5 Most Ignored Factors in Big Data Initiatives

Big Data is no more a monster lurking in shadows at the next corner. Rather, it is a new species of draught animal domesticated and put to good use by most organizations worth their salt. Although there still remain a few hiccups on its way to total assimilation, these are mainly to do with how’ rather than with why’. It has long been accepted that organizations need to embrace Big Data in order to stay relevant.

Now, however, it is a demonstrable fact. Recent studies by three major consulting firms indicate that not every enterprise is deriving the desirable benefits. The massive data is imprecise yet predictive, CXOs are yet to find out how exactly their companies can use the Big Data. Following 5 factors can help the business leaders to define and create a road map to make Big Data work for their business.

1. Getting Right Data Experts:

Getting right talent on board is critical. Big Data is really not just about the technologies and platforms. Enterprises need to invest in the right kind of people who clearly understand the business objectives of their enterprise and harness the Big Data accordingly. The right kind of people need to be equipped technically as well as analytically who can understand and make sense of co-relations and trends thrown by the data analysis. Enterprise leaders should not only train the internal resources for data handling, but also bring in new talent. Certain processes in Big Data strategy can also be outsourced to firms dealing exclusively in certain data analysis areas.

2. Define what Matters:

Big data is really big and can be analyzed in various ways. Ambiguity can be a massive killer for Big Data initiative. It is important to have absolute clarity of the objective and what niche components need to be analyzed in what way, to gain what kind of insights. Reductionism ‘the practice of breaking down complex issues into their component parts is one of the best practices and can be implemented only with clarity of the objective, which will define the process. This is what will define what is to be done with the data.

3. Optimize focus through testing:

Testing is an often ignored factor by IT leaders. Whenever new technology is implemented, it is important to test and further adjust the process to gain what is required. This is called big testing in some industries. An optimized focus can only be gained through fostering a culture of experimentation. It is a lesser known fact that data-driven experiments have let to finding new ways of data interpretation and creation of innovative data based products.

4. Gaining and applying Actionable Insights:

Although Actionable insights is such an oft-repeated term, it still gets ignored at the implementation level. CIO’s need to extract actionable information from big data analysis. Providing filtered and relevant information to decision-makers has enormous importance in industry. Also, managers need to understand and change or create processes that incorporate the insights gained from Big Data.

5. Evaluation and Refinement:

Industries have a tendency to stick to a process or a policy once it is formed, however, when it comes to Big Data initiative, constant evaluation and refinement is required to achieve any big goal. Enterprise leaders, typically CIO’s need to evaluate through right monitoring solutions that give real time feedback, and respond by changing and refining. Although this seems like a time consuming process, it turns out to be a time saving process in the long run.

It is through paying close attention to these factors that the Enterprises would be able to set clearly defined goals and set milestones for success in their data analysis systems. These key points can identify early opportunities and help the business leaders to harness maximum benefits through Big Data solutions.