Today, organizations across all industries are struggling to achieve enterprisewide visibility and maximize use of data and related digital assets for business advantage.
Recently, Ben Sharma, co-founder and chief product officer of Zaloni, a provider of enterprise DataOps software whose flagship product is the Arena platform, spoke with BDQ about the challenges companies are facing in their efforts to gain more value from data.
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BDQ: The past year has been a really volatile time and it's put a lot of pressure on companies to be agile and flexible. What are the challenges that have emerged that you're helping companies navigate around?
Ben Sharma: There are several challenges that come to mind as we have worked with customers in various different verticals over the last several years and, more importantly, the last couple of months. One is that there are more data silos being created. As you think about the speed to execute, oftentimes, that actually means going as fast as you can and doing whatever you need to do to get the results or create the outcomes. And that's creating more data silos.BDQ: What is happening?
Sharma: Organizations that lack a data strategy for managing data across silos are struggling because once they create the data sprawl, they don't have the governance, they don't know how to manage data, and they don't know what data exists where. Having a single unified view of governance has become critical from our perspective and from what we're seeing from various customers and their use cases. Along with that, organizations that do not have a strong approach in terms of how they think about DataOps and automation are struggling because it just takes too long for them to get access to the data and to give data to the right people for the right business use cases. The two critical things are that, one, we see companies struggling with governance and then, two, we see organizations struggling with time-to-market or time-to-insight.
'Having a single unified view of governance has become critical from our perspective and from what we're seeing from various customers and their use cases.'
BDQ: What is Zaloni’s definition of DataOps?
Sharma: Our view is quite simple. DataOps has emerged as a discipline taking the learnings from some of the best practices in the DevOps world: having an automated approach in terms of bringing in the data and making sure the data gets validated, making sure that you can trust this data. And at the same time, there is governance, and other principles being applied to that data in an implicit way based on your data strategy and the policies that you have adopted within your organization. Those are the critical aspects of making sure that you have a DataOps mindset or approach.
BDQ: Automation and governance are the two critical pillars there.
Sharma: That's right. And when I say “automation,” it's not just moving data from point A to point B; it's also validating it, running your test cases, and making sure that they succeed before you promote from one environment to another environment, before you actually make the data available from one zone to another zone in a trusted manner for the rest of your data consumers. All of that is front and center in terms of a DataOps approach.
BDQ: Has the rise of hybrid architectures combining multi-cloud and on-prem deployments made data management more difficult for organizations?
Sharma: Absolutely. If you think about it, every cloud provider has their own way of doing things, which fits their use cases and how they're bringing their services to the market. Now if you're the customer and you're trying to do these things across multiple infrastructures and multiple platforms, you don't have a common way of thinking about data. You don't have a common way of thinking about security. You have a very fragmented approach and unless there is an abstraction layer that allows you to think about this in an organized manner, you have to build this in a very proprietary manner each time you are standing up these environments. That creates more challenges in terms of thinking about governance and compliance to various regulatory requirements that you may have depending on your industry. All of that adds and multiplies in terms of the challenges that you have to deal with as you manage data.'Organizations that do not have a strong approach in terms of how they think about DataOps and automation are struggling because it just takes too long for them to get access to the data, and to give data to the right people for the right business use cases.'
BDQ: What is at stake for companies that really don't take a comprehensive approach?
Sharma: To put it very simply, it's a question of survivability. How do companies survive, given that they have to adapt to change? In the past 12 months, we saw retailers whose approaches were no longer valid because they didn't have any traffic coming into their stores. Changing to an online model, accelerating in terms of digital transformation, and making adjustments sooner than later in a very kind of agile mode was critically important for these businesses to survive. What we see is that using data to make informed decisions so that you can retain and grow your customers is at stake. You must be able to use data effectively in a timely manner so that you can adapt to change and so that you can reinvent some of the business models.
BDQ: That brings us to the idea of data democratization which has been a major theme for Zaloni.
Sharma: At the highest level, what we mean by that is that companies need to be able to provide or enable access to data across their organization but do it in a meaningful way where they are providing the right data to the right people. As an organization, you also have responsibility to safeguard sensitive data. If there is PII data, you need to think about complying with CCPA- and GDPR-type regulations so that you're protecting the data, have role-based access control on the data, and are making sure that you're not letting the data be available in an ungoverned manner because that, from our perspective, reduces the trust in the data. You need to have an approach where you can say that this is the original data, which may or may not be trusted, but then do something to the data to apply checks and balances and make it more trusted so that as people in the rest of the organization consume it, they can know that this data has been approved by a centralized data authority.
Having that mindset and approach—and people, processes, and technology to enable that—is the first step in terms of going toward data democratization and making data widely available across the organization so that you can empower your employees to make critical business decisions based on the datasets that are available.