Data analytics converts raw data into actionable insights. Through data analytics, business users looking to uncover key trends or solve problems have access to a wide range of tools, technologies and processes. Over time, data analysis can improve decision-making, shape business processes, and accelerate business growth.
As data plays an increasingly important role in business viability and success, businesses are leveraging analytics in new and innovative ways. Here’s a quick overview of the top five current trends in data analytics.
1. Data sharing between companies
This happens all the time. A company attempting to develop a specific information solution encounters a critical gap in its knowledge, creating a data gap. “Maybe they don’t have data on actual product usage and need to get it from a customer, or maybe they don’t have data on market behaviors and need data from their peers or other industry players,” says Barbara H. Wixom. , a senior researcher at the MIT Center for Information Systems Research (CISR), in an email interview.
When a business fails to close its data gap, the solution is doomed to failure because it is not powered properly. “Data sharing involves using data from outside the company and/or providing data to other companies,” explains Wixom. She notes that interorganizational data sharing is essential to creating value in the digital economy.
2. Data-centric analytics
The biggest trend in data analytics today, besides AI, is data-centric analytics, Shri Santhanam, executive vice president and general manager of software, platforms and technology, said via email. ‘AI from consumer credit reporting company Experian North America. “This trend focuses on leveraging high-quality, well-governed data as a central asset to drive analytics, modeling and insights,” he explains. “It highlights the importance of data management, integration and governance to ensure organizations can get the most out of their analytical capabilities. »
Data-centric analytics is crucial because it highlights the need for reliable, accurate and comprehensive data as the foundation of any analytical process, says Santhanam. “In an era where data volumes are growing exponentially, having a solid data management strategy ensures that businesses can derive actionable insights from their data. He adds that data-centric analysis also supports the democratization of access to data within teams, making it more easily accessible to non-data scientists and allowing different departments within the company to take informed decisions based on consistent and reliable data sources.
3. Integrating AI and ML into Analytics Frameworks
One of the most notable current trends in data analytics 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 company Instaclustr by NetApp, during an online interview.
“We are seeing the emergence of a new era of Data 4.0, which builds on previous changes focused on automation, competitive analysis and digital transformation,” says Inamdar. “This new, distinct phase leverages AI/ML and generative AI to significantly improve data analysis capabilities,” he explains. Although the potential for transformation is now within reach, businesses must carefully strategize in several key areas. “How they achieve this will determine their success with Data 4.0 in the near term.”
Inamdar believes that strong, forward-thinking IT leadership is necessary to shift the organizational culture from traditional thinking to an innovative and more data-driven mindset. “Leaders must drive the adoption of AI/ML technologies for data analysis, ensuring the entire organization is aligned with this vision.
4. Double down on data governance
Data governance should be a top concern for all businesses. “If it’s not yours, you’re headed for a world of hurt,” Kris Moniz, national data and analytics practice lead for business and technology consulting firm Centric Consulting, warns via email.
Data governance dictates the rules by which data should be managed, Moniz explains. “It’s not just about who has access to what,” he notes. “It also does this by defining what your data is, defining processes that can ensure its quality, creating frameworks that align disparate systems to common domains, and establishing standards for common data that all systems should consume.”
The rapid evolution of AI makes strong data governance necessary. “Without a mature data governance practice – and the data management processes it dictates – any attempt to broadly add new AI capabilities will result in massive collateral damage,” Moniz warns.
5. A growing focus on data quality
One of the biggest trends in data and analytics is the growing focus on improving the data itself. “We know that the greatest output comes from the highest quality input. We now see organizations working to collect more comprehensive data and become the foundation for better analysis,” says Scott Chambers, director of the analysis at NTT DATA Business Solutions, via e-mail. “When the process of capturing this data is effectively homogenized, the results work better for everyone, especially with the recent emergence of AI in conjunction with analytics.”
According to Chambers, when information is in different places, often appearing in different ways, quality problems arise. “We’re seeing more and more people realize this and become personally engaged in improving data on the front end instead of just analyzing problems on the back end,” says he.
