Tag

governance

Browsing

Do you know whether your company data is clean and well managed? Why does it matter anyway?

Without a working governance plan, you may have no company to worry about – data-wise.

Data governance is a collection of practices and procedures establishing rules, policies and procedures that ensure data accuracy, quality, reliability and security. It ensures the formal management of data assets within an organization.

Everyone in business understands the need to have and use clean data. But making sure it’s clean and usable is a bigger challenge, according to David Kolinek, vice president of product management at Atacama.

This challenge is compounded when business users have to rely on scarce technical resources. Often, no one person oversees data governance, or that person doesn’t have a complete understanding of how the data will be used and how to clean it up.

This is where Atacama comes into play. The company’s mission is to provide a solution that even people without technical knowledge, such as SQL skills, can use to find the data they need, evaluate its quality, understand any issues How to fix that and determine if that data will serve their purposes.

“With Atacama, business users don’t need to involve IT to manage, access and clean their data,” Kolinek told TechNewsWorld.

Keeping in mind the users

Atacama was founded in 2007 and was originally bootstrapped.

It started as a part of a consulting company, Edstra, which is still in business today. However, Atacama focused on software rather than consulting. So management spun off that operation as a product company that addresses data quality issues.

Atacama started with a basic approach – an engine that did basic data cleaning and transformation. But it still requires an expert user because of the user-supplied configuration.

“So, we added a visual presentation for the steps enabling things like data transformation and cleanup. This made it a low-code platform because users were able to do most of the work using just the application user interface. But that’s right now.” was also a fat-client platform,” Kolinek explained.

However, the current version is designed with the non-technical user in mind. The software includes a thin client, a focus on automation, and an easy-to-use interface.

“But what really stands out is the user experience, made up of the seamless integration that we were able to achieve with the 13th version of our engine. It delivers robust performance that is crafted to perfection,” he said. offered.

Digging deeper into data management issues

I asked Kolinek to discuss the issues of data governance and quality further. Here is our conversation.

TechNewsWorld: How is Atacama’s concept of centralizing or consolidating data management different from other cloud systems such as Microsoft, Salesforce, AWS and Google Cloud?

David Kolinek: We are platform agnostic and do not target a specific technology. Microsoft and AWS have their own native solutions that work well, but only within their own infrastructure. Our portfolio is wide open so it can serve all use cases that should be included in any infrastructure.

In addition, we have data processing capabilities that not all cloud providers have. Metadata is useful for automated processing, generating more metadata, which can be used for additional analysis.

We have developed both these technologies in-house so that we can provide native integration. As a result, we can provide a better user experience and complete automation.

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

David Kolinek
David Kolinek
Vice President of Product Management,
atacama

Kolinek: Standardization is just one of many things we do. Typically, standardization can be easily automated, in the same way that we can automate cleaning or data enrichment. We also provide manual data correction when resolving certain issues, such as missing Social Security numbers.

We cannot generate SSN but we can get date of birth from other information. So, standardization is no different. It is a subset of things that improve quality. But for us it is not just about data standardization. It is about having good quality data so that the information can be leveraged properly.

How does Atacama’s data management platform benefit users?

Kolinek: User experience is really our biggest advantage, and the platform is ideal for handling multiple individuals. Companies need to enable both business users and IT people when it comes to data management. This requires a solution for business and IT to collaborate.

Another great advantage of our platform is the strong synergy between data processing and metadata management that it provides.

Most other data management vendors cover only one of these areas. We also use machine learning and a rules-based approach and validation/standardization, both of which, again, are not supported by other vendors.

Furthermore, because we are ignorant of technology, users can connect to many different technologies from a single platform. With edge processing, for example, you can configure something in the Atacama One once, and the platform will translate it for different platforms.

Does Atacama’s platform lock-in users the same way proprietary software often does?

Kolinek: We have developed all the main components of the platform ourselves. They are tightly integrated together. There has been a huge wave of acquisitions in this space lately, with big sellers buying out smaller sellers to fill in the gaps. In some cases, you are actually buying and managing not one platform, but several.

With Atacama, you can buy just one module, such as Data Quality/Standardization, and later expand to others, such as Master Data Management (MDM). It all works together seamlessly. Just activate our modules as you need them. This makes it easy for customers to start small and expand when the time is right.

Why is the Integrated Data Platform so important in this process?

Kolinek: The biggest advantage of a unified platform is that companies are not looking for a point-to-point solution to a single problem like data standardization. It is all interconnected.

For example, to standardize you must verify the quality of the data, and for that, you must first find and catalog it. If you have an issue, even though it may seem like a discrete problem, it probably involves many other aspects of data management.

The beauty of an integrated platform is that in most use cases, you have a solution with native integration, and you can start using other modules.

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

Kolinek: Machine learning enables customers to be more proactive. First, you’ll identify and report a problem. One has to check what went wrong and see if there is anything wrong with the data. You would then create a rule for data quality to prevent repetition. It’s all reactive and based on something being broken down, found, reported and fixed again.

Again, ML lets you be proactive. You give it training data instead of rules. The platform then detects differences in patterns and identifies discrepancies to help you realize there was a problem. This is not possible with a rule-based approach, and is very easy to measure if you have a large amount of data sources. The more data you have, the better the training and its accuracy.

Aside from cost savings, what benefits can enterprises gain from consolidating their data repositories? For example, does it improve security, CX results, etc.?

Kolinek: This improves safety and minimizes potential future leaks. For example, we had customers who were storing data that no one was using. In many cases, they didn’t even know the data existed! Now, they are not only integrating their technology stack, but they can also see all the stored data.

It is also very easy to add newcomers to the platform with consolidated data. The more transparent the environment, the sooner people will be able to use it and start getting value.

It is not so much about saving money as it is about leveraging all your data to generate a competitive advantage and generate additional revenue. It provides data scientists with the means to build things that will drive business forward.

What are the steps in adopting a data management platform?

Kolinek: Start with a preliminary analysis. Focus on the biggest issues the company wants to tackle and select platform modules to address them. It is important to define goals at this stage. Which KPIs do you want to target? What level of ID do you want to achieve? These are questions you should ask.

Next, you need a champion to drive execution and identify the key stakeholders driving the initiative. This requires extensive communication between various stakeholders, so it is important that one focuses on educating others about the benefits and helping the teams on the system. Then comes the implementation phase where you address the key issues identified in the analysis, followed by the rollout.

Finally, think about the next set of issues that need to be addressed, and if necessary, enable additional modules in the platform to achieve those goals. The worst part is buying a device and providing it, but not providing any service, education or support. This will ensure that the adoption rate will be low. Education, support and service are very important for the adoption phase.