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Experts claim "data is the lifeblood of every organization." But the unwritten caveat to that claim is that the data must be of good quality, as working with poor data can lead to the death of your organization.
Ensuring good data quality can be a struggle because things can go wrong between accessing, processing, and distributing data. You can discover that your data sets are incomplete, inconsistent, and inaccurate, leaving you frustrated and at a loss in handling the data you're left to work with. This is why every organization needs to take data quality management seriously.
Data quality management is a way of ensuring every data your organization works with can be trusted to get the work job done.
To do justice to this topic in today's piece, we'll discuss the meaning, importance, implementation plan, challenges, and best practices for data quality management.
Data quality management refers to practices and principles for maintaining data integrity, usefulness, and accuracy. These practices are enforced at different data lifecycle stages to ensure consistent data quality. The success of a data quality management plan is measured by the metrics set for each use case, and these data quality metrics are:
Data quality management is sometimes likened to data management, which is erroneous, as the latter is about the overall data architecture, with less focus on data integrity.
However, in discussing data quality management, understand that data quality is contextual. What counts as quality data in one scenario may be below the benchmark for another data use case. For instance, the data used in a survey about customer satisfaction won't be sufficient for creating a campaign to launch a new product or feature. In essence, the ultimate check for data quality beyond accuracy is its relevance.
Data quality management positively impacts your organization in the following ways:
Making decisions about product development and marketing, corporate strategy, and stakeholder relations is faster and more effective with access to quality data.
Working with the wrong type of data and its insights wastes employees' time and the company's resources. For example, executing a marketing campaign based on wrong insights about customers' needs will drain the company's marketing budget with no results.
Your organization's ability to obtain, use, and maintain high-quality data in different ways makes it trustworthy and reliable. This will, in turn, attract stakeholders and other corporate bodies who are willing to partner with your organization.
Here's an easy step-by-step process for implementing data quality management within your organization:
There's no better way to work towards improved data quality than looking at where the data journey begins, the data lifecycle.
This is because the most common and impactful data issues can be matched to a data lifecycle stage. For instance, incomplete data, the most common data quality problem, is traced to data collection and storage. Doing this also nips data quality issues before they escalate.
You can take this step further by running a data quality assessment. That is, you investigate the existing data within your organization, check for recurring data issues and areas that are most hit by these issues, and document your findings. Doing this will provide a foundation for setting up the data quality management plan.
This kicks off the implementation of a data quality management plan. While the general metrics on data quality apply to organizations in different industries, you should have your criteria for data quality. For example, in defining data accuracy on data sets, how much percentage of data error do you allow?
At this stage, you can develop a playbook for your data personnel to fall back on, which should include the following:
Even with the best tools, you still need qualified persons overseeing the data lifecycle and executing the data quality management strategy. An excellent place to start for this is with a chief data officer. Other roles are data quality manager, data analyst, and data engineer.
Data quality management tools help identify data quality bottlenecks. Your tool of choice should be able to automate the following functions, which are necessary for upholding data quality:
Some tools that help with these are Atacama, Talend, Informatica, and Precisely Trillium.
After executing the data quality management plan, you'll still need data monitoring to observe the changes even after implementation. This will help me detect suspicious changes, identify areas that need more work, and the processes you need to scale up.
Sometimes, even with a plan, your team may still struggle to ensure data quality. This is normal and can be linked to any of the following factors:
With data pouring in from different sources and people accessing it differently, controlling and determining quality data takes time. Therefore, experts have more work to do deciding which data is relevant, which can be worked on, and which to ignore.
As helpful as industrial regulations surrounding data usage are, these requirements are stressful to implement. Hence, companies would use shortcuts (mostly illegal or unethical) to beat these regulations. However, in doing this, they also miss out on improving their company's data quality, which is the most significant benefit of data governance. And the ones who comply sometimes fail to enforce the policies uniformly across the organization.
Imagine spending money and efforts ensuring data quality just to have your cyberspace invaded overnight and sensitive information lost. That's the new reality for data experts, making the fight for data quality seem like an effort in futility.
Data lakes and data warehouses are two standard options for storing big data but use them differently to maintain data integrity.
For instance, data lakes can house structured, unstructured, or semi-structured data. On the other hand, data warehouses are best for structured and refined data. Hence, a data warehouse is a better point of call if you need data for immediate use. Learn more about the difference between data warehouses and data lakes.
A zero-trust environment provides a hands-on approach to security concerns around data. It involves device monitoring and gatekeeping employee access to the organization's network.
Gaining visibility into how your organization's data is processed and the performance of your data pipeline can help you improve data quality, which is what data observability helps with. You'll be able to track data sources and records, engineer better data workflows, and identify issues within these workflows before they escalate.
Use extract, transform, and load, a three-step data process for creating a standard and unified database free of errors and inconsistencies.
Be selective in sourcing data, and focus on introducing insights relevant to your business or customers' needs, no matter how trendy or game-changing it seems.
Data quality management won't work if it's implemented in a silo. The necessary data quality tools need handling by the right data professionals. These professionals should be familiar with the data quality metrics and match them to the correct use case. Anything short of this, and there'll be lapses in your data quality management plan.
This posting does not necessarily represent Splunk's position, strategies or opinion.
The Splunk platform removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.
Founded in 2003, Splunk is a global company — with over 7,500 employees, Splunkers have received over 1,020 patents to date and availability in 21 regions around the world — and offers an open, extensible data platform that supports shared data across any environment so that all teams in an organization can get end-to-end visibility, with context, for every interaction and business process. Build a strong data foundation with Splunk.