This is part three of our series on the findings and text from IBM Institute for Business Value’s latest study and paper: “Analytics: A blueprint for value - Converting big data and analytics insights into results,” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley.
In part two, we introduced the three levels of value impact that classify the nine levers identified in part one of this series: Enable, Drive and Amplify. These levers represent the sets of capabilities that most differentiated Leaders from other respondents of the study. Leaders were identified in the study as substantially outperforming their industry and market peers and attribute much of their success to analytics. At the conclusion of part two, we discussed Source of value, the first of the three Enable levers. Today we will dig into the second Enable lever: Measurement.
Influencing business outcomes is the primary purpose of analytics investments. Organizations realizing value from analytics solutions are those that can readily measure the impact on key performance metrics and recognize their ability to forecast future outcomes.
To sustain success with analytics, results must be measured; leaders know this intuitively and take proactive steps to ensure they can define how much impact information and analytics have on business outcomes and also when anticipating future events. They use information and analytics to predict future events and impact business outcomes, and then measure the outcomes.
Almost half of Leaders report that data and analytics have a significant impact on their organizations’ business strategies and operational outcomes. Leaders use analytics across their core processes to inform and guide most operational actions and drive departmental-level decisions. The top one-fifth of Leaders base all their business decisions (that is, both strategic and operational) on information provided by analytics.
Once a project has been implemented, a majority of Leaders use a set of metrics-based processes to evaluate the outcomes. One-third of Leaders evaluate analytics efforts based on both the tangible and intangible impacts, while another quarter of Leaders benchmark pre-defined metrics and evaluate success based on the level of change (see Figure 4).
Measurement is important in demonstrating a return on investment for analytics initiatives. We found that a majority of all organizations realize a return on their analytic investments within the first 12 months following implementation. More than 40 percent of Leaders report realizing a return within the first 6 months, while another 25 percent report a return between six and twelve months.
For one South Asian communications company, the ability to measure the performance of its communications towers is an essential part of its business model. The company rents space on the towers to service providers throughout India, including rough, remote areas. To guarantee the necessary levels of service quality, the company needed a clear view into asset usage, tower tenancy and other factors to aid in monitoring, management, operational efficiency and cost control, both overall and by business circle.
For example, some towers only had a few tenants renting space. Those towers brought in less revenue for the company, which also missed opportunities to market the extra space to other potential tenants. The company needed to identify those less populated towers to rent them to capacity.
The company now uses business analytics to monitor 34 different key performance indicators (KPIs) for each tower to spot high costs and inefficiencies. The analytics dashboard has automated 74 percent of KPI monitoring, all of which was previously measured manually. The solution also provided first-time insights into market share, leading to increased sales and marketing opportunities. The system also has reduced the energy cost per tenant and, as a result, reduced customer churn.
In my next post, we will discuss the third and final Enable lever: Platform. This lever includes the integrated capabilities delivered by hardware and software components in support of analytic activities.
Catch up on the entire series so far with parts one and two:
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