This isn\u2019t just a technical headache. When your CEO asks why last quarter\u2019s customer acquisition cost varies by 40% between the Marketing and Finance reports, both teams can defend their numbers. They\u2019re both pulling from “reliable” sources, using “correct” definitions, and following “established” processes. Yet somehow, you\u2019re still debating basic metrics in quarterly business reviews.<\/p>\n
In more than 20 years of providing data and analytics solutions<\/a>, our experts have seen firsthand how poor data quality hampers organizational success. In 2023, Forrester<\/a> found that when data and analytics workers reported poor data quality at their organization, more than 25% estimated their business lost $5 million annually as a result. A further 7% estimated the cost at $25 million or more.<\/p>\n We\u2019ve written this article in our quest to help organizations worldwide improve data integrity. Read on to learn about four battle-tested steps that have helped organizations transform their data from a liability into a strategic asset. You\u2019ll also learn what to include in your data stack and how to overcome common challenges.<\/p>\n Oversee, control, and optimize the business value of your data <\/p>\n Every data practitioner knows that successful data quality initiatives start with understanding what you\u2019re trying to improve. Garbage in, garbage out isn’t just a saying \u2013 it\u2019s the difference between trusted insights and costly mistakes. Before diving into how to improve data quality, let\u2019s examine what “quality” means for your specific use case.<\/p>\n DAMA (UK) has defined six dimensions of data quality<\/a> that cover most situations: accuracy, completeness, uniqueness, consistency, reliability, timeliness, and validity.<\/p>\n Think of the dimensions of data quality as flavors to pick and choose from to suit the end user, not as a checklist of criteria for all data to meet.<\/p>\n That is to say: Various industries and internal functions have different data quality requirements. For example, a rough estimate of a team\u2019s travel expenses may be good enough for a manager to make a budget forecast but not accurate enough for an audit. Last year\u2019s stock market data is not timely enough for investment decisions but is good enough for an AI model for economic forecasting.<\/p>\n Improving data quality is critical to your company\u2019s data maturity journey.<\/p>\n A data-na\u00efve organization may only use data passively, store it in silos, and have little in the way of data governance. At the other end of the spectrum, a highly data-mature organization will have a deeply ingrained data culture backed by a unified data architecture, enterprise data management<\/a>, and comprehensive data governance tools, standards, and practices.<\/p>\n Gartner<\/a> has outlined 12 actions to improve data quality along your data maturity journey:<\/p>\n Source: Gartner<\/a>\u00a0<\/em><\/p>\n We\u2019ve distilled this set of actions into four practical steps for improving data quality:<\/p>\n There is no cookie-cutter approach to improving data quality because data quality means something different to every organization. You\u2019ll need to choose data quality pillars that make the most sense for your stakeholders.<\/p>\n Depending on your end users\u2019 needs and tolerances, you could focus your data quality initiative on improving accuracy or decide that you don\u2019t need to improve data accuracy and instead prioritize other dimensions of data quality. Knowing what data quality dimensions to focus on will help you effectively allocate resources.<\/p>\n Actions to take:\u00a0\u00a0<\/strong><\/p>\n Once you\u2019ve established your data quality pillars, it\u2019s time to build an improvement strategy to ensure data quality and integrity in practice.<\/p>\n One unintuitive aspect of data maturity is letting go of the notion that data quality is binary: \u201cgood\u201d or \u201cbad.\u201d Organizations generally start with a truth-based model, in which the goal is total correctness. However, a more mature approach to data quality leans into the greater business value of \u201cgood enough.\u201d<\/p>\n If an end user doesn\u2019t need perfect data, pursuing perfection will only create a bottleneck. The answer is shifting to a trust-based model that gives users more immediate and contextualized access to usable, if imperfect, data.<\/p>\n Actions to take:\u00a0\u00a0<\/strong><\/p>\n While data quality initiatives typically get off the ground thanks to high-level data quality champions, those aren\u2019t the people who will do the work. Establish clear ownership of granular data quality responsibilities to ensure data quality plans become a reality.<\/p>\n An accountability structure makes it easier to coordinate efforts, track progress against data quality improvement targets, and verify that improving data quality is having an impact on business KPIs.<\/p>\n Actions to take:\u00a0\u00a0<\/strong><\/p>\n At the most mature organizations, data quality improvement becomes part of the organization\u2019s DNA. These organizations recognize that data quality initiatives aren\u2019t time-bounded projects but ongoing practices embedded into daily operations and aligned with business objectives.<\/p>\n At an organization with high data maturity, data literacy is ubiquitous. Interdepartmental communications emphasize the importance of data quality. Internal communications include data success stories to celebrate colleagues who improve data quality and reinforce data quality best practices.<\/p>\n Actions to take:\u00a0\u00a0<\/strong><\/p>\n Data management experts<\/a> at Intellias believe that data observability is the key to a data stack that supports your data quality strategy. With end-to-end visibility into enterprise-wide data, you can proactively monitor data collection and data management processes. That way, you can catch any issues during data capture and find and resolve problems that arise later.<\/p>\n Core elements of a comprehensive tech stack for improving data quality include:<\/p>\n Look for tools with robust data version control features, which will help keep your data safe and organized. These features allow you to track changes, test updates without affecting live data, and quickly revert to a previous version of your database if needed. If data gets corrupted or if changes introduce errors, version control can help your team recover quickly and maintain consistent, high-quality data.<\/p>\n One of the most efficient and effective ways to improve data quality is to build your data management system to address issues early in the pipeline, before errors or omissions flow into analytics or production environments.<\/p>\n This aligns with the software development Rule of Ten, which holds that at each step of the process, the cost and time to fix a problem increases tenfold.<\/p>\n Source: Researchgate<\/a><\/em><\/p>\n Data engineering services<\/a> professionals at Intellias can help you build a data management architecture that moves quality checks upstream, closer to the point of data entry or collection. That way, your team can detect errors at the source and prevent data quality issues (and their costs) from compounding.<\/p>\n Stay in the know about main data and analytics trends.<\/p>\n Managing a big IT project is always an uphill battle. If you\u2019re pushing to improve data quality at your organization, you will encounter all the typical barriers to technology projects \u2013 and more.<\/p>\n Here are eight specific challenges you may run into (and how to plan for them):<\/p>\n Leaders don\u2019t always understand the value of data quality efforts. Without leadership support, you\u2019re unlikely to get the resources you need for success.<\/p>\n What to do<\/strong>: Start speaking their language. Gain the support of leadership by clearly connecting the dots between data quality improvements and business outcomes. Highlight benefits like increased operational efficiency, improved decision-making, and stronger regulatory compliance.<\/p>\n Data quality initiatives require a solid governance framework to provide structure and accountability. What should you do if the teams managing data don\u2019t follow consistent processes?<\/p>\n What to do<\/strong>: Establish or enhance data governance policies and practices as part of your data quality initiative. Define clear roles, set data quality standards, and implement processes that guarantee consistent data handling. You can ensure long-term success by tying data quality improvements directly to data governance<\/a>.<\/p>\n As your volume of data grows, manual processes will become too slow and error-prone to keep up. If your plan doesn\u2019t account for automation at scale, it may not be an effective strategy for long.<\/p>\n What to do<\/strong>: Choose modern, scalable tools that can grow with your organization\u2019s data needs. Automate data quality checks and validation processes to make sure you\u2019ll have the capacity to handle them even as data volumes increase.<\/p>\n Data flows into your organization from diverse systems in a variety of formats. While some data is structured in tables and lists, an increasing amount is unstructured, including text documents, video and audio files, and social media posts. It arrives in different cadences, in batches or in streams. With all that complexity, ensuring the consistency and high quality of data is anything but straightforward.<\/p>\n What to do<\/strong>: Use data integration tools to standardize formats and ensure consistency across systems. Managing data quality from the start helps prevent errors and inconsistencies from escalating into bigger problems as data moves through your pipeline.<\/p>\n Data quality improvement initiatives are resource-intensive. Without outside support, it’s easy for these projects to stall \u2013 especially for smaller or less experienced teams.<\/p>\n What to do<\/strong>: If your team is stretched too thin to handle a data quality improvement initiative, call on Intellias for data strategy consulting<\/a>. We\u2019ll give you as much help as you need to bring your data quality initiative to life without sacrificing normal operations. In the long term, you can make the most of your IT resources by prioritizing high-impact data quality tasks and using automation to handle repetitive processes like data cleansing.<\/p>\n If your organization is early in its data maturity journey, employees may see new data quality processes as unnecessary or time-consuming. You could encounter pushback, or uncooperative colleagues could fail to follow new processes.<\/p>\n What to do: Engage key stakeholders early in the process. Show how improving data quality can reduce errors and inefficiencies. Celebrate quick wins along the way to demonstrate the initiative’s value.<\/p>\n Without consistent data standards across departments, data users will encounter confusion and errors. Trouble sharing and using data across the organization reinforces existing data silos.<\/p>\n What to do<\/strong>: Establish clear definitions of terms and standards for data across the business. A data glossary will keep everyone aligned on how data should be labeled, formatted, and interpreted. Regular audits can help to ensure adherence to these definitions, leading to more accurate and reliable data.<\/p>\n If your organization is still running on old systems, they may not support modern data quality tools. Outdated instruments make it harder to manage data effectively or implement real-time monitoring.<\/p>\n What to do<\/strong>: Where possible, upgrade legacy systems or implement integration solutions that connect them with newer, more advanced tools. Modernizing your business software and implementing cloud governance will help your organization maintain high data quality, even with older infrastructure.<\/p>\n Improving data quality is one of the most essential actions you can take to support your business \u2013 but it\u2019s more nuanced than focusing on 100% complete and accurate data. To progress along your data maturity path, you\u2019ll need to reflect on what constitutes \u201cgood enough\u201d data for your end users to achieve their business objectives. That knowledge will enable you to select the data quality strategy that best suits your organization\u2019s needs.<\/p>\n Intellias is passionate about helping businesses succeed in their data quality initiatives<\/a>. The business benefits of this are astounding. Without trustworthy data, companies can\u2019t pivot quickly to avoid risks or take opportunities. But when an organization uses a trust-based model to ensure data quality and integrity, the sky is the limit.<\/p>\nUnderstanding data quality improvement<\/h2>\n
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<\/p>\n4 practical steps to improve data quality<\/h2>\n
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<\/p>\nStep 1. Start with targeted data quality principles<\/h3>\n
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Step 2. Define a fit-for-purpose data quality strategy<\/h3>\n
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Step 3. Assign accountability for data quality improvement<\/h3>\n
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Step 4. Make improving data quality part of your culture<\/h3>\n
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Building a comprehensive data stack: A guide to ensuring data quality<\/h2>\n
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![\"How](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2024\/12\/BP-Practical-Steps-to-Improve-Data-Quality-04.jpg\")
<\/p>\nImprove data quality by moving logic upstream in your pipeline<\/h2>\n
![\"Rule](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2024\/12\/BP-Practical-Steps-to-Improve-Data-Quality-08.jpg\")
<\/p>\nOvercoming challenges while improving data quality for your organization<\/h2>\n
1. Lack of executive buy-in<\/h3>\n
2. Inadequate data governance<\/h3>\n
3. Scalability issues<\/h3>\n
4. Data complexity<\/h3>\n
5. Time and resource constraints<\/h3>\n
6. Resistance to change<\/h3>\n
7. Inconsistent data standards<\/h3>\n
8. Legacy IT systems<\/h3>\n
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<\/p>\nLet Intellias enhance your data quality improvement process<\/h2>\n
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