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Four Analytics and AI trends to watch in 2021

1/28/2021 by Steve Cavolick

By Steve Cavolick

Happy New Year! The start of a new calendar year is the perfect time for self-evaluation and goal setting. With that in mind, we begin each year with a blog that showcases technology trends in analytics that can help you better leverage all of your data.

But this year is a little different. The pandemic is still raging and vaccine rollout is slower than expected. This means that companies are still impacted by more than technology. New business models, new ways of working, and new ways of finding and satisfying customers are all part of the new normal that is here to stay. AI is also here to stay.

With that in mind and through a lens of analytics, LRS is expanding its trends discussion beyond technologies to also include leadership, society, and the economy.

Without further ado, these are the trends in 2021 we think you should be watching and thinking about how to best incorporate them into your business strategy.


The shift towards automation and online activities happened quicker this past year than ever before. AI is the engine for efficiency and will be able to do some activities faster and better than humans. While a study from McKinsey & Company suggests that worldwide, 60% of all jobs will see at least 30% of their main tasks automated by 2030, newer jobs around AI will also be created. The companies that prepare for this accelerating trend will be the ones that outperform their peers in the re-engineered future. Before deploying your AI models, though, you must have a rock-solid information architecture.

Changing Consumer Behavior

How many packages were delivered to your house today? Spurred by the pandemic, online shopping exploded by 20% in 2020, accelerating the adoption of eCommerce by 5 years. The biggest shift in consumerism has been the role e-commerce now plays in attracting and growing customers. Retailers must not only have competitive pricing, but their websites and apps must provide the fastest search, easiest payment process, a trusted recommendation engine, and the ability to chat 1:1 in real-time to assist customers. Besides that, you must also collect data that measures customer experience. All of these capabilities depend on highly organized and trusted data on the back end.

Evaluation Of Success Metrics

Before COVID, were you able to measure your financial success, risk, and vulnerabilities with ease? When the pandemic hit, you may have been forced to re-engineer your supply chain or find new markets. It is paramount to maintain corporate concentration on these changes over a multi-year period. Determining targets, success criteria, and milestones is critical for each new initiative. It’s also a perfect opportunity to add new metrics around the areas of safety, environmental responsibility, and performance in underserved communities. Analytics platforms that allow you to see, react, and predict will help you measure success and stay on track.

Data Ops

Data Ops is the orchestration of people, process, and technology to automate the preparation and availability of data for analytics and AI applications. When your data is governed properly, you can comply with governmental and privacy regulations, and ensure that your AI models are accurate by monitoring data bias. These automatic data pipelines run data validation rules at ingestion, and metadata catalogs use Machine Learning to automatically discover and classify data, score its quality, generate a business glossary, and dynamically mask sensitive data.

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.