Looking to the coming year to predict what is in store for data-driven organizations, we take comfort in knowing that respected tech industry prognosticators see more investments and better results for organizations that emphasize analytics. In fact, Gartner says that, “Data and analytics (D&A) leaders are increasingly central to business strategy,” predicting that chief data officers who are able to deliver on the promise of data analytics “will significantly outperform their peers in driving cross-functional collaboration and value creation.”

Kyligence agrees, and from our position as originator of the Apache Kylin analytics engine and developer of the AI-augmented data services and management platform Kyligence Cloud, we have our own perspective. Based on conversations with our customers, partners, and an extensive network of subject matter experts, Kyligence believes five key areas of big data and analytics will build momentum to emerge as the biggest trends of 2023.

• Data & Analytics ROI is Key Metric

Data and analytics continue to be the focus of IT transformation for many organizations, but as the market evolves–and in an environment of economic uncertainty–most will demand that their investments in analytics show a path to a clear return on investment in the near-term.

Lessons learned during the pandemic and from the experiences of organizations that are already a good distance down the analytics road, show that producing faster, more accurate business intelligence is an attainable goal. That puts an emphasis on production rather than proof-of-concept, and ROI is a key metric for enterprises to consider before adopting any analytics platform or product.

• Analytics as a Service & BYOD

As the power of analytics becomes more obvious, SaaS analytics applications will emerge as a powerful option to get in on the action, especially for organizations without the human capital or data infrastructure to build and manage their own data operations. Analytics as a Service tools will allow organizations to operationalize their own data on sophisticated tools in the cloud, avoiding costly and time-consuming DevOps cycles.

As a result, Analytics as a Service will further democratize the power of data by delivering a faster time-to-insight while allowing organizations, departments, and even individuals to bring their own data to the application and focus on extracting its value rather than fussing with technology.

• Re-emergence of Key Performance Indicators

As more enterprises focus on IT operations and data efficiency, refined management has emerged as an important element of enterprise management. In turn, that has placed an emphasis on measuring and managing performance based on quantitative results. Because of this, key performance indicators (KPIs) will overtake objectives and key results (OKR) as a means of evaluating performance, and for driving decisions at the operations level.

That means goals must be translated and progress tracked using measurable data, including leading metrics that take lag into account so that adjustments can be made in near real-time as performance tracks to established KPIs.

• Dashboards are Dead

Experienced business intelligence and data engineers have long been aware that dashboards–often counted as a business asset–tend to generate a great deal of technical debt that accrues over time, hampering performance. That’s because, for all the ease-of-use glitz and democratization acclaim, dashboards are just dirty tools for connecting data silos.

With enterprises rushing to adopt public cloud applications and services, using business intelligence dashboards for managing processes and reviewing results will create more problems than they solve. But while BI dashboards die off, the metrics store concept will displace them as the preferred method for efficiently managing data on one platform across the entire enterprise.

• AI-Augmented Analytics will Enable Democratization

The democratization of analytics means unskilled data consumers across the enterprise are joining the data party. That’s a great thing… provided your data operations are prepared to manage the chaos.

Using artificial intelligence, data consumers can quickly discover insights like anomalies, root causes, and trends without the skills of a data scientist or engineer, adding value to your organization at every level. The secret to extending analytics to everyone without creating headaches is to augment your organization’s analytics processes with AI to empower users on the front-end, while driving efficiencies on the back-end.