6 key differentiators between old-school and forward-looking data pros

The role of data professionals is evolving at an unprecedented pace. As businesses generate and consume more and more data every day, data leaders have never been more crucial and can spell the difference between a thriving and a lagging behind business.

With constant changes impacted by technological advancements, political conditions, market shifts, and the global economy, to name just a few factors, it’s imperative for data leaders to constantly be on their toes. And, their data needs to do the same

While some data professionals stick to traditional approaches, others embrace a forward-thinking, strategic mindset that prioritizes efficiency, scalability, and AI-powered insights.

The divide between these two mindsets—old-school data professionals and forward-looking data leaders—is widening, impacting how organizations use data to drive success.

In this article, we’ll explore the key differences between these two groups, highlighting why forward-thinking approaches are essential for businesses to stay ahead in a data-driven world.

1. Choosing an analytics solution

Choosing the right analytics solution is essential for success in today’s data-driven world. While traditional professionals rely on multiple specialized tools that create inefficiencies, modern leaders prefer integrated, scalable platforms that streamline operations and enhance collaboration.

Old-school data professionals: Fragmented, fit-for-purpose tools

Traditionally, data professionals have followed a tactical approach embracing a fit-for-purpose purchasing model. This means acquiring separate tools for each function—ETL, visualization, data science, etc.—as the need arises. In many cases, even different departments within the same organization end up using separate tools fit for their specific use cases.

This fragmented approach leads to disjointed ecosystems that require extensive integration efforts, weaken governance, and increase inefficiencies over time.

Moreover, old-school data pros often tend to rely on default solutions based on their existing technology stack. So, if they’re Microsoft users, for example, they will automatically choose Power BI, without considering other options or various use cases within their organization.

If they consult analyst reports such as Gartner’s Magic Quadrant to make their choice, they are more likely to choose services and tools already ranked as market leaders. What they fail to acknowledge, however, is that this report is based largely on market share, not product capabilities. So, they’re choosing a popular bet, but without fully assessing whether the tool aligns with their organization’s actual needs.

Forward-looking data leaders: Consolidated platforms

What differentiates forward-looking data leaders is their more strategic approach to choosing an analytics solution. They prioritize consolidation and operational efficiency, by selecting an end-to-end analytics platform that streamlines processes, instead of multiple fragmented tools. An integrated platform enables seamless data integration, governance, and company-wide collaboration.

Instead of focusing on market share or brand recognition, forward-looking leaders evaluate scalability and long-term capabilities—ensuring their chosen analytics solution can continuously support complex business needs, large data volumes, and true self service for users of all technical levels.

2. Approach towards reporting

Reporting is a critical aspect of business intelligence, but the way it’s handled differs greatly between old-school data professionals and forward-thinking data leaders.

Old-school data professionals rely on centralized teams for report generation. Forward-thinking leaders, on the other hand, enable self-service reporting, empowering business users with real-time insights and fostering swift responses to changes in the market.

Old-school data professionals: Static, IT-dependent reporting

Traditional data pros view reporting as a specialized function, with data or IT teams responsible for generating reports for other business units, while most employees have nearly no knowledge of analytics. This creates bottlenecks, slows down decision-making, and reinforces data silos that limit business agility.

Furthermore, old-school professionals treat business intelligence (BI) as a static tool, relying on dashboards to present predefined insights rather than enabling dynamic, real-time data exploration.

Forward-looking data leaders: Self-service, collaborative analytics

Today’s data leaders recognize that reporting should be democratized and accessible to all business users. They do so by:

• Investing in governed multi-user collaboration at scale.
• Providing data literacy programs to upskill teams and enable self-service analytics.
• Encouraging a data-driven culture, where non-technical users can independently explore data and generate insights without heavy reliance on IT or data teams.

This shift frees up data and IT teams from creating endless reports and fielding requests from every other team, letting them focus on strategic projects. It also fosters agility and improves decision-making, allowing teams to act on insights in real time.

3. Embedded Analytics

Data must be accessible without disrupting workflows, otherwise it gets lost in the busy schedules of most users. Traditional analytics require switching between platforms, causing oversights. Modern leaders embed analytics directly into business applications for real-time insights and faster decision-making.

Old-school data professionals: Siloed analytics

A major limitation of old-school analytics strategies is that data remains separate from day-to-day workflows. Users must leave their operational tools to access reports and dashboards, either in PDF files or dedicated web apps, making insights less actionable and delaying critical business decisions.

Adding this extra step to their flow means business users, who already have a hard time deciphering analytics, are even less likely to embrace data as a driving force.

Forward-looking data leaders: Seamless, embedded analytics

The more advanced approach is to embed analytics directly into business applications, allowing users to access insights without leaving their workflow.

Whether it’s on the company’s website, apps, or any internal infrastructure that’s used every day, embedding ensures analytics becomes an integrated component of daily operations, enabling faster, data-driven decision-making across the organization.

4. Using AI to empower analytics

Artificial Intelligence (AI) is changing the way organizations approach data analytics, yet not all data professionals are taking full advantage of its potential.

Old-school data professionals: Basic AI adoption

AI changes everything and analytics is no exception. Still, many old-school data professionals limit their AI usage to basic automation features.

They fail to recognize the broader potential of AI in enhancing data preparation, predictive modeling, and insight generation.

Forward-looking data leaders: AI-driven transformation

Forward-thinking data leaders understand that AI should be integrated into the entire data lifecycle. They leverage AI to:

• Automate data cleaning and preparation.
• Boost data discovery and insight extraction with AI-powered analysis and recommendations.
• Enable natural language processing (NLP) to make dashboard and report creation more accessible to non-technical users.
• Unlock conversational analytics so business users can gain instant self-served insights from their data.

5. Considering the costs of analytics solutions

Cost is always something to consider but should never be the main factor when choosing critical business functions such as an analytics solution. Not all data pros agree with this sentiment.

Old-school data professionals: Short-sighted cost focus

Many traditional data professionals focus primarily on upfront costs and official price tags when selecting analytics solutions.

While cost control is important, this approach often results in organizations adopting tools that don’t fully meet their needs, leading to inefficiencies, higher maintenance costs, and poor user adoption.

This being the case, they’re already paying too much, as their tools don’t necessarily deliver the impact they expect. And, then they have to purchase additional tools on top of that, to complement their stack, which ends up costing even more.

Additionally, old-school data professionals often fail to evaluate the true cost of ownership (TCO), neglecting hidden expenses related to integration, additional software, training, and scaling analytics capabilities. And, with lower adoption, there’s another price point they should be considering: missed opportunities leading to lost revenue.

Forward-looking data leaders: ROI-driven decision making

Instead of focusing solely on software costs, modern data leaders evaluate the ROI of analytics investments based on efficiency, insight generation, and business impact. In addition to examining the cost they:

• Assess how a tool improves decision-making speed and accuracy.
• Factor in operational efficiency gains from automation and self-service analytics.
• Consider scalability to ensure that solutions can grow alongside the business.

This long-term, value-driven perspective ensures that analytics tools provide meaningful business advantages rather than simply meeting a budget constraint.

6. Data depth and business impact

A strong data strategy is not just about collecting information—it’s about extracting valuable insights that drive action. Many organizations struggle to maximize the full potential of their data, often due to outdated approaches that limit data depth and visibility.

Forward-thinking data leaders recognize that inefficient analytics can lead to missed opportunities, while proactive exploration can uncover hidden patterns, trends, and optimization paths by exploring underlying layers of data. The difference between old-school and modern approaches lies in how deeply they analyze data and whether they are willing to challenge existing limitations.

Old-school data professionals: Limited depth, missed opportunities

Old-school data professionals often fail to quantify missed opportunities caused by outdated analytics approaches. They may not recognize the inefficiencies introduced by fragmented tools, manual processes, and static reporting, leading to slower business insights and lost competitive advantages.

Forward-looking data leaders: Proactively addressing inefficiencies

In contrast, forward-thinking leaders actively seek to identify and address inefficiencies. Here’s how they do it:

• Use analytics to pinpoint bottlenecks and process gaps.
• Implement tools that enable deep data exploration rather than surface-level reporting.
• Leverage AI and automation to accelerate insight generation and decision-making.

By focusing on depth and impact, modern data leaders ensure that analytics plays a transformative role in business strategy rather than merely supporting basic reporting functions.

The future of data leadership

While old-school approaches to data and analytics are characterized by fragmented tools, static reporting, and basic AI usage, modern leaders embrace integrated platforms, self-service analytics, and AI-driven insights to enhance agility, efficiency, and decision-making.

For organizations looking to stay ahead in today’s competitive landscape, the choice is clear: adapt to a forward-looking analytics strategy or risk being left behind. The businesses that thrive will be those that empower all users with scalable, in-context, and AI-powered analytics, driving better decisions and long-term success.