Data plays a pivotal role in our lives – especially when strategic or operational decisions.
And how ensure you’re making good data-driven decisions? With quality data and your ability to understand it – your data literacy.
So – how do you increase the visibility of your data quality metrics to amplify data literacy, which leads to improving your data quality?
Everyone is aware that good decisions are based on good data. But the quality of your decision-making, as well as the reliability of strategies and operations, invariably depends on the robustness of your data quality metrics – not just data in itself.
Understanding these metrics and your capacity to use data effectively – that’s data literacy. This is increasingly becoming a critical skill set in the modern enterprise.
Conversations you have about data invariably revolve around its quantity – big data, data lakes, data warehouses. But isn’t it high time you shifted the focus to ‘quality’ over ‘quantity’?
Wouldn’t it be wise to start talking about ‘good data’ instead of just ‘more data’? By making your data quality metrics more visible, you’ll elevate data literacy and that will lead to ‘good data’.
By the end of this read, you’ll know why and how to make data quality metrics more visible to improve both data literacy and data quality.
In simple terms. you have quality data if it meets the needs it was intended for.
The key factors are:
- uniqueness, and last but not least,
- its fitness for purpose.
We deem data to be high quality if it accurately satisfies its purpose and remains consistent over time. It should be complete, unique to prevent any overlaps and must be updated as of when required source.
So – data quality boils down to whether your data is good enough to support the outcomes it’s being used for source.
There’s a long list of undesirable dire consequences of poor data quality:
- Missed opportunities,
- Steep revenue losses,
- Misguided analytics,
- Internal fingerpointing and disputes,
- Diminished operational efficiency,
- Tarnished relations with customers, and significantly,
- Making wrong decisions source.
IBM released startling estimate in 2021 …
Lackluster data quality can strip $3.1 trillion from the U.S. economy annually, attributable to:
- lower productivity,
- system outages, and
- higher costs source].
Imagine bringing together the finest minds in your enterprise to develop a innovative business strategy… only to have it falter, and ultimate wither and die due to low-quality data.
Imagine nurturing a customer base over many years, only to destroy their trust with misinformation or bad service.
Inaccurate data causes flawed analyses. The ripple effect is often poor decisions with potentially far-reaching negative consequences.
Playing marketing or operational catchup due to setbacks from flawed decisions drains your financial resources and saps your employees’ morale.
It’s clear that the repercussions of poor quality data aren’t limited to the realms of business; the society at large indeed bears the brunt.
It’s time we started capitalizing on high-quality data – these data quality discussions having been going on for decades!
The key is to begin with consistently validating the quality of the data at your disposal. And we achieve thise through effective data quality metrics.
Understanding Data Literacy
In the era of big data – where facts, figures, and raw data are the backbone of your business – understanding how to use these data assets is vital. But what does it mean to understand data? It means to be data literate!
Data literacy means you have the ability to draw meaningful insights from data.
It’s the capacity to:
- interpret, and
- evaluate data in various contexts,
enabling you to make effective data-driven decisions that align with the organization’s goals.
But, why is data literacy so crucial, especially in today’s data-driven enterprise?
Data literacy allows stakeholders at all levels – from front-line workers to key decision-makers – to quickly and accurately extract actionable insights.
It enables easy interaction with data, leading to effective decisions that drive organizational success.
McKinsey asserts that in the data-driven enterprise of 2025, data literacy will be a non-negotiable skillset.
Data literacy is expected to go from a ‘nice-to-have’ asset to a ‘must-have’ capability.
This should be a wake-up call for organizations to immediately invest in data literacy improvement. They need to prepare their staff for the upcoming challenges and opportunities.
Imagine an organization where everyone understands the language of data … where every decision draws upon the power of informed data insights. It would not only lead to a rise in productivity, but also encourage a culture of learning, growth, and innovation. .
Isn’t it time we started to cultivate this culture?
Metrics for Data Quality: What Are They?
Data quality metrics are specific agreed measures that gauge the health and effectiveness of your organizations data.
Properly designed and continuously tracked data metrics lead to more accurate data, and consequently more informed decision-making in your organization.
Consider a captain navigating a ship without a compass, or a pilot flying an aircraft with no flight data available.
Seems absurd, right?
In today’s data-driven world, directing a business without robust data quality metrics is no different.
These metrics are your business’ compass, the flight data that guides your organization’s journey on the path to success.
One of the strategic roles data quality metrics play is to highlight areas of strength and pinpoint areas needing improvement.
The absence of data, is data … 🙂
Missing data a clear sign of:
- a poorly configured system,
- poor process design,
- inadequate training, or
- business unit leaders that either don’t value data or are poor supervisor.
Missing data is either cleaned up before your leadership gets access to a dashboard, or the problem is hidden.
Data quality metrics quantify the value of good quality data, impacting the bottom line by ensuring:
- reducing redundancy, and
- spotlighting opportunities for cost savings and revenue growth.
This distilled, actionable knowledge powers decision making:
- inspiring confidence in strategic planning,
- reducing risks and uncertainties, and
- enhancing the overall competitive advantage.
But what makes a data quality metric useful? It’s not just about crafting metrics; it’s about crafting the right ones.
Some Best Practices:
- Relevance: Focus on metrics that matter the most to your business. If a metric doesn’t align with your business goals, it’s merely clutter.
- Quantifiability: Good metrics bear numeric or comparative values, making them easier to track over time.
- Accessibility: Your metrics should be readily accessible, easy for everyone to understand and act upon.
Erwin’s case study on an organization leveraging data quality tools to manage enterprise data asset management is enlightening.
Through their data quality metrics, the organization was able to streamline their decision-making processes, fostering an environment where data quality, data literacy, and decision-making operate in a virtuous cycle.
Metrics for data quality are the foundation of any data-driven decision. They create the framework for the critical transition from data awareness to being data driven.
Without them, you’re simply flying blind…
How to Make Data Quality Metrics More Visible
Greater visibility of your data quality metrics means greater transparency. And greater transparency directly correlates to better decision-making.
So … how can you elevate the visibility of these critical metrics?
Firstly, recognize that visibility begins with accessibility.
A good starting point is to ensure that both your staff and your key stakeholders have easy access to the relevant metrics.
You need to make the vital information readily available to the right people at the right time.
Fostering this culture of visibility and accessibility hinges on four primary aspects:
- Technological infrastructure: Make use of cutting-edge technological platforms that allow real-time tracking and representation of metrics.
Dashboards and data visualization tools provide your team a bird’s-eye view of the data universe.
- Frequency: Frequent updates helps identify problems early on. You’re then able to resolve them before they escalate into a crisis. More updated information equates to better actions and decisions.
- Education and Training: Empower your team to comprehend the metrics. Put in place training programs to enhance your team’s data literacy. Also look at retraining in areas where data audits show a high number of quality issues caused due to human error.
- Publish Audit Results Whenever your conduct an audit, and update a process, publish the before-and-after results. This will raise the data literacy levels, as well as reinforce that tracking is continuously taking place.
One highly effective method of increasing visibility is through the use of ‘Data Quality Dashboards’.
Offering an easy-to-grasp visual representation of the metrics, they build a collective understanding, ensuring everyone is “on the same page”. They not only exhibit real-time updates of your data quality metrics but also provide historical views, spotting trends, and analyzing the progress
See AltexSoft’s “Data Governance Explained
An even more effective technique is to publish a data quality indicator on every dashboard. Data quality dashboards are great, but executives are far less likely to visit them than their business dashboards.
Making a topline data quality indicator visible on every dashboard makes it impossible to ignore. And if they want more detail, they can click on it, and then go through to the more detailed DQ dashboard.
These methods will share the status of your data quality measurements throughout your organization.
The status of data quality will no longer be hidden from most of the staff. A higher literacy will help them appreciate the downstream effects of their data handling actions.
Impact of Increasing Data Literacy on Quality
Do you want to safeguard your organization against misguided decisions? Do you wish to unlock the full potential of your data? Then, cultivating a culture of data literacy could be the key!
Improved data literacy plays a significant role in enhancing data quality. Let’s explore the ‘how’ part.
- Enhanced Accuracy:
Data-literate employees can identify and rectify inconsistencies, inaccuracies, or outliers in data sets, leading to improved data accuracy.
- Reduced Redundancy:
A data-literate workforce can effectively manage and use data, reducing redundancy, and duplication, thereby improving the cleanliness and quality of organizational data.
- Improved Validation:
The more literate your team is, the better they can validate the quality of incoming data, ensuring only good quality data is fed into your systems.
A shining example of an organization that reaped these benefits is that shared by LinkedIn. The organization launched a data literacy initiative targeting not just front-line managers but all employees. This initiative resulted in enhanced decision-making, increased efficiency, and significantly improved data quality.
Increased data literacy indeed fuels continuous improvement in data quality. It promotes a sense of ownership amongst employees, who feel empowered to question, explore, and utilize data efficiently.
Remember, an organization’s data is only as good as the people who interpret it.
So, that’s one more reason to invest in building your team’s data literacy skills …
Data Quality vs Data Governance
At first glance, data quality and data governance might seem interrelated. Indeed, they are, but each one has a unique function and an important role within an organization.
Data governance sets the strategy for data management. It lays out guidelines and protocols on how data should be:
- stored, and
- procedures, and
These aim to maintain:
- data integrity,
- consistency, and
across the organization.
Data governance is the playbook – the ‘how-to’ guide for achieving high-quality data [Techtarget, “What Is Data Governance and Why Does It Matter?”](https://searchdatamanagement.techtarget.com/definition/data-governance).
Data quality refers to the actual condition and suitability of the data in meeting the purpose it was intended for. It’s a measure of the data’s:
- completeness, and
Going by the playbook analogy, if data governance is the playbook, data quality is then the performance of the team on the pitch in accordance to the playbook’s guidelines.
Though distinct, neither can be successful without the other.
Data governance acts as the guiding light, illuminating the path to superb data quality.
Implementing robust data governance assures:
- reliable, and
- high-quality data.
This is turn drives:
- informed decision-making,
- boosts operational efficiency, and
- gains a competitive edge.
But, the ability to accurately follow the playbook – to implement the data governance strategies successfully and thereby achieve excellent data quality – demands a data-literate workforce.
That’s your team.
So, the circle completes.
Data governance, data quality, and data literacy create a powerful trinity that enables you to exploit your data to the maximum.
Data quality metrics, visibility, and literacy – these are not just buzzwords within your data management domain. They are the pillars that can become the basis for a flourishing data-centric culture within your organization.
Hopefully you’re convinced that …
‘Quality data, when accessible and interpreted accurately by a data-literate team, is the ultimate game-changer’.
Through data quality metrics, your organization gets a precise measure of their data’s health – making sure you have a data lake, and not a data swamp.
By increasing the visibility of these metrics, you ensure transparency that bolsters:
- team spirit, and
Simultaneously, the data-literacy improvement of our team guarantees that they’re not drowning in your data lake, but swimming with confidence, extracting value with every stroke.
We live in a fast-paced world driven by a flood of data. Technology is advancing at a blistering pace, converting every action and interaction into data points.
Your organization’s ability to make decisions based on this onslaught of data will determine whether you are a leader, or a laggard.
Taking all these necessary steps to:
- ensure the highest data quality,
- improve your data literacy, and
- make your data quality metrics more visible
will endows your organization with a superpower. A power where you don’t merely transform into information, but into actionable insights and informed decisions.
You’ll quite literally be carving the path to success in today’s data-driven world.