Why Businesses Need Data Reliability Engineering

Hitachi Vantara’s Pratik Dakwala talks data reliability engineering and how it helps businesses be proactive so they can detect data issues before they become critical.

Many businesses today say they are, or aim to be, data-driven. As they use more and more data sources and large volumes of data, data management tools only do so much. Increasingly, what’s needed are Data Reliability Engineering services and tools that ensure accurate and up-to-date data is available to users and applications.

Recently, RTInsights sat down with Pratik Dakwala, Sr. Marketing Lead for Data Reliability Engineering Services at Hitachi Vantara, to discuss the data challenges businesses face today, why data reliability engineering is so important, and how Data Reliability Engineering services help.

Here is a summary of our conversation.

RTInsights: Why is data reliability so important in business today, especially when it comes to helping them meet their goals as they seek to digitally transform?

Hitachi Vantara: There is quite a bit of confusion in the market or in some people’s minds. The usual argument is, why do I need data reliability when I have data management or data quality tools?

I would tell them that there is a difference in focus and techniques, and the scope is totally different. Although there are some similarities, there are differences. You need to be able to build an automated and optimized data ecosystem to improve data quality, reduce downtime, enhance data observability, strengthen security and compliance, and increase overall decision-making efficiency.

That’s where Data Reliability Engineering comes in. It focuses more on reliability and making sure that good, quality data is available consistently in complex data-driven systems. In contrast, data management’s focus is on the overall management of data throughout its lifecycle. It does not focus on quality. And so, that’s a fundamental difference.

And the goals are different. When we talk about data reliability, we are talking about reliable and scalable data that is available throughout the ecosystem and consistently to all data consumers.

You have to keep in mind that data is growing consistently, and it is being produced at a very faster speed. When you have that kind of situation, it is very important for data consumers to get access to the data in real time. You do not want to have them wait for anyone to look at it and ensure that the data is correct.

Additionally, the techniques offered by Hitachi Vantara’s professional services, methodology, tools, and frameworks through our unique approach to jumpstarting your data reliability engineering journey. In contrast, data management does not help in those areas and does not rely on these techniques. Data management tools focus more on data governance, data modeling, and metadata management. So, again, there are fundamental differences.

And coming back to your question, why is data reliability important to today’s business? I would say that businesses are facing phenomenal challenges. And for them to make better-informed decisions, they need reliable data. It is that simple.

First, they need access to reliable data to help them improve operational performance and reduce cost with effective data management. Because when we are looking at IoT devices or sensors, we are capturing the data. But if that data is not translated and not reliable, then any automation will not give good returns.

A second thing is a competitive advantage. By improving operational performance and reducing cost with effective data management, companies that have reliable data will definitely have a significant advantage over their competitors because they will be able to respond intelligently to changing customer needs.

The third thing is improved efficiency by reducing complexity, and scaling innovation with data observability across the entire data pipeline. All the ML models use that captured data. And if you have poor data, it’s garbage-in, garbage-out from the service models. And the ML models deteriorate. So, it’s very important to have reliable data to improve overall business and operations efficiency.

Fourth is compliance and security. Many industries have very strict regulations. In fact, nowadays, it’s pretty much all industries have their own regulations, as well as regional and global regulations. And so, for them, having reliable data ensures that they are compliant with all these regulations and don’t have to pay hefty fines or don’t end up in any kind of legal situation. And there is customer trust. That is the most important thing because customers expect companies to not only protect that data, but they are also now expecting more valuable insights from these companies. For example, if customer behaviors and preferences are changing, customers are expecting to be served personalized products and services. They don’t want to look into something different and waste their time. So, customer trust and customer experience are important.

Finally, there is increase enterprise efficiency by enabling managers to drive the business forward with self-serving, self-correcting data. If you have reliable data, then you can definitely identify potential threats. What happens if there’s a data downtime? What happens to your operations, or what happens to the customer-facing applications? I mean, recently, you probably have heard about Wells Fargo, where there was some technical glitch, and their applications went down, and people were thinking, “Oh my goodness, is this another Silicon Valley Bank?”

And so, with reliable data, you can take proactive measures and avoid such problems.

See also: Data Reliability Engineering: You Can’t Fly Blind in the Clouds

RTInsights: How do things like cloud migration and multi-cloud architectures make data reliability more challenging?

Hitachi Vantara: Moving to cloud or adopting a multi-cloud strategy involves multiple systems, applications, and data sources. And so this increases the complexity, making it challenging to ensure that reliable data is delivered.

There is the issue of data consistency. In multi-cloud environments, data is stored in different locations and accessed by different applications. How do you make sure that consistent data, or the same data, appears from different systems to the same application or different applications?

In cloud or multi-cloud environments, network connectivity and network latency play a major role in data reliability. What if a network experiences downtime or if there is congestion that leads to delays as data is being transferred? Applications or users reliant on the data may not have access to it or have the most up-to-date version of it.

Then there is the issue of data movement. Moving data from one environment to another when migrating can be complex. During that process, data can get corrupted, altered, or lost. If any of these things happen, decision-making based on the data will be incorrect.

RTInsights: How does Data Reliability Engineering help?

Hitachi Vantara: Data Reliability Engineering is a fairly new concept. Most companies deal with data management and data quality. They have tools to help in these areas.

But if you look at some of the pain points the data stewards, like the Chief Data Officers, who want to build a data culture, are facing, you see there needs to be something more.

For example, Data Scientists want to make sure that they are using the right data. But finding the right data and getting access to it is sometimes difficult. Similarly, the Data Governance Officer’s primary responsibility is to ensure that the data is properly governed. Everybody that needs access to the data has it. Those who don’t have privileges to the data cannot access it. 

And finally, there are the data engineers who, whenever something happens to the data, are going to be blamed. Sometimes, they are being asked to create data pipelines without even knowing what business problem they’re trying to solve.

There is a lack of recognition for all these data stewards. So, what do we do about all of this?

Data Reliability Engineering and Services is the answer. Data reliability helps these data stewards be proactive. For example, with proactive monitoring, they can detect issues before they become critical.

Data Reliability Engineering helps businesses consistently check if something is wrong with the data while the data is flowing or data is in motion. They can use self-healing capabilities. Data Reliability also helps reduce burnout by automating the tasks of backups and updates. It also helps with capacity planning because when the data is growing at a faster speed, understanding the current and future capacity needs is very important so that one can prevent data outages.

Data Reliability Engineering also has a role to play in disaster recovery planning. What happens when there is a catastrophe? Having disaster recovery planning under Data Reliability Engineering involves testing and refining the data all the time to help make sure something does not happen.

And then, there’s collaboration. All the different data stewards are working hard. And if they are not aware of the actual objectives, goals, or business problems they are trying to solve, they are doing something that will not help. As such, data reliability is a shared responsibility that creates the need for strong collaboration between different teams.

RTInsights: How does Hitachi Vantara help in this area?

Hitachi Vantara: At Hitachi Vantara, we know what it means to be data-driven because data is in our DNA. And so, we have seen the challenges data engineering teams go through. If something happens as planned, nobody cares about data engineers or data stewards. Nobody gets the credit. But if something goes wrong, they are the first ones to get blamed. So, there is a lot of pain and burnout. And then there is the lack of skills.

To address these issues, Hitachi Vantara has come up with Data Reliability Engineering services. We offer a comprehensive suite of services so businesses can derive the true value of their data.

If you ask whether a company’s data-driven, around 40 to 45% of the companies will say, “Yes, we are data-driven.” That’s fine, but what about reliability? Are you using reliable data? Are you getting the most out of the data? That’s where they will basically take a step back and start thinking.

And so, our suite of services helps businesses analyze their situation. Our Data Reliability Advisory Services help companies to see where they are in terms of current data state and discover compliance and security risks. We then help them understand their ability to detect and resolve any kind of data or data pipeline incidents faster than what they have right now. In some cases, they cannot.

Another offering is Hitachi Data Reliability Engineering Services. Once we have looked at the advisory services findings, we know where companies stand in terms of their data state and where they want to be. Based on that input, we can design a framework that addresses end-to-end data observability as well as governance.

And the customers can then use this blueprint to modernize their capability with self-healing engineering. With that, a problem is detected and fixed before the data consumer has a problem or an application goes down.

And then, our Implementation Service comes in. We provide the best tools and resources to help businesses manage their data effectively. We use AI and ML technology to deliver accurate, consistent, and secure data. That helps companies not only detect but also prioritize and isolate issues, which are then quickly resolved before they get out of hand.

We also have a dashboard that provides businesses with KPIs and self-service reports, so they can use lessons learned and information intelligence to improve operations. Additionally, we adopt a workflow that lets your data team gain end-to-end visibility. So, your data teams can overcome modern data management challenges and ensure that data is always reliable, secure, and available when needed.

RTInsights: What benefits are your customers realizing?

Hitachi Vantara: Consistent and accurate data is very important for data consumers. So, one tangible benefit of our Data Reliability Engineering services is improved data quality.

Another benefit is reduced downtime. Anytime any issue happens with data or data pipelines, there is a substantial amount of downtime. By implementing our Data Reliability Engineering practices, organizations can identify the issues and address them, reducing downtime or data loss.

We save time. Addressing data loss is a very manual effort. Data stewards end up spending tons of their time trying to fix and ensure that there is no data loss. With Data Reliability Engineering, we increase their productivity. And now, they can focus on something that they are hired for or something that they love the most.

Data Reliability Engineering allows for increased scalability. Data is growing at a fast speed, and Data Reliability Engineering helps make sure that data systems can handle the increased amount of data and traffic. Of course, improved security and compliance are other benefits that customers will enjoy.

And finally, there is disaster recovery. With Data Reliability Engineering practices, businesses can come up with a robust disaster recovery plan that can be executed quickly and effectively.

RTInsights: Any final thoughts on the topic?

Hitachi Vantara: Most of the time, data reliability is an afterthought, meaning something has happened, and then companies start thinking about it.

By optimizing data ecosystems with Hitachi Vantara’s help, you can improve data quality, reduce downtime, and enhance data observability while strengthening security and compliance. Being proactive in today’s business environment is very important. And with data observability tools and continuous monitoring, businesses can stay proactive and make sure that data consumers don’t experience data problems. With the same tools and services, data stewards don’t have to wait for data consumers to inform them that the applications are not working, or data is not correct, or whatnot.

So, when we talk about data reliability, we focus on data observability and the need to be proactive.

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