Study: Most Companies’ Data Doesn’t Meet Basic Quality Standards

Study: Most Companies’ Data Doesn’t Meet Basic Quality Standards

At the Harvard Business Review, Tadhg Nagle, Thomas C. Redman, and David Sammon present the findings of a study they conducted to assess the quality of data available to managers at 75 companies in Ireland. Using Redman’s Friday Afternoon Measurement method, they asked managers to collect critical data on the last 100 units of work conducted by their departments and mark them up, highlighting obvious errors and counting the number of error-free records to produce a data quality score. “Our analyses confirm,” they write, “that data is in far worse shape than most managers realize”:

  • On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error. A full quarter of the scores in our sample are below 30% and half are below 57%. In today’s business world, work and data are inextricably tied to one another. No manager can claim that his area is functioning properly in the face of data quality issues. It is hard to see how businesses can survive, never mind thrive, under such conditions.
  • Only 3% of the DQ scores in our study can be rated “acceptable” using the loosest-possible standard. We often ask managers (both in these classes and in consulting engagements) how good their data needs to be. While a fine-grained answer depends on their uses of the data, how much an error costs them, and other company- and department-specific considerations, none has ever thought a score less than the “high nineties” acceptable. Less than 3% in our sample meet this standard. For the vast majority, the problem is severe.
  • The variation in DQ scores is enormous. Individual tallies range from 0% to 99%. Our deeper analyses (to see if, for instance, specific industries are better or worse) have yielded no meaningful insights. Thus, no sector, government agency, or department is immune to the ravages of extremely poor data quality.

The data quality challenge should sound familiar to HR leaders attempting to implement talent analytics strategies.

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ReimagineHR: 6 Shifts in the Digital Age and Their Implications for HR

ReimagineHR: 6 Shifts in the Digital Age and Their Implications for HR

At our ReimagineHR summit in London on Thursday, CEB (now Gartner) Principal Executive Advisor Clare Moncrieff led a session on creating a common vision of digitalization for the business and HR. After examining hundreds of trends, our research councils serving chief HR officers and chief information officers have identified six deep shifts in the business environment that will result from digitalization. These shifts should act as the framework for heads of HR to:

  • Ensure talent conversations with the line are grounded in business context
  • Identify the current talent implications of these shifts, project future implications, and partner with the line and C-suite peers to prioritize and respond to each
  • Improve their teams’ business acumen (to underscore the importance of this, 58 percent of HR business partners indicated in one of our surveys that building business acumen was their top development goal in 2017)

(The case studies we link to below are available exclusively to CEB Corporate Leadership Council members)

1) Demand Grows More Personal

As customers seek personalized products that align with their preferences and values as individuals (rather than as segments), companies will rely on digital channels and digital innovations in logistics and customer service to achieve personalization at scale. Customers will continue to expect lower-effort, nonintrusive service.

This could, for example, affect how HR functions look for new talent. Attraction of critical talent now requires differentiated, customized branding and career coaching. Candidates will demand a more effortless, personalized application experience. AT&T approached this shift by creating a more personalized “Experience Weekend” to show the innovation of its brand to campus candidates and make top talent more likely to accept job offers.

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ReimagineHR: Key Challenges for Leaders in Talent Analytics

ReimagineHR: Key Challenges for Leaders in Talent Analytics

In a breakout session at the ReimagineHR conference hosted by CEB (now Gartner) in London today, a group of several dozen HR leaders came together for a peer benchmarking session to compare notes and discuss common challenges in the field of talent analytics. The attendees at Wednesday’s session had a variety of roles, including some CHROs, some heads of employee experience, HR business partners or other leadership positions within the HR function: Just as in our peer benchmarking session last year, very few identified themselves by title as heads of talent analytics. The diversity of titles and roles in the room illustrates both the breadth of the impact talent analytics is having on the HR function and the fact that many organizations do not have a dedicated talent analytics team.

The discussion centered on several key themes in the sphere of talent analytics and the challenges attendees were facing at their organizations in bringing data analysis to bear on their talent strategies. Enabling the use of talent analytics, making the function more strategic, building analytic capability, and improving data quality were all areas of concern. These are some of the key challenges that came up in Wednesday’s discussion:

Aligning Talent Analytics to Critical Business Questions

Asked where they were primarily focusing their efforts to drive action in enabling the use of talent analytics, a plurality of attendees identified this as their main focus. Some attendees noted that they are gathering robust data but were still struggling to translate that data into actionable insights to solve business problems. Attendees at last year’s session shared the same frustration. To some extent, the degree to which data can be leveraged is a matter of the analytics function’s maturity. One component of solving this problem is ensuring that the data is “clean,” accurate, and helpful in making decisions: As one HR leader remarked, she is often presented with the data that is easiest to gather rather than the data that is most useful.

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Are Organizations’ Talent Strategies Hung up on Benchmarks?

Are Organizations’ Talent Strategies Hung up on Benchmarks?

Benchmarking surveys can be a useful tool to understand how your organization compares to its peers across a variety of metrics, including talent metrics. However, Scott Mondore writes at Talent Economy, some organizations become over-reliant on benchmarks in defining their talent strategies, which “takes away from the value of the metric as a strategic tool.” Rather than chasing potentially arbitrary benchmarks, Mondore, co-founder and managing partner of the human capital analytics advisory Strategic Management Decisions, argues that talent leaders should use their data and analytics capabilities to figure out what talent metrics really matter to the organization’s performance:

Consider that a benchmark is just an average. Thus, the pursuit of outperforming a benchmark is simply a chase to be better than average against a number that may not reflect a true reality — it just reflects your particular vendor’s database. Benchmarks are also subjective. They’re a number that can change when, for instance, a vendor surveys more clients or you switch vendors. If the target is arbitrary and highly fluctuating, why spend time and money aiming for it? Shouldn’t leaders spend time and money focusing on improving metrics that have proven connections to building their business, and not just trying to outscore the average organization?

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Do You Need a Data Scientist to Lead Talent Analytics?

Do You Need a Data Scientist to Lead Talent Analytics?

Talent analytics is a relatively new and rapidly evolving function at most organizations, many of which are still figuring out where their small but growing analytics teams fit in with their broader HR function. Our ongoing research at CEB (now Gartner) finds that while the vast majority of organizations are investing in talent analytics, very few are seeing results yet.

In David Creelman’s opinion, voiced in a blog post at TLNT, one mistake many organizations are making in developing their analytics teams is appointing a data scientist to lead them. While a talent analytics leader needs an appreciation of data science, a team headed by a specialist runs the risk of focusing on the most interesting data problems rather than those with the greatest business impact:

Here’s what can go wrong if you hire someone mainly for their data analysis skills:

  • They’ll lead you down the path to making unnecessarily large investments in data and technology infrastructure — and postpone producing actionable results until that is in place.
  • They’ll overlook simple solutions in favor of complex ones.
  • They’ll spend months on interesting analysis that does not have a prominent business impact. …

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Major League Baseball Searching for the Formula for Team Chemistry

Major League Baseball Searching for the Formula for Team Chemistry

Whether in baseball or business, the value of a data-based approach to decision-making is at this point unquestioned. In baseball, however, one attribute data skeptics usually bring up as immeasurable or impervious to analytics is “chemistry”—the intangible factor that glues great teams together for sustained success. Even that may now be quantifiable, however, as recent studies profiled by Jared Diamond for the Wall Street Journal last week reveal that there is a budding movement to understand the science of interpersonal chemistry.

“In a sweeping shift, many of the industry’s wonkiest stat-heads now acknowledge that how players get along with each other likely can affect how they perform on the field over a six-month season,” Diamond wrote.

The San Francisco Giants, winners of three of the past seven World Series, are putting cameras in the dugout of their minor-league affiliate in San Jose to try to find a link between physical behavior and production. The organization and researchers will not comment on the study until it is complete, but the Giants will not have exclusive access to the results.

At the MIT Sloan Sports Analytics Conference, two Federal Reserve economists and a professor at Indiana University presented their findings of a study attempting to measure team chemistry in baseball. They focused on teams that were outperforming the sum of their individual parts, hypothesizing that good chemistry accounted for the difference. In their study, they were able to identify players with unspectacular individual performance statistics who nonetheless were consistently part of successful teams.

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Microsoft Launches Workplace Analytics Tool

Microsoft Launches Workplace Analytics Tool

Last week, Microsoft announced that its Workplace Analytics product was now generally available as an add-on to Office 365 enterprise plans. The program “taps into Office 365 email and calendar metadata, including to/from data, subject lines and timestamps, to shine a light on how the organization collaborates and spends time.” TechCrunch’s Ron Miller takes a closer look:

Microsoft is providing an overview dashboard inside Workplace Analytics and 4 standard views of organizational productivity including Week in the life, which looks at how the entire company spends time and collaborates; Meetings, which looks at the quantity and quality of time spent in meetings; Management and coaching, which measures how much one-on-one time employees are spending with their managers and Networks and collaboration, which looks at how employees are connecting across the company.

You may be thinking if it can look at positive behaviors and productive employees, it could also be used conversely to identify employees who are being less productive, but [Alym Rayani, director for Office 365,] says throughout the private beta, not one company was using it to call out employees.

Instead he said it was about looking at output versus behaviors and finding ways to improve the outcomes. For example, managers could look at the activities of top performers and learn how those people spent their day, then use that data to teach other employees to use those techniques to improve productivity