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Doctors are writing "social prescriptions" to get people engaged with nature, art, movement and volunteering. Research shows it can help with mental health, chronic disease and dementia.

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The case, which stems from a deadly crash in 2019, raises broader questions about the safety of Tesla's driver-assistance systems, and whether the company has exaggerated their capabilities.

Congressional Republicans have become more aligned with President Trump since he first took office. That makes even a single vote against him a "moderate" stance, even for staunch conservatives.

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It started as a one-off dinner with a chatbot — a night of shrimp, sarcasm — then veered into something unsettlingly human.

Today’s data and business analysts have a wealth of tools at their fingertips to do their jobs effectively. Behind every analyst team, however, is an IT leader being asked to communicate how such tools offer a Return on Investment (ROI).

Without being able to answer these questions resolutely, IT budgets will be scrutinized and, at worst, put at risk. However, the arrival of AI and IT automation may be about to flip the switch and make it much easier to prove ROI. Let’s explore…

The reporting gap

A lack of answers boils down to reporting gaps. To date, companies that have invested in advanced data analytics and data visualization platforms have fallen into the trap of neglecting the reporting that shows impact. If no one tracks the time saved from that platform or its influence on decision making, its impact goes unnoticed in the wider business.

Take a retail business as an example. Deploying predictive analytics to optimize inventory management counts for nothing without structured reporting on stock reductions, cost savings or sales improvements as outcomes.

Analysts themselves feel the drag when they aren’t given the space to communicate outcomes. A third believe reporting should be a core part of their role but is currently overlooked.

With the introduction of AI into analytics workflows, as well as increased automation, it’s high time for enterprises to turn a page. The improvements that these changes offer (both to working with data and its outcomes) make possible a more systematic approach to reporting.

New possibilities

Our recent research found that 97% of analysts are integrating AI into their workflows, with 87% using analytics automation to streamline routine tasks.

With the right platform, it's possible for analyst teams today to automate data exploration, insight generation and the way that workflow’s function. This leads to faster time-to-value, improved decision-making and, crucially, analysts can report on progress against set KPIs in a reliable manner that doesn’t rely on manual input. Automated collection of performance-based data can track things like time savings in data preparation, cost per project and even the tracing of revenue back to insights generated via analytics.

Automation also makes the output of analytics more accessible. No-code platforms allow users to visualize key findings and insights without technical knowledge. This makes it easier for any business end users to come to, and communicate, data-driven conclusions.

Finally, sophisticated analytics platforms that come with integrated generative AI functionalities allow analysts to spin up presentations, reports and workflow summaries simply through a natural language prompt. The significant relief this provides analysts in terms of time and resources saved is obvious.

A focus on results communication

While AI and automation in analytics offer immense advantages, IT leaders need to shepherd a strategy to streamline and optimize the communication of results.

First and foremost, IT leaders should define success metrics that directly measure the enterprise impact of analytics tools, such as cost savings, revenue growth or operational efficiencies. Aligning these metrics with broader organizational objectives makes reporting coming out of data initiatives much more likely to resonate in ways that other teams care about – rather than heralding technical achievements without context.

Regular and proactive communication of insights is also crucial. Data analysts should go beyond ad-hoc reporting and establish a cadence for sharing comprehensive updates with leadership teams. These reports can highlight key metrics, emerging trends and measurable outcomes, ensuring that executives remain engaged with the impact of AI and analytics automation.

By demonstrating clear ROI through ongoing reporting, organizations can secure buy-in for further investment and scale their analytics capabilities effectively. There’s also no excuse not to do it, given how frictionless automation makes the collation and reporting of such insights.

Finally, fostering a culture of data literacy is an important step toward realizing the ROI of analytics and the tools that enable it. When more business users are working with data, IT teams have greater scope to gather day-to-day feedback from their analytics investments. A wider range or team are empowered to make smarter decisions and create tangible examples of ROI that make the case for continued investment.

Workforces in which the foundations of working with data are understood are more likely to apply new AI technologies in impactful ways. It also stands to improve their own productivity. Put in these terms, the strategic, long-term case for investment in analytics and data stacks becomes easier to articulate to any internal stakeholder.

Settling a debate

Proving the ROI of data analytics tools has long been a tough challenge for IT leaders. Without tracking the impact of their investments, many organizations will have been sitting on high ROI without even knowing it. That’s changing. Advances in AI tools and automation make it easier to track and show the value of analytics with clarity. This is in addition to the democratization of analytics that can unlock all-new levels of operational efficiency.

This shift is helping IT leaders make the case to stay the course with nascent or mature analytics programs, rather than dismantling efforts that were highly viable but difficult to link to business value until now. Enterprises mastering data and analytics will readily demonstrate measurable returns, giving them the most to gain in the upcoming intelligence era of AI disruption.

We list the best business intelligence platforms.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

In today's data-rich environment, enterprises find themselves custodians of vast, largely untapped repositories of unstructured data. These troves, encompassing documents, emails, videos and more, represent a latent competitive advantage—a wealth of potential insights awaiting activation.

The challenge lies not in the accumulation of data, but in the effective extraction of actionable intelligence. Artificial Intelligence (AI) serves as the transformative tool, capable of converting this “dark data” into tangible business value.

Nearly 90 percent of enterprise data remains unstructured. The most significant opportunity for enterprise growth and innovation in the current landscape is thoughtful AI application. The key is moving beyond mere data collection to strategic data operationalization.

Decision-Making Challenges in the Age of Information

The sheer volume of data does not automatically translate to accelerated or improved decision-making. In fact, teams often struggle to derive relevant insights and take decisive action amid the noise. To address these challenges, enterprises should focus on three critical areas of improvement:

Breaking Down Departmental Data Silos: Siloed data impedes cross-enterprise information sharing, hindering comprehensive analysis and strategic alignment. Establishing seamless data flow between departments unlocks a holistic view of the enterprise, allowing for better and more informed decision-making.

Upgrading Legacy Systems: Legacy systems often cannot fully leverage modern data processing capabilities, limiting the potential for advanced analytics and AI integration. Modernizing infrastructure is essential to unlock the full value of enterprise data.

Transforming Regulatory Compliance: Viewing regulatory compliance as a structured framework, rather than a mere obligation, allows enterprises to proactively leverage compliance data for strategic insights and confident action. This approach transforms compliance from a cost center into a value driver.

To drive this point home, let’s consider the example of a major healthcare provider grappling with fragmented patient data dispersed across 15 disparate systems. By implementing a unified data platform, the provider can empower physicians with comprehensive patient histories during critical situations, reducing treatment delays, minimizing redundant testing and ultimately improving patient outcomes.

Enterprises don’t need more data—they need better ways to use the data they already have. When enterprises combine data quality, governance and scalable AI systems, they turn a passive asset into a strategic differentiator.

Navigating the Critical Data-AI Relationship

The symbiotic relationship between data and AI demands careful navigation. Several key considerations are paramount:

The Data Quality Imperative: The performance of AI systems is inextricably linked to the quality of the underlying data. Poor-quality data can severely limit AI's potential, leading to inaccurate outputs and flawed insights. Enterprises must prioritize data excellence as the bedrock of any successful AI initiative.

Preserving Trust in AI: AI-driven decisions are only as reliable as the data upon which they are based. Inaccuracies, biases, or "hallucinations" can erode confidence in AI outputs, hindering adoption and potentially leading to adverse outcomes. Enterprises must implement robust data validation and governance mechanisms to ensure the trustworthiness of AI systems.

Impact Multiplication: The impact of poor data quality on AI performance is not merely additive; it's multiplicative. Failing to address data quality issues can lead to compounded losses in efficiency, accuracy and competitive advantage. Enterprises must recognize the long-term consequences of neglecting data quality.

Industry Reality Check: The Real Cost of Untapped Data

Untapped data represents more than just a missed opportunity; it's a tangible competitive disadvantage. Consider the following industry-specific realities:

Financial Services: Financial institutions often struggle with outdated data systems that are ill-equipped to detect sophisticated modern fraud patterns, leaving them vulnerable to financial losses and reputational damage.

Healthcare: Fragmented patient data within healthcare systems compromises the quality of care, increases costs and hinders the development of personalized treatment plans.

Retail & CPG: Retailers collect vast amounts of consumer data but often fail to translate these insights into the personalized customer experiences now expected, resulting in lost sales and diminished brand loyalty.

The key takeaway is clear: data hoarding is not a viable strategy. Enterprises must prioritize data monetization and operationalization to unlock the full potential of their data assets.

The Data-to-Intelligence Revolution: AI as the Catalyst

A modern data engineering approach must encompass every stage of the data lifecycle, from legacy data migration and real-time ingestion to robust governance and AI-driven analytics. Key components include:

AI-Accelerated Data Migration: AI/ML-powered accelerators streamline the transition from legacy systems to cloud-native environments, minimizing disruption and accelerating time-to-value. Automated workload discovery and dependency mapping provide a structured migration plan, while AI-driven schema conversion, code refactoring and optimization reduce manual effort. Self-learning AI models analyze historical workloads and recommend performance-optimized architectures for modern platforms.

Advanced Data Engineering: Real-time data processing is essential to power AI-driven decision-making. Generative AI enhances ETL/ELT pipelines, automating data transformation and quality checks. Automated, real-time ingestion pipelines leverage AI to detect, clean and process data at scale. Predictive optimization models dynamically allocate computing resources based on workload demand, while event-driven architectures ensure instant data availability for analytics and decision-making.

Knowledge Graphs for Enterprise Data Intelligence: Generative AI-powered knowledge graphs transform fragmented enterprise data into an intelligent, structured and interconnected ecosystem. AI algorithms detect patterns and uncover insights that would otherwise be missed, while enhanced data lineage tracking ensures accuracy, transparency and trust in AI-driven decisions.

Building an AI-Ready Data Foundation: A robust data foundation is essential to support AI initiatives. This includes:

• Robust Infrastructure: Ensuring high-quality, integrated data for AI-driven insights.
• AI-Driven Governance: Automating compliance, preventing mismanagement and securing access to sensitive data.
• Smart Metadata Management: Enabling automated tagging for organization, searchability and auditability.

The data-to-AI revolution isn’t about isolated initiatives—it’s about integrating every layer of enterprise data into a responsive, scalable foundation for innovation.

Transforming Data with AI Agents: From Raw Information to Powerful Insights

We are rapidly moving beyond the era of static business intelligence dashboards and reactive data analysis. The future of enterprise decision-making lies in the hands of AI agents: intelligent, autonomous systems that proactively transform raw information into actionable insights. These aren't just souped-up analytics tools; they represent a fundamental shift in how enterprises interact with and leverage their data assets.

The key to unlocking the full potential of AI agents lies in their ability to:

Contextualize Data: AI agents don't just process data; they understand its context, relevance and implications.

Automate Insights: AI agents automate the process of extracting insights, eliminating the need for manual analysis and freeing up human resources for more strategic tasks.

Enable Proactive Decision-Making: AI agents empower enterprises to anticipate and respond to change in real-time, enabling proactive decision-making and a competitive edge.

For example: imagine a retail enterprise deploying AI agents to continuously monitor customer behavior, social media trends and competitor pricing strategies. Instead of waiting for a weekly report, these agents dynamically adjust stock recommendations, personalize marketing campaigns and optimize pricing in real-time. This level of agility was previously unattainable, but AI agents make it a reality.

This is where dark data turns into an enterprise superpower. It enables every employee—not just data scientists—to make informed decisions, guided by always-on, always-evolving intelligence.

Conclusion: From Data Possession to Data Power

In the modern enterprise, the emphasis must shift from simply possessing data to effectively leveraging it. Enterprises don't need more data; they need better ways to use the data they already have. Failing to operationalize data comes with the risk of falling behind competitors who are actively harnessing the power of AI.

The enterprises that will thrive in the decades to come are those that can successfully unlock and activate their untapped data assets using AI. The question is no longer “How much data do you have?” but “How intelligently are you using it?”

The time to act is now. The future belongs to those who can harness the hidden power of their dark data, transforming it into AI-driven business value.

We list the best data visualization tools.

This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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