Clean data and governance planning business case

Do you know if your company’s data is clean and well managed? Why is it important anyway?

Without a practical governance plan, no company may be worried about data.

Data governance is a collection of practices and processes that establish rules, policies, and procedures that ensure data accuracy, quality, reliability, and security. Guarantees formal management of data assets within your organization.

Everyone in the business understands that they need to have and use clean data. However, ensuring clean and usable is a major challenge, said David Kolinek, Vice President of Product Management. Attack..

This challenge is exacerbated when business users have to rely on the lack of technical resources. In many cases, no one oversees data governance. Or, the individual does not fully understand how the data is used and how it is cleaned up.

Attackers play an active role here. The company’s mission is to find the data you need, evaluate its quality, understand how to fix problems, and determine if that data is useful for your purposes, without the need for technical knowledge such as SQL skills. Provides a solution that can be used for.

“With Ataccama, business users don’t have to involve IT in managing, accessing, and cleaning up their data,” Kolinek told TechNewsWorld.

With the user in mind

Ataccama was founded in 2007 and is basically bootstrapped.

It started as part of the consulting firm Adastra and is still in business. However, Ataccama’s focused on software rather than consulting. As a result, management has spun off its business as a product company addressing data quality issues.

Ataccama started with a basic approach: an engine that performs basic data cleansing and transformation. However, due to the user-provided configuration, this required an expert user.

“So we added a visual presentation of the steps that enable data transformation, cleansing, etc., which allows users to do most of their work simply by using the application’s user interface, making it a low-code platform. But it was still a sick client platform, “Kolinek explains.

However, the current version is designed with non-technical users in mind. The software includes thin clients, an automation-focused one, and an easy-to-use interface.

“But what really stands out is the user experience, which builds on the seamless integration achieved with the 13th version of the engine. It provides perfectly tuned and robust performance,” he said. Suggested.

Dive deeper into data management issues

We asked Kolinek to discuss more about data governance and quality issues. This is our conversation.

TechNewsWorld: How does Ataccama’s concept of centralizing or integrating data management differ from other cloud systems such as Microsoft, Salesforce, AWS and Google Cloud?

David Corineck: We are platform agnostic and not targeted to any particular technology. Microsoft and AWS have their own native solutions that work well, but only within their own infrastructure. Our portfolio is wide open to cover all use cases that need to be covered by any infrastructure.

In addition, there are data processing capabilities that not all cloud providers have. Metadata is useful for automated processing, and you can generate more metadata and use it for additional analysis.

We have developed both of these technologies in-house to provide native integration. The result is a great user experience and a lot of automation.

How is this concept different from the concept of data standardization?

David Corineck
David Corineck
Vice President of Product Management,

Corineck: Standardization is just one of many things we do. Standardization is usually easy to automate. This is the same way you can automate cleansing and data enrichment. You can also manually correct the data when resolving some issues, such as missing social security numbers.

You can’t generate an SSN, but you can figure out your date of birth from other sources. Therefore, standardization is the same. This is a subset of what improves quality. But for us, it’s not just data standardization. It is important to have high quality data so that the information can be used appropriately.

What benefits does Ataccama’s data management platform bring to users?

Corineck: The user experience is really our greatest benefit and the platform is ideal for handling multiple personas. Enterprises need to enable both business users and IT professionals when it comes to data management. It requires a solution for business and IT to work together.

Another major advantage of our platform is the powerful synergies between data processing and the metadata management it provides.

The majority of other data management vendors cover only one of these areas. We also use machine learning, a rule-based approach, and validation / standardization, which are often not supported by other vendors.

And because it’s technology agnostic, users can connect to different technologies from the same platform. For example, in edge processing, once you configure something in Ataccama ONE, the platform transforms it into different platforms.

Does the Ataccama platform lock in users the way proprietary software often does?

Corineck: We have developed all the core components of the platform in-house. They are tightly integrated. There has been a wave of major acquisitions in this area lately, with major vendors buying smaller vendors to fill the gap. In some cases, you may not actually buy and manage one platform, but buy and manage many platforms.

With Ataccama, you can purchase just one module, such as Data Quality / Standardization, and later extend it to other modules, such as Master Data Management (MDM). It all works seamlessly. Just activate the module as needed. This makes it easier for customers to start small and expand at the right time.

Why is the integrated data platform so important in this process?

Corineck: The biggest advantage of the integrated platform is that enterprises are not looking for a point solution to solve a single problem like data standardization. It’s all interconnected.

For example, to standardize, you need to verify the quality of your data. To do this, you first need to find and catalog your data. If there is a problem, it may look like an individual problem, but it may involve many other aspects of data management.

The advantage of the integration platform is that for most use cases, you have one solution with native integration and you can start using the other modules.

What role do AI and ML play today in data governance, data quality and master data management? How are you changing the process?

Corineck: Machine learning allows customers to be more proactive. Previously, we identified and reported problems. Someone needs to investigate what went wrong and see if there was something wrong with the data. Next, create a data quality rule to prevent recurrence. It’s all reactive and is based on something broken, discovered, reported, and fixed.

Again, using ML makes it proactive. Provides training data instead of rules. The platform then detects pattern differences and identifies anomalies that warn you before you realize there is a problem. This is not possible with a rule-based approach, and scaling is much easier if you have a large data source. The more data you have, the better your training and its accuracy.

Besides cost savings, what benefits can companies benefit from integrating data repositories? For example, will security and CX results be improved?

Corineck: It improves security and reduces potential future leaks. For example, there was a customer storing data that no one was using. Often they didn’t even know that the data existed! Not only are they now consolidating the technology stack, they are also able to view all the stored data.

With integrated data, it’s also much easier to onboard new people to the platform. The more transparent the environment, the sooner people can start using it to gain value.

It’s not about saving money, because it’s about leveraging all your data to create a competitive advantage and generate additional revenue. It provides data scientists with a way to build something that drives their business forward.

What are the steps to adopt a data management platform?

Corineck: Start with the first analysis. Focus on the biggest issues your company wants to address and choose a platform module to address them. At this stage, it is important to define your goals. Which KPI do you want to target? What level of ID do you want to achieve? These are the questions you need to ask.

Next, we need a champion to identify the key stakeholders who can move forward and drive the initiative. This requires extensive communication between different stakeholders. Therefore, it is important to have someone focused on educating others about the benefits and helping the team to participate in the system. This is followed by an implementation phase that addresses the key issues identified in the analysis, followed by rollouts.

Finally, think about the next set of issues that need to be addressed, and if necessary, enable additional modules on the platform to achieve those goals. The worst thing is to buy and provide the tool, but not the service, education, or support. This will reduce the adoption rate. Education, support, and service are very important to the recruitment phase. Clean data and governance planning business case

Back to top button