TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

CEO Perspective: Future Trends in BI and Analytics

What are the big trends enterprises face in BI and analytics? Satyen Sangani, CEO and cofounder of enterprise data catalog vendor Alation, explains why he thinks machine learning, data literacy, data governance, and data catalogs are so important right now.

• By James E. Powell
• July 27, 2020

Upside: What technology or methodology must be part of an enterprise's data or analytics strategy if it wants to be competitive today? Why?

For Further Reading: The Broad New Powers of Modern Data Catalogs How Data Catalogs Accelerate Decisions, Boost Productivity Making Applications More Intelligent and Protecting Sensitive Data in 2020

Satyen Sangani: Driving data culture is consistently ranked as a top chief data officer (CDO) priority, and data catalogs are the key to driving data culture. Data catalogs serve as a platform for a broad range of data intelligence solutions, from search and discovery to data governance and digital transformation. Data catalogs drive a virtuous cycle of collaboration where the more the catalog gets used, the better it gets, and the more people want to use it. That breaks down organizational silos and fosters a culture where users think "data first," resulting in more informed, data-driven decisions.

What one emerging technology are you most excited about and think has the greatest potential? What's so special about this technology?

Machine learning (ML). We're at the dawn of the age of machine learning. We originally used machine learning to improve data search and discovery in a data catalog, allowing us to sort results in relevance order determined not by keyword matching or through manual creation but by actual data usage.

This was a big breakthrough because in a world of too much data, it's not good enough to say, "you can find customer data in these 20 places." That's the wrong solution. You want to solve for "which of the 20 places that have customer data is the best source for analyzing customer profitability?"

We then applied ML to data stewardship -- deciding what data to assign stewards based on popularity and which stewards to suggest based on usage. You can apply machine learning to 100 other data problems, including data quality, metadata management, cloud migration, active metadata, decision intelligence, and digital transformation.

My theory is simple: in a world of exploding data volumes, manual curation will never be able to keep up, so you need a data platform that leverages machine learning across the board.

What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?

Making rational decisions based on data. To do so requires three things: data search and discovery (so you can find the data you need), data literacy (so you can properly interpret and analyze the data you're using), and data governance (to ensure both the quality of the data you're using and your policy-driven rights to use it).

Each of these three challenges is way harder than meets the eye. Finding data, as we've discussed, has a poverty of riches. Data literacy is hard and involves everything from training people to use data and avoid cognitive biases in analyzing it, to collaboration support where you can get people working together to increase their joint fluency in the data and how to make decisions based on it. Data governance is essential; using data when you're not authorized is a big problem. If you're making decisions based on incorrect data, it's potentially an even bigger problem. All of this is hard.

Is there a new technology in data or analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?

The cloud is creating challenges and benefits on many fronts, data being no exception. The goal of any cloud migration is to host applications and data in the most effective IT environment possible. Enterprises must embrace the move, which requires a well-thought-out strategy that can be accelerated with the use of data catalogs. Data catalogs enable enterprises to quickly identify and prioritize popular data sets for cloud data migration. Ultimately it is a combination of technology and approach that turns the challenge into a long-term benefit to the business.

What initiative is your organization spending the most time/resources on today?

We believe in "drinking our own champagne." To carry through on this belief, Alation employees, also known as Alationauts, leverage an internal instance of the Alation Data Catalog called A@A. It allows employees to find, understand, trust, use, and reuse data and information that exists within Alation. It serves as a central hub for data discovery and solves for siloed functional knowledge through cataloging, documentation, and cross-functional collaboration.

All pillars of data management that serve our customers so well are utilized internally. By using our own product, Alationauts come to better understand product nuances and make them more impactful. It also empowers employees to be more involved in the business and to have a voice.

Where do you see analytics and data management headed in 2020 and beyond? What's just over the horizon that we haven't heard much about yet?

I see analytics and data management moving beyond the typical data user to a larger scope including all business users. This move coincides with businesses becoming more aware that data, and the need to understand it, is becoming pervasive to every role and decision within an organization.

Our priority at Alation is to broaden the audience for data and enable people within an enterprise to be proficient in their ability to access, use, and understand data. We work hard to empower people who are less technically skilled to embrace their data. As participation in data initiatives and the use of technologies that enable data-driven decisions increases, the user community, technology, and conversations get richer, attracting more participants, yielding increasingly impactful insights.

Describe your product/solution and the problem it solves for enterprises.

Alation is a pioneer in the data catalog market, leading the evolution of data management toward driving data culture. With our powerful Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation supports data intelligence solutions by combining machine learning with human insight to tackle the most demanding challenges in data management. Nearly 200 organizations drive their data cultures and improve their organizations' decision making with Alation including DraftKings, Exelon, Finnair, Genentech, GoDaddy, Marks & Spencer, Mercado Libre, Munich Re, New Balance, New Relic, Pfizer, Scandinavian Airlines, Scout24, and US Foods.