Happy New Year!
As we look back at 2022, we see plenty of bad news that disrupted business and economies: war in Ukraine, energy shortages, inflation, bigger natural disasters, broken supply chains, and monkey pox, for starters. Is crisis the new normal?
If COVID taught us anything, it is that we now must have actionable data and systems that allow us to pivot our business at a moment’s notice. Having a flexible information architecture that connects all of your disparate data to feed AI and operational decision making will let you react in a second when change is needed and prepare you for events you don’t expect.
These are the data and AI trends we think you should be watching and thinking about how to incorporate into your business strategy in 2023.
What’s the best way to use your data for quick wins with a positive ROI while simultaneously laying the analytics foundation for future business needs? Data-As-A-Product (we wrote about this in detail here) as an approach to data is picking up steam among customers we are talking to and helping them become data-driven. It’s neither Big Bang nor Grassroots, but with the Data-as-a-Product strategy you can access data in place with built-in protection, ensure governance and compliance that’s centrally defined but executed in a distributed way, and automatically funnel your data to wherever it needs to be. This lets you meet the challenges of exploding data volumes and types while supporting all major categories of analytics (operational reporting, data science, process automation, e.g.) and the specific way those categories need to collect, organize, and govern data on a single platform.
You don’t want incorrect insights delivered to your executives or used to make an automated decision. Data observability platforms collect metadata and build historical baselines of data pipeline behavior. When deviations occur, intelligent workflows remediate data quality workflow issues in-flight and keep SLAs on track. Data observability protects your data fabric and allows organizations to create trusted real-time data products.
Data browsing and self-service visualization platforms have made access to insights easier than ever. Not every worker is an analyst though, and many still need more than data and appealing dashboards to make an actionable decision. They also need narrative. When narrative is coupled with data, it tells a person what is important and why it is important. Data stories are enabled through platforms that use AI to explain to users what is in the visualizations and call out additional hidden connections and patterns in your data they may not have thought to look for by exploring.
Synthetic Data Proliferates
Whether you are a nanotechnology company trying to accelerate molecular optimization, or a community bank trying to build a fraud detection model, finding enough useful data to train an AI model can be problematic. In addition, new privacy laws and social calls for ethical AI make obfuscating data an imperative. Synthetic data can be the solution to both of those issues. Synthetic data is data that has not been generated from your real operational systems, but by technology that creates data by following your rules around data types and volume, the relationships between the data, and any transformations that are required. Gartner believes synthetic data will overshadow real data in AI models by 2030.
Which of these trends are you currently employing? Which ones are you planning on implementing this year?
If you are interested in learning more about how LRS can help you find value in your data using modern data architectures to implement advanced analytical applications, please contact us 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.