Board-level interest in building a data advantage is growing, but that does not always translate into a clear understanding of how insight and analytics work together. They are not separate disciplines. Analytics helps connect and organize information. Insight helps explain what matters and what should happen next. 

Many organizations still are not using data effectively because their data is not connected. Eight in 10 business leaders say data is critical to decision-making, but only half truly understand their data. Only 40% of available data is actually used in decision-making, and one in four organizations is working across more than 10 BI tools. That is not a data shortage, but a connection problem. 

That is why leading insights teams are focused less on creating more information and more on creating more value from the data they already have. The opportunity is not just better access. It’s better connection, better context, and better use of data in decisions that drive growth. 

Leadership teams are focused on growth, agility, profitability, and risk. Data connectivity matters when it helps improve decisions in those areas. 

The commercial upside is significant. A 10% increase in data usability can generate $2 billion in revenue for the average Fortune 1000 company. Organizations that report extensive use of consumer analytics capabilities also report 93% higher profits than competitors.  

That matters because connected data makes it easier to tie consumer understanding to real business decisions.  

• It can help teams optimize campaigns while they are still live, rather than reviewing performance after the opportunity to act has passed.  

• It can connect brand awareness and consideration to commercial outcomes more clearly.  

• It can combine campaign tracking with POS data to show where investment is working and where it is not.  

• It can also bring in conversational data, customer feedback, and behavioral signals to improve experience, loyalty, and innovation decisions.  

This is where connected consumer data becomes part of how the business prioritizes investment, evaluates performance, and responds to change. The key is not just to show that data exists, but to show how connected datasets create a more complete view of the consumer and a clearer basis for decision-making. That is what helps leaders move from dashboards and reports to action. 

Connected data only creates value when more of the business can use it well.  

To deliver better consumer experiences, teams need expertise in their own areas supported by a clear view of consumers, markets, and performance. That is why data democratization matters. It gives more people access to the insight they need to make better decisions in their own part of the business.  

Used well, that can improve more than speed. It can help teams identify opportunities earlier, respond faster to market changes, and make stronger calls on innovation, experience, and execution.  

But democratization is not just about wider access. It is about usable access. The difference between a data-informed organization and a data-driven one usually comes down to whether people have the tools, training, and structure to turn information into action. 

Connected data gives teams a broader view. AI helps them use that view faster. 

AI can help connect insight data to other datasets, reduce the time spent analyzing information, and surface patterns sooner. That means faster answers to business-critical questions and less delay between seeing change and acting on it. 

This matters most in areas like marketing, where timing is often the difference between measurement that shapes the outcome and measurement that simply documents the past. AI can help teams move faster from campaign data to interpretation, from consumer feedback to action, and from fragmented inputs to a clearer view of what is working. 

The commercial context makes that need more urgent. Global advertising spend reached $754 billion in 2024, yet much of that investment still does not translate into timely, usable learning. The CMO Council reports that just 5% of marketers can respond to campaign data in meaningful ways in real time, and most still struggle to anticipate future consumer needs. There is also a clear execution gap: many organizations recognize the importance of generative AI, but far fewer are able to apply it effectively in practice, leaving a 25 percentage-point gap between ambition and execution. 

That is where AI-powered insight can help. Not by removing the need for judgment, but by making connected consumer, campaign, and performance data easier to analyze, easier to access, and easier to use. The value is not AI on top of the business, but helping the business make better decisions with better timing. 

More data does not automatically lead to better decisions. In fact, it can do the opposite. Thirty percent of business leaders say they are overwhelmed by the amount of data available across the organization. As AI connects more real-time sources, the risk is not just complexity, but also confusion. Without enough structure, businesses can end up with multiple versions of the truth and too many signals competing for attention. 

That is especially risky when consumer sentiment is moving quickly. Political and social attitudes can shift fast, and so can their impact on brand perception. If those changes are read too quickly or too shallowly, teams can end up reacting to noise rather than responding to something that genuinely matters. 

That is why instant insight does not always need instant delivery. Senior leaders need reporting rhythms that make insight usable, not overwhelming. The real value comes from curation: knowing which insights should be shared automatically, which need analyst interpretation first, and which should be reviewed in a more structured decision-making window. 

The principle is simple. Do not let fast, easy-to-collect data push stakeholders off course. Give them the information that matters, break it down clearly, and make it usable enough to support action. That is how connected data helps teams prioritize better, rather than just react faster. 

The value of connected insight becomes much clearer in practice. Leading brands are not building these ecosystems simply to centralize more data. They are doing it to make consumer understanding easier to access, easier to apply, and more useful in business decisions. 

The advantage does not come from a single tool, but from connecting sources, improving access, and building a model that helps teams use insight in context. 

Unilever 

Unilever offers a useful example of what this looks like in practice. Its shift from a purpose-led brand agenda to a more performance-focused business model has been supported by connected consumer insight and stronger use of data across the organization. 

Unilever’s CMI function uses AI to connect forecasting and actual sales data in real time, with cross-functional teams accessing insights through an in-house cloud platform. It also processes large volumes of unstructured consumer data from sources such as social media and product reviews, making the Digital Consumer Voice more visible across the business. Machine learning and natural language models help analyze sentiment and identify the themes that matter most, giving marketing, quality, and research teams easier access to insight they can use. 

The value of that model is not just speed. It is that connected data becomes easier to apply in context. In Unilever’s case, this approach has supported decisions that improved product quality, strengthened consumer experience, and delivered cost savings of €350k in individual cases. 

It also highlights an important point for senior stakeholders: connected data creates more value when it is tied directly to business priorities. Unilever has paired its use of AI and connected insight with ongoing leadership engagement, linking consumer understanding more closely to commercial performance. 

Coca-Cola 

Coca-Cola shows what a connected ecosystem looks like at global scale. Its broader model brings together data from 300 brands, 100 sources, and 75 agencies to create a more complete view of how brand and sales activity are performing across markets. 

That matters because Coca-Cola operates across a wide mix of categories, geographies, audiences, and purchase occasions. To make sense of that complexity, the business needs a connected view that can work globally while still allowing for local interpretation. Bringing these sources together makes it easier to see how campaign activity, consumer response, and commercial performance are moving in relation to one another. 

Its ecosystem combines inputs such as campaign performance, customer satisfaction, social interaction data, localized weather patterns, purchasing behavior, sales reports, and real-time POS data from connected vending machines. The value is visibility. By connecting those sources, Coca-Cola gives stakeholders a much clearer picture of what is happening across the brand experience and where action may be needed. 

Delineate connects Coca-Cola’s research and global campaign tracking data to give senior stakeholders a more complete view of the brand experience across 50 countries. That broader view helps teams understand how campaigns are influencing awareness, resonance, and action across markets while activity is still live. 

Just as importantly, it improves decision timing. Coca-Cola’s teams moved from a slower monthly fieldwork model to a faster cadence that helps answer key business questions in hours rather than weeks. That makes it easier to optimize campaigns in flight, improve effectiveness while activity is still in market, and connect insight more directly to marketing and sales outcomes. 

Nestlé 

Nestlé partnered with IBM and used Microsoft Power BI and Azure to build a central business intelligence hub that democratizes consumer insight. 

Its AI-powered Customer Data Platform integrates data from more than 20 brands, creating a more holistic view of consumer interaction across D2C channels, promotions, and campaign landing pages. Nestlé has also used automated demand signal-driven forecasting to suggest new concepts in just over a minute, helping teams test ideas faster and move products to market more quickly. 

Nestlé used its FAIR data framework — Findable, Accessible, Interoperable, and Reusable — to support democratization training for 40,000 Citizen Analysts across the company. Its Freedom Box approach gave teams more autonomy to explore analytics at a level that matched their capabilities, while still creating accountability for how those insights were used. 

PepsiCo 

PepsiCo transformed its global capability from manual reporting to AI-powered analysis by building Ada, a centralized consumer insights ecosystem that democratizes data from 95 million menu items, 226 billion recipe interactions, and 22 billion social posts. 

Teams use natural language querying to ask questions in a much simpler way, using sentiment analysis and promotion effectiveness insights gathered in one region to inform decisions in others. That creates a more connected model for learning across markets, rather than treating each region as a separate reporting environment. 

PepsiCo has also shifted from a more rigid BI model to a self-serve approach that allows regional teams to filter data, customize dashboards, and carry out more detailed analysis independently. AI-assisted reports provide drill-down views and scorecards across departments, while senior executives receive more curated communication on the insights and recommendations that matter most. 

Across all four examples, the pattern is the same. The advantage does not come from making data easier to access. It comes from making consumer understanding easier to scale, easier to share, and easier to apply in business decisions. 

Building a connected insights ecosystem is one thing. Scaling it across the business is another. 

The challenge is not just opening up access. It is making sure that access is useful, sustainable, and tied to better decisions. That usually comes down to a few basics: executive sponsorship, the right level of training, and a clear balance between self-serve access and expert support. 

• The role of executive sponsorship 

One of the most important starting points is executive sponsorship. When a senior leader backs the effort, connected data is more likely to be treated as a business priority rather than a tooling project. Johnson & Johnson’s enterprise-wide Data Science Council, for example, was designed to track the value of scaling consumer insight and GenAI across the organization, helping create stronger alignment around how that work supports the business. 

• The need to avoid data overwhelm 

Just as important is avoiding data overwhelm. Not every team needs the same level of access, and not every team is ready for the same level of complexity. Some functions will need more support on data privacy, governance, and interpretation. Others will be more ready to adopt automation and self-serve tools. Scaling successfully means designing access around how people actually work, not assuming that one model will suit everyone. 

• The importance of data and AI literacy 

Training matters for the same reason. Seventy-five percent of the workforce believe AI will accelerate job losses, with more than half of younger professionals especially concerned. That makes data and AI literacy a practical requirement, not a nice-to-have. The most effective programs do not just teach people what AI can do. They help people understand how to work with it, what questions to ask of it, and where human judgment still matters most. 

That also means teaching people what data they do not need. One of the risks of broader access is that teams can be drawn toward data that is easy to collect rather than data that is genuinely useful. Better democratization depends on focus as much as access. 

• The value of the right foundation 

As organizations scale, infrastructure choices also start to matter more. Mars, for example, invested in data lakes and a broader vendor ecosystem to support the next stage of its data and analytics development. That kind of foundation becomes increasingly important as data volumes grow, source types multiply, and more teams expect faster access to insight. 

• The balance between self-serve access and expert support 

Organizations also need to strike the right balance between self-serve access and expert support. As analytics capability matures, more teams will want to explore data independently. That can be a strength, but only if it is supported by the right tools, training, and collaboration. Otherwise, wider access can create inconsistent interpretation rather than better decisions. 

• The role of governance at scale 

Governance becomes more important as scale increases. Effective governance is not about locking systems down. It is about maintaining quality, consistency, and responsible use as more people work with more connected sources of data. The more connected the ecosystem becomes, the more important it is to keep confidence in what the data is showing and how it should be used. 

In practice, this is what scaling data democratization really means: not simply making more data available, but building the structure that allows more of the business to use connected consumer insight confidently and well. 

There has never been more data available to businesses. But the advantage does not come from volume alone. It comes from building a better system around that data: one that connects sources, improves access, supports interpretation, and delivers insight in time to influence decisions. 

The best consumer insights are supported by the best data partnerships. 

Delineate’s always-on tracking captures consumer data across all communications channels in real-time to instantly connect you to global audiences. Gain the competitive advantage enjoyed by Coca-Cola, Ancestry.com and Nomad Foods: get insights on demand to make the best business decisions fast. 

Here’s how Delineate can skyrocket the impact of your consumer insights.