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6 Key Data Quality Dimensions: Insights and Practical Application

Data Quality Dimensions

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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 quality and “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).

Data Quality Dimensions

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.

Read more: All about that data – sources and collection methods

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.

Read more: Why is data integration important and how can we achieve it?

Managing Data Quality Dimensions and KPIs

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.


Editor’s Note: This article was originally published in Performance Magazine: Issue No. 26, 2023 – Data Analytics edition and has been updated as of July 4, 2024.

Data Management Best Practices: 4 Fundamental Tips to Get Started


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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.

Read More: Why is Data Integration Important and How Can We Achieve It?

Here are the four fundamental tips to start data management:

  1. Develop a clear data storage systemData 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 controlData 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 procedureAlthough 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 simpleFirst, 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.

How Data Enrichment Can Help Us Use Big Data Better


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Big data is a major asset for businesses that can access its insights. Making this happen, though, is a complicated job that needs the right tools. Enter data enrichment.

Understanding how it works and its impact on current industries is a great way to get to know what data enrichment can do for your organization. How it benefits the use of big data will become clearer, too.  

What Is Data Enrichment? 

Data enrichment is the process of identifying and adding information from different datasets, open or closed, to your primary data. Sources can be anything from a third-party database to online magazines or a social network’s records.

People and organizations use data enrichment to gather legitimate intel on specific things, like a customer, product, or list of competitors. And they can start with just their names or email addresses.

As a result, the original data becomes richer in information and more useful. You can find education trends, profitable news, evidence of fraud, or just a deeper understanding of users. This helps improve your conversion rate, customer relations, cybersecurity, and more.

The most popular method of making all this a reality is specialized software. Their algorithms vary in strengths and weaknesses, as SEON’s review of data enrichment tools shows. They can target human resources, underwriting, fraud, criminal investigations, and more. However, the goal is the same: to support the way we work and give us better insights.

Data Enrichment and Big Data: What Statistics Say

Data enrichment is a good answer to the problem of big data, which often sees masses of disorganized and sometimes inaccurate information that often needs cleaning, maintenance, and coordination. 

A 2021 survey by NewVantage Partners on data-driven initiatives highlights some key difficulties in using big data for corporate improvement. These challenges include:

  • Managing data as an asset
  • Driving innovation
  • Beating competition
  • Creating a data-driven culture within organizations

Despite the benefits of smart data management and major investments already in place, only 24% of firms have become data-driven, down from 37.8%. Also, only 29.2% of transformed businesses are reaching set outcomes.

What this shows is that, yes, big data is difficult to deal with but not impossible. It takes good planning and dedication to get it right.

There are several promising big data statistics on FinancesOnline. For starters, thanks to big data, businesses have seen their profits increase by 8-10%, while some brands using IoT saved $1 trillion by 2020. 

Also, the four biggest benefits of data analytics are:

  • Faster innovation
  • Greater efficiency
  • More effective research and development
  • Better products and services

These achievements are taken further with data enrichment, which adds value to a company’s datasets, not just more information to help with decision-making.

Read More: How Data Analytics Can Improve Company Performance

How Does Data Enrichment Help Different Industries? 

The positive impact of constructively managing data is clear in existing fields that thrive because of data enrichment and other techniques. Here are some examples.

Fraud Prevention

Data enrichment helps businesses avoid falling victim to fraudsters. It does this by gathering and presenting to fraud analysts plenty of information to identify genuine people and transactions.

For example, you can build a clear picture of a potential customer or partner based on information linked to their email address and phone number. Do they have any social media profiles? Are they registered on a paid or free domain? Have they been involved in data leaks in previous years? How old are those? 

It’s then easier to make informed decisions because we know much more about how legitimate a user looks.

Banking services, from J.P. Morgan to PayPal, benefit from such intensive data analytics, as do brands in the fields of ecommerce, fintech, payments, online gaming, and more. 

But so do online communities, where people create profiles and interact with others. For example, fake accounts are always a problem on LinkedIn, mainly countered through careful tracking of user activity. Data enrichment can help weed out suspicious users in such communities, keeping everyone else safe.


Data enrichment in marketing tracks people’s activities and preferences through cookies, subscription forms, and other sources. To be exact, V12’s report on data-driven marketing reveals Adobe’s survey findings regarding what data is most valuable to marketers.

  • 48% prefer CRM data
  • 40% real-time data from analytics
  • 38% analytics data from integrated channels

Companies collect this data and enrich it to create a more personalized experience for customers in terms of interactions, discounts, ads, etc. Additionally, brands can produce services and products tailored to people’s tastes. 


The more information your human resources department has, the better it’s able to recruit and deal with staff members. Data enrichment is a great way to build strong teams and keep them happy.

Starting from the hiring stage, data enrichment can use applicants’ primary data, available on their CVs, and grab additional details from other sources. Apart from filling in any blanks, you can flag suspicious applicants for further investigation or outright rejection.

As for team management, data enrichment can give you an idea of people’s performance, strengths, weaknesses, hobbies, and more. You can then help them improve or organize an event everyone will enjoy.

Summing Up

As we saw in these examples, data enrichment already contributes to the corporate world in different ways, both subtle and grand. 

With the right knowledge and tools, we can tap into this wealth of information even further, allowing it to make a real difference in how we work and what we know, rather than simply amassing amorphous and vast amounts of data.

Learn more about data enrichment by exploring our articles on data analytics.


About the Author

Gergo Varga has been fighting online fraud since 2009 at various companies – even co-founding his own anti-fraud startup. He’s the author of the Fraud Prevention Guide for Dummies – SEON Special edition. He currently works as the Senior Content Manager / Evangelist at SEON, using his industry knowledge to keep marketing sharp and communicating between the different departments to understand what’s happening on the frontlines of fraud detection. He lives in Budapest, Hungary, and is an avid reader of philosophy and history.

Beyond remote work: insights and strategies for enhancing employee productivity and performance


Remote work and the implications of continuing the process, including its potential impact on employee performance, are widely discussed. However, there is no right answer, and it is not one-size-fits-all. 

The future of work includes flexibility, employee experience, agility, and the responsible use of artificial intelligence (AI)—these significant shifts impact where and how employees work. With an increase in remote work options, we have seen positive trends in work-life balance, employee empowerment, inclusivity, and an increase in diverse talent. These factors are also known to increase employee productivity and retention. According to BCG, a considerable population of employees are ready to leave their jobs if they find their flexible work arrangements unsatisfactory. Based on their survey, approximately 90% of women, caregivers, individuals identifying as LGBTQ+, and those with disabilities, deem flexible work options as crucial in determining whether they will continue or resign from their current employment.

Remote work productivity is subject to debate due to various factors that must be considered. Some suggest remote work can increase productivity due to a flexible schedule, no commute, and fewer interruptions. While many employees thrive in a remote work environment, some find it challenging due to the discipline it demands.

Remote work was on the rise even before the COVID-19 pandemic. A July 2023 report from Stanford University found that working remotely has doubled every 15 years. Then, when the pandemic occurred, although devastating, it provided a new perspective for those previously constrained, forced to relocate, or live in less favorable locations to work for a specific company and advance their career. Worldwide ERC states that around 56 million Americans moved to new residences between December 2021 to February 2023 due to COVID-19-related shutdowns and the surge in remote work and online education. With such a huge increase in their number over the past few years, this begs the question: do employees working remotely demonstrate productivity?

Taking a deeper look into the study by Standord University, researchers shared that remote work employees’ productivity differs depending on perceptions—the nature of the research and the conditions under which it was conducted. The report revealed that workers believed productivity was higher at home (approximately 7% higher), while managers perceived it lower (around 3.5% lower). Another example, according to a poll by the video presentation applications mmhmm, 43% prefer office work and 42% favor working from home for peak productivity. Moreover, 51% of employees stated that working asynchronously or having the flexibility to set their schedules contributed positively to their productivity. Perceptions aside, the Stanford analysis found a 10% to 20% reduction in productivity across various studies.

The bottom line is today’s company culture is crucial. Ensuring work-life balance and putting the employees in the driver’s seat are the best ways to retain and increase productivity because they will feel valued and empowered. In a 2022 Microsoft employee engagement survey, 92% of employees say they believe the company values flexibility and allows them to work in a way that works best for them. An even higher percentage (93%) are confident in their ability to work together as a team, regardless of location. People have different preferences—some individuals opt for a hybrid approach, while others choose either remote or in-person work exclusively. 

Regardless of the work setup, company leaders and human resources (HR) or human capital management (HRM) executives should ensure that they can still make a lasting impact on employee performance. One measure involves establishing key performance indicators (KPIs) that assess innovation, program, project, and product success—the output, not the physical location. Another crucial step is developing a strategy that includes all future work options, such as in-person, hybrid, and remote choices. Employees tend to be more productive if there is a level of empowerment that allows them to decide where to do their best work.

Planning in person events makes a difference. Leaders who bring new hires and internal transfers, new to the team, on-site for several days should see an uptick in productivity post-gathering. In-person team or company-wide gatherings 1-4 times per year provide employees an opportunity to reset and socialize. Moreover, managers should bring teams together for major program and project kick-offs. When onsite in person, people being present makes a difference. Discourage using Teams or Zoom when employees are in the general vicinity. I have seen companies spew the importance of in-person just to fly employees into a specific location and have people take meetings from their desks or in a different on-site building-conference room, defeating the purpose of in-person interaction.

Having organizations foster all work options is critical and foregoes having to decide which is best. There is no right or wrong answer to this challenge; it should be considered a new way of working and requires future-forward ways of thinking, just as we do with emerging technologies. 

About the guest author:

Dr. Malika Viltz-Emerson is a Senior Global Human Resource Leader at Microsoft. She has over 20 years of experience in human capital management. Her mission is to identify and address the real-world challenges and opportunities for employees and the company, and design and implement optimal solutions that leverage the latest tools, technologies, and processes.

Standardization for aligning operational processes in a healthcare setting


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The case below explores the implementation of a standardized Operational Deployment System (ODS) at Corewell Health West, a healthcare system in West Michigan. The goal of the system was to align operational processes and improve efficiency across physician and non-physician stakeholders. By implementing ODS, the organization aimed to enhance quality, increase patient satisfaction, optimize operational efficiency, and reduce costs while ensuring staff and physician satisfaction.

The authors are Aiesha Ahmed MD, MBA (VP, Population Health, and Chief of Neuroscience); Rashelle Ludolph (Operations Director, Medical Specialty Services); Cheryl Wolfe MD, MBA (VP, Chief of Women’s Health), and Sonja Beute (Director of Strategic & Operational Deployment).


Corewell Health West is a complex large healthcare system in West Michigan with 31,000 employees (4600 providers). Due to its large footprint in West Michigan, it aims for transformation to improve quality, increase patient satisfaction, deliver operational efficiency, and reduce costs. Foundational to all this work is staff and physician satisfaction. There was a need for shared language to communicate critical goals in a way that allowed us to be efficient while creating a standard approach to work. To move such a large team in one coordinated direction, Corewell Health needed to engage in focused efforts in a way that was respectful to its teams and leaders.

The Operational Deployment System (ODS) was designed to help leaders clarify what is most important and align the right resources to meet the goals set.  This system, composed of best practices from individual project management and process improvement methodologies, was implemented to provide clarity, cascade goals appropriately, and help prevent employee burnout by creating a system of intentional alignment.

ODS implementation process

ODS begins with an annual goal-setting process led by the executive team and subject matter experts in the areas of cost, quality, people, and value. There is then a multi-week process of cascading these goals from the executive team through various levels of physician and operational leadership to front-line staff. Subsequent conversations called “catch-ball” follow in which each level of leadership discusses and eventually finalizes goals in each of the four categories. This process culminates with executive sign-off, confirming the roll-up of goals at each level to ultimately achieve the system goals.  These goals are captured in a document called an Operational A3 (see sample). Each level of leadership, starting at the director level, has an OA3 that outlines the annual goal in each category and provides space for monthly data updates and explanations.

The manager level of leadership does not have an OA3 but instead utilizes a reporting tool called a gate chart (see sample). Each goal has a separate gate chart featuring a leading metric (the metric that aligns with the director OA3), a lagging metric, and specific tactics and timelines for impacting performance.

Reporting and communication

Following this goal-setting process and after populating the OA3 and gate charts, weekly report-outs begin each week focused on one of the four priority areas. Report-outs take place in a virtual meeting with managers reviewing the gate chart performance with front-line staff. This is followed by managers reporting their gate chart update to directors, who then provide a similar report to Physician and Operations Vice Presidents (VPs), and so on. Each of these report-outs follows the TAPE methodology, which stands for Target (what was the goal), Actual (what is the actual performance metric), and Please Explain (what were the actions or factors that contributed to that month’s performance).

Change management

The ODS process inherently supports change management surrounding efforts to meet annual goals by engaging the front-line staff and every level of physician and operational leadership in goal setting, action plan development, and performance tracking. A key component of successful implementation is training leaders and teams in the ODS process. Training sessions for all levels of leaders included a review of the principles of ODS, the OA3 and gate chart templates, and the TAPE reporting format, and included time for discussion and questions. Implementing operational goals, management for daily improvement and cascade reporting, and communication were key areas of discussion during these training sessions.

Stakeholder experience

To gauge the stakeholder experience, VPs and Director-level physician and operational leaders were surveyed about their experience with ODS. Among the 54 respondents, 61% agreed or strongly agreed that ODS has allowed them and their upline to focus on key areas for operational success. Moreover, 69% agreed or strongly agreed that ODS effectively aligns operational tactics with system strategy.

Lessons learned and next steps

The ODS at Corewell Health initially faced challenges as leaders at all levels adjusted to this new form of tracking and presenting metrics. As the process matured, these perceived notions morphed into support, engagement, and eagerness to introduce new ideas.

Survey results indicate that the leaders perceive improved focus in key operational areas due to ODS. The system has been adopted outside of service lines as well. Hospital medical staff leadership embraces value in aligned goals and now reports on the executive dashboard. Independent physicians are looking at ways to use ODS to improve their private practice structure and function.


Implementing ODS at Corewell Health has been thought-provoking, enlightening and rewarding. Previously top-down leadership in this space has moved to shared decision-making. As ODS progresses through year three, physician and operations leaders will build on lessons learned and broaden skills to make ODS an even richer process and a model for other organizations to follow.


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