The term “high-performance” implies that the team’s performance is measured and either meets or exceeds expectations. Thus, for organizations seeking objective measurement and the precision necessary for informed decision-making, having a strong data analytics framework is indispensable. One of the most practical applications of this is the use of key performance indicators (KPI) dashboards to track progress and align efforts.
Before developing a KPI dashboard, it is important for organizations to first understand how goals are set, including the processes, roles, and considerations involved, as these form the foundation for effective measurement and improvement.
Analytical Goal Setting
Step 1: Defining High Performance
The first step to achieving high performance is to define it. This is done by setting specific objectives for your company, team, and team members. This process is usually top-down for most businesses, meaning top management defines the company-wide goals first and then cascades them down the totem pole.
Let’s take a small marketing consultancy firm as an example. The firm’s top management may define high performance as achieving a 10% year-over-year growth in revenue. Achieving this target means the company is high-performing.
Step 2: Identifying Key Processes
At this point, everyone in the company should already understand what management sees as high performance. However, what does high performance mean for individual teams? They must understand what they can specifically do to help the company achieve high performance.
Let’s continue with our marketing consultancy example. If the company aims to achieve 10% year-over-year growth in revenue, here is how each department can contribute to this goal.
The marketing team can work on increasing the number of generated leads.
The sales team might implement new strategies to upsell current and old clients. They can also train the salespeople to increase the conversion rate of leads into customers.
The finance team can collect invoices from customers faster, leading to higher reported revenue and lower bad debt.
The client services team can work on increasing customer satisfaction, therefore retaining clients for longer.
If every team member understands which of the processes they can influence, then they can all work together to achieve high performance. This clarity around goals and processes forms a critical part of the overall data analytics framework, ensuring that every metric tracked later truly reflects business priorities.
It can be tempting to set targets for each team member related to every process. However, achieving high performance does not come from setting aggressive targets alone. This is something that Richard Rumelt talks about in his book Good Strategy/Bad Strategy. In the book, he states that growth targets should be justified by:
A wider industry growth
A new product/service that a company launches
An improvement in a current business process
New source of leads
For example, before the sales team commits to a 10% year-over-year increase in sales to existing clients, management should decide what exactly will contribute to this growth:
Is this in line with the wider industry trend? If so, management should define specific strategies for how the sales team could capitalize on it.
Did the company launch a new product? If so, management can work together with the sales team to identify new upsell opportunities.
Does the sales team need more capacity for the sales process? If so, management can hire an additional salesperson.
Are there any salespeople who have better conversion rates than others? If so, management can train the other salespeople based on the best practices of high achievers.
Essentially, the whole team needs to agree on what specifically would drive business growth. Once this is done, management will be able to identify specific tasks and start assigning them to team members.
Data Analytics
Step 4: Determining KPIs
Once the process is identified and broken down into tasks, there are two types of key performance indicators (KPIs) that the team needs to measure:
How tasks are being completed
The output of the process
For example, here are the KPIs that measure how effectively the sales team is following the process:
# Prospecting calls to existing clients per month
% Clients contacted with new offers
# Demos to existing clients
Measuring and tracking the team’s performance based on these KPIs can help identify any potential problems in the execution process.
The company should also identify KPIs that measure whether the new process is productive, such as:
# Sales closed
$ Additional revenue
% Conversion rate from prospecting calls to demos
These KPIs can help the company understand if the process is bringing the desired results to the business. If not, the company may need to re-engineer the process or pivot toward a different one altogether.
The final step is to set targets based on every KPI. This will set performance expectations for every team member and ensure clarity on how their performance is measured.
Step 5: Developing KPI Dashboards
KPI dashboards help management keep their finger on the pulse of the company by providing the latest updates based on identified KPIs. The process of building these KPI dashboards involves:
Extracting the data needed to calculate these KPIs
Visualizing this data by using business intelligence (BI) services like Power BI or Tableau
Refreshing the data in these dashboards either manually or automatically
Using BI tools to build KPI dashboards enables businesses to automate their reporting process, which makes it easy to refresh the dashboards every day. By having almost real-time reporting, management can quickly identify if the process needs to be optimized and which part of the process is responsible for underperformance.
For example, the CRM KPI dashboard (see Figure 1) analyzes the performance of every sales representative within a team based on their activity metrics. Notice how there are targets for every sales activity KPI and for each team member.
Many companies also choose to share these KPI dashboards with every team member so that everyone knows their performance and how it compares with others in the team.
Sharing KPI dashboards with the whole team is impactful for several reasons:
Every employee understands which KPIs are used to measure their performance.
Everyone is clear at all times whether they are on track to hit their targets.
Team managers can identify high performers and team members who need the most help.
However, creating data analytics reports alone is not going to drive high performance. Data analytics reports simply improve the management of business processes, which can lead to higher performance.
It is also important to note that working to achieve certain targets can be quite stressful for the team. A good practice is for management to take a helpful stance on achieving targets by supporting team members when they need it, instead of putting more pressure on them when their performance lags.
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Editor’s Note: This article was written by Eugene Lebedev, managing director of Vidi Corp, a UK-based data analytics consultancy. He has delivered over 1000 data analytics solutions for more than 600 international clients, including Google, Teleperformance, and Delta Air Lines.
As dashboards, performance reports, and meeting invitations are piling up, a decision must be made or a strategy needs to be developed. This typical workplace scenario can be overwhelming and can make people fixated on numbers. Unfortunately, organizations sometimes heavily rely on such figures to make decisions that prioritize the company’s interests over those of people and the planet. To ensure that a company’s approach to performance reporting and strategic decision-making remains effective and holistic, it is important to understand the different statistical factors causing inaccuracy and inconsistency.
1.Weighted performance scores
Setting priorities and focusing on what matters by correlating importance with a weighted value will definitely facilitate the performance reporting process and is integral to calculating an overall performance score. However, according to The KPI Institute, establishing weight could be subjective and misleading if one is not careful about how to report it because changing weights from one period to another leads to inconsistency of data in time and creates a fallacy of performance improvement or regression.
For example, a report could be presented to the management regarding the improvement in the performance of the sales department from the year 2021 to the year 2022 and that the department performance score has improved by 4%. However, there is a possibility that their performance has not changed at all if all they have done was change the weight of the underperforming key performance indicators (KPIs) to get different results.
To avoid such a misleading presentation, it is important to redirect the conversation to the metrics’ results and not the weighted score or the target. This will drive the decision-maker to concentrate on what matters and see the bigger picture.
Figure 1. Sales department’s performance in 2022 and 2021
Arguably, the most common form of misrepresentation in graphs is when its Y-axis is manipulated to exaggerate the differences between bars. The truncated graph is produced when the axis starts from a value other than zero. This might give an illusion that the differences are high.
Even if the audience is informed that the Y-axis was truncated, a study found that they still overestimate the actual differences, and the results are often substantial.
Start the axis at zero to show the true context of the data. This will ensure that the data is presented naturally and accurately, reducing the chances of being misinterpreted.
Figure 2. The differences between a regular graph and a truncated graph
3. Small sample size
Darrell Huff, the author of “How to Lie with Statistics”, believed that samples can be trusted only if they are statistically significant. Hence, to be worth much more, a report or analysis based on sampling must use a representative sample, and for it to be relevant, every source of bias must be removed.
For example, a skincare company might advertise its facial wash by putting “users report 85% less skin breakout” on its packages. Upon closer inspection, one may discover that the test group of users consisted of only 15 people. This sample size works well for the skincare company because it is easier to repeat the experiment in a smaller group until they get their desired result while ignoring any findings that do not support how they want to promote their product. Sooner or later, a focus group will show a significant improvement worthy of a headline, and an advertising campaign based on a deceiving result will be created.
There is no clear-cut answer on what the appropriate sample size is since it will be highly dependent on the research type and population size, and there are many statistical methods and equations that could help determine the appropriate sample size, such as Herbert Arkin’s formula or Krejcie and Morgan Table. In addition, when reporting the survey or research results, it’s important to be transparent with the recipient audience and communicate the survey methodology, population, and the logic behind determining the sample size.
4. Misleading averages
While averages can give a simple summary of large data, they might also give half-truths. Case in point, if the range between numbers is too high, then the average would be meaningless. It is optimal that when an average is presented in data, it should be introduced along with supporting facts to provide in-depth analysis and make the right decision at the right time for the business. It is, therefore, important not just to rely on an average but also to look at the distribution of data, understand the range, and consider other statistical measures like median and mode. The median represents central tendency measurements instead of the average or mean because it is less impacted by outliers.
To conclude, it’s crucial for all stakeholders—whether they’re performance specialists or executives at the top management—to understand how human biases can infiltrate numbers and form a narrative that is completely different from reality. This can be avoided by ensuring that the reported data covers all the quality aspects of completeness, uniqueness, conformity, consistency, and timeliness. In addition, organizations must establish a data validation process to ensure the credence of performance reports.
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About the Guest Expert:Wedad Alsubaie, a seasoned Senior Strategy Management Officer at the National Unified Procurement Company (NUPCO), holds certifications in strategic business planning and KPI and performance management. With extensive experience in enhancing corporate and individual performance, she led the performance development program in Mobily and is now focused on corporate strategy and performance management at NUPCO.
Editor’s Note: This was originally published in Performance Magazine Issue No. 29, 2024 – Strategy Management Edition.
Despite the continuous hype around data analytics and the rapid acceleration of data technologies such as machine learning (ML) and artificial intelligence (AI), most companies are lagging behind with low data capabilities and no in-house data team in place. These companies have their data either fully unleveraged or marginally analyzed by executives on the side of their jobs to produce limited reports.
In such a situation, pushing the organization up the hill of data maturity would require building a team of data-specialized personnel. Building such a team can be daunting, as every company would have different conditions and no one way can fit all cases. However, covering the following main grounds can help cut miles on the road to building a data team from the ground up.
First, nurture the environment and plant the seeds. Data teams cannot grow in a vacuum. To prepare the organization to become data-driven with a data team, enhancing the organizational data culture is a good starting point. Having employees at all levels with a data-driven mentality and an understanding of the role of data analytics can significantly prepare the room for the planned team.
Second, connect with stakeholders and recognize priority needs. Carrying out data culture programs inside the organization can open up opportunities to have meaningful discussions with stakeholders on different levels about their data needs, what they already do with data, and what they want to achieve, in addition to having better insights into the pre-existing data assets. This is a good stage to recognize the organization’s data pain points, which would then be the immediate and strategic objectives of the future data team.
Third, define the initial structure of the team. According to the scale of the organization and the identified needs, data teams can have one of three main structures:
Centralized: This involves having all data roles within one team reporting to one head, chief data officer (CDO), or a similar role. All departments in the organization would request their needs from the team. This is a straightforward approach, especially for small-size companies, but can end up in a bottleneck if not scaled up continuously to meet the organization’s growing needs.
Decentralized: This requires disseminating all data roles and infusing them into departmental teams. This mainly aims to close the gap between technical analysis and business benefits as analysts in every team would be experts in their functional areas. However, the approach may lead to inconsistencies in data management and fragile data governance.
Hybrid: This consists of having governance, infrastructure, and data engineering roles within a core team, along with embedding data analysts, business analysts, and data scientists in departmental teams. The allocated personnel would report to the respective department head as well as the data team head. This approach combines the benefits of both centralized and decentralized structures and is usually applicable in large organizations as they require more headcount in their data teams.
Fourth, map the necessary tech stack and data roles. As the previous stages have uncovered the current uses and needs of data in an organization, it should be easier to start figuring out the tech tools that the team would be initially working with. Mapping the needed tech stack would be the first pillar before moving on to the hiring process. The second pillar would involve defining the roles that the team would need in its nascent stage to meet the prioritized objectives.
Several data job titles can be combined in a data team, with many of them having specializations that intersect with or bisect each other. However, there are three main role areas that should be considered for starting data teams:
Data engineering: implementing and managing data storage systems, integrating scattered datasets, and building pipelines to prepare data for analysis and reporting
Data analysis: performing final data preparation and extracting main insights to inform decision-making
Data science: building automated analysis and reporting systems, usually concerned with predictive and prescriptive machine learning models
Fifth, follow step-by-step team recruitment. Hiring new employees for the data team is one option. The other option can be upskilling existing employees with an interest in a data career and with minimum required skills. Even employees with just interest and no minimum required skills can be reskilled to fill some roles, especially within an initial data team.
The team does not need to take off with full wings. It can start small and gradually grow. Typically, data teams would start with data analysts who have extra skills in data engineering, data engineers who have experience with ad-hoc analyses and reporting, or a limited combination of both. In later stages, other titles can join onboard.
The baby-step-building approach is more convincing for stakeholders as it can be more efficient from a return-on-investment (ROI) perspective. Starting with a full-capacity team may end up being too costly for the organization, which could lead to the budding project being cut off in its prime.
Sixth, deliver ad-hoc analyses, heading towards long-term projects. In the beginning, data analytics experts at the organization would be expected to answer random requests and solve urgent data-related problems, like developing quick reports and reporting on-spot metrics. This is a good point to prove how data personnel can be of direct benefit to the organization.
However, along with delivering said ad-hoc requests, the data team should have strategic goals to enhance and develop the overall data maturity of the organization, like organizing, integrating, and automating the analytics processes and installing advanced predictive models. These long-term projects should foster the organization’s data maturity, which should result in ad-hoc requests being less frequent as all executives should be self-sufficient in using the installed automated reports and systems. In such a data-mature environment, the team would have time to advance their data products continuously, opening up new benefit opportunities.
Seventh, fortify the team’s presence. Strategic projects with shorter implementation periods and more immediate impact should be prioritized over longer ones, especially in the beginning. That would help continuously prove the benefits of the data team and the point of its foundation. Owning the products of the data team by having its name on it can help remind decision-makers of the team’s benefit. In addition, it is highly useful for the data team’s head to have access to top managerial levels to keep promoting the team’s presence and expansion.
Building a data team from scratch requires careful planning, investment, and commitment from organizational leadership. By following these guidelines and adapting them to their specific needs, organizations without prior data capabilities can establish a robust data team capable of driving innovation and offering a competitive advantage through data-driven insights.
Learn more about data management by exploring our articles on data analytics.
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Editor’s Note: This post was originally published on April 23, 2024 and last updated on September 17, 2024.
You’ve probably heard tech buzzwords like data-driven decision making, advanced analytics, “artificial intelligence (AI), and so on. The similarity between those terms is that they all require data. There is a famous quote in the computer science field — “garbage in, garbage out” — and it is a wonderful example of how poor data leads to bad results, which leads to terrible insight and disastrous judgments. Now, what good is advanced technology if we can’t put it to use?
The problem is clear: organizations need to have a good data management system in place to ensure they have relevant and reliable data. Data management is defined by Oracle as “the process of collecting, storing, and utilizing data in a safe, efficient, and cost-effective manner.” If the scale of your organization is large, it is very reasonable to employ a holistic platform such as an enterprise resource planning (ERP) system.
On the other hand, if your organization is still in its mid to early stages, it is likely that you cannot afford to employ ERP yet. However, this does not mean that your organization does not need data management. Data management with limited resources is still possible as long as the essential notion of effective data management is implemented.
Here are the four fundamental tips to start data management:
Develop a clear data storage system – Data collection, storage, and retrieval are the fundamental components of a data storage system. You can start small by developing a simple data storage system. Use cloud-based file storage, for example, to begin centralizing your data. Organize the data by naming folders and files in a systematic manner; this will allow you to access your data more easily whenever you need it.
Protect data security and set access control – Data is one of the most valuable assets in any organization. Choose a safe, reliable, and trustworthy location (if physical) or service provider (if cloud-based). Make sure that only the individuals you approve have access to your data. This may be accomplished by adjusting file permissions and separating user access rights.
Schedule a routine data backup procedure – Although this procedure is essential, many businesses still fail to back up their data on a regular basis. By doing regular backups, you can protect your organization against unwanted circumstances such as disasters, outages, and so forth. Make sure that your backup location is independent of your primary data storage location. It could be a different service provider or location, as long as the new backup storage is also secure.
Understand your data and make it simple – First, you must identify what data your organization requires to meet its objectives. The specifications may then be derived from the objectives. For example, if you are aiming to develop an employee retention program, then you will need data on employee turnover to make your data more focused and organized. Remove any data that is irrelevant to the objectives of your organization, including redundant or duplicate data.
Data management has become a necessity in today’s data-driven era. No matter what size and type of your organization, you should start doing it now. Good data management is still achievable, even with limited resources. The tips presented are useful only as a starting point for your data management journey.
Learn more about data management by exploring our articles on data analytics.
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Editor’s Note: This post was originally published on December 9, 2021 and last updated on September 17, 2024.
One of the most common challenges faced by professionals in working with key performance indicators (KPIs) relates to data. They grapple with collecting and analyzing data to establish targets accurately, as indicated by 42% of respondents in The KPI Institute’s State of Strategy Management Practice 2023 Report.
This is particularly important as the collected data is expected to be of high qualityand “fit for their intended uses in operations, decision making, and planning,” according to the book “Modern Data Strategy,” by Mike Fleckenstein and Lorraine Fellows. Drawing from its advisory experience, The KPI Institute recommends employing the following data quality dimensions as a framework for assessing your data (see Figure 1).
Figure 1. Data Quality Dimensions | Source: Certified KPI Professional training program
Overcoming Issues with Data Quality Dimensions
Figure 2 highlights a dataset that has encountered significant data quality issues. Through an initial audit, several faulty elements have been identified, revealing potential inaccuracies that could have an adverse impact. This section presents approaches for effectively resolving these faulty elements to improve data reliability.
Figure 2. Sample quality troubled dataset | Source: The KPI Institute
A – Completeness: There is a missing value in the Actual Result column. One way to prevent this is to develop and utilize a data collection template that clearly outlines the necessary data fields. It is also important to regularly review the completeness of the data and address missing information that affects analysis.
B – Consistency: The structure of the data does not correspond with the template, the name, and the position of the Data Custodian being switched. To prevent this issue, one must make sure the data presents the same values across different systems and follows the same structure.
C – Timeliness: This issue pertains to the data being received after the specified deadline. One potential solution is to establish a data collection cycle time and set clear deadlines for data submission. Communicating these deadlines to all relevant parties and sending reminders for data submission can also help address this issue.
D – Conformity: The KPI is expressed as a percentage rate, but the data provided for the result includes a numerical value. To ensure conformity, organizations must provide clear guidelines on data format and how the KPI should be calculated.
E – Accuracy: This issue concerns the usage of an inappropriate sign. The KPI measures a rate, but the sign used in the KPI name is “$.” To ensure accuracy, one should make sure the data reflects real information, including the use of appropriate units. To adhere to accuracy, The KPI Institute developed a naming standard, which designates the symbols ”#” for units, ”%” for rates, and ”$” specifically for monetary value.
Maintaining data quality is essential to generate meaningful and effective KPIs. Reliable data ensures that business decisions are based on trustworthy information, resulting in improved marketing, increased customer satisfaction, enhanced internal processes, and reduced costs.
On the other hand, unreliable data can cause significant challenges. KPIs based on inaccurate data lead to wrong decisions, resulting in wasted resources and a negative impact on the organization’s performance. Poor data quality can impede the identification of trends or the accuracy of forecasts, leading to missed opportunities. In addition, it can hold back innovation, causing businesses to lose competitiveness.
Therefore, it is recommended that organizations prioritize data quality management and take actions to assess and improve data quality to enhance KPIs and drive business success.
Enhance your understanding of KPIs and read more about them on our KPI section.
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Editor’s Note: This article was originally published in Performance Magazine: Issue No. 26, 2023 – Data Analytics Edition and has been updated as of September 17, 2024.
