Analyzing the business value of analytics
This is part eight in a series presenting, in small easily consumable bites, findings and insights from the IBM Institute for Business Value’s latest study and paper: “Analytics: The speed advantage - Why data-driven organizations are winning the race in today’s marketplace,” by Glenn Finch, Steven Davidson, Christian Kirschniak, Marcio Weikersheimer, Cathy Reese and Rebecca Shockley. In part seven of this series, we looked at the study’s second key stage within the analytics lifecycle (Analyze), which focuses on analyzing the data and identifying the insights most likely to create a positive business impact. In this, part eight, we will examine recommendations and practical actions for the Analyze stage.
Recommendations and practical actions for the Analyze stage
Organizations striving for speed-to-action should focus on the data and insights most likely to create a positive business impact.
Get insights from the outside
- Add depth to customer profiles, interactions and operations by integrating external data. Knowing basic account details about your customers or operations is no longer enough. Organizations need to augment this basic data with external details. For customers, this may mean adding preferences, behaviors, socioeconomic factors and influencers; for operations, it may be external financial and economic data or internal sensors and actuators. These details provide a depth of understanding most organizations today ignore. Being among the first to spot new trends in the market or preventing operational downtime can facilitate growth even in low-growth markets.
- Tap into behavior patterns, trends and sentiments using social media and customer-generated text. Use these outlets to quickly understand customers’ preferences and habits better, and identify product and service strengths and weaknesses. A rapid response to product flaws or service disruptions is critical to keeping competitors at bay. But social analytics involves more than just customer patterns; it also includes data on trends and events. For example, analysis of trends relating to hospital check-ins and status updates could help more quickly identify disease outbreaks or emergency service needs in the event of a disaster.
Make pervasive use of deeper analytics a priority for everyone
- Make pervasive use of predictive analytics a priority. Gut instincts and history alone are poor predictors of the future in today’s rapidly changing marketplace. Using analytics to spot fraudulent behaviors, forecast outcomes and guide actions reduces the likelihood of marketplace missteps, lost opportunities and unidentified risks.
- Use prescriptive analytics to empower the workforce. Few things are more frustrating for both customer and employee than a service representative who either cannot act on a request or who offers only generic responses. Empower employees by embedding analytics into front line processes, enabling them to act quickly and precisely at each opportunity. The same is true for back-office personnel, often confronted with a myriad of choices and little guidance on the course of action most likely to create value.
Confront the skills gap —it is not going away
- Learn from the best within your organization. Tap into the pockets of talent within the organization (those few using predictive or prescriptive analytics) to expand the skills of others. Create a strong internal professional program to arm analysts and executives who already understand the organization’s business fundamentals with analytics. Sharing resources and knowledge is a cost-effective way to build skills and helps limit the need to seek talent elsewhere.
- Externally supplement skills based on business case. Not all organizations need a data scientist full-time; the same is true for niche analytics skills that may be used only to solve specific challenges. Organizations should invest in the talent and skills they need to solve the majority of their analytics demands, and consider vendors to supplement critical niche skills that are hard to find and expensive to employ.
In part nine, we will begin examining the final step necessary to create the speed-to-action demanded today: Act, and act quickly on the data. While this may sound simple, it is in many ways the hardest part about using analytics to create a competitive advantage.