What Is Analytics Amplifying in Your Organization?

With the current attention to analytics, it is easy to forget that business processes are the core, not the analytics about them. Analytics is about making an organization do what it usually does, only better.

Analytics amplifies. Data analysis helps organizations understand, predict and optimize by taking data — a collection of signals from underlying business processes — and magnifying it until managers can detect the business implications. Yet as promising as that may seem — and despite the considerable hype — analytical approaches are not a panacea, able to correct fundamental flaws with a sprinkle of mathematical fairy dust.

A cautionary tale can be found in the saga of British supermarket chain Tesco. Once a poster child for retail analytics, recent difficulties at Tesco show that embracing analytics does not make organizations immune to trouble. Tesco has enviable analytics capabilities, but intense competition, changing consumer preferences and potential accounting irregularities have been difficult problems to overcome.

Did Tesco’s focus on analytics contribute to its decline? Or did it stave off an even more extreme fall? The answer is likely “yes” to both questions, and underscores the challenge of applying analytics approaches well in organizations. Analytics may have exacerbated some problems such as kindling a complex loyalty system. But, conversely, data analysis may also have informed it to scale back expansion plans. The amplification effect of analytics remains difficult to manage.

Analytics may be able to take your organization up to eleven, but bear in mind these four principles of amplification.

Amplification needs signal

A Spinal Tap-style metal band goes nowhere if it doesn’t have the right equipment to perform its ear-shattering brand of music. Similarly, companies bent on producing data-driven insights are muted when they find their existing information systems don’t provide the infrastructure to generate the data signals required for analytics to yield novel insights.

The banking industry, in particular, was an early adopter of information systems, but these older systems can be inadequate for current needs. Citigroup, for example, struggled to make fragmented legacy systems support more sophisticated emerging needs. But despite limitations from existing systems, replacing core infrastructure is daunting. As a result, banks such as Deutsche Bank end up with a mishmash of legacy infrastructures and modern systems instead of a unified approach.