Analytics isn't necessarily about what the data is; it is about what the data means.
Big Data can yield big opportunities, but analytics ROI is elusive because the majority of practitioners believe that analytics starts with data and software. Precise ROI calculations are much more art than science. Here are some causes why advanced analytics ROIs are elusive.
Lack of well-set guidelines: If practitioners and leadership don't set the right guidelines to establish a comprehensive assessment and resulting project definition, it includes a clear plan to define, collect and calculate KPIs which truly have impact for the business, then Big Data ROI will continue to be elusive. And businesses are not to blame for their failed initial attempts. The approach to start with data and software works well in many other BI projects, but in advanced analytics, and particularly standing up overall analytics practices, is essentially a discovery process, it just won't work. Aggregate success comes from a rhythm of discovery and execution that falls into place only when programs are in line with business sponsorship.
Think Big Data is the end. Analytics isn't necessarily about what the data is; it is about what the data means. Transparent decision support analytics can have an exceptional ROI when it allows decision makers to best understand the information they are making decisions from, and when it increases the confidence of those decisions. ROI isn't made on single points, but on the aggregate success of the decisions made using analytics. And in business, the product of data analysis is not the analysis. The real products of the analysis are the insights gained and the impact measured.
Multiple gaps: The gaps between strategy and implementation, and the communication gaps between different roles. To understand why organizations are ultimately directly at fault if they cannot arrive at clear, understandable, accountable and actionable results for their analytic initiatives, it boils down to a lack of strategic implementation. It is typically not the responsibility of analysts and IT specialists to focus on strategic-level decision processes. Yet, analytics will fall short of its potential without adequate context, sound problem definition and results translation. The advancement of analytic and reporting options, along with the proliferation of big data delivery platforms and analytic software suites create an environment where functional managers must rely heavily upon their analysts and IT staff for critical insight. At the same time, statisticians and IT professionals are often misguided by managers who lack core analytic skills to effectively communicate their needs, or fully understand the results. The gap between these roles leaves the manager to subjectively interpret results from analytical models that emphasize quantitative sophistication and artificial metrics instead of objective, data-driven solutions.
Too much focus on short-term return: Adoption of analytics in business is an investment. It may require a change in IT infrastructure and tools to be used, which are costly. So, even though adoption of analytics will result in proactive decisions that result in increased ROI for the business, the cost for the IT infrastructure or Tools might initially offset the ROI in the short term. Realistically one can expect to see ROI to increase dramatically in the medium to long term. Business is investing heavily on Analytics to determine strategies for customer retention and identify new customers at the same time the investments on Analytics for employee retention, vendor retention needs improvement as well.
Low adaption rate due to varying reasons: Analytics is key in unraveling insights and future opportunities. An important piece is adoption into a business process to drive outcomes. Here are some of the reasons why adoption may be impaired a. Inability for business managers and analytic experts to connect on "below the hood" intricacies b. Data used not representative of the business process that it seeks to represent c. Resources on both sides of the table don't have deep competency in analytics and business d. Analytics delivered not in line with strategic corporate imperatives hence poor sponsorship e. Lack of disruptive thinking within the business f. Re-branding BI/ reporting as analytics, pull in a few better competency resources and hope that something substantial should turn up to be considered "transformational".
There are many ways analytics ROIs can get tricky, but the focal point is to set the principles to invest, implement and measure effectively by avoiding the pitfalls listed above. As analytics is the digital capability to make effective and timely decisions driven by valuable information hidden within a rapidly increasing mass of data, and it is critical to the success of digital organizations and managers.