AiThority Interview with Sean Knapp, Founder and CEO at Ascend.io

Hi Sean, what advice do you have for the CIOs and Chief Data Officers?

The year 2021 would be the defining year for chief data officers.

In years past, chief data officers, and therefore the teams they direct and influence, have approached their position from a very technical and tactical mindset – operating primarily as a cost center for the enterprise as they lay a foundation for the future. However, as data becomes more intertwined with the fundamental success of the business, CDOs must refocus their efforts on strategy and transformation of how the business interacts with and benefits from data, rather than the technologies employed along the way. I predict that 2021 will be the defining year for the chief data officer, where we will see the role take shape and truly establish whether they will go the route of a cost or profit center.

How do you see EETL / ELT models further revolutionizing the Big Data and Deep Learning industries?

In 2021, ETLT will emerge as the new standard for data engineering

Nearly a decade ago, we saw the introduction of modern big data technology, offering the tremendous promise of near-infinite scale and superior customizability, flexibility, and control. This rise of big data technology was underpinned by ETL (extract, transform, load) pipelines; however, ETL was primarily constrained to deep IT and engineering organizations, leaving out line-of-business users that needed faster access to data. With the rise of the cloud data warehouse over the last five years, ELT (extract, load, transform) pipelines surged in usage. This architecture won over huge amounts of business for its speed, ease of use, and ability to connect to various data sources. Despite these benefits, companies are still experiencing significant pain with ELT pipelines, including high costs, a lack of flexibility, and an inability to customize to meet each business’ specific needs. This created a dependency between business users, data scientists, and data analysts on the IT and engineering organization to grant data access and write and productionize custom data pipelines.

In the past year, we’ve seen a tremendous amount of uniform buy-in around the notion of self-service from both line-of-business teams and IT/engineering departments. Over the course of 2021, I anticipate this demand for self-service pipelines will lead to the rise of ETLT, a best-of-both-worlds approach that offers the same speed and ease of use of ELT, while also providing scalability and flexibility of ETL.

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What do you think about Metadata economy in 2021?

Metadata is big data

As digital transformation initiatives accelerated significantly in 2020, massive volumes of structured, semi-structured, and unstructured data have become scattered throughout the enterprise. However, Gartner predicts that through 2022, only 20% of the organizations that invest in information governance will succeed in scaling that governance. To achieve full governance and audibility, businesses are turning to metadata to provide deeper context into where data came from, the entire series of code that ran on it, and where it went. With this continuing surge of data and increasing governance requirements, organizations are realizing that the ability to track and automate the management of metadata is the new growing challenge. In the coming year, I anticipate that with the sheer volume of metadata continuing to increase, enterprises and vendors alike will be looking for new, scalable ways to solve the metadata challenge, and increasingly lean on AI to make sense of it all.

How complex are data science projects getting? How can Chief Data Officers ensure better operations in the enterprise set up?

With conflicting team priorities, data mutinies will be on the rise

Data teams today are on a collision course with conflicting priorities. For infrastructure teams, building for scale, security, and cost are of the utmost importance while engineering teams prioritize flexibility, development speed, and maintainability. Meanwhile, data scientists and analysts are focused on the availability and discoverability of data, and the connectivity of tools. As enterprises scale their efforts and their teams to build new data products, the interconnectedness and resulting complexity can be paralyzing for these groups. If organizations continue to cater to one group’s needs amidst these conflicting priorities, we can anticipate a rise of “data mutinies” in 2021 – in which internal users create their own engineering organizations with a mandate to move quickly and free themselves from these conflicts.

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Tell us more about “Flex code” in SaaS and Cloud business landscape.

The future of software is “flex-code”

In 2020, we saw the rise in popularity of low-code and no-code solutions, which leverage visual interfaces and abstract away the bulk of coding required to build logic inside of software applications. These tools democratize access to technology by bringing it within a broader reach of less technical individuals. Less code also results in fewer potential errors, making technology easier to maintain over time. However, businesses have come to find out there are often serious limitations with these tools as they frequently don’t offer the ability to go deeper and customize code when needed. This has diminishing returns on the developer experience and significantly limits the applicable use cases over time. In 2021, I predict that more enterprises will demand flexible coding options, or “flex-code,” to provide the benefits of low- and no-code solutions, but advanced customization and flexibility when needed.

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Thank you, Sean! That was fun and we hope to see you back on AiThority.com soon.