Marketers, project managers, human resource professionals, domain experts, everybody is talking about Big Data as the single most important new age catalyst for innovation, competition and productivity. It therefore comes as no surprise that everybody is getting onboard the Big Data bandwagon. But not everybody is able to leverage its immense potential. One reason for this is of course lack of understanding of Big Data and an inability to align its use with company objectives. But, there is yet another reason why businesses/professionals are unable to optimize its use they cannot get around some of the biggest Big Data myths.
In an era where there is growing emphasis on Big Datas importance, it makes sense to approach 2014, without getting entangled in the various myths that can put a damper on Big Data adoption.
Some of the Biggest Big Data myths
Myth 1: Collecting, Synthesizing and Mastering High Value Data is All about Volume
The Debunk: Yes, volume is an important part of what Big Data is all about but so is variety and velocity. Its the three Vs that are the defining parameters of Big Data. Volume refers to the amount of data, while variety refers to the various types of data and velocity refers to the speed at which all data is processed. The challenge is to tame data variety and join all this data into a unified analytic. If youre able to do this youll be able to analyze all the data at your disposal and make critical strategic decisions. If not, even if youre able to manage the data volume, youll still be left with nothing tangible at the end of the day.
According to the Big Data Executive Survey, companies whore successfully using Big Data are more focused on getting a variety of data rather than its volume. Improved data-driven decision making does not result from managing very large data sets, but by analyzing diverse data sources.
Myth 2: Big Data is Only about Accessing Opportunities outside Ones Enterprise
The Debunk: The only data that is important for your organization is the one that isnt available on the mainframes within the enterprise. Take a close hard look at the statement. This is the myth doing the rounds of enterprises. But, this is incorrect to say the least. Before, Big Data analytics came into the picture, organizations were unable to optimize the use of data that was already sitting in their databases. Remember, more than the unstructured data available outside the enterprise, of more importance is the criminally mismanaged and underutilized data in existing data stores.
Your company can reap rich dividends from Big Data if it first looks inwards for investigating existing data opportunities and then moving outwards. Your transactional and log data might offer a bigger payoff in the decision making sweepstakes rather than data that you need to source from outside the enterprise framework.
Myth 3: Big Data has Made Traditional Project Management Practices Redundant
The Debunk: With businesses fast realizing the importance of data sources from financial systems, social media, customer call centers, surveys and more, Big Data is being merged with project management. It is offering tremendous opportunities to project managers for analyzing performance and benchmarking. So does the sheer enormity of the data make the fundamentals of project management irrelevant? Do project managers need to overhaul their skill sets to blend Big Data into the project management process?
The simple answer to these question is No. The best practices still stand and there is plenty of progressive project management software like WorkZone etc. that bring the power of Big Data to project management. A Big Data project is like any other large scale project, the only difference being that you now need to add personnel to the project management team that have the skill sets required to analyze, evaluate and implement the data in accordance with an actionable plan. Together the team should not only plan but also utilize Big Data initiatives.
Myth 4: Big Data has a Clear Definition
The Debunk: This myth has been going around for quite some time, but it still makes sense to mention it. Although experts have tried to explain Big Data by defining various parameters for it, the term is still nebulous. Its being used in different contexts and while there is very little consensus on its scale and scope, there is a general agreement amongst experts as to what constitutes Big Data.
Its important to understand, there is no confusion as to its use, but whether it can subscribe to a definition etched in stone. As a Big Data user, your focus shouldnt be narrowed down by trying to put this data in a definition silo. This wouldnt be doing justice to it. However, with the massive amounts of data we can mine, its imperative that you lay out the ground rules for mining this data by identifying the reasons why you want this data in the first place. While you cant easily define Big Data, you can definitely ensure you have a handle on objectives behind Big Data mining to help you reduce the number of variables and make datasets manageable.
Wrapping it up
Busting myths is difficult business, because most of them are well-entrenched in the mindsets of the people who believe them. Hopefully, the myths mentioned in this article have been debunked to your satisfaction, at least, enough to make you start thinking about your Big Data missteps. This will allow you to capture and create value from Big Data and ensure your business gets the competitive edge, as a result.
