Data Quality Approach

Approach

Traditional Data Management practices have failed.

Organizations have spent extensive time and money on disciplines in the realm of Data Governance, Data Cataloging, and Metadata yet these efforts have not scaled or met expectations without a focused approach towards Data Quality.

Current trends show that data is generated faster than data can be consumed and, in this climate, as a business you need a change in thinking from traditional data management practices towards a “Data Quality First” approach.

What is “Data Quality First” approach?

As organizations grow, you must be aware of where your internal practices land within the data maturity life cycle as this can drastically enhance or hamper the overall growth of your organization. DQLabs defines this data maturity life cycle in four stages:

Data Quality First Approach
Beginner

No data management practices implemented and more people-dependent processes using offline tools and methodologies

Low

Have used or tried individual or departmental efforts either using custom solutions or off the shelf solutions with no centralized focus

Medium

Have implemented a Data Quality, Data Catalog or Data Governance solution and are engaged in an effort towards centralized management practices, however, they are still struggling to streamline processes and have not seen real ROI.

High

Have a centralized data governance team and implemented technology and processes for Data Governance, Data Catalog, Data Lineage, Data Quality, DataOps, and Data Science but many are still struggling to have a modern collaborative data workspace with a focus on Data Quality.

Here’s the bad news - irrespective of which category you belong to, many organizations have no understanding of what percentage of bad data exists. This is because most of the efforts taken towards Data Quality are in conjunction with other disciplines of Data Governance, Data Catalog, or Data Lineage but there is not a primary focus or an actionable effort towards Data Quality.

That is why we preach a Data Quality first approach – an approach that focuses on understanding data from a business context via automated processes like semantic discovery and classification. This is further fueled by automation using proven machine learning technology that helps organizations measure, monitor, and remediate data quality issues in a more practical way and derive immediate value. If you are in the high maturity cycle, the good news is that a Data Quality First approach still takes into consideration your current learnings of business glossaries, governance practices and seamlessly integrates to automate an actionable data quality process.

Using this approach, you can benefit from trustable, actionable data and answer

  • What data is important and what is not?
  • What data needs to be improved or remediated?
  • What data can be used in reporting and analytics?
  • What data pipeline changes are needed to address schema/data deviation or drift?
  • What models can be built to enrich customer experiences?
  • Can I trust this report I am signing off on and am I as compliant as I think I am?

Recently published

5 Most common data quality issues

BLOGS

5 Most Common Data Quality Issues

Introduction to Data Quality Issues

With the advent of data socializing, many firms can effectively acquire, exchange, and make data accessible to all employees. However, while most businesses benefit from having such information resources at their fingertips, others have issues with the accuracy of the data they employ. This is especially relevant today that most businesses consider deploying artificial intelligence systems or integrating their operations via the Internet of Things.

Duplicate, unstructured, missing, multiple data formats, and trouble accessing the data can cause quality issues. This article will go over 5 of the most frequent data quality issues, how to fix them, and how DQLabs helps overcome these data quality issues in businesses.

Duplicate Data

Duplicate data occurs when the same data is recorded multiple times in a database, sometimes in slightly different ways. Oftentimes, duplicate data produces incorrect insights if it goes undetected.

The data quality monitoring tools, such as DQLabs Monitor, that has an appropriate data verification process in place, complete with these tools will help you to comb through all your data and identify duplicate records. The deduplication tools will even identify data records that are not exactly named in the same way but even those with some similarities. The tools use advanced data verification processes where algorithms will automatically remove duplicates.

Unstructured data

Numerous times, on the off chance that information has not been entered accurately within the framework, or a few records may have been undermined, the remaining data has numerous lost factors.

With an information integration apparatus, you’ll offer assistance converting unstructured information to organized information. Additionally, move data from different designs into one reliable shape.

Security Issues

The security of data is based on three fundamental principles; confidentiality, integrity, and availability. Business-critical data as well as private and personal information must be protected by an organization. A strong data security strategy provides differentiated protection of the organization’s data assets, giving the most critical data the highest level of priority in protection.

Human error

Human error is perhaps the biggest challenge to achieving high data quality. Personnel are prone to making errors such as typos, misplaced alphanumerals, leading to data quality issues, and even incorrect data sets. 

The most effective way to minimize this issue is to minimize human effort while inputting data as much as possible. The use of AI is making automation more possible every day. The use of AI-based systems, as well as advanced algorithms, ensures that human error is minimized.

Inaccurate data

There’s no point in running enormous information analytics or contacting clients based on information that’s fair plain off-base. News can rapidly get wrong. By not gathering all the covered-up data, your information isn’t total and limits you from making choices based on complete and precise information sets. The more self-evident way for wrong information is information in frameworks filled with human botches, like off-base data given by the client or contributing subtle elements within the off-base field.

There’s no remedy for human mistakes, but guaranteeing you have clear procedures taken after reliably could be a great beginning. Robotization tools to decrease manual work when moving information between frameworks are also colossally valuable in reducing botches by tired or bored laborers.

How can DQLabs help your business with Data Quality Issues?

DQLabs can help solve these data quality issues by leveraging DQLabs’ augmented data quality platform that scans various types of data sources and data sets in real-time and generate a trustable DQScore™ with the ability to track, manage and improve data quality over time.

For your ease of understanding, listed down some of the advanced features of DQLabs Measure:

  • Out of the box Data Quality Measurement
  • Semantics-based DQ Visual Learning
  • Create and Integrate Issue Workflows
  • Create Domain Level Scoring
  • Create Complex Rules with Ease
  • Integration with other Data Catalog/Governance Platforms

Want to learn more about how DQLabs solves data quality issues with its ML and self-learning capabilities in detail? Request for a free demo.

Also, watch our on-demand webinar to learn more about how DQLabs uses AI and ML to manage data smarter and simplify the data management processes with just a few clicks.

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