In part 1 of this post, we explained what really is poor data and the many ways in which it can impact your business. Now, going ahead, we will learn how data quality can be preserved, and the role of artificial intelligence in helping preserve large data sets. Here’s how to tackle and prevent dirty data.
One of the first things any organization has to do to stop dirty data from getting into the system is to set up a data-friendly company culture. Whether yours is a big, medium, or small-sized Enterprise, it is very important to have a culture to encourage the proper use of data quality and for its 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 harmful effects 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. That’s also because the data gets connected from the master database across a company’s CRM, DRM, and other such services so data management requires a certain 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 relating to 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, after this too, you will need to continuously monitor and better the quality of incoming data.
HERE ARE SOME COMMON ROLES TO ENSURE MASTER DATA MANAGEMENT
There are 3 ways this can be bifurcated: You can have 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:
- Play a pivotal role in data domains
- Defines data requirements
- Preserves data quality and accessibility
- Decides who in the team gets what kind of access rights and
- 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 normally 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 next? Remember, a fruitful data quality and management project require 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 take care of this. Your designation team with the given infrastructure first needs to identify all the problem areas from where bad data is likely to come in. Remember, all this effort is towards establishing a single source of truth.
Thereon, your Enterprise will then have to develop a data quality program, and with the help of a data steward, apply the business processes that ensure all future data collection and use meet regulatory frameworks and eventually adds value to the business at hand.
The correct method of matching high data quality with technology is to integrate the different stages of the data quality cycle into operation procedures and tie them in with the individual roles.
Use of AI in manipulating large data sets
In an earlier post, we had written how with the entry of AI, data stewards could now use data cleansing and augmentation solutions based on machine learning (ML).
ML and deep learning allow the analysis of the collected data, making estimates, to learn and change as per the precision of the estimates. As more information is analyzed, so also the estimated progress.
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 deal with data integrity issues at the very start of data processing, and quickly convert such vast volumes of data into dependable information.
Tracking, analyzing, and correcting/updating incoming data will eventually help an Enterprise in making well-informed business decisions, providing a single source of truth, and eventually increased productivity.
An Engine That Drives Customer Intelligence
Oyster is not just a customer data platform (CDP). It is the world’s first customer insights platform (CIP). Why? At its core is your customer. Oyster is a “data unifying software.”
Liked This Article?
Gain more insights, case studies, information on our product, customer data platform