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The Rise of X Analytics in an Uncertain Economy

Last week, as part of the Great Lakes Digital Transformation Summit, I spoke about an interesting new trend called X Analytics. Gartner coined the concept as a sort of umbrella term referring to the ability to run analytics on a wide range of structured and unstructured data, and new data alongside old or core data. It’s about analyzing any data, no matter where or in what format that data resides.

 

Today this capability is more important than ever. The businesses that find a way to mine all of their data for insights – especially the newest data – are more likely to adapt to and even thrive in this unusual economy.

Behavior and data patterns have completely changed in the past 6 months

The pandemic forced massive change in consumer and organizational behavior. Retailers that relied on point-of-sale transactions for insight suddenly had little or no new data to query. Digital transformation accelerated across industries. Microsoft CEO Satya Nadella said his company had seen two years of digital transformation in just two months. The commercial banking industry witnessed mobile app usage spike by 80%. Across industries, all the work that was done analyzing behavior, creating machine learning models to detect new patterns, and running predictive analytics models was rendered irrelevant by the pandemic. As Angel Evan and Amber Rivera wrote recently in Harvard Business Review: “In our post-stay-at-home reality, companies need to recognize that their existing predictive models, forecasts, and dashboards may all be unreliable, or even obsolete, and that their analytic tools need recalibrating.”

The data that drove past insights isn’t entirely irrelevant now, but it’s nearly useless without data collected in the last six months. X Analytics is about giving business intelligence analysts and data scientists the chance to extract value from all enterprise data, matching the old against the new to understand how behavior has changed, what patterns have remained, and how to capitalize on these shifts to drive new business.

The "single-source-of-truth" data strategy is a roadblock to faster analytics

One of the roadblocks to X Analytics is that this data is rarely stored in a single place. Data might be spread across cloud data lakes, traditional data warehouses, and on-prem data lakes. This data might exist in completely different and often proprietary formats. A company’s foundational or core data will typically reside in a data warehouse, where it’s available for real-time access. The newer data, though, is often harder for data scientists and analysts to access and explore.

Until recently, most large companies tried to circumvent this roadblock by working to establish a single source of truth for their data. The idea was to move all your data – semi-structured, structured, unstructured – into one place, often a traditional data warehouse. This was great for the Oracles and Teradatas of the world. The more of your data converted to their proprietary format, the stickier you were as a customer. For enterprises, unfortunately, this translated into vendor lock-in and rising costs.

Today it’s all but impossible for a large enterprise with multiple data warehouses and data lakes to move all of its data into a single source. The data is streaming in too quickly, from too many new places, and it exists in too many different formats. But this single source of truth model is also unnecessary.

X Analytics addresses the need for speed

X Analytics is all about speed and timeliness. It’s about extracting maximal value from all your data as soon as possible, so your analysts and data scientists can make smart decisions today. One of our clients, a telecommunications giant, offers a great example. The company has data everywhere, from traditional warehouses to a growing cloud data lake, and they’re always looking for ways to better serve their customers and grow subscription revenue.

At one point, the Marketing department wanted to find a way to analyze a subscriber’s behavior to predict what they might enjoy, then offer them an upgraded package based on this prediction. To do that, the company had to tap into both basic user profiles and more recent behavioral data, such as which channels a given subscriber watched, explored, turned to, etc. These pools of data were stored in different formats and different locations. The traditional approach would have been to extract, transform, and load (ETL) one of these datasets into the other warehouse or cloud. When Marketing asked IT what it would take to make this happen, they were given an 18-month timeline.

Instead of establishing a single source of truth, we worked with the company to implement a query fabric layer that functions as a single point of access. This allowed them to start extracting insights in a matter of weeks. Soon they were able to drive net new subscription revenue from patterns picked out in this data – no ETL necessary. This is a perfect example of the power of X Analytics. The company found the hidden value in disparate, disconnected data sources, and did so quickly.

Your team needs access to new and newly critical data now

Every industry has been impacted by the pandemic, and X Analytics has been used to help organizations in Manufacturing, Financial Services, Healthcare, Government, and other industries query new data for the kind of insights that will help them adapt. Traditional retailers, for example, might not have as much point-of-sale data, but they can use X Analytics to analyze customer service interactions, website behavior, and more.

By moving from a single source of truth to a single point of access, you remove the pressure to hurry all your data into one location and one uniform format. All data – from foundational data stored in legacy warehouses to new data streaming into your cloud data lakes – remains in place, and business intelligence analysts and data scientists query it where it lies. They have consistent, unlimited access to all data.

This is X Analytics – with the variable being any data you wish to query. It’s going to shorten your time to insight. It’s going to help your organization adapt to this ever-changing, unprecedented economy. And we think it is going to be an increasingly powerful enterprise tool in the coming months and years.

Justin Borgman

Justin is the co-founder, Chairman and CEO of Starburst. Prior to founding Starburst, Justin was Vice President and General Manager at Teradata (NYSE: TDC), where he was responsible for the company’s portfolio of Hadoop products. Prior to joining Teradata, Justin was co-founder and CEO of Hadapt, the pioneering “SQL-on-Hadoop” company that transformed Hadoop from file system to analytic database accessible to anyone with a BI tool. Hadapt was acquired by Teradata in 2014.

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By | on 30, Sep 2020 |   presto Big Data

Last week, as part of the Great Lakes Digital Transformation Summit, I spoke about an interesting new trend called X Analytics. Gartner coined the concept as a sort of umbrella term referring to the a[...]

By | on 30, Sep 2020 |   presto Big Data

Last week, as part of the Great Lakes Digital Transformation Summit, I spoke about an interesting new trend called X Analytics. Gartner coined the concept as a sort of umbrella term referring to the a[...]

By | on 30, Sep 2020 |   presto Big Data

Last week, as part of the Great Lakes Digital Transformation Summit, I spoke about an interesting new trend called X Analytics. Gartner coined the concept as a sort of umbrella term referring to the a[...]