4 Trends Powering the Future of Data Analytics

Data analytics as a whole is the art and science of transforming information in its raw form into actionable insights. Demographic, behavioral and psychographic data about consumers, for instance, has limited use unless you can refine, study, parse and model it so that it provides information to help you better market to them, retain their loyalty and encourage them to spend more money with you.

How Has Data Analytics Evolved Over Time?

Data analytics was initially restricted to only capturing data from the past — annual sales volume and year-on-year revenue growth, for example. But it struggled to look ahead to the future and translate that historical data into strategic next steps.

Technological advancements soon enabled statistical modeling and led to what was perhaps the first analytics trend: predictive analysis, which helped organizations forecast future outcomes.

Then came generative AI (GenAI), which doesn’t just interpret existing data but also creates new content from it. GenAI enabled prescriptive analytics, going beyond predictions to recommend actions.

Given this trajectory, the future of data analytics promises to extract even more actionable insights from ever-increasing amounts of data to enable even quicker, more accurate decision-making.

What Trends are Shaping the Future of Data Analytics?

To paraphrase Ferris Bueller, data analytics moves pretty fast. Being alert to emerging trends in data analytics and how they can improve your business gives you a significant competitive edge. Our data and technology experts cited several trends in particular that are determining the future of data analytics.

1. AI-driven automation

Automation in data analytics is nothing new. Before AI, however, humans needed to ensure the data was structured within the confines of a predetermined format, and analysis was confined to rules-based systems, typically to answer well-defined queries. Now, AI-powered models “learn” from previous use cases to better identify patterns and hone algorithms for continual improvement. This enables faster, more accurate outcomes with a minimum of manual queries or assistance.

As GenAI becomes more fully integrated into tech stacks and operations, automation of even more tasks — from schema matching to feature engineering to creation of data visuals — will become the rule rather than the exception. A subtrend is data-centric AI, in which the technology concentrates at least as much on improving the data sets and the data within them as it does on refining its models and features.

2. Democratization of data

No longer will marketers, operations managers, merchandisers and members of other teams have to request data reports and interpretations of those reports from the IT and data teams. The future of data analytics will break down the barriers between data experts and end users by empowering the latter with the necessary data tools and training.

Data-as-a-service, MLaaS and other SaaS offerings are playing a huge role in providing cost-effective access to intuitive, low- and no-code apps and dashboards. With non-tech employees eliminating the middleman and creating their own reports, decision-making becomes more immediate. Just as important, when the end users are able to experiment with the variables, parameters and data that are their expertise, they’re apt to come up with innovative ideas and solutions they, or the data teams, might not have otherwise.

3. Greater emphasis on data governance, ethics and transparency 

One recent study reported that only 32% of organizations had a formal data governance program in place; another found that 71% do. Neither percentage is high enough — which is why prioritizing data governance, ethics and transparency is among the emerging trends in data analytics.

Keeping track of global, national and local legislation surrounding data, consumer privacy and AI ethics is already challenging, and governing bodies continue to introduce and amend regulations as the industry continues to change. Data democratization adds to the need for greater governance: as more people gain access to data, the risk of data breaches and misuse rises.

Skepticism and mistrust among consumers is also fueling this trend. A survey found that 87% of consumers want more control over how their data is collected and used. This ties in with an ongoing macroshift of institutional mistrust. To regain consumer trust and loyalty, the future of data analytics will see organizations becoming more open about their practices, even using this transparency as a competitive advantage.

4. Unified, scalable data architectures

Data architectures traditionally grew in an almost ad hoc manner. Each data source, format or use brought its own pipelines and customization requirements, resulting in data siloes, technical debt and inefficiencies due in part to the need for manual coding and maintenance. The future of data analytics is leading to more integrated, modular, scalable architectures with low- or no-code data preparation components.

One such unified architecture model, data mesh, takes data democratization a step further: The teams that use the data — such as marketing, planning and operations — take ownership of its security, management and governance. By having more immediate access to only the data they know and need, end users enjoy increased business agility.

While data mesh is decentralized, data fabric is a centralized approach. Relying heavily on AI automation and other advanced technologies, it integrates, manages and analyzes data from multiple sources, with access available across the organization. Generally speaking, moving to a data mesh model is more of an organizational change, while adopting a data mesh architecture requires tech streamlining and updating to support enterprise-scale data volumes and workloads.

Keeping Current with Data Analytics Trends

Data technology and governance are only going to grow more complex. Success in the future of data analytics requires expertise in multiple spheres, from AI to regulatory compliance, data hygiene to architecture. And it requires adapting to continual business, governmental and cultural shifts.

At Material, we use our data & AI consulting expertise to create custom data-driven solutions that enable businesses to grow. We take a bespoke approach that meets each organization’s unique needs to solve their unique problems.

Contact us today to learn how we can help meet your data, analytics and AI needs.

FAQ

Why should companies care about data analytics trends?

Data analytics enables organizations to make better-informed decisions in a gamut of areas, including marketing, product development, operations and merchandising. AI-driven automation, scalable architectures and other emerging trends in data analytics will enable businesses to transform information into insights more quickly, efficiently and accurately than ever. Organizations that fail to keep up with these data analytics trends will ultimately fall behind in the competition for customers, sales and market share.

What industries are most affected by shifts in data analytics practices and technologies?

The future of data analytics offers benefits to just about every industry. Healthcare is one of the most affected: AI-powered analysis helps identify disease patterns and patient anomalies, improving diagnostics, while also forecasting potential outbreaks of diseases so that healthcare providers can prepare in advance. In fields such as finance and insurance, it enables more accurate, faster risk assessment; in manufacturing, it improves the accuracy of demand forecasts and limits equipment downtime by predicting equipment failures. For retailers and brands, emerging trends in data analytics will improve the efficiency and effectiveness of customer segmentation, dynamic pricing and personalization, among other aspects.

What’s the difference between predictive and prescriptive analytics?

Predictive analytics mines and models data to forecast future outcomes. For instance, by analyzing past and current sales patterns, market changes, audience shifts and other factors, a brand could forecast product demand for the next quarter. Prescriptive analytics uses AI-powered optimization algorithms, simulations and scenario analysis to generate actionable suggestions based on predictions. If a decline in demand is predicted, for example, predictive analytics might recommend changes to production volume or shifts in marketing tactics.

How is AI changing the way businesses use and analyze data?

AI-powered data analytics enables organizations to clean, organize and analyze datasets more quickly and accurately than ever before. It is also enabling the automation of many tasks that had been manually intensive, such as schema matching and report processing. This automation is leading to low- and no-code solutions that allow users with domain expertise but little technical experience to directly access data and outcomes, speeding decision time and accuracy. Perhaps most significant, GenAI has given rise to prescriptive analytics, which goes beyond predictive analysis to offer recommendations based on forecasts.