9 Ways to Maintain Data Quality

#Data Management #Data Management Strategy #Data Quality

Summary

This blog emphasizes the importance of data quality in informed decision-making and operational efficiency, offering actionable strategies to ensure data integrity across various organizational processes.

Implement Data Profiling: Utilize tools to analyze data sources, identifying anomalies, inconsistencies, and errors to enhance data quality before integration.
Establish Data Quality Rules: Define and enforce rules for data validation, cleansing, and enrichment to maintain accuracy and consistency across datasets.
Monitor Data Quality Continuously: Employ real-time monitoring systems to detect and address data quality issues promptly, ensuring ongoing data reliability.

Data quality is essential to informing decisions, predicting and resolving problems, and enabling desired outcomes, but do you know how to maintain and deliver the quality your analysts and other data users need? A data management strategy is one essential component to ensure that data meets your quality standards. Likewise, it’s important to understand and address common factors that reduce data quality.

At Actian, we define data quality management as “the mature processes, tools, and in-depth understanding of data you need to make decisions or solve problems to minimize risk and impact to your organization or customers.” The data must be accurate, current, complete, trusted, and usable by the various teams that need it.

Here are 9 ways to improve and maintain data quality:

1. Determine the Data Quality Standard You Need

You’ll need to define your standard for data quality. This standard should align with your business goals and anticipated uses to ensure the data meets your needs. The standard should also meet your data compliance and data governance requirements. Performing a data quality assessment lets you determine the current state of your data, then you can identify what needs to be improved to reach your data quality standard. When your data is trusted and meets the standard for its intended use, analysts and others will have confidence in the data and the analytics insights.

2. Create a Data Governance Framework

Data governance establishes the protocols and framework for maintaining data quality. It assigns the policies, processes, and roles within your organization to make sure data meets your quality standard for integrity, availability, and security. The framework also ensures your data meets compliance standards for regulated industries and for individuals’ personal data. A robust governance framework delivers quality data to all users, when and where it’s needed.

3. Implement Data Quality Tools

The right tools give you a modern approach to enabling data quality by automating processes for assessing data and identifying quality issues. The tools also help with essential processes such as profiling, cleansing, and standardizing data. Data management tools vary wildly in capabilities, so look for products that can provide a quick ‘at-a-glance’ view of data quality based on the rules you’ve established. These tools can also be integrated into data pipeline processes to automate data quality checks as data is ingested.

4. Profile Data to Identify Issues

Data profiling is essentially performing an audit to find quality issues. As Gartner notes, “Data profiling is a technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness.” Data profiling tools also look at data sources and metadata to uncover data errors. The process allows you to fix quality issues before the data is analyzed or integrated with other data, and it also allows you to solve problems to prevent them from reoccurring.

5. Cleanse Data to Address Inconsistencies

Gaps and inconsistencies can exist in datasets, which impact quality. Data that’s incorrect, incomplete, or has missing fields will not deliver the granular, trusted results users need. Data cleansing is a critical process that lets you find and fix inaccuracies, fill in missing information, and identify inconsistent data. The right approach to cleansing data helps ensure datasets are accurate, reliable, and complete.

6. Standardize Data into the Correct Format

Data standardization can be considered part of data cleansing. This process ensures data is in the required format for data users. It also makes sure you’re using a common format for all of your data for consistency and easier integration. Likewise, standardizing data makes it easier for you to perform data analytics and store the data because it’s in the most optimal format for your organization. Transforming the data into a usable, accessible, and shareable format ensures analysts and others can leverage it for maximum value.

7. Use Deduplication Processes to Eliminate Redundancies

Data redundancy, which results in multiple versions of the same data, is a common problem. Copies of data are made for backups, testing, specific uses, or other reasons. This can lead to data silos, which in turn increases costs by storing the same data several times. Data deduplication is the process that looks for and eliminates duplicate, or redundant, versions of data. The process identifies extra copies and deletes them so only a single instance of the dataset is stored. Deduplication helps with quality by eliminating data copies that can quickly become outdated, and it encourages analysts to use the current, verified data that’s available on a centralized data platform.

8. Train Employees to Recognize Quality Issues

Building a data-driven culture entails more than creating an environment in which everyone has access to and utilizes data. It also involves giving employees the proper tools and training them on best practices for maintaining data quality so they can identify issues and either fix them or report them. Many organizations have employees who focus on data stewardship, a role that’s responsible for the oversight and usage of data assets. Each department can have its own data steward to ensure data meets quality standards and that data governance policies are followed.

9. Monitor Data on an Ongoing Basis

Maintaining data quality is a continuous process. You can streamline much of it by using automated monitoring tools that routinely check and evaluate data quality, and identify any issues. When there is an issue, alerts are sent to notify the proper stakeholders to take corrective action. Continuous monitoring ensures that data maintains your quality standard as it’s shared and reused across the organization.

Making High-Quality Data Easy to Use and Analyze

Analysts, decision-makers, and others throughout the company must be able to trust the data in order to have confidence in the insights. Providing quality data is one way to establish that trust. Actian can help. We offer tools and expertise to help you identify and correct data anomalies to give you high-quality data that improves the effectiveness of your data-driven initiatives. We also make data easy. The Actian Data Platform simplifies how you connect, manage, and analyze data. This makes trusted data readily and easily available to everyone in your organization to accelerate your growth.

Additional Resources:

Mastering Your Data With a Data Quality Management Framework
Data Profiling and Data Quality: What You Need to Know
The High Costs of Dirty Data – Why Data Profiling with DataConnect Version 12 Will Improve Your Business Outcomes

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About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale. Organizations trust Actian data management and data intelligence solutions to streamline complex data environments and accelerate the delivery of AI-ready data. Designed to be flexible, Actian solutions integrate seamlessly and perform reliably across on-premises, cloud, and hybrid environments. Learn more about Actian, the data and AI division of HCLSoftware, at actian.com.

Analytics Program and Project Justification

By William McKnight

#Business Data #Data Analytics #Data Management #Enterprise Data Management

By William McKnight

#Business Data #Data Analytics #Data Management #Enterprise Data Management

The economy is currently in a state of flux based on analytics, and there are both positive and negative signals regarding its future. As a result of factors, such as the low unemployment rate, growing wages, and rising prices, businesses find themselves in a spectrum of states.

Recent pullbacks appear to be driven primarily by macro factors. I have a positive outlook on IT budgets in 2024 because I anticipate a loosening of IT expenditures, which have been limited by fears of a recession, since 2022. This will allow pent-up demand, which was cultivated in 2023, to be released. Because data is the key to success for these new endeavors, the demand for data cleansing and governance technologies has increased to address broad data quality issues in preparation for AI-based endeavors.

Taking a broader perspective, despite the instability of the macro environment, the data and analytics sector is experiencing growth that is both consistent and steady. However, there is a greater likelihood of acceptance for business programs that concentrate more on optimization than on change. As a means of cutting costs, restructuring and modernizing applications as well as practicing sound foundational engineering are garnering an increasing amount of interest. For instance, businesses are looking at the possibility of containerizing their applications because the operation costs of containerized applications are lower.

At this point, in this environment, project approval is taking place; nonetheless, the conditions for approval are rather stringent. Businesses are becoming increasingly aware of the importance of maximizing the return on their investments. There has been a resurgence of interest in return on investment (ROI), and those who want their projects to advance to the next stage would do well to bring their A-game by integrating ROI into the structure of their projects.

Program and Project Justification

First, it is important to comprehend the position that you are attempting to justify:

A program for analytics that will supply analytics for a number of different projects.
A project that will make use of analytics.
Analytics pertaining to a project.
The integration of newly completed projects into an already established analytics program.

Find your way out of the muddle by figuring out what exactly needs to be justified and then getting to work on that justification. When justifying a business initiative with ROI, it is possible to limit the project to its projected bottom-line cash flows to the corporation in order to generate the data layer ROI (which is perhaps more accurately referred to as a misnomer in this context). In order for the project to be a catalyst for an effective data program, it is necessary for the initiative to deliver returns.

The question that needs to be answered to justify the starting of an existing data program or the extension of an existing data program is as follows: Why architect the new business project(s) into the data program/architecture rather than employing an independent data solution?  These projects require data and perhaps a data store, if the application doesn’t already come with one, then synergy should be established with what has previously been constructed.  

In this context, there is optimization, a reduction back to the bare essentials, and everything in between. The bare essentials approach can happen in an organization in a variety of different ways. All of these are indications of an excessive reach and expanded data debt:

Deciding against utilizing leverageable platforms like data warehouses, data lakes, and master data management in favor of “one-off”, and apparently (deceptively) less expensive, unshared databases tight fit to a project.
Putting a halt to the recruiting of data scientists. Enterprises that take themselves seriously need to take themselves seriously when it comes to employing the elusive genuine data scientist. If you fall behind in this race, it will be quite difficult for you to catch up to the other competitors. Even if they have to wrangle the data first before using data science, data scientists are able to work in almost any environment.
Ignoring the fact that the data platforms and architecture are significantly more important to the success of a data program than the data access layer, and as a result, concentrating all of one’s efforts on the business intelligence layer. You should be able to drop numerous BI solutions on top of a robust data architecture and still reach where you need to go.
Not approaching data architecture from the perspective of data domains. This leads to duplicate and inconsistent data, which leads to data debt through additional work that needs to be done during the data construction process, as well as a post-access reconciliation process (with other similar-looking data). Helping to prevent this is master data management and a data mesh approach that builds domains and assigns ownership of data.

Cutting Costs

If your enterprise climate is cautious spending, target the business deliverables of your data project and use a repeatable, consistent process using governance for project justification. Use the lowering of expenses to justify data programs. Also, avoid slashing costs to the extreme by going overboard with your data cuts, since this can cause you to lose the future. 

Although it should be at all times, it’s times like these when efficiencies develop in organizations, and they become hyper-attracted to value. You may have to search beyond the headlines to bring this value to your organization. People in data circles know about Actian. I know firsthand how it outperforms and is less costly than the data warehouses getting most of the press, yet is also fully functional.

All organizations need to do R&D to cut through the clutter and have a read on the technologies that will empower them through the next decade. I compel you to try the Actian Data Platform.

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