Big data analytics is driving enterprise technology investments, with 37 percent of IT leaders saying the technology will occupy a larger share of the budget than other important areas like security or risk management according to a recent CIO survey.

With such significant investment comes greater expectations for results, so it’s critical that organizations get their big data analytics initiatives right. However, this is often easier said than done. There are some common challenges that hinder data analytics success, among them:

  • Poor data foundations. As Mary K. Pratt put it in a recent CIO article, “Without a fully implemented data governance program, organizations can’t expect to have sound data hygiene practices in place. They can’t access or integrate the data they have, as it remains locked away in departmental silos. They might not even know what data they need to be effective.” That’s why the first step to long-term analytics success is instituting a formal data governance framework, ensuring that an adequate budget is allotted for data cleansing and integration and securing cross-departmental support and buy in.
  • The wrong strategy. Data analytics strategies should always be rooted in solving business problems, otherwise, the technology can easily become underutilized or even ignored. With this approach, IT leaders can create manageable, achievable goals capable of generating measurable value, that can also easily evolve as the analytics program matures. Pratt writes, “Organizations should build analytics capabilities business case by business case, incrementally expanding their data program by adopting more advanced tools and enabling more users to tackle increasingly complex problems.”
  • Failure to balance freedom and control. A common reason behind failed analytics initiatives is the organization’s inability to recognize and respect different user needs. Companies need to strike the right balance between a completely permissive approach in which there are no organization-wide standards and limited support and a centralized strategy in which savvy business users’ efforts are curtailed and the program is prohibited from reaching its full potential.
  • Shortchanging the need for culture change. As Pratt puts it, “Executives need to devise more than just a holistic data program that’s aligned with strategic goals. They also need to change the culture of their organizations so that users embrace the use of real-time, data-driven insights and actually view engagement with data as the norm.” It appears many companies may be underestimating the importance of this cultural element.  Ninety-one percent of respondents in a recent survey pointed to people and process challenges as their company’s biggest barriers to becoming more data driven.

Big data analytics programs have the potential to deliver tangible benefits with a bottom-line impact in virtually every aspect of operations. As such, it’s critical that organizations overcome the stumbling blocks outlined above and ensure that they are reaping the largest returns from their big data analytics investments.