Here’s How to Tackle Your Organization’s Dirty Data

One of the first things any organization must do to prevent dirty data from entering the system is to establish a data-friendly company culture.

Whether yours is a large, medium, or small-sized Enterprise, it is imperative to have a culture that promotes data quality and analytics.

But the term ‘culture’ can be vague and intangible. So here’s a quick definition – the Enterprise head honchos need to constantly talk to employees at all levels about the use of data analytics and its benefits.

This should also include getting across the importance of accurate data and the impact of dirty data.

What’s more, roles and tasks have to be assigned to designated members of the team; more so to those who will be responsible for ensuring the consistent accuracy of all incoming data.

Data quality and its importance, and its link to data management, cannot be stressed enough.

Managers need to constantly emphasize to all team members that to make the right data-driven decisions, the first and foremost task is to get the correct data in.

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That’s also because the data is connected from the master database across a company’s CRM, DRM, and other such services. Hence, data management requires a certain level of consistency across all. Wrong or poor data can even become dangerous for an Enterprise’s survival.

By now, you would have also understood that monitoring incoming data for inconsistencies or other errors is not a one-off thing. It is always a priority.

Once you have done all of this and are confident that the message on dirty data and its avoidance has gone down the file and rank, you can go ahead and make your investment in creating the processes, including the software, and assigning the people to man them.

Yet do not forget that, after this, you will need to continuously monitor and improve the quality of incoming data.

Here are Some Common Roles to Ensure Master Data Management

There are 3 ways this can be structured: a 3-tiered structure comprising a data owner, a data steward, and a data manager. At first, these may sound like overlapping or similar roles, but they are not.

For example, here’s what the data owner does:

1. Play a pivotal role in data domains
2. Defines data requirements
3. Preserves data quality and accessibility
4. Decides who in the team gets what kind of access rights and
5. Permits data stewards to manage data

So the data owner operates on a macro level in the ecosystem.

On the other hand, the data steward is actually the one who lays down the rules and the plans and then coordinates data delivery. He is the operations guy.

Last in this change is the data manager. This guy operates at a micro-level in the Enterprise. He usually coordinates between the data owner and the technical part of implementing the plans.

Now that you have the systems, procedures, and manpower in place, what's next? Remember, a fruitful data quality and management project requires a holistic approach.

This is where the ‘How’ part comes in – how to go about ensuring consistent good data?

To determine the quality of data, here are some aspects to look out for:

Accuracy, completeness, adherence to standards, and duplication. A combination of IT software, hardware, and human resources will handle this.

Your designated team, with the given infrastructure, first needs to identify all problem areas where insufficient data is likely to enter. Remember, all this effort is towards establishing a single source of truth.

Thereon, your Enterprise will have to develop a data quality program and, with the help of a data steward, apply business processes to ensure that all future data collection and use meet regulatory frameworks and eventually add value to the business at hand.

The correct method for aligning high data quality with technology is to integrate the different stages of the data quality cycle into operational procedures and tie them to individual roles.

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Use of AI in manipulating large data sets

In an earlier post, we wrote that with the advent of AI, data stewards could now use machine learning (ML)-based data-cleansing and augmentation solutions.

ML and deep learning enable the analysis of collected data, making estimates, learning, and adapting as per the forecast's precision. As more information is analyzed, the estimated progress is updated.

While identifying where your data is lacking or erroneous, large data sets always present a problem. How do humans track say a million data points? And say, in real-time? But with ML getting into the mix, that hurdle, too, can be surmounted.

AI can be used to detect anomalies in data sets by being “trained” to continuously track and evaluate data, even as the data is being processed.

What is even more important is that an ML solution can detect and address data integrity issues at the very start of data processing and quickly convert vast volumes of data into reliable information.

In conclusion:

Tracking, analyzing, and correcting/updating incoming data will help an enterprise make well-informed business decisions, provide a single source of truth, and increase productivity.

Handling dirty data in analytics isn’t simply a technical fix—it’s a strategic need.

Understanding how to clean dirty data not only improves your analytics—it lets your team act with confidence and saves time.

References:

How to Clean Dirty Data – The Life of a Data Janitor

Data Quality