Check out the key performance indicators of data management
Data management needs performance indicators to guide you. Also known as Key Performance Indicators (KPIs), they are relevant to determine the way a company makes decisions and to point out if the new tools, technologies, and methodologies used by the company are making a difference.
Indicators are used to control the objectives of an organization because they can put the data as protagonists in the decision-making process. When we are guided by data, it is easier to determine what is expected of each team member or department, facilitating communication within companies and the distribution of responsibilities.
In addition, KPIs bring more visibility and transparency to an organization, but you need to know how to define each one and which cannot be left out in data management. Check out the indicators that should be evaluated constantly within your business and how each of them is associated with the digital transformation, used analytical solutions, and Big Data.
Less than 15% of companies have the resources to analyze how digital transformation affects their business, that is, starting to consider this type of KPI will be a competitive differential. The main challenge in measuring the impact of digital transformation is that traditional indicators are not always appropriate to measure the immediate efficiency of efforts.
Most of them offer a long-term view. However, as changes in digital transformation occur at a high speed, we need to work with efficient performance indicators. It is important to analyze, for example:
Digital transformation requires continuous improvement. These metrics impact on the mentality of professionals, their behaviors, and the results they obtain.
Adoption metrics can help you pinpoint whether the tools the company has acquired are actually being used by employees. Evaluate the quality, productivity, and performance of your applications to acquire operational metrics. Monitor the tools, their use, how much they become part of the employees’ daily routine, and in what types of activities they are applied in more frequently.
People see user experience KPIs as “vanity metrics,” that is, information that cannot be used in decision making. However, UX (User Experience) metrics are one of the most important indicators for evaluating digital transformation.
How easy your team uses data management tools influences how often they add data to systems and how they use the insights they get there in their routines. Consider how long a tool is used depending on how much work is done with it. Thus, it will be easy to assess whether resources increase or decrease employee effectiveness.
Digital transformation leaders need to be concerned about their financial impact. To measure it, they must achieve significant growth targets. Since the impact of the transformation is hard to see with traditional KPIs, which are measured over large periods of time, daily or weekly monitoring works best to evaluate it.
Increasing productivity or revenue generated by collaborators is an important financial indicator as well as the cost of acquiring new customers. With transformation it is possible to perceive those values in smaller cycles. Recurring revenues and growth are also metrics that measure the impact of digital transformation on business finances.
Assessing how the systems employed by a company in data management is imperative to scale if in fact the organization is moving towards a paradigm shift. Consider the following metrics to understand the impact of analytical solutions.
Indicators of success
When you try to understand if your company performs better when using data to make decisions, it is more interesting to evaluate the success of the initiatives than their quantity. Having too much data is not the same as using them well.
Evaluate how many business decisions are data driven and whether their outcome is better than what was previously achieved. Here, qualitative approaches, documenting the change in corporate processes, can be used in surveys with employees.
Good governance makes it easier to understand whether data management occurs efficiently within companies. Is there a defined policy? Is it clear and accessible to all workers and departments?
To define this, use metrics such as the effort required to adapt to the policies, the time the employee spends to feed information into the system, the accuracy of the data that he submits, and the grades he obtains in audits.
Big Data initiatives require dedicated performance indicators to determine if the amount of data a business owns is used strategically. In this area, the indicators can point to whether technology is efficient enough to bring optimized responses to multiple business areas.
How a company analyzes and processes the data it has is a relevant point to evaluate the use of Big Data. So measure the frequency of data collection, the time it takes to make them available for analysis, and how easy it is to turn them into performance indicators.
Synthesis capability is one of Big Data differentials, so in order to evaluate it, note the quality and frequency of inter-departmental communication, such as IT and Marketing.
Big Data makes millions of information pieces readable in seconds. But how do you assess if this is being achieved by your data management? By measuring the speed of systems. Identify the response time of the business areas to achieve increased agility.
Some of the KPIs used by the business to determine whether data management is efficient can be applied to more than one area of the company. Performance indicators ensure that, in data management, important information is collected for decision making. Defining good KPIs is the starting point for data analysis tools to bring results.
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