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KPI Governance Structure: Understanding the Roles of KPI Owners and Data Custodians


Image Source: relif | Canva

When implementing a Performance Management System (PMS) based on Key Performance Indicators (KPIs), the organization needs to create a favorable context to plan, organize, coordinate, communicate, and control performance. Such endeavor implies multiple initiatives, resources, and most of all, employee engagement. However, challenges are inevitable. These challenges often arise from the mechanisms and relations by which the KPI Measurement Framework and KPI-related processes are controlled and directed. 

As such, unclear definitions and overlap of roles and responsibilities and lack of ownership, commitment, or clarity in terms of target achievement accountability are some of the most common challenges that may endanger the achievement of strategic business objectives and goals. The root cause of these dysfunctions is that KPI governance structure has never been clearly defined or described.

KPI governance structure

There are multiple parties involved in governing and managing KPI-related processes, and all play a specific role in promoting, supporting, designing, implementing, and maintaining the KPI measurement framework. A typical KPI governance structure includes the following  components:

  • Performance Manager – Responsible for supervising the entire process
  • PMO specialists – Support the persons involved in the process, analyze data and check it for accuracy
  • KPI owner – Responsible for KPI target achievement
  • Data custodian – Responsible for KPI results collection/ data collection

KPI owners and data custodians have two of the most operational KPI governance roles within the organization. While the data custodians are responsible for ensuring that high-quality KPI data is gathered and communicated to all interested stakeholders, the KPI owners are mainly responsible for the KPIs under their management, making sure that they are viable and measurable.

KPI owners’ role and responsibilities

Within a standard Data Governance Framework, a data owner is in charge of ensuring that processes are followed to guarantee the collection, security, and quality of data. Frequently in a senior or high-level leadership position, a data owner has a role in planning the data, supervising access to it, ensuring data security, and defining a repository to contextualize the data.

Similarly, a KPI owner is responsible for overseeing the process, function, or initiative that the KPI is monitoring. That person has access to the data, knowledge of how that domain functions, and, most of all, is empowered to make decisions on improving operations. 

In a nutshell, the KPI owner is responsible for reaching KPI targets through the following actions:

  1. Monitoring (looking at) the measure over time
  2. Interpreting its trends and patterns and seeking causes for them
  3. Communicating this information to people affected by that performance area
  4. Initiating action to improve performance in that area
  5. Following up to be sure that actions have the desired effect on performance

Data custodians’ role and responsibilities

Within a KPI governance framework, data custodians are involved in the design of performance data collection, receipt and storage, process, analysis, reporting, publication, dissemination, and archival or deletion of data. The daily processing and management of performance data are therefore under the control of appointed data custodians. The person assigned with such a role must demonstrate high levels of data literacy as well as skills in data management software systems and tools. 

Other required competencies for a data custodian are as follows: 

  • The ability to intuitively identify and recognize any variance from the data quality dimensions
  • Can contribute to better ways of collecting data
  • Focused on the improvement and automation of the process
  • Can competently apply the behaviors and skills of managing change
  • Uses change as an opportunity to advance business objectives
  • Works to minimize complexities, contradictions, and paradoxes or reduce their impact
  • Unifies leadership support for direction and smoothens the process of change

We may say that the data custodians are the guarantors of a sound performance data gathering process. Because of that, the profile of such an individual should also cover an analytical mind, experience in measuring and reporting metrics/ KPIs, information technology skills (basic Microsoft Excel or more advanced data analysis tools, depending on the data architecture`s level of automation), and a strong sense of integrity and ethics.

While some companies may hire specialized professionals, such as data analysts, other organizations may assign the data custodian roles to the existing employees. 


Building a strong KPI governance team is a key part of the KPI-related processes and functionalities and of successfully overcoming the inherent challenges of implementing a PMS. Once the right people are on board, they need to be guided towards making the right decisions and focusing on the correct issues, ultimately making sure that information is being governed for a purpose that aligns with business objectives.

Discover the world of KPIs, from best practices to developing scorecards and dashboards, by signing up for The KPI Institute’s KPI Professional and Practitioner training courses.

How To Establish Self-service Bi System Using Google Sheets and Datawrapper


Image Source: Oleg Magni | Pexels

To learn more about implementing a self-service business intelligence system, read the author’s previous article: “Embracing Data Visualization: What Is a Self-service BI System?”


Google Sheets needs no further introduction, but let me make a reminder that it builds on arguably one of the simplest cloud-based storage solutions, Google Drive. That will help us build a basic data warehouse that will be connected to the visualizations, as explained later in the steps. Datawrapper is also a famous online-based data visualization tool that is commonly used by news organizations, but also can be used by any business, as we will see in the how-to section of the article.

How-to time!

The how-to section will be divided into two parts; the first on how to establish the system, and the second on how to use it afterwards. These two will be followed by a final part that includes additional tips to keep the system sustainable and in best shape. So, let’s get started!

Part 1: Establishing the system

Step 1: Create your Google Drive data warehouse
  • If you are not using Google Drive for file storage at your organization, you can easily do so by creating an account.
On Google Drive > Click: Go to Drive > Click: Use another account > Click: Create account > Follow instructions  

GIF 1: Creating a Google Drive account | Source: Author

  • Create folders to categorize and build a hierarchy for the data files that you’ll add later. For instance, you can create a folder for each division in your company and then subfolders for each team within the divisions.
On Home Page > Click: New > Click: Folder  

GIF 2: Adding a folder on Google Drive | Source: Author

  • In each folder, upload the relevant data files.
Inside the desired folder > Click: New > Click: File Upload  

GIF 3: Adding a file on Google Drive | Source: Author

OR   Drag the desired files/folders from your computer > Drop it in Google Drive
  • Give suitable access permissions to your team members to relevant folders.
Right Click on desired folder > From dropdown menu, Click: Share > Enter colleagues email addresses > Click: Done  

GIF 4: Sharing Google Drive folder with colleagues | Source: Author


Step 2: Prepare datasets for Google Drive

As we will be connecting the datasets you’ve just uploaded to Datawrapper, we need to structure them in the way that the visualization tool can best read and interpret them. Here are a few things to make sure of:
  • There must be only one header row.
  • Do not merge cells.
  • Eliminate thousand separators as they will be automatically inserted on Datawrapper.
  • In the columns of values, do not mix letters with digits (You can add prefixes and suffixes later on Datawrapper, if you need to).
  • Inside the tables, do not leave any blank cells:
    • If the value is unknown/non applicable, put down a dash “-”.
    • If the value is zero, write a zero digit “0”.
  • Do not write any notes, source… etc. below the table in the spreadsheet. That can be added later on Datawrapper.

Step 3: Initiate your visualizations catalogue

  • Create a Datawrapper account.
On Datawrapper > Click: Login > Click: Create a new account > Follow instructions  

GIF 5: Creating a Datawrapper account | Source: Author

  • Start creating one type of visualization for each type of data you have. The visualizations you’ll create will serve as templates for your colleagues when they start using the system. 
On Datawrapper Dashboard > Click: Create New > Click: Chart  

GIF 6: Creating a chart on Datawrapper | Source: Author

  • Connect the spreadsheet from your Google Sheets with the chart
First: Get a shareable link for the sheet | Go to your spreadsheet on Google Sheets > Click: Share > Click: Change to anyone with the link > Click: Copy link  

GIF 7: Getting a shareable link from Google Drive file | Source: Author

  Second: Paste the link in Datawrapper chart | Go to Upload Data page on Datawrapper > Click: Connect Google Sheet > Paste the earlier copied link in the text box  

GIF 8: Linking Datawrapper to Google Sheets| Source: Author

  Hint: You may need advice from a data visualization expert on what visualization type to choose for what type of data to achieve what purpose. Here are a few general guides for the most common types of data:
  • For timeseries data with one or two values, choose a line graph.
  • For timeseries data with more than two values, choose an area graph.
  • For a simple comparison, like comparing values over a few months, choose grouped column chart.
  • For a little bit more complex comparison, choose a single column chart with a filter above.
  • Publish your chart.
Go to Publish & Embed > Click: Publish Now  

GIF 9: Publishing a Datawrapper chart | Source: Author

  • Create the previous steps to create one chart for each type of data as explained above.
Congratulations! You’ve successfully established your SSBI system with Google Sheets and Datawrapper!

Part 2: Using the system

Use 1: Creating new visualizations Users in your team will be able to create new visualizations by following the same previous steps that you followed in Part 1, except:
  • Instead of creating a new visualization from scratch, they can duplicate an already formatted existing one, and just change the data source.
First: Duplicate an existing chart | On Datawrapper dashboard > Click: Archive > Choose the closest chart representing similar data > Click on the chart > Click: Duplicate Second: Change data source | Copy and paste the new data source in the duplicated chart’s Upload Data page
  • Republish the chart to get the changes in the new chart reflected.
Go to Publish & Embed page > Click: Republish  

GIF 10: Duplicating a Datawrapper chart | Source: Author


Use 2: Editing or updating existing visualizations

Users can also edit or update an existing visualization
  • If editing or updating the data itself is what is needed, you’ll need to edit or update the source data spreadsheet, and then go to the connected chart on Datawrapper to republish it, so it reflects the new changes in the data source.
Go to Google Sheets > Edit/Update the dataset > Go to the connected chart on Datawrapper > Go to Publish & Embed page > Click: Republish  

GIF 11: Updating a Datawrapper chart | Source: Author

  • If editing or updating the chart itself is what is needed, you can head directly to the chart, make your changes and republish as in the previous steps.

Use 3: Exporting and embedding visualizations

Now, once the visualization is created, it can be exported in both interactive and static forms, to be embedded in web pages, pdf reports… etc.
  • To get an interactive version of your chart, you’ll need to get its HTML code and paste it into the editor of your web page.
First: Get the code | Choose the desired chart > Go to Publish & Embed > Under Embed code for your visualization > Click: Copy Second: Paste the code in your web page | Go to the backend of your web page > In the text editor > Click: Text (Name and form below may differ) > Paste the code > Save the page > Should Appear on your front end  

GIF 12: Embedding an interactive Datawrapper chart | Source: Author

  • To get a static version of your chart, you’ll just need to export it as an image, and then you can use it as any other image.
Choose the desired chart > Go to Publish & Embed > Under Export or duplicate visualization > Click: PNG  

GIF 13: Embedding a Datawrapper chart as an image | Source: Author

  Congratulations! The SSBI system is now up and running!

Additional tips

For this system to keep working efficiently and with as little flaws as possible, here are a few tips of good practice:
  1. Make your data folders and files naming systematic and understandable. Then, name the connected chart with the same name of the spreadsheet. That will enable easier reaching for users in the future, using the search function on both tools.
  2. You can let your team use the same account you created for Datawrapper, but that of course is not advisable. Instead, you can create teams within your account, and invite relevant members through email, assigning specific access authorities (in the same fashion of giving access on Google Drive). For more on this, you can see Datawrapper Academy articles on how to create a team and how to invite others.
  3. Create a shared guide document with your team containing detailed steps, color codes and all standards for all the uses above (You are also welcome to embed a link to this article as well!).
  4. As an appendix to that guide, add a part for the technical issues that faced your team when working with the system, and document how you solved them. Keep that part updated with every new issue coming up.
  5. Assign yourself or one of your team members the responsibility of doing regular checkups on datasheets and newly created or updated charts, to make sure you are avoiding human errors and random technical ones as well.
  6. Depending on your needs, you might consider paid plans of Google Drive and Datawrapper. Click here and here respectively to see what they have to offer.
That’s it! Thanks for bearing up with us all the way down to this point! Now before you go, we have one more thing to say. If you would like to discover new knowledge and the practical application of best practices used in analyzing statistical data, sign up for The KPI Institute’s Data Analysis Certification.

Embracing Data Visualization: What Is a Self-service BI System?


Image Source: Buffik | Pixabay

Gone are the days when analyzing and visualizing data to get information was a job that was limited to the IT and business intelligence (BI) divisions. Gone also are the days when the sole possession of knowledge, skills, and tools for data processing was in the hands of the “data guy.”

Data is becoming more and more abundant and essential for various business operations. This makes centralizing data processing on one or two divisions an inevitable bottleneck. On the other hand, analytics and visualization tools are becoming easier to use, with more intuitive user-friendly interfaces that require less and less technical expertise.

What SSBI Is About

Self-service business intelligence (SSBI), also called self-service data exploration, has become an important approach for data-driven insights in business. It means giving the ability to the wide range of employees who are not experienced with data to drive insights from relevant datasets and create exploratory visualizations to help them better understand the data and to use it in reports. It’s also a part of what is called data democratization if you’d like another fancy term on the plate.

It should be, however, distinguished from the second approach called dashboarding. While the latter should still be the responsibility of the experienced BI team, turning amounts of data to finely curated reports on the most important KPIs within a well-developed narrative can happen. The SSBI approach aims to:

  • Avoid time delays in data-driven decision making among the low and mid-level teams that may happen due to the centralization of analytics responsibilities.
  • Minimize intuition-based decisions that can be made by low and mid-level teams on a daily basis due to lack of analytical capabilities.
  • Enhance internal communication within the teams by making data-driven insights and visualizations easier to generate, and therefore more frequent integration of reports.
  • Enhance external communication of the organization as the insights and visualizations can also be easily used in developing publications, like blog posts for example.

Google Sheets and Datawrapper

There are tons of visualization tools out there that can enable you to create an SSBI system for your organization, some of which are technologically advanced, but each has its best uses and downfalls. 

Just like Google Sheets and Datawrapper. The advantages of using these tools are the following:

  • – Businesses with no capabilities of experienced teams or infrastructure can implement the system.
  • – Anyone can use it as it requires little to no technical expertise.
  • – Visualizations can be easily duplicated and edited, suiting fast-based work routines.
  • – Visualizations can be easily well-formatted and laid out, leading to efficient reporting.
  • – Generate both interactive and static visualizations that are suitable for embedding in various forms of reports, from web-based all the way to paper-based.
  • Using a self-service BI solution can help streamline operations and support critical decisions. It also encourages collaboration, simplifies daily business needs, and increases one’s competitive advantage. With the efficiency brought by SSBI, businesses can focus on what matters most to them.

    Want to understand how visual representations can support the decision making process and allow quick transmission of information? Sign up for The KPI Institute’s Data Visualization Certification course.

    Data Management Best Practices: 4 Fundamental Tips to Get Started


    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 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:

    1. 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.

    2. 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.

    3. 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.

    4. 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. 

    How Innovation Helps the Growth of Digital Sales


    Digitalization is nothing new in the business industry as the world has shifted toward digitalization for the past few decades. However, the Covid-19 pandemic has catapulted the digital model of business to another level. 

    In a 2020 study, Salesforce showed that 60% of customer interaction took place online compared to 42% in the previous year. Meanwhile, up to 88% of customers also expect digital innovation from companies during and after the pandemic. This shows how customers start to put emphasis on company value by what they are seeing online. The sudden surge of the online presence of the majority forced businesses to rethink their existing strategy, especially when it was directly related to their customers.

    The changes brought by digitalization

    The increasing use of digital-based platforms has affected several aspects of businesses. Demand to be available digitally has changed the marketing industry even before the pandemic hit. We can easily spot how large to small companies transitioned their marketing strategy into a digital approach. Even though it sounds like most companies are already familiar with digital marketing, the fast-changing nature of it requires constant learning on what is relevant at the moment.

    The second change mostly catalyzed by the pandemic is the change in how companies do their business. Many employees have been forced to work remotely and moved most of their workflow online. Occasionally, companies have been required to modify their products or services to fit the current demand or trend.

    The adaptation of businesses on their strategic planning and performance measurement to fit the ongoing and upcoming challenges is a conversation that is often missed. The fast-changing digital world has caused a lot of developments in companies towards important matters that can sustain their business by upgrading and preparing their resources

    Innovation is the key for digital sales

    Similar to other sectors, sales activities also demand to have a digital model more than ever. Data shows that digital sales, in general, can boost revenue up to 28%. As much as digital sales sound promising, it also demands a constant upgrade and innovation.

    Innovation is one of the most crucial parts to achieving maximum digital sales growth. Just like traditional sales, the ability to engage with the customer is still a major factor in the success of sales. However, the digital model demands companies to be more attentive to the changes in customer behavior. Companies and even salespersons are required to see the need and trends in the market.

    The innovation in sales technology is also predicted to have a big impact on how long-term revenue is generated. The use of more efficient CRM and even the use of AI can be a huge booster in sales growth. For example, now the customers have become more digitally savvy, this also means that they are more aware of cyber security. Things such as transparency in sales activities and data collection are just some of the things they look out for. In turn, the growth in technology would also mean an increase in demand for people who are knowledgeable in the digital space and can operate the business.


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