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Data Trends for 2022: How Many Have You Adopted?

Data drives business decisions. We know this. But a business’s ability to evolve with emerging data trends and practices can mean the difference between stagnation and innovation. Today, the way we live, work, and connect looks vastly different than it did a few short years ago. Since the onset of the pandemic, it’s become increasingly more important for organizations to embrace a data-first mindset leveraging the latest digital advancements.

Here are 5 data trends to keep in mind as you consider your data strategy for 2022 and the foreseeable beyond.

  1. Artificial Intelligence (AI)

Okay, so AI isn’t exactly a new concept. What is new, however, is the way AI is being used today. What was once considered an experimental technology is now a strategic priority for the most successful businesses. As more businesses leverage artificial intelligence to drive transformative customer experiences and real-time business decisions, a new era of AI development is being ushered in: AI 2.0. Addressing accuracy, speed, and security, this latest phase of AI is expected to deliver significant enterprise-changing benefits.

  1. Real-time data processing

In today’s digital world of instant gratification, businesses need to up their game when it comes to making real time decisions. Gone are the days of analyzing static data sets at specific points in time. In order to uncover important new patterns and seize opportunities as they arise to enable the transformative experiences that customers expect, businesses need to glean insights from streaming data in real-time. That’s where real-time data processing comes in. Faster and more accurate than batch processing, real-time data processing involves streams of data that are captured in real-time and processed with minimal latency to generate real-time (or near-real-time) insights or automated responses.

  1. Composable data analytics

A software-defined method of disaggregation, composable infrastructure makes all network, storage, and compute resources available over the data center network. With composable data analytics, organizations combine and consume analytics capabilities from various data sources across the enterprise to allow for broader user accessibility and more intelligent decision-making.

  1. Supply chain analytics

As supply chain challenges continue to dominate the world, businesses are faced with ongoing disruptions, lack of visibility into the supplier network, and challenges managing supply and demand. Given the prolonged supply chain challenges, tracking and managing supply chain data has evolved from a that of a tactical function to a strategic imperative. Moving forward, you can expect more businesses to step up their supply chain data management using data from sensors and other emerging technologies, AI, and machine learning to inform supply chain decisions that maintain business continuity.

  1. Predictive analytics and “The Great Resignation”

As I’ve discussed here many times, “The Great Resignation” continues to wreak havoc on the business community across the country. Today, some businesses are getting creative in how they use their data analytics to address the potentially crippling staffing issue. Specifically, they’re using predictive technologies to analyze data around job satisfaction, compensation, productivity, and other metrics to identify which employees may be on the verge of resigning—so they can take proactive steps to prevent it.

As the business landscape evolves and customer expectations grow, staying one step ahead in how you use data to power critical decisions is essential to maintaining a competitive advantage. But knowing you need to use data more intelligently and knowing exactly which steps to take on that journey are two different things. With a continued finger on the pulse of emerging technologies, our data experts can partner with you to offer guidance on next steps.


What if any of the above trends have you adopted in your business?