Current Top Trends in Data Analytics

Data analytics converts raw data into actionable insights. With data analytics, business users seeking to uncover key trends or solve problems have access to a wide range of tools, technologies, and processes. Over time, data analytics can improve decision-making, shape business processes, and accelerate business growth. 

With data playing an increasingly important role in business viability and success, enterprises are leveraging analytics in fresh and innovative ways. Here's a quick rundown of five current top data analytics trends. 

1. Cross-company data sharing 

It happens all the time. A business trying to develop a specific information solution stumbles upon a critical gap in what they know, creating a data gap. "Maybe they don't have data about actual product usage, and need to get it from a customer, or maybe they don’t have data about market behaviors, and need data from peers or other industry players," says Barbara H. Wixom, principal research scientist at the MIT Center for Information Systems Research (CISR), in an email interview. 

When a business fails to fill its data gap, the solution is doomed to fall short, since it's not being fed properly. "Data sharing involves using data from outside the company and/or providing data to other firms," Wixom says. She notes that interorganizational data sharing is essential for value creation in the digital economy. 

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2. Data-centric analytics 

The current top trend in data analytics, aside from AI, is data-centric analytics, states Shri Santhanam, executive vice president and general manager of software, platforms, and AI with consumer credit reporting company Experian North America, via email. "This trend focuses on leveraging high-quality, well-governed data as the central asset for driving analytics, modeling, and insights," he explains. "It emphasizes the importance of data management, integration, and governance to ensure that organizations can make the most out of their analytical capabilities." 

Data-centric analytics is crucial, since it highlights the need for reliable, accurate, and comprehensive data as the foundation for any analytical process, Santhanam says. "At a time when data volumes are growing exponentially, having a robust data management strategy ensures that businesses can derive actionable insights from their data." He adds that data-centric analytics also supports the democratization of data access across teams, making it more easily accessible to non-data scientists and allowing various enterprise departments to make informed decisions based on consistent and trustworthy data sources. 

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3. Integrating AI and ML into analytics frameworks 

One of the most impactful data analytics trends right now is the integration of AI and machine learning (ML) into analytics frameworks, observes Anil Inamdar, global head of data services at data monitoring and management firm Instaclustr by NetApp, an online interview. 

"We are seeing the emergence of a new data 4.0 era, which builds on previous shifts that focused on automation, competitive analytics, and digital transformation," Inamdar states. "This distinct new phase leverages AI/ML and generative AI to significantly enhance data analytics capabilities," he says. While the transformative potential is now here for the taking, enterprises must carefully strategize across several key areas. "How well they do this will dictate how successful they can be with data 4.0 in the near term." 

Inamdar believes that strong, forward-thinking IT leadership is needed to shift organizational culture away from legacy thinking and toward a more data-driven, innovative mindset. "Executives need to champion the adoption of AI/ML technologies for data analytics, ensuring that the entire organization is aligned with this vision." 

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4. Doubling down on data governance 

Data governance should be a top concern for all enterprises. "If it isn't yours, you’re heading for a world of hurt," warns Kris Moniz, national data and analytics practice lead for business and technology advisory firm Centric Consulting, via email. 

Data governance dictates the rules under which data should be managed, Moniz says. "It doesn’t just do this by determining who gets access to what," he notes. "It also does it by defining what your data is, setting processes that can guarantee its quality, building frameworks that align disparate systems across common domains, and setting standards for common data that all systems should consume." 

Driving the need for strong data governance is AI's rapid evolution. "Without a mature data governance practice -- and the supporting data management processes it dictates -- any attempt to broadly add new AI capabilities will result in massive collateral damage," Moniz warns. 

5. A growing emphasis on data quality 

One of the biggest trends in data and analytics is a growing focus on improving the data itself. "We know the greatest output comes from the highest-quality input, now we're seeing organizations work toward more comprehensive data being gathered and becoming the bedrock of better analytics," says Scott Chambers, director of analytics at NTT DATA Business Solutions, via email. "When the process of capturing that data is homogenized effectively, the results work better for everyone, especially with the recent emergence of AI in conjunction with analytics." 

When information lives in various places, often appearing in different ways, is when quality issues pop up, Chambers says. "We're noticing more people realize this fact and are taking a personal stake in improving data on the frontend instead of just analyzing issues on the backend," he states.