge into both the system design stage and ongoing operations. Second, many organizations underestimate the extent of the analytical skills that are needed. If Big Data analysis is only about building reports and dashboards, enterprises can probably just leverage their existing BI expertise. However, Big Data analytics typically involves more advanced processes, such as data mining and predictive analytics. That requires analytics professionals with statistical, actuarial, and other sophisticated skills, which might mean new hiring for organizations that are making their first forays into advanced analytics.

- Treating the project like a science experiment. Too often, companies measure the success of Big Data analytics programs merely by the fact that data are being collected and then ana-lyzed. In reality, collecting and analyzing the data is just the beginning. Analytics only produces business value if it is incorporated into business processes, enabling business managers and users to act on the findings to improve organizational performance and results. To be truly effective, an analytics program also needs to include a feedback loop for communicating the success of actions taken as a result of analytical findings, followed by a refinement of the analytical models based on the business results.

- Promising and trying to do too much. Many Big Data analytics projects fall into a big trap: Proponents oversell how fast they can deploy the systems and how significant the business benefits will be. Overpromising and underdelivering is the surest way to get the business to walk away from any technology, and it often sets back the use of the particular technology within an organization for a long time—even if many other enterprises are achieving success. In addition, when you set expectations that the benefits will come easily and quickly, business executives have a tendency to underestimate the required level of involvement and commitment. And when a sufficient resource commitment isn’t there, the expected benefits usually don’t come easily or quickly—and the project is labeled a failure.

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