Despite an industry obsession with data analytics, most companies lack confidence in their ability to pull off advanced big data initiatives. Instead of being paralyzed by perceived shortcomings and lack of mature practices, more successful companies are getting the ball rolling by pinpointing analytics projects that can deliver short-term value while setting the stage for follow-on efforts.
A recent survey confirmed that the lion’s share of companies is saddled with a low analytics IQ. Specifically, Gartner classified 87% of survey respondents as having low business intelligence (BI) and analytics maturity, severely hampering their ability to derive value from their data assets. The lack of maturity also impedes the likelihood of companies leveraging emerging technologies like machine learning and artificial intelligence (AI) to bolster analytics efforts.
Another Gartner survey found that despite data analytics being a No. 1 investment priority for CIOs in recent years, a whopping 91% of firms have still not reached a “transformational” level of maturity in the discipline. Even more revealing, 60% of survey respondents rated themselves in the lowest rungs of Gartner’s five-level maturity model for data and analytics. The most common barriers to achieving a more sophisticated maturity posture were an inability to adequately define data and analytics strategy, lack of value from projects and trouble hammering out a plan for solving risk and governance issues, the Gartner survey revealed.
While the hurdles are hardly insignificant, they do not need to stop analytics cold. Like any significant technology project or business transformation, big changes don’t happen overnight. Companies must have the wherewithal and foresight to pick a starting ground for analytics, which can evolve at a manageable tempo over time. In fact, the best strategy for jump-starting analytics efforts is to zero in on a defined and manageable project that can deliver demonstrable benefits and serve as a test case for developing and refining best practices moving forward.
One potential area that can bear fruit fairly quickly is software usage analytics. This category of analytics, embedded in common software applications, tracks and analyzes user interaction within the program. By collecting and distilling data about feature usage, installation and run-time data, among other information bits, software usage analytics comes up with insights and trends that can help organizations more easily identify cross-sales opportunities, evolve the feature set of an upcoming product release and even pinpoint little-used features that could be dropped from legacy applications.
The best part is, you don’t have to boil the ocean to glean benefits from software usage analytics. That makes it a natural candidate for implementations capable of showcasing the tangible benefits that can win over analytics skeptics. For example, it’s a relatively straightforward analytics exercise to determine what feature sets are most widely used among a customer population, which can go a long way toward channeling development resources most effectively. As maturity advances, organizations can get more bang for the software usage analytics buck by tapping into filtering and segmentation capabilities, which allow for a deeper dive into understanding usage patterns and targeting specific user groups.
With successful pilot projects underway, organizations build a backbone of experience to start formalizing best practices and evolving to a more mature data analytics posture. These are among the recommended steps to support a mature data analytics posture:
As opposed to an ad hoc approach, it’s important to establish a framework for the management, availability, integrity and security of data within the enterprise. While governance approaches vary, a program should include a defined set of procedures for data access, a governing body that provides oversight and an evolving set of master data definitions.
Cultivate analytics maturity through both a top-down and bottom-up approach. Identify key stakeholders in the business ranks and empower them to be data stewards, responsible for the management of specific data-related assets as well as advocates for data-driven decision making. At the same time, align with an executive sponsor, who has the clout and leadership profile to promote the importance of analytics and data governance.
Effective analytics programs will require specific skills that may transcend the knowledge base of current employees. It’s important to upskill key individuals so they are up to speed on new analytics tools and practices while also bringing in new talent versed in emerging areas like machine learning and predictive analytics.
As maturity advances, a CoE can help expand analytics use and codify best practices. The shared services group supports analytics functions across the enterprise, provides training, introduces new tools and technologies and helps formulate and promote best practices.
As with any complex initiative, it takes time and commitment to establish data analytics maturity. No one (smart) wakes up one day and runs a marathon without planning and training. But there are hundreds of thousands of marathon finishers each year who started out with something more achievable like running a 5K. Don’t be paralyzed and wait until you’ve figured out all of data analytics. Instead, move forward with quick-win projects that serve as building blocks and pave the way for deeper analytics success.
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