Data Governance Might be the Solution to Your Performance Management Pain
Whether you’ve adopted OKRs, cascading metrics, balanced scorecards, or another framework, the key to success is trust in the data. Here’s how to get there.
By Amy Hsiung and Brian Leitner
One of the leading industries using data analytics is professional sports, where Performance Management—also known as Enterprise, Corporate or Business Performance Management (EPM, CPM, and BPM, respectively)—has been successfully implemented for several decades. Probably most famously, the Oakland A’s played “moneyball” and pioneered the use of data and analytics in Major League Baseball. The Golden State Warriors capture and track millions of data points from every moment of play on the basketball court, winning back-to-back NBA titles.
But why does the world of sports make performance management appear so easy? One reason could be that controversy over the stats driving on-field and off-field performance rarely happens. Players, teams, leagues, and critics have all agreed on what metrics are the right metrics, and there is a high degree of trust that the results are accurate and correct.
When you think of performance management, is your next thought of data, and data governance? Maybe it should be. For many of our clients, the intersection of these two disciplines can be a pain point from the C-Suite down. However, it doesn’t need to be like this! Investment in data governance, and close engagement with the data governance function in your organization, is essential to trusted, accurate, and actionable performance management to which all stakeholders are aligned.
Performance management without data governance is nothing
Performance Management comes in many shapes and sizes: Balanced Scorecards were popularized in the early 1990s, while Objectives and Key Metrics (OKRs) were most famously adopted by Google to guide them to greatness in the 2000s. Your organization may already have implemented a performance management approach — or at least picked some metrics or key performance indicators (KPIs) to track in dashboards and reports. If so, your organization is probably also scheduling regular planning sessions to act on the results.
Of course, that’s only the foundation. Your organization may have encountered conflicting reports, multiple “sources of truth,” or time-consuming reconciliation processes. As a result, decisions may have been called into question, and strategic plans or tactical execution may have failed. Ultimately, if your company does not invest in data governance, it does not matter how modern and cutting edge your performance management approach of choice is, or how fancy your dashboards are. Whether you have adopted OKRs, cascading metrics, balanced scorecards, or another framework, the key to success is trust in the data. That is where strong data governance comes in.
What is data governance, and how can it help?
Data governance is the set of policies, standards, and resulting ways of working that protect data quality in an efficient manner while increasing accessibility. Data governance involves ongoing agreement on what particular data and metrics represent; tracking where the data comes from, how it is prepared for analysis, and where it is used; and automating business rules to minimize manual processing. Documenting data and their definitions in central repositories not only enables self-service analytics, but fundamentally ensures quality because potential users will have the same interpretation. Other downstream benefits are fewer errors, improved traceability, and more accurate reporting and decision-making.
Central to data governance is a forum where both business and technical stakeholders can coordinate and make joint adjustments as business realities and analytics evolve. In the world of performance management, data governance provides quality assurance, and in turn, more reliable performance reporting and decision-making. Data governance, data quality, and improved access to data are core components of a company’s ability to instill a modern culture of data, where everyone is enabled to use the right data to make decisions.
What are some considerations for bringing data governance into my organization?
Let’s start by addressing one major misconception: Data governance is not solely the responsibility of data analysts, data architects, or database engineers. When developing performance measures, business leaders are already engaging stakeholders across the organization to define strategic metrics. In conjunction, business leaders should include considerations about data governance by investing efforts to align terminology related to metrics. For example, what does “product” represent? Is it a feature? A service product bundle? A SKU? Similarly, the business logic required to calculate each metric must also be aligned across the organization.
While helping a client migrate to a new travel and expense platform, we discovered that the business intelligence reports slated for migration had not been used for years. Some teams were instead downloading the reports, combining them with other data, and calculating new metrics using the enriched dataset. In addition, the data used to categorize and aggregate employee expenses were not useful to business leaders, who had diverse performance goals such as fraud, abuse, compliance, mobility, and sales and marketing effectiveness. The various performance management requirements led to a slew of spreadsheets and databases (and people to manage them) to consume travel and expense data and generate performance metrics. With the move to the cloud, all of these downstream performance management solutions would be disrupted, and new reporting metrics needed to be defined.
These activities can be coordinated through governance forums. Governance forums are integral to strong data governance as they bring together cross-functional representatives across the business and IT or engineering. While the forum’s makeup may vary, its role is to facilitate alignment across relevant organizations or across the enterprise on terminology, metrics, and components of metrics, as well as weigh in on changes and additions that may impact existing databases. For example, how does a change in how a performance metric is calculated impact other metrics that may depend on it? Does it change the business definition, and is the revised business definition already in use elsewhere? Before changes are made, it is important that the right business leads vet that the metric reflects current business processes. Otherwise they risk quickly becoming irrelevant and unused, substituted with offline analyses, spreadsheets, and an amalgam of data sources.
Slalom helped our client set up governance teams consisting of key business stakeholders who used travel and expense data and representatives from the corporate analytics, employee travel and expense, and IT/engineering teams responsible for the new cloud systems. With Slalom’s data governance experience, the client was able to develop sustainable cross-functional teams that met regularly and expand dialogue about performance metrics and the underlying data structures required to drive them. The migration plan was adjusted to ensure that new reporting would provide the data structure required for effective employee expense performance management.
Once the business has agreed on the metrics, definitions, and business logic, engineering teams can start to design data models (think of a sketch of the future database) that will facilitate regular reporting and faster insights that require less manual manipulation. It is through engineering that the metrics, definitions, and calculations defined earlier are implemented and codified. To protect and democratize how the metrics are understood, the common terms and definitions that the enterprise previously aligned on can be stored and made accessible through a centralized portal (sometimes referred to as a metrics catalog, dictionary, or library). As organizational processes change with the speed of business, Engineering can leverage the portal in conjunction with governance forums to partner effectively with the business in maintaining the timeliness and relevance of performance metrics.
Closing comments
The intent of performance management is to remain competitive, and just like sports teams, businesses must leverage data to drive the metrics that will best engage their employees, customers, and stakeholders. Early and regular investment in understanding how business leaders consume data and manage metrics to drive performance can yield better results. Strong data governance is the key and this requires an adjustment to your business culture and ways of working, driven by leadership alignment, open dialogue and inter-departmental cooperation. Each function must regularly adapt new forms of collaboration, take the time to learn to speak the same language (of data), and invest and trust in a single source of truth. This is part and parcel of what we at Slalom call a Modern Culture of Data.
To learn more about Slalom’s Modern Culture of Data framework and how you can use it to help transform your company’s use of data, check out our website.
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