Technology continues to innovate and provides business users today with so many good tools to provide more actionable insights to aid decision making than have been previously available.
While there are many top data analytics technology trend views and perspectives, this post comes at the trends from the business user perspective.
More insights/Less number crunching. Artificial Intelligence (AI) and Machine Learning is readily available and becoming smarter by the day. Both of these technologies have taken over much of the “manual” human effort tasks and analysis. They augment the human capabilities and deliver more “throughput” analysis and insights which enable the business analyst to focus on interpreting patterns and applying those insights to their business and product decisions. And, over time the business user can reap additional value since the AI/ML is adaptive and learns more and more over time – and can be tuned further to the specific business needs. This brings a more strategic focus to the business analyst role beyond the traditional “number cruncher” skill set.
Lessening need for coding skillset. Today’s tools are becoming more business user friendly and providing more context-driven and self-service analytics. The tools are also becoming more decision-centric and employing more no-code/low code capabilities so a broader base of business users can employ them, lessening the need for a deeper technology or coding expertise, with broadens the base of business users who can access, interpret and use data analytics.
Increased visualization uncovering more extensive insight opportunities. Context-enriched analytics built on graph and other visualization technologies helps identify patterns that were not easily identifiable previously. Deeper analysis using relationships between data points is now more readily available, augmenting the business user’s arsenal of information to generate relevant insights for decision-making. The visualization aids in both the analysis and also the reporting and communication of the implications of the insights and consequent decision-making.
Expanded collaboration. The proliferation of secure cloud and multi-cloud eco-systems enables collaboration between parties that could not easily or safely collaborate previously. This is both inside an enterprise and across enterprises. Vendors, suppliers, customers and end users have broader and deeper access to more data sets and information now. This data unification leads to more complete pictures for the business user to interpret, which increases an enterprise’s agility.
Heightened data-driven cultures. The pandemic and remote work accelerated the movement toward data-driven business cultures. This further enhances the value of business analysts and data scientists, and the measuring and tracking of data, behaviors and activities from a 360 perspective. Personalization will continue to evolve further and more dynamic and real-time insights will be generated for business users to interpret and leverage.
Data seen as an asset. Netting it out, companies are making more investments in systems and business users that help them mine their data better. Data is now seen as a significant asset that can help reduce costs, increase revenues, and improve decision-making. This higher prioritization gives the business user easier and more robust access to more relevant information for decision-making.
These trends increase the business user’s ability to more quickly and accurately manage their business and aid in improved decision-making, which leads to more successful business results.