Every company that invests in big data wants that data to yield actionable insights to help their organization remain competitive and achieve its goals. In order to generate real return however, big data specialists must be able to do their jobs well. Unfortunately, big data teams often face a host of (preventable) obstacles that stand in the way of success. If you employ a big data team, these are some of the biggest headaches that they experience.
The Perceived Democratization of Big Data
Thanks to widespread availability of big data tools and dashboards, everyone from entry level marketing assistants to the CEO believes they are experts in big data. Even if the company has invested in high-level tools to make data accessible to employees, there is a lot of technical work that goes in to collecting, organizing, managing, securing and providing that data, and it takes experts to truly make sense of that data.
The impression that “anyone can do big data” has led companies to reduce investments in personnel, putting additional strain on the remaining team and reducing the effectiveness of big data initiatives. Any time the company considers scaling back on big data, leaders should consult directly with the technical team to ensure that the right people remain in place to ensure the company can achieve its big data goals.
Failure To Communicate
Here’s a not-so-fun-fact: Nearly 55% of big data projects don’t get completed, and most fall short of expectations, leading to millions of wasted big data dollars each year. One of the biggest reasons for failure, according to a recent study, is a lack of communication between organizational leaders who set the vision and the technical staff tasked with implementation.
Leaders often do not invite those who will be doing the work to the table when planning a new project, which often leaves the project vulnerable to a host of challenges that could have been anticipated and prevented if the right people had been invited to the table. In many cases, challenges are dealt with reactively rather than proactively, which increases the odds that the project will either change dramatically in scope, or fail altogether.
The Resource-Draining Power Of the Back End
When it comes to big data projects, infrastructure does matter and the technology stack has to be capable of moving the team towards their end goals, but big data professionals have to focus on the entire picture, and their priorities go far beyond infrastructure.
One of the biggest frustrations of big data teams is the amount of time spent on back-end work like processing and ongoing data management. These functions can all be outsourced, but many organizations are reticent to take this step, keeping their internal teams hamstrung and unable to be agile, responsive or innovative. When teams are tied to back-end work, it limits their ability to help the company realize true ROI through timely, actionable insights.