How to Maintain Data Quality in Your Databases
Data has become a core part of enterprise operations. With organizations around the world using data analytics to drive decision-making, refine processes, and boost success. Although data is now a central part of these endeavors, they are only effective if the data that organizations use is high quality. So, how to increase and maintain data quality?
In order to get the best possible results from data analysis, the data that databases and data lakes ingest and store must have a high data quality. Data quality refers to the degree to which data is free from errors, accurate, complete, and correctly structured for its location. These may seem fairly obvious requirements. Only 3% of organizations meet acceptable data quality standards.
Almost one in two data records has one or more critical errors. This reduces the chance of maintain data quality. Without effective data management strategies, it is harder to regulate, store, query, and draw meaning from data. In order to create better data analytics, organizations must strive for better data quality and data management.
There are a number of useful strategies to incorporate that can help to increase the quality of the data that organizations ingest. We’ll explore the following data quality strategies.
- Create Foundational Governance Guidelines.
- Engage with Data Profiling.
- Optimize, Reduce, or Eliminate Manual Data Entry.
- Create Educational Programs for Employees.
- Utilize Alternative Data Storage Systems.
Let’s dive in.
Create Foundational Governance Guidelines
Organizations create and upload data governance guidelines. This is in order to ensure that all data that they ingest has a consistently high quality. Without clear structures and governance in place around data standards, collection procedures, maintenance, and storage, it is easy for the quality of data to slip.
The first step for any company that wants to maintain data quality is to ensure its governance guidelines are up-to-date, comprehensive, and clearly outline expectations. Not only does this provide guidance for employees. But it also creates a sense of accountability.
Improving data quality starts from this bottom line. Once the governance is in place, everyone involved in the collection and management of data will have clear guidelines to follow, helping to improve consistency and quality.
Engage with Data Profiling
Data profiling is where an organization analyzes its own data to pinpoint data quality issues. Often this is a larger process. But one that can reveal incredibly useful results when done correctly. By profiling data, companies will understand where low-quality data is most frequent. Once you have identified this data, you’re then able to rapidly eliminate these sources. Or begin the process of cleaning up that data before your company ingests it.
If there is consistently a missing value or there are inconsistencies in a data source, you are then able to further investigate to improve its quality. By repeating data profiling many times over any given year, organizations can continually optimize the data that they use. Over time, this creates higher-quality data. And ensures that all sources are accurate, consistent, and continually providing high-quality data.
Optimize, Reduce, or Eliminate Manual Data Entry to Maintain Data Quality
When it comes to the accuracy of data, manual data entry is one of the biggest areas where errors occur. Although many legacy systems rely on manual data entry, it is a slower and less accurate system. While humans are prone to making a mistake when entering and copying data, machine tools perform the same work at a quicker pace, all without committing errors.
In order to create high-quality data, reducing or eliminating manual data practices where possible will greatly help the cause. If manual data entry is absolutely necessary, then companies should take additional actions to create stringent data capture policies and peer-review systems.
Create Educational Programs for Employees
If there is consistently a missing value or there are inconsistencies in a data source, you are then able to further investigate to improve its quality. By repeating data profiling many times over any given year, organizations can continually optimize the data that they use. Over time, this creates higher-quality data and ensures that all sources are accurate, consistent, and continually providing high-quality data.
In your organization, take time to create a short and effective data quality management course. Cover how to maintain the quality of data and why data quality is important. As more employees in a company learn about data quality, you are less likely to experience a reduction in quality due to accidental mistakes when handling data.
Utilize Alternative Data Storage Systems
The primary storage system that organizations will opt for is typically data warehouse. While data warehouse provide a number of benefits when it comes to storing and querying structured data, they’re not as effective when it comes to unstructured data. Their inefficiency is due to their pre-defined schema. That many data formats simply will not fit into.
However, if an organization was to only collect structured data, they would miss out on a rich set of opportunities for analysis. Instead of only relying on a data warehouse, it’s a good idea to have a number of data storage structures in place. For example, also adding a data lake and a delta lake to the stack. It can increase the amount of data an organization can collect. Hence, ensuring that structured and unstructured data always have a set location it can go to.
While comparing a delta lake vs data lake, it initially seems that they both simply help with data storage. While they both store data, organizations can use data lakes for raw-unprocessed data and delta lakes for when they need additional data management features like ACID transactions. By investing in having a wide array of data storage tools and systems in place, organizations can unlock a whole range of data analysis opportunities.
Instead of transforming data to fit into pre-defined schemas and potentially compromising its integrity, utilizing alternative data storage systems will allow organizations to collect data in its raw form without damaging its quality.
Final Thoughts on How to Maintain Data Quality
Any organization that regularly uses data, especially to inform or optimize its processes, should focus on improving the quality of the data that they use. A high-quality standard for data ensures that analytics tools can rapidly process, ingest, and produce the analytical understanding they’re built for.
By incorporating the five strategies that this article outlines, organizations will be able to radically increase the quality of their data. From increasing governance and ensuring employees understand the need for good quality data to create comprehensive data ecosystems and eliminating manual entry. There are a number of impactful strategies to improve data quality.
Adhar Dhaval is experienced portfolio, program and project leader with demonstrated leadership in all phases of sales and service delivery of diverse technology solutions. He is a speaker sharing advice and industry perspective on emerging best practices in project leadership, program management, leadership and strategy. He is working for the Chair Leadership Co.