Analytics efforts can help companies improve efficiencies in business operations, grow sales, increase business agility, and attract and retain customers. But, in order to truly understand the value analytics can bring, you need to think about both what you’re currently doing as well as identify new possibilities and opportunities. Technology has come a long way from standard BI reporting to self-serve reporting and analysis dashboards, and from structured data to semi-structured to an unstructured data set.
In a recent survey conducted by IBM and MIT Sloan Management Review across 3000 executives over 100 countries and 30 industries, one of the key findings was top-performing organizations use analytics five times more than lower performers. Top performers approach business operations differently from their peers. Specifically, they put analytics to use in the widest possible range of decisions, large and small. They were twice as likely to use analytics to guide future strategies, and twice as likely to use insights to guide day-to-day operations. They make decisions based on rigorous analysis at more than double the rate of lower performers. The correlation between performance and analytics-driven management has important implications to organizations, whether they are seeking growth, efficiency or competitive differentiation. The leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to improve the business. Over the next two years, executives say they will focus on supplementing standard historical reporting with emerging approaches that make information come alive. These include data visualization and process simulation, as well as text and voice analytics, social media analysis, and other predictive and prescriptive techniques.
At Encore Software Services, we have gained deep experience and expertise in implementing analytical solution in various industries and have had repeated success with our STRATEGYZE Methodology.
Quite often, organizations are tempted to start by gathering all available data before beginning their analysis. Too often, this leads to an all-encompassing focus on data management – collecting, cleansing and converting data – with little time, energy or resources to understand its potential uses. One of the clients that I had worked with in the past had a similar issue. The customer was storing all possible data it could and was keeping all the history it could. Too much time and effort was spent in getting the data in-house vs. analyzing the data. Analysts were trying to mine the huge amount of data to find the golden nugget. The data structure was not suitable for complex queries, and in some cases the needed attributes were not collected by source application.
These are typical challenges with organizations that do not being with strategy. Organizations should implement analytics by first defining the insights and questions needed to meet the big business objective, and then identify those pieces of data needed for answers. Companies that make data their overriding priority often lose momentum long before the first insight is delivered. Organizations should first pinpoint insights to be leveraged, use available data to test analytic models, and then the actions based on those insights can help define the next set of insights and data needed.
A recent study by the Nucleus Research says that analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in business intelligence, performance management and predictive analytics. Not surprising are the areas where they saw ROI increase – revenue, gross margin and expenses. Companies need to move more toward being “analytics-driven” instead of “data-driven.” The term data-driven has been misinterpreted and abused by implementation teams focusing more on getting the data and storing the data. Nucleus Research has found that the ROI of business intelligence continues to show significant growth as time goes on. The research group analyzed 60 different deployments of business intelligence solutions and found that there was an average ROI of 188 percent during the initial phases of implementing a business intelligence solution, but it grows to 1,209 percent in later predictive phases.
A variety of technologies form the basis for business intelligence tools and analytic applications. Predictive analytics utilize mathematically oriented techniques (such as neural networks, rule induction, and clustering) to discover relationships in data and make predictions. The category of business intelligence tools includes predictive analytics (in the form of data mining tools) as well as tools for query, reporting and multidimensional analysis. Analytic applications can incorporate either or both sets of technologies – predictive and nonpredictive.
Research group IDC also conducted a study on “Predictive Analytics and ROI.” Among the key findings highlighted in that study:
- Both predictive and nonpredictive projects yielded high median ROI, 145% and 89%, respectively.
- The major benefits of business analytics projects that employed predictive analytics centered on business process enhancement, especially improving the quality of operational decisions.
- Predictive analytics projects required higher investment levels and yielded significantly higher overall returns over five years, implying that these projects tackled problems of greater scope and complexity.
The point here is not to prove the ROI from BI and analytics investment, but to highlight that companies need to first ask the right questions for business decision making, build the analytics use cases, identify data and then implement analytical solutions. BI and analytics solutions should not only address the tactical day-to-day operational questions, but they should also lead the way to address strategic business needs and steer decision makers toward achieving the vision of the company.