Is Data Quality a nightmare for you? For your information, you are not the only one who might be afraid of data quality. Thousands of practitioners feel the same way. The main reasons are:

• 01

  Perception of opening a can of data issues that might be too much to handle

• 02

  It is not a priority until a fire breaks loose

• 03

  It is treated like a massive project

• 04

  External Subject Matter Experts are consulted

• 05

  Millions need to be budgeted

• 06

  Mammoth platforms must be implemented

• 07

  Numerous multi-year projects get ideated

Finally, the business team is asked to carve out time to identify the list of problems that might be there in their own data.

Does it have to be this difficult? No. Data Quality is not a project – it needs to become your routine (period).

Data Quality needs to be least threatening yet most effective – it must be non-invasive!

Introducing Non-Invasive Data Quality

In all organizations (big or small), data quality as a function already exists and always does. It might not have a formal designation and might not be as effective and efficient as required but it exists. It exists within the systems and within the users of those systems. Challenges arise when users make mistakes, the system does not have enough checks/validations, or data is ingested from other systems or external sources. In these situations, users have no way to identify and monitor the quality of their data – hence the need for a solution that blends with the user’s activities and constantly identifies and alerts the user of the issues in their data. Suddenly this concept transforms a business user to also become a Data Stewart for their own data. Now, data quality becomes their daily routine activity without making any significant change in their behavior or skill – making them yet more efficient and accountable.

Key Attributes of Non-Invasive Data Quality

• 01

  Should be a Routine Activity instead of a project task

• 02

  No additional system training should be required. Even if required, it should be simple and intuitive for the business user.

• 03

  No separate team should be set up – data users should become the Data Stewarts

• 04

  Data users should be the first one to learn about the issues in their data

• 05

  Data users should be alerted promptly and be specific about the data issue that was created or generated.

If possible, data users should be suggested on the fix required that the user should be able to correlate directly with their business activities that were performed recently.

Tenets of a Non-Invasive Data Quality Solution

A perfect Non-Invasive Data QualityTM Solution needs to work like a spy that is set up to continuously work in the background to find the needle in the data stack. Such a solution should have the following characteristics:

• 01

  A standalone system that can access all or required data sources

• 02

  Implementation should be a breeze (same day not a 3–6-month project)

• 03

  Minimal to no technical skill should be required to setup, configure, and start running the solution analysis

• 04

  Discovery analysis should require minimal human intervention. Most of the rules should be automatically extracted by the solution

• 05

  The solution should be able to identify the critical data elements in a data source, but the user should be able to override its interpretation

• 06

  The system should be able to automatically generate rules that are in the data without any human guidance

• 07

  The system should be able to identify anomalies in the generated rules and also

• 08

  Business-specific rules should be easily set and configured

• 09

  Continuous monitoring should be available that should alert the user of any issues that were identified in the latest processing

• 10

  User should be able to monitor logs to understand if certain issues are repeating regularly or if there is any trend in the process

Dashboard to track and report data quality should never exist

Could we encourage a working environment where Data Quality management is redundant? If data quality becomes a habit, then data quality remediations should become “auto-correct”. In such a situation, your need to continuously track and police data quality issues becomes redundant. Escalating these as issues become irrelevant. And suddenly Data Quality becomes a Routine and not a forced externally enforced activity.

Non-Invasive Data Quality Solution is a win-win outcome for the employees and the corporations

Good Data Quality is a need for every corporation, but the cost of implementation (in terms of money, resources, and time) is so big that most corporations shy away from it or deprioritize it. Non-Invasive Data QualityTM Solution enables corporations to take this step forward quickly with minimal business or financial impact. This is a big WIN for the corporations.

On a similar note, Non-Invasive Data QualityTM empowers Data Users and Employees to become accountable for the quality of their data with minimal to no changes to their daily tasks and activities. That means no extra project to get involved in; no extra hours to burn; and no new systems to learn or train on. This is a huge WIN for the employees.

RoutineAI is one such tool that is built for Non-Invasive Data Quality.