Top 5 Trends in Data Analytics That Will Impact Business Operations in 2023

The “cloud wars” have made way for the “data wars.” With the ongoing economic headwinds, enterprises are focusing on driving actionable insights using data, but leaders are facing tech investment fatigue. Today’s talent is being leveraged to deploy cloud investments.

In 2023, we anticipate that organizations will spend more time on data analytics. Here are the top five trends to watch for:

1. Digital immunity and data resiliency. To react swiftly to market changes, enterprises need to design resilient data environments, faster delivery cycles and superior employee experience. CXOs are saying their key objective in making digital investments is to improve employee experience. Resilient data environments can speed up the data-to-insights cycle and reduce data processing downtime. The result is enhanced employee experience (and thus, increased revenue).
2. People-based aspects of data analytics. A key challenge for organizations in 2023 will be figuring out how to go beyond technology and leverage people to gain competitive advantage. We expect effective use of “data executives” will be a differentiator. Organizations will need to promote disciplines like data culture, data mesh and data ops. Not only will this improve the operational efficiencies of the data executives, but it will help organizations retain their talent and intellectual property.
3. Vision AI. The ability to convert textual, graphical and audio data into actionable insights is now mainstream, but the same advances have not been achieved in video data. Edge technology and computer vision are making it easier to build vision AI tools. Some real-world applications are enabling law enforcement officers to pre-detect crime, helping senior citizens pre-detect eminent falls and helping professional athletes pre-empt accidents. Retailers have successfully dabbled with vision AI technology. We expect to see healthcare providers, government agencies and medical equipment companies benefit from vision AI as they use it to help them reduce human errors and generate business efficiencies.
4. Ethical and responsible AI. Effectively using first-party data (consumer data), second-party data (supplier data) and third-party data (paid databases) has always been a challenge. Add in data ethics (as they pertain to demographics and socio-economic factors), data regulations (including GDPR, CCPA) and data transparency (involving data sources and treatment) and now you have a complex problem to solve. Consumer sector and supply chain companies stand to benefit substantially from designing and implementing ethical and responsible AI tools.
5. Data lakehouse and self-serve analytics. Most organizations have already made heavy investments in technology, cloud platforms and digital systems. To get a higher ROI on these investments, companies need to combine data lakes and warehouses to form a data lakehouse that brings higher speed, storage and operational efficiencies. Real-time insights can be achieved when self-serve analytics tools are built on top of a data lakehouse. This exercise requires a redesign of the data architecture, which is an operational activity. Pharma, automotive and consumer sector companies with a large volume of data stand to benefit by operationalizing their data lakehouses.

ISG helps companies map, design and operationalize the best data analytics strategy for them. Contact us to find out how we can help.