In data we trust: optimizing reporting through KPIs
The CEO Agenda Insights research study performed by Gartner, in 2015, indicates that IT investments have increased since last year and 50% of them are now dedicated to business intelligence analytics tools. These statistics reveal an increased interest in terms of collecting and processing big data volumes.
The age of “big data” has come, and most companies are shifting from “intuitive management” towards decision making based on data. It has become quite popular, nowadays, to find a number that will justify your strategy. Looking from a distance, this seems to be a management best practice, but if we take a closer look, the age of big data does not have a pretty face yet.
In practice, many organizations are facing challenges like:
- Too much data, losing sight of what is important;
- Lack of expertise to process data, to provide visibility and clarity;
- Lack of expertise in terms of understanding the true meaning of data;
- Data gaming;
- Too much focus on past performance.
- Too much data – there is a common expression used to express the inability to handle large volumes of information, and most managers are familiar with the saying: “drowning in data”. One problem is related with having the right technology to collect and work with data efficiently, and another one is about strategy. Just because we have the infrastructure to measure everything, it does not mean we should actually do so. Having in mind a well-articulated strategy will enable management to identify what kind of information is essential for decision making, and focus on providing the data that makes a difference.
- Lack of expertise to process data – working with numbers is not a job for everyone and, like in any other field of expertise, organizations need subject matter experts such as data analysts, or statisticians, to provide quality data. Key quality dimensions of data are related to accuracy, timeliness, completeness and consistency. However, these type of specialists may be difficult to recruit and will incur a supplementary cost for the organization.
- Lack of expertise in terms of understanding the true meaning of data – we can agree that numbers have the ability to reflect an objective reality of our business, but the way we choose to interpret and use those figures can vary widely from one manager to another. In some cases, managers may not have the ability to truly see the picture shaped by the performance report or the skills to ask the right questions, to verify what lies beyond the numbers, in order to get a true understanding of what is happening. It is a dangerous trap not to question the underlying assumptions based on which data was generated, just because data was produced by the latest analytics model does not mean it is unquestionable.
- Data gaming – taking advantage of the lack of expertise, such as the one mentioned above, makes it very easy for people to manipulate data. Employees have no interest in presenting data that is not working in their interest, therefore it is important to ensure the reporting process takes place in accordance to a framework that has the ability to diminish data gaming risks. Managers can choose what and how to report to serve their interests, but the person in charge with decision making should have the ability to know what to look for in data.
- Too much focus on past performance – reporting is retrospective and many performance review meetings have become an opportunity for managers to present the data they need to justify their actions. There should be more interest towards moving forward, towards planning next steps and identifying key areas of action that will ensure the progress of the organization.
Managing data in a manner that benefits the organization is possible, despite all challenges but it needs to rely on a well-functional system and it has to be connected to value drivers, to strategy. To avoid drowning in data, the strategy should be able to define what is important for the organization through clearly articulated goals and objectives. To ensure that objectives are reached, the company should identify several KPIs for each objective. Besides reporting a hand of 30-50 KPIs that a corporate scorecard may have, other important metrics for the business can be monitored. These usually refer to tracking key processes necessary for objective fulfillment.
By providing clarity through a scorecard and a dashboard, top management knows what figures are needed to take decisions, and it also makes it easier for them to verify the reliability of the data, to ensure a proper KPI measurement and reporting process.
It is not enough to develop a performance management system within the company, but it is also essential to develop the organizational capability to work with data. Key stakeholders need to be trained into how to collect data, how to report, analyze and take decisions based on information provided by performance reports.
By bringing together 3 components: performance management system, the right technology and organizational capability, you can transform the organization from being blinded by the data mirage to generating true value from working with data.
References:
- Gartner (2015), CIO Agenda Insights
- HugeInccom (2013), People, not numbers: How to create data driven culture
- The KPI Institute (2015), Certified Performance Improvement Professional Training Course
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Tags: Data analysis, operational performance
