3 Big Data Roadblocks and How to Tackle Them
Grow Your Business, Not Your Inbox
The benefits of big data are well documented, especially for organizations looking to create more personal and impactful customer experiences. However, as I've previously described, few have been successful in this realm, and a huge disconnect still remains between brands and customers.
That disconnect doesn't look like it will improve, any time soon. According to Gartner, through 2015, 85 percent of Fortune 500 organizations will still be unable to exploit big data for competitive advantage.
So, what is keeping companies from going full throttle on big data? Fear of change? Lack of manpower? Something else? From my vantage point, here are the most prevalent big data roadblocks, and the steps you can take to overcome them.
1. Ability to evolve
Times are changing. What customers want and how they engage with brands is different than it was even two years ago. The “we’ve always done it this way” mentality doesn’t work, and companies have to be nimble and fast in this data-driven world to quickly gain relevant, real-time insights.
Organizations looking to be more data-driven, then, need to consider big data technologies, including machine learning. These technologies make data accessible across the entire organization and enable data scientists to be more productive and deliver consistent insights to marketing, CRM groups, BI teams, customer intelligence groups, call centers, etc., for a more customer-oriented company.
It’s also not only about the technology. Many companies think they already have the skill sets to manage their data; but assuming that current technology and skills are going to suffice is unrealistic.
2. Sponsorship at the C-suite level
Big data projects, like most large IT projects, have a huge business impact and therefore require sponsorship from true C-suite-level visionaries. Unless an initiative is fully defined and its business value clearly understood, the project is often dead in the water before it begins. C-level executives need to understand and recognize how truly taking advantage of their customer data can help their company and its business.
Therefore, big data evangelists within an organization must work with individuals across lines of business and job silos to ensure that stakeholders in every part of the company are on board and able to lend support to, and gain the benefits from, a big data initiative.
3. Changing the mindset
To evolve, an organization must set out to change the mindset of those, from C-level execs to data managers and data users, who are unsure of big data or how their roles will change because of it.
For example, data scientists have created models and analyzed data that have worked well enough in the past. But big data is a whole new world, offering new challenges and new business benefits, if used correctly. Data scientists will need to relinquish control over the data (and that also means embracing the rise of the citizen data scientist,) and rely on technologies like machine learning that are radically transforming the way organizations interact with, process and use data to make decisions.
Machine-learning techniques, with their emphasis on real-time and scalable predictive analytics, use fully automatic and generic methods that identify trends and individual preferences. In this way, they change and “simplify” some of the typical data scientist tasks -- from doing the work to “managing” the process to gain greatest value for the data.
The bottom line
Companies have a golden opportunity to leverage the massive amount of user data at their disposal to significantly improve their customer experience, which will ultimately increase customer loyalty and improve sales and margins. Those that have significant data, yet lack the initiative to take steps to derive value from it, are handicapping their businesses.
By clearly outlining your organization’s big data goals and honestly assessing where you stand now in terms of both skill and technology, you’ll be able to create a clear pathway to big data success.