The need for speed in business analytics

Global Banking Industry Marketing, Big Data, IBM

This is part four 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." In part three we looked at the final two of four important shifts that occurred in the world of big data that are outlined in the study. Today we look at the capabilities required to enable speed to action, examine four distinct clusters of organizations that were identified as a result of the study and introduce three key stages within the analytics lifecycle to outline how leading organizations are outpacing the competition.

The need for speed

Given the shift toward speed we discussed in part three, my colleagues Glenn Finch, Steven Davidson, Christian Kirschniak, Marcio Weikersheimer, Cathy Reese and Rebecca Shockley sought to identify the organizations most capable of delivering and consuming insights quickly based on their survey responses.

To effectively meet accelerating demands, organizations need capabilities that enable speed to action and minimize the lag time between raw data and insight-driven actions. This requires both a pervasive adoption of analytics throughout the organiza­tion and the technical capabilities to quickly act on insights.

  • Pervasiveness of analytics
    • Broader usage of analytics generates an enterprise-wide ability to act with speed and precision
    • Data diversity enables organizations to create more robust and meaningful insights, increasing the likelihood of greater business impact
  • Technical capabilities to support analytics
    • Speed-driven organizations must be able to manage the volume, variety and velocity of the data available
    • Agility and flexibility within the data architecture is a key speed-enabling characteristic

Strengthening both of these capabilities is a cultural acceptance of the use of analytics, which requires executive support, leadership and funding.

Four clusters of capabilities

Through organic clustering based on 31 data points reflecting analytics capabilities, my colleagues identified four distinct groups of organizations: Front Runners, Joggers, The Pack and Specta­tors

  • Front Runners: Data-driven organizations using analytics to drive business processes within most business functions (10 percent of respondents)
  • Joggers: Primarily use analytics to automate and optimize operations, but do not use analytics pervasively (14 percent of respondents)
  • The Pack: Analytically minded organizations using analytics to drive or inform some business processes within multiple business functions (45 percent of respondents)
  • Spectators: Use only the bare minimum of analytics within business processes, yet have aspirations — often unrealistic — to increase their analytic capabilities in the near future (31 percent of respondents)

IBV Speed Fig 3.png

Becoming a speed-driven organization

The research clearly reveals that the speed at which an organization is able to transform the volume and variety of data available from raw bits and bytes into insight-driven actions is the key differentiator in creating value from data and analytics today. Underpinning this speed is the use of big data technologies.

While big data adoption in the broader marketplace has remained flat since 2012 (the first year it was measured in our survey), leading organizations (69 percent of Front Runners) are rapidly adopting big data, piloting and imple­menting technologies to support speed-accelerating capabili­ties throughout the analytics lifecycle.

We believe other organizations should follow the lead of the top performers by ensuring they have the capabilities needed to become a speed-driven organization. It’s important to understand that creating analytics speed within an organization is not a single step; organizations must excel at each key stage within the analytics lifecycle: Acquire, Analyze and Act.

  • Acquire: Source and manage data more quickly by blending traditional data infrastructure components with newer big data components.
  • Analyze: Focus on analyzing the data and identifying the insights most likely to create a positive business impact.
  • Act: Use the insights derived from data to create value for the organization.

While a clear majority of Front Runners report strong capabilities in each of these areas, Joggers are also strong competitors: close to a majority report very strong capabilities in acquiring, analyzing and acting on data. Joggers don’t report as pervasive a use of analytics as Front Runners do, which may dampen their confidence; however, like Front Runners, they are focused on optimizing the value that can be extracted from big data.

In part five we will take a closer look at the first key stage in the analytics lifecycle—Acquire—to begin to outline how leading organiza­tions are outpacing the competition, and how they are accelerating the end-to-end data process to consume data more quickly and act with agility and speed.