Levi, Ray & Shoup, Inc.

Top analytics trends for 2020

1/22/2020 by Steve Cavolick

By Steve Cavolick

Happy New Year! Analytics is changing fast and 2020 is already shaping up to be an interesting year. Many styles and facets of analytics are starting to merge, making data easier than ever to manage and analyze, while creating even more power for decision making.

Without further ado, here are the top trends that we believe will influence analytics in the coming year.

Augmented Analytics

It’s no longer enough to design and deploy colorful dashboards and reports to keep your business ahead of the competition and market trends. From the same interface, you need to understand the weight of correlations, see connections that are not overtly apparent, and predict outcomes. You don’t have a data scientist on staff yet? No problem! While enhancing traditional BI with AI that can be used by the masses is a giant step in the right direction, augmented analytics will never completely replace the role played by an actual data scientist. Analytics leaders such as IBM and SAS are at the forefront of this merging of capabilities.

A Quickly Rising Tide Of Graph Databases

“Only through our connectedness can we really know and enhance the self” is a saying attributed to Harriet Goldhor Lerner. Connections do enhance the “self” (think about your Facebook and LinkedIn networks), but understanding your connections and the strength of those relationships is valuable to companies who make recommendations to you or want to prevent fraud. That’s where graph databases come in. Due to some design tenets we won’t go into here, they are designed to understand and traverse relationships. They don’t replace a data warehouse on a traditional RDBMS or a Hadoop cluster, but they do complement them in some specific use cases. Organizations such as Walmart (recommendation engine), Pitney Bowes (master data), NASA, and eBay (AI) are already using graph databases in production today.

Natural Language Processing (NLP)

NLP and augmented analytics are complementary. Providing search criteria to find content in BI platforms has been around for years, but now that capability is graduating to allow simpler interaction with data by typing questions in the spoken language of the user. Not every person is comfortable creating a database query, and having a BI platform narrate the interesting discoveries in a dashboard, or being able to type a question to the BI platform helps democratize corporate data even more.

Model Ops

The goal of DevOps is to automate and monitor the delivery of software products, resulting in better repeatable process while increasing human productivity. Combining AI with DevOps will help development teams become even more successful through better monitoring of their applications. Software generates tons of data in the form of performance metrics and log files. Using machine learning to sort through this data, spot problems, and proactively alert team members with recommendations on fixes, provides the continuous, automated feedback loop that is a tenet of DevOps.


Blockchain is digital storage where transactions are put into blocks and appended to a chain of existing records. Each transaction is duplicated across an open network, so everyone involved with the data sees updates in real-time, and each update is validated with a public verification process that guarantees its accuracy. Blockchain includes a layer of cryptography that makes tampering with the data very difficult because you would have to hack the data in every block where it exists, and in all locations for every user. From an analytics perspective, this is mostly about providing trusted, immutable data that can be moved into a traditional analytics platform and analyzed to look for relationships between the blockchain and other data sources, discovering fraud, or fighting money laundering. Amazon Web Services and other RDBMS are positioning ledger databases as parts of their database platforms now, so this type of environment will soon be more common in platforms many people already use.

The LRS Big Data and Analytics team has over 20 years of experience in analytics, information management, and data warehousing. If you are interested in discussing current trends in analytics and understanding how we can help you find value in your data, please fill out the form below to request a meeting.

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.