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The Wrong Analytics Priorities Will Squander Your Investments

This article is from the Q3 2017 issue of Fi|r|st: The CEB Journal of Finance|Risk|Strategy

 

Corporate and support functions have torrents of new data available. So they’re starting to recognize—often with some prodding from senior management—that doing their jobs and delivering value to the business requires digitalizing.

Departments like Quality, Internal Audit, and HR must move rapidly to keep up with the company’s vast harvests of information and its need to make business decisions with data. But the pivot involved can be difficult, especially for functions used to traditional methods.

Unfortunately, these functions tend to reflexively assume the answer is more technology—Audit departments, for instance, spend an average of $100,000 each year on analytics systems. That assumption can be costly, according to our studies this year of these three functions’ efforts to catch up.

A second misstep is concentrating too hard on building and, if they have reached a certain level of quality already, scrubbing datasets. Leaders of Quality, Audit, and HR all rank limited access to data and low-quality data as key impediments to data analytics progress.

That way of thinking risks squandering money, effort, and— perhaps most importantly—time. These functions can’t afford to spin their wheels, and they are better served by doing more with currently accessible data.

Instead of making additional investments in technology or focusing on the state of the data, departments should set up their staff for success. To do so, they must tackle often-overlooked (and more prosaic) tasks:

  • Getting employees more comfortable working with imperfect data (which has twice the impact on Quality effectiveness that getting access to better data has, and 5 times the impact of improving technical skills)
  • Adapting and resequencing workflows (which has four times the impact for Audit of either technology, staff skills, or access to data)
  • Improving relationships with stakeholders across the enterprise (which can improve talent analytics effectiveness in HR by 40%)

Three of our research teams provided detail.

The View from Quality

Quality and analytics go back a long way. In the early 20th century, Quality launched statistics in business with the introduction of statistical process control in manufacturing (it all started with a chemist at Guinness who wanted to standardize batches of stout).1

One hundred years later, the amount of available data is exploding—pouring in from machine performance, Internet of Things for products, social media, and customer calls.

Quality organizations that effectively use this data are reaping the benefits—they see a 9% reduction in the frequency of recurring quality errors and take 30% fewer days to complete an investigation.

But Quality traditionally is about accuracy and precision. Of course, these attributes are critical given the potential risks to employees, customers, and consumers of a product that doesn’t meet specifications. As a result, though, large datasets, particularly with unstructured data, inspire little confidence among Quality staff.

The function’s employees must develop what we call “analytic comfort”—the ability to use these new troves to spot trends, generate insight, and effectively communicate insight. Analytic comfort allows Quality staff to ask questions starting with, “I wonder if.…” It means they can use their intuition to drive better quality outcomes through analytics.

To build analytic comfort, Quality should focus on three sets of activities:

  • Developing a framework for prioritizing potential data sources, differentiating between what needs precision and what doesn’t, and determining what is insight and what is merely “interesting” or simply “noise”
  • Reducing apprehension about using new data sources, creating a lower-risk investigative approach to analytics, and carving out space for ideas that may fail
  • Spurring creativity for analytics insight

Case in Point: Keurig created a physical collaboration space for Quality and business partners where they could explore new and creative ways to use data and drive continuous improvement.

—Lynne Tappan and Bryan Kurey

The View from Audit

As early as the 1960s, Audit started trying to use com- puters in its work. But it took user-friendly software and access to data in organizations for Audit’s analytics ef- forts to take off. Now these initiatives are at the top of the agenda for Audit departments worldwide.

Analytics is a smart priority. In fact, Audit departments that embed data analytics into their daily work are see- ing significant progress toward their goals, including the ability to cover a larger part of the audit universe, deliver on their board mandate, and influence manage- ment to take action. But only 16% of Audit departments have reached this level.

Virtually all (98%) of Audit departments participating in our recent benchmark said they have made at least one investment in analytics technology. Yet even for those that have made an above-average number of investments in technology, talent, and training—sustained for a year or longer—only 26% use analytics routinely.

The key to success, it turns out, is addressing the process and methodology that auditors follow.

Audit departments don’t need a process overhaul—they just need an update. Considerations of data analytics must be inserted at key points. This applies to the department-level process of risk assessment and audit planning as well as to the methodology auditors follow in individual engagements.

Reinforcement is also essential. Signals of the importance of these changes should come from the CAE, managers, and peers. Addressing process and methodology doubles the likelihood of success over investments in technology and talent alone.

—Malcolm Murray

The View from HR

HR is still chasing the analytics dream. Three out of four HR organizations plan to increase their investments in talent analytics, but only 12% of talent analytics leaders believe their organizations are effective at using talent data to make decisions.

To improve ROI, talent analytics leaders have set aggressive priorities for 2017:

  • Improve data quality.
  • Enable the use of talent analytics among clients.
  • Make the talent analytics function more strat How can they deliver? In a world full of data, the most

important strategy that talent analytics leaders can de- ploy is strengthening relationships. These relationships may be with data owners throughout the organization to gain access to datasets that talent analytics teams need. Or they could be relationships within the team—understanding who has which roles and responsibilities, and how the team works together to meet shared objectives. Or it could be the relationships talent analytics profes- sionals have with clients, whether those are colleagues in HR or executives elsewhere in the organization.

Talent analytics teams must learn how to interact with, influence, and align priorities with their stakeholders’ priorities to build better relationships and meet their goals for 2017. In fact, talent analytics teams should take a relationships-focused approach to their priorities.

For example, instead of striving to build clean datasets on their own, the best talent analytics teams enable other data quality stakeholders to input and maintain clean data in HR systems continually. To enable the use of talent analytics among clients, think about restructuring workflows to establish solid relationships with clients at the start of analytics projects and to bring action planning (or even pre-action planning) into those relationships as early as the scoping phase.

Overall, these investments in relationships create a ripple effect that will go a long way in transforming talent analytics work and impact.

—Blakeley Hartfelder

Next Steps

Members of CEB Audit Leadership Council can register for a series of small-group meetings through November to discuss these findings with counterparts.

1 Stephen T. Ziliak, “Retrospectives: Guinnessometrics: The Economic Foundation of ‘Student’s’ t,” Journal of Economic Perspectives, 22 (2008); 199–216, https://www. aeaweb.org/articles?id=10.1257/jep.22.4.199.

 

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