KPI Data Analytics: what metrics should be measured?

One of the most important elements for the success of an industry is the management and control of data, since from these data business decisions can be made to determine the direction of an organization, and above all, it allows an analysis to discover opportunities for improvement.

A good way to maintain efficiency with the information provided in this reading, is through the use of key performance indicators or KPIs, as they help to have a clear vision of how the work teams focused on this area are performing and to identify new strategies.

In this article we will learn which are the main KPIs that should be taken into consideration for data analysis and why it is important to have a solution that responds effectively to these processes.

The main KPIs you should measure

Data is the key to every organization and the teams in charge of data management have a challenge ahead of them. In the face of technological advances, the use of enterprise tools can define a before and after.

However, before understanding how these solutions can help a company, it is important to understand what are the main KPIs that you should take into account, so that there is clarity in the team regarding the information collected and its subsequent analysis.

KPI for data analysis

Among the most important KPIs we have the following, each one is oriented to different groups, such as analysts, engineers and data scientists. The first one we are going to look at is the analytics managers:

1. Insights generated in the month

A first key performance indicator is found in the discoveries or behaviors of the data that are developing, which shows how effective your team is at identifying and understanding patterns.

As insights increase throughout the month, it will become easier to make predictions and recommendations to respond to the challenges ahead with the information obtained.

2. The number of times data is used to make decisions

The people in charge of making decisions should always use the data that is being obtained, analyzed and classified; this way they are more intelligent and optimal.

This KPI allows us to verify that the reading of information is constant and not occasional, since the latter is likely to yield lower efficiency results, and be detrimental to an industry.

3. Accuracy of predictions made

A fundamental goal of data analytics is the ability to predict future trends and behaviors of a market, so it should be considered essential to the performance indicator.

The simplest way is to track the accuracy of these to ensure that the team is providing valuable insights to the industry and that there is constant improvement.

4. Time in which the work team generates results

With the hectic market we find ourselves in today, diligence is a key element, but so is accuracy. The faster you can generate results from data analytics the better competitiveness will exist.

'Fast and accurate', is the right answer to deliver the best value to an industry. 

KPI for data engineering

Now we will see what happens in the case of the data engineering team and the KPIs to be measured:

1. Uptime for data flows

This key performance indicator can signal the effectiveness of the data engineering team, and the easiest way is to read the uptime as it unfolds.

If there is a lot of time you are more likely to reduce disruptions in your company's data flow to a minimum.

2. Errors occurred during the month

Here you can have accurate data on the number of errors made during the course of a month. The way is simple, the lower the number of errors and incidents the more effective the strategy is.

3. Response time to errors

This KPI can help us to know the time that the team has to deal with the incidents that occur during a work cycle. It is the way in which the problems are solved and the time used to do so.

The faster you can respond, the less downtime there is within the organization, which is a good indicator for the success of your business.

4. Production success rate

Data also allows implementing strategies for the production area, as it can help save time to reduce the amount of manual work thanks to automations.

5. Number of changes delivered per month

Here we can make a reading to review the activity of the data engineering team, if there are many changes or new features the higher the probability of improving the data flow, which always has benefits for any kind of industry.

KPIs for data science

Finally, we have some points to consider for key performance indicators in the area of data science:

1. Models developed per month 2

The productivity of the data science team is determined by the number of models developed during a month. This KPI shows the efficiency of the team, the higher the number the better for uptime.

2. Prediction accuracy

As with data analysts, the accuracy rate indicates that the models they have been developing are effective in solving any type of problem that arises within the industry.

3. Problems solved by models

Problem solving is measured in this KPI, it has to do with the effectiveness of data science in responding to problems. A high number of positive cases indicate that the models are providing useful data and insights.

4. Projects on time and on budget

This KPI allows us to measure whether a team is efficient and effective in project management, as a high percentage of projects on schedule indicates that we are doing a good job.

Importance of KPIs in data analysis

Because they are specific and measurable metrics, the performance, quality and impact of data within any type of company can be tracked and used to optimally manage data and make better decisions.

Its primary objective is to provide actionable information so that companies can improve their operations and achieve goals in a much simpler way. Thus, KPIs function as vital tools for this purpose.

Among the reasons that make them crucial we can point out the following:

• Strategic alignment: KPIs ensure that activities are aligned with the company's objectives.
• Objective measurement and accountability: provide an objective basis for measuring management performance and data analysis.
• Identification of areas for improvement: highlights performance gaps in order to initiate corrective actions.
• Resource optimization: it is possible to identify where resources are allocated to make processes more efficient.
• Improved decision-making: when you know the data and have a proper analysis you can make better responses to any event.
• Increased data quality: they help industries to always maintain top-level information.
• Risk management: they allow tracking compliance with information-related requirements and rules in order to always maintain a current regulation.
• Competitive advantage: when a company adopts a data analytics strategy it can easily gain a competitive advantage in the market.
• Customer satisfaction: some of the KPIs can directly or indirectly impact the satisfaction of our main consumers.

Finally, KPIs offer a simple way to understand and improve data management within any type of business, and are essential to ensure that strategy is effective and aligned with an organization's objectives.

A tailored solution for data analytics

The importance of having an enterprise solution is definitive. In this case, Analytics, Data and BI from London Consulting Group, which is focused on data analytics through different technologies to enhance processes.

London Consulting Group's solution is ideal for any industry, as its objective is to conduct exhaustive analysis, enabling a company's collaborators to correctly handle the tools and thereby enhance the efficiency of the analysis. 

The objective is simple, to have the possibility of issuing customized reports that facilitate the precise measurement of all incoming information, and that all teams always have the data in real time.

In addition, the clear commitment to the use of reliable data is always a priority, so that businesses have the ability to drive their analysis and actions to obtain and work with them.

With London Consulting Group the implementation of KPIs is a simple process and will provide an organization with the ability to increase its efficiency and productivity, making them the perfect strategic ally.

Conclusion

The KPIs for data analysis in an industry are very important as they allow us to know the metrics of all the processes that are being developed and see where we can improve the way it is carried out.

If a company wants to achieve competitiveness in a market it is necessary to measure the results all the time. Implementing strategies without the ability to verify their effectiveness is a wasted effort.

Although the project is not straightforward, with the help of partners such as London Consulting Group, the process can be facilitated, enabling successful implementation in a more efficient manner.