People analytics is at the center of Human Resources (HR) strategy. Companies rely heavily on data and analytics to find and retain talent, drive engagement, and improve productivity. However, analytics are only as good as the quality of the data, which aims to be error-free, trustworthy, and transparent.

According to a Gartner report, poor data quality costs organizations an average of USD $12.9 million each year. Poor data quality compounds the complexity of data ecosystems which can lead to inaccurate results and poor business decisions.

To address these challenges, IBM Human Resources and the IBM Data Office partnered in the development of Workforce 360 (Wf360), IBM’s platform for people data.

Wf360 delivers one integrated HR profile spanning career, skills, performance, learning, and compensation, incorporating both daily snapshots and historical data. Built on IBM’s Cognitive Enterprise Data Platform (CEDP), Wf360 compiles data from more than 30 sources and now delivers monthly insights to HR leaders 23 days earlier than before. Flexible application programming interface (APIs) enable technical teams and data scientists to deploy AI solutions at scale and cost, resulting in a seven-fold faster time-to-delivery.

Wf360 offers a wealth of data and AI-powered HR experiences on one platform, which is compliant to local privacy regulations, eliminating the need for dedicated infrastructures. For instance, the Job Recommendation assist 180,000 IBM employees in identifying internal career opportunities; the Compensation Advisor offers recommendations to managers for annual Employee Salary Program increases (note: managers make all final salary decisions); and the Performance to Skill indicator measures the scarcity of skills in the market.

Despite this solution’s ability to effectively compile data and deliver insights to HR and IBM business units, improving data quality and reducing manual checks of the data – which can be labor-intensive and error-prone – remained a challenge.

To address this problem, IBM HR and the IBM Data Governance team built a solution that automates business data quality rules while enhancing trust on the platform, using IBM Knowledge Catalog, which operates within Cloud Pak® for Data.

What is data quality?

Data quality measures how well a dataset meets criteria for accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose. Data quality is critical for data governance. Data quality standards make sure that organizations are making informed data-driven decisions to meet their business goals.

High data quality gives organizations confidence that they can accurately interpret the data and derive meaningful insights that improve overall business performance. This helps drive efficiency and creates intelligent workflows that free up staff to dedicate their time to high-value tasks.

Data quality and people analytics

When it comes to data quality, IBM needed a more proactive way to monitor business integrity of the data. Take this example:there is a 1% spike in total headcount of a Business Unit. While this change might be justifiable from a business standpoint—such as IBM just acquired a new company which led to an increased number of headcount or mission moves—the HR team member analyzing the data still needs an approach to proactively identify such anomalies to properly communicate to senior leadership.

With its automation capabilities, IBM Knowledge Catalog enforces business data quality rules and identifies anomalies. This enables the HR team member to derive trusted talent insights that accurately tell the story behind what has occurred in the company.

With the help of IBM Knowledge Catalog, IBM HR has established dozens of data quality rules using a simple interface that allows them to consistently monitor for errors.  Data quality rules are run weekly against all of IBM’s 250,000-plus employee population that spans across more than 170 countries.

By leveraging these capabilities, IBM HR can provide leaders with trusted insights and proactively detect and resolve data anomalies before they impact clients. This empowers managerial decision-making and successful implementation of data-driven strategies. In contrast to the previous weeks-long timeline, HR teams can now accomplish these tasks within minutes, instead of weeks, significantly enhancing operational efficiency and agility.