Big Data for Business Managers
Why do Big Data projects fail to deliver the promised value, that too despite the “clearly” established potential? What should business managers do to avoid the media hype and focus on achieving sustainable benefits from big data investments?
What are the most common errors in Big Data processes that keep these projects aloof of desired results? How much do the business managers need to know about the underlying technology to take the right decisions?
In today’s world, media is buzzing with Big Data, its potential, “successful” projects and thought leaders. So much to the extent that selling the idea of Big Data is no more a major challenge. However, relying on their superficial knowledge and superior delegation skills, business managers face the true challenge when the Big Data investments are evaluated for delivered value and sustainable advantage. Senior executives are not willing to embrace Big Data investments as merely for anticipated benefits in future. Thus, leaving the Business Managers in a big dilemma:
How to ensure that Big Data projects deliver their true value and provide sustainable benefits?
Often the failure of Big Data projects has been attributed to either social factors such as lack of organizational buy-in or talent factors such as competence of the data engineers. While these factors do certainly play a vital role, it’s very difficult and time-consuming to control them and modify to achieve desired results.
If we dare to look “under the hood” of these projects, we see a lot of factors that seem very small yet have tremendous impact on the end value. Most of these factors are operational and part of the business / technical processes followed towards Big Data goals. Even more interestingly, most of these factors lie on the periphery of being related to business or technical team, and thus, are highly susceptible to fall through the cracks. Finally, the best part is that most of these process related errors are easy to avoid provided the business managers are analytics-educated and ask the right questions to their Big Data team.
Big Data ROI will remain an inevitable challenge, until the business managers take serious efforts to understand the Big Data related business and technical processes. This will enable them to understand and improve the data quality at each step of the process. Better understanding of data quality will make them better readers of the end product - Reports (numerical or visual). These insights will help them not just in delivering value but also in setting up the right expectations. Thus, it is high time for business managers to develop a solid conceptual understanding of all the business and technical processes involved – overview, dependencies, key metrics and common errors at each process step.
The list of examples of Red Flags for Business Managers (refer image on right) can be used for self-assessment. How many of these examples stand out as Red Flag to you?
Refer the following research paper presented at IEEE Big Data 2013 for concise explanation and actionable recommendations for Business Managers to achieve better end-results from their Big Data projects (Note: IEEE Subscription may be required):
All of the five points mentioned in the box above are Red Flags. Refer the above paper for detailed explanation.