How to Set and Implement Great Data Quality Standards

Many companies want to create a culture of data quality but don’t know where to start. So, we put together this guide to help you tackle this project!

Assess the Current State of Your Data

To know how to fix your data, you must know where you stand. Assessing the current state of your data is the critical first step in setting and implementing effective data quality standards within your organization. This phase involves a comprehensive evaluation of your data landscape to identify the quality of your existing data and understand its structure, sources, and how it’s being used across different departments.

Here are the steps we recommend:

1. Data Auditing: Begin with a thorough audit of your data repositories. This means examining databases, data lakes, and any other storage systems to catalog the types of data you have. Look for inconsistencies, duplicates, incomplete entries, and any signs of outdated information. Tools and software designed for data auditing can automate much of this process, highlighting areas that require immediate attention.
2. Identifying Key Data Metrics: Determine what metrics are most important for your business’s success. This process involves categorizing data based on its impact on your business objectives, regulatory compliance requirements, and its significance to critical business operations. Start by identifying mission-critical data sets that directly influence decision-making, customer satisfaction, and revenue generation. These might include customer data, financial transactions, and operational metrics. Mid-tier data could involve supplementary customer information, inventory levels, or secondary financial metrics that support broader analyses but are not immediately mission-critical. Lastly, classify as low priority the data that, while useful for long-term trends or peripheral analyses, does not directly impact day-to-day business outcomes. By establishing a clear hierarchy of data prioritization, your organization can allocate resources and efforts more effectively during cleanup.
3. Stakeholder Interviews: Engage with stakeholders from various departments to understand how they use data in their daily operations and the challenges they face. This step can uncover hidden issues that may not be apparent from a simple audit and help you understand the practical implications of data quality problems on business processes.
4. Benchmarking Against Industry Standards: Assessing your data also means understanding how it stacks up against industry standards or best practices. This comparison can provide a clearer picture of where your data quality stands in relation to your competitors and what areas need improvement to meet or exceed these benchmarks.
5. Setting a Baseline: With the insights gathered from your audit, metrics identification, stakeholder interviews, and benchmarking, establish a baseline for your data’s current quality. This baseline is your starting point, against which all future improvements will be measured, allowing you to track progress over time and quantify the impact of your data quality initiatives.
6. Documentation: Document your findings comprehensively. This documentation should include the current state of data quality, identified issues, stakeholder insights, and the established baseline. It serves as a reference point for developing your data quality standards and guides the subsequent steps in your journey toward better data management.

By rigorously assessing the current state of your data, you lay the groundwork for setting realistic, impactful data quality standards. This initial assessment highlights where you are and helps chart a course toward where you need to be, making it an indispensable step in cultivating a culture of data quality within your organization.

Actually, Clean the Data

Once you know the above, you can get to the task of cleaning the data. For many companies with massive amounts of data, this is a monster project. To make it more realistic, you need a heavily planned strategy (based on the above) and a staged approach cleanup, starting with high-priority data first.

One of the common places to tackle data cleanup right now is ERP migrations. Companies want the new tech, but they have ancient ERP’s full of crappy data they need and use every day. This data, often riddled with inaccuracies, duplications, and redundancies, is nevertheless essential for the day-to-day operations of the company. The migration process demands a meticulous approach to data cleansing – ensuring that only clean, accurate, and relevant data is transferred to the new ERP system.

You will likely need a dedicated team for this project (either in house or third party) who will utilize the information you have outlined above as guidelines for the cleanup.

Invest In Data Management Systems

DQaaS systems allow you to do this and can teach you how to be data stewards of your own data. Without them, unless you have a large, experienced, and expensive data scientist team, you are essentially shooting into the dark during data cleanup and hoping you fix the problem.

DQaaS providers specialize in delivering comprehensive data quality services, from data cleansing and validation to governance and stewardship. These services not only ensure that data is accurate, consistent, and reliable, but also equip organizations with the knowledge and tools necessary to maintain these standards independently. By partnering with a DQaaS provider, companies can benefit from expert guidance and advanced technologies tailored to their specific data challenges.

For companies with their own data teams, the journey towards effective data management can be daunting. The sheer volume and complexity of data, coupled with the rapid pace of technological change, can lead to analysis paralysis, where teams are overwhelmed by the scope of work and unsure of where to start. And we have seen this in real time.

A client of ours was going through proof of concept with an ELT tool. The initial setup of data pipelines aimed at replicating data across various systems for testing purposes unveiled the “monstrous” nature of the task. The overwhelming complexity and the realization of the additional burden on the team’s existing responsibilities highlighted the gap between the ambition for high-quality data management and the practical realities of achieving it in-house.

Sometimes, it is just not possible for companies to tackle this data cleanup on their own without significantly hindering the day-to-day operations of the business.

The choice between leveraging external expertise or developing in-house capabilities should be informed by an organization’s specific needs, resources, and strategic objectives. What is clear, however, is that inaction is not an option. Data is a critical asset for every company. Ensuring its quality and effective management is essential for operational success and long-term competitiveness.

Remember The Value

When faced with daunting projects like this, it can be tempting to once again “kick the can” down the road, especially when we can’t articulate a business case for the time and money spent on the project.

To build a compelling business case, translate the benefits of data quality into concrete, measurable outcomes. This involves identifying specific instances where poor data quality directly impacts the business, whether through operational inefficiencies, missed opportunities, or heightened risks. By quantifying the costs associated with these issues – such as the time and resources expended to rectify data-related failures – you create a tangible context for the value of improving data quality.

Focus on how enhanced data quality can mitigate operational and strategic risks. For example, accurate and reliable data can prevent costly downtime in critical business processes, avoid compliance penalties, and enhance decision-making confidence. Equally important is demonstrating the potential for increased profitability. This can be achieved by showing how improved data quality leads to better customer insights, more targeted marketing efforts, more efficient operations, and, ultimately, higher revenue.

Value always comes back to the impact of business summarized as mitigating risk and increasing profitability. You have to find a way to show a path to these two things.

As an example, for your data categorized as mission critical, do an analysis and look back at the last 6 months of data. Try to identify failures across the business. By meticulously documenting past inefficiencies and forecasting the benefits of improved data integrity, you can convincingly argue for the investment needed.

Remember, the goal is to showcase data quality not as a cost center but as a strategic investment that safeguards the company’s operational integrity and paves the way for enhanced profitability. You can secure the buy-in necessary to tackle your data quality projects through careful planning, clear demonstration of past pain points, and a focused approach to mission-critical data.

Some Final Advice

DataGence has been helping our clients do everything we outlined in this article. And because of our experience, we have a few tips and tricks to offer when tackling this process:

1. Start Small and Scale Gradually: Before attempting to overhaul your entire data ecosystem, begin with a manageable, clearly defined segment – your “pond”. This approach allows for more focused efforts, quicker wins, and less risk of project overrun. It sets a precedent for success that can be built upon in subsequent phases.
2. Embrace Master Data Management (MDM): Adopt a phased, wave-based strategy for rolling out data management improvements. Tackling data quality projects in manageable chunks increases the likelihood of timely and successful completion.
3. Prioritize for Greatest Impact: Focus your initial efforts on areas of your data landscape that will provide the most significant benefit. Identifying and improving these key areas can offer immediate business value, helping secure further buy-in for data quality initiatives.
4. Secure an Executive Sponsor: Find a champion at the executive level who genuinely understands and believes in the importance of high-quality data. This sponsor can be instrumental in advocating for the necessary resources and budget, providing a critical voice of support within the leadership team.
5. Adopt a Cost-Efficient Approach: Strive to execute your data quality projects within budget by seeking the most efficient solutions. Don’t hesitate to consult with external experts and leverage their knowledge. The industry is rich with professionals who have navigated similar challenges and can offer invaluable insights.
6. Leverage Community and Industry Knowledge: You’re not alone in your quest for improved data quality. You can crowdsource some of your problems. Engage with the broader community, participate in forums, attend webinars, and read up on industry trends. Collaborating on your ideas can provide fresh perspectives and innovative solutions for your challenges.
7. Cultivate a Supportive Culture: Ensure your organization’s culture is aligned with the goals of your data quality projects. Resistance to change or a lack of understanding about the value of data can significantly hinder progress. If the cultural groundwork isn’t laid, even the most well-conceived projects risk being derailed by internal friction.
8. Manage Ambition with Pragmatism: While ambition is crucial for driving change, overly ambitious data projects without clear, achievable milestones can lead to cancellations or failures. Set realistic goals and manage expectations to maintain momentum and ensure long-term success.