Customer expectations have reached an all-time high and industry competition is ever increasing -- putting businesses under constant pressure to increase efficiency and improve results. At the same time, the amount and type of available data have grown exponentially -- companies can now collate this information from across their organization and across broader industry sources. Access to this growing pool of information creates a significant competitive advantage and provides a unique opportunity to conduct in-depth market research -- offering rich insight into recent sales trends, critical business improvements and gaps in the market to exploit.
As a result, analytics has become one of the most important tools at an organization’s disposal. When data and analytics work in tandem, the benefits become obvious. Companies can leverage data to improve cost savings, redefine processes, drive market strategy, establish competitive differentiators and, perhaps most importantly, build an exceptional and truly personalized customer experience.
Today, most corporate leaders understand the value of data to their organization. A recent study (registration required) by IDG found that 47% of chief information officers (CIOs) predict their spending will increase most in the areas of analytics and business intelligence over the next 12 months. Yet, while analytics can provide real-time insights into multidisciplinary business functions and improve overall decision making to achieve better results, many organizations struggle to leverage data in a truly meaningful way.
The Gap Between Promise And Reality
Despite analytics being around for well over a decade and significant improvements to the tools and techniques available, most organizations have not figured out how analytics can play a transformational role within the enterprise. In fact, when looking at the endless possibilities, the application of analytics technology today has likely only reached 5-10% of its maturity and even leading companies are still using analytics to drive decision making in a very limited way.
While analytics can be a source of competitive advantage, if done correctly, there are four main reasons why most analytics initiatives fail to deliver the value they promise.
1. Gap in business alignment: For most analytics projects, there tends to be a gap in understanding of the business context and its translation into the analytics problem that is being solved. Analytics projects tend to overly focus on solving an analytical problem very well when instead they should focus on solving the business problem by understanding the linkage between the business and functional drivers. Every analytics exercise must start with a sharply defined business problem based on specific use cases and business key performance indicators (KPIs). This can be achieved by the upfront involvement of business teams in problem definition and designing of the solution.
2. Lack of end-to-end approach: There are four different layers in analytics. First is the strategy layer, which is about identifying the core business problems; second is model building, which hinges on choosing the right kind of data modeling for your specific problem; the third layer is the actual data (access and collection) and the fourth is intelligent applications. Where many data analytics projects fall short is in recognizing that to achieve true business impact, change needs to be driven as an end-to-end transformation, where business problem definition, analytics modeling, data platform, technology and operations implementation are tightly integrated. A disproportionate focus is often placed on the outcomes of modeling and not enough attention is paid to the core building blocks to connect all four layers. Companies need to adopt an integrated approach towards building a solution -- only then can they derive significant advantage from their analytics investments.
3. Big bang implementation: Analytics problem solving is inherently iterative. It needs a fair degree of experimentation to get it right. You need to start small and then build up in a progressive manner. Starting with too large a problem makes specific actions, iteration and insights discovery difficult. To reiterate, there is no “first time right” in analytical problem-solving. Trying to do a big bang implementation with large data sets that have a cross-organizational impact is most certainly a recipe for disaster.
4. Inefficient operating models: Most organizations today are structured linearly with negligible cross-functional collaboration. Successful analytics projects need cross-functional teams comprising of data scientists, architects, engineers and teams from business, operations and even finance, who can work through agile implementation cycles until they find the solution to meet their business needs. This lack of an agile mindset and culture, and structures that inhibit cross-functional collaboration are often the hidden cause of failure in analytics-driven transformations.
To sum up, an analytics implementation is not a silver bullet. Analytics projects are messy, complicated and iterative. Unless they are approached with an end-to-end business transformation mindset, they will fail to deliver the intended results. However, the ability to connect the dots within analytics is easier said than done and there are very few players who are currently able to manage each piece of the equation in unison to deliver true business impact. As companies fall victim to the challenges above, data ceases to hold any value and actually creates additional work and effort with little return.
On the other hand, firms that are able to view analytics as a strategic source of competitive advantage and implement it effectively are better equipped to unlock new market opportunities as they arise. With the help of analytics, these companies can make calculated business decisions that not only improve the bottom line but also deliver a premium customer experience.
