Pointers for Project Managers during Big Data Implementation
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Pointers for Project Managers during Big Data Implementation

By CIOReview | Tuesday, August 16, 2016

Touted as the next big thing for businesses four years ago, so far, Big Data has lived up to its reputation helping businesses improve productivity while propelling their revenues. Just like the internet, it all started with the U.S. military as they skimmed through gigabytes and terabytes of structured and unstructured datasets to locate terrorists in the barren lands of Afghanistan and Iraq. Soon, the Big Data wave ran into businesses, both big and small. Retail was the first sector to jump aboard the Big Data bandwagon and shortly after manufacturing joined in the groove as well.

Big Data has indeed driven businesses forward, however, the hard truth lies in the fact that most projects have either failed miserably or have been abandoned prematurely. When things go wrong, the blame game begins and fingers will automatically be pointed at project managers for poor planning or implementation. Well, how to make it right then? Listed below are some pointers for project managers to look into while deploying a Big Data program in the enterprise.

Well-defined Objectives

In a bid to gain maximum profitability within the shortest period of time, enterprises often tend to put unrealistic expectations which in turn contribute to the downfall of a Big Data implementation project. Instead of setting unfeasible objectives, project managers ought to have a look within their setup and clearly define their objectives before proceeding to the next step. If the enterprise has the required resources, capabilities, along with the right personnel to run big data and analytics, then the project managers can go forward with their implementation strategy. If not, there is a high chance for enterprises to flop “spectacularly” in their endeavours.

Talent Acquisition

Back in 2014, a survey conducted by Accenture revealed that 41 percent of enterprises failed in their Big Data implementation project due to lack of required talent. Data scientists are one of the most sought after professionals; so acquiring them won’t be cheap or easy. It would have been easy for every enterprise if they had enough budget to cherry pick the best data professionals available. Sadly, that is not the case. However, there is an alternative hidden within their organization itself—business analysts. Rather than spending a ransom on acquisition and later training those data scientists about the enterprise’s goals, it is always prudent to train a group of business analysts on Big Data. Moreover, the existing team members will give enterprises the added benefit of cohesion in realizing the company objectives.

Agile Application Development

Adopting an agile methodology for any project management process, Big Data implementation in particular, has twofold benefits for enterprises. Firstly, it helps organizations counter unpredictable scenarios through a flexible, incremental, and iterative work cadence approach. Next, it ensures that each person in the hierarchy, from project managers to developers to analysts, are accountable for the project’s success. In addition, by maintaining a two week cycle of project development, the enterprise can undertake an “inspect and adapt” approach, thus eliminating any “analysis paralysis” in the project. An agile methodology also gives the project manager the added responsibility of keeping the team motivated through unforeseen predicaments.

Time-boxed Development

Time-boxing helps Big Data project managers ensure that the team members do not “over-engineer” processes and abide by the given time frame for implementation. It also enables team members to work with existing resources, thus allowing enterprises to stay within the allocated budget of a project. Moreover, time-boxing does not encourage setting deadlines without the involvement of any team member. Finally, it helps project managers measure employee productivity levels, enabling managers to assign high-priority processes to peak productive periods while scheduling the less important works for times when employees are more likely to get distracted.

Big Data is like a treasure chest for enterprises and the key to unlocking it lies with the project managers’ ability to motivate their teams in times of uncertainty and following agile project management practices.

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