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From Data to Intelligence

A practical guide to turning marketing data into strategic advantage

Data

Data

Information

Information

Knowledge

Knowledge

Intelligence

Intelligence

Introduction

It is no secret that data is one of the most precious resources a business can own in today's world. Businesses of every size and across all industries have had to invest heavily in their data and cloud infrastructure to fully harness its value. However, as many will attest, this is not always straightforward.

The actual value in your data is the information that you can glean from it; the actionable insights. But to transform the raw data into truly intelligent and meaningful information is not an easy feat.

The path from raw data to intelligence is not a straight line. More often than not, it is a 'Choose your own adventure'. This is a journey involving technology, process, and culture, where patience will often need to match persistence.

The four stages in this guide are not check boxes to tick once-and-done, they are ongoing capabilities which will grow and mature with your business.

 

 

Data image

Stage 1: Data

Disparate, unorganized facts from multiple platforms.

The strength of your entire intelligence journey depends on the work you do here. A clean, unified, and properly governed marketing dataset will give you the ability to convert raw facts into useful insights in Stage 2.

Challenges 

What you need to address at this stage:

  • Breaking Down Silos

    Marketing, sales, and customer information can often be stored separately, so the whole picture cannot be seen.

  • Data Quality Issues
    Format differences, empty fields, and duplicates make integration difficult.
  • Restricted Access
    Some teams might only be able to see their own data, reducing collaboration and shared insights.
  • Storage Costs Creep
    Keeping large quantities of raw data without a retention strategy can be costly.

Opportunities

What you can achieve at this stage:

These are the outcomes you should aim to accomplish before progressing to Stage 2. They are the foundations which will support all future analysis and intelligence activity.

  • Establish a Single Source of Truth

    Bring your marketing data together and, where possible, bring in sales and customer datasets to begin building a complete view of the customer journey.

  • Standardize Collection and Storage
    Use uniform formats, naming conventions, and data types from all sources so that the data is reliable and easy to combine.
  • Establish Basic Governance
    Create clear guidelines on data ownership, security, and access rights so that the data is both trusted and secure.
  • Enable Cross-Team Access
    Give marketing, sales, and other relevant teams visibility to the same dataset so they can work together towards shared objectives.

Dangers

What you need to be wary of at this stage:

  • Gut-Feel Decisions

    Without an integrated dataset, campaign performance is measured without full business context.

  • Risks to Data Security
    Uncontrolled and scattered data is vulnerable to more breaches.
  • Competitive Advantage Lost
    Competitors with a clean and consolidated data foundation will be able to make better decisions faster.

"If you don’t do it really, really thoroughly from the beginning, you have to spend a lot more time fixing things down the line."

Diana Gonzalez, Director of Revenue Operations, Riverside.fm
The Undiscovered Metric, Episode 3, Season 3

Information image

Stage 2: Information

Harmonized datasets that can be queried and visualized.

Stage 2 is your opportunity to make your data a trusted, shared asset. Create crystal-clear definitions, consistent reporting, and easy access so that by the time you reach Stage 3, your teams can put their energy into interpreting the "why" behind the numbers, not questioning if the numbers are accurate.

 

Challenges 

What you need to address at this stage:

  • Inconsistent Reporting

    Different departments may create their own reports using different metrics and definitions, resulting in conflicting conclusions and ambiguity.

  • Scalability Issues
    As the amount of data goes up, basic reporting and dashboarding tools tend to slow down or become unwieldy, so it becomes challenging to generate insights quickly.
  • Limited View of the Business
    Data can be siloed at the department level. As an example, marketing data may not be connected to sales data, so one cannot have an end-to-end view of the customer journey.
  • Data Illiteracy
    Employees are provided with dashboards, but no training on how to effectively read the data or what questions to ask.

Opportunities

What you can achieve at this stage:

  • Set up a Centralized BI Platform

    Deploy a business intelligence platform to generate standardized dashboards and reports providing a consistent view of key performance indicators across the organization.

  • Build Data Models
    Develop models that integrate data across different sources such as CRM, ERP, and web analytics to obtain a holistic view of the business.
  • Improve Operational Efficiency
    Use information to identify process bottlenecks, from campaign workflows to customer service, and make targeted improvements.
  • Democratize Access
    Share information with a larger group of people so that employees across different departments are empowered to use data in their various roles.

Dangers

What you need to be wary of at this stage:

  • Analysis Paralysis

    Having lots of reports and dashboards without pre-defined goals or action plans can lead to information overload and stagnation.

  • Mistrust in Data
    Employees may lose trust in data if it is proven to be inconsistent or unreliable, and they may switch back to intuition-based decision-making.

"When you have poor data management, people start to not trust the data."

Landon Perry, VP of Ad Measurement and Data Analytics, Green Line Digital
The Undiscovered Metric Season 2, Episode 10

Knowledge picture

Stage 3: Knowledge

Understanding the “why” not just the 'What'.

Stage 3 is where your data truly starts to tell a story. The more adept you are at digging up the "why" behind your results, and sharing those insights across teams, the more your company can act with confidence and adaptability. This is the gateway to Stage 4, where knowledge becomes intelligence and can automate and optimize decisions at scale.

Challenges

What you need to address at this stage:

  • The "Why" is Elusive

    Connecting disparate pieces of information to determine the underlying cause of a trend may prove difficult and require advanced analytical expertise.

  • Talent Gap
    Some businesses may lack data scientists or analysts to carry out in-depth analysis and build prediction models.
  • Scale Issues for Insights
    An insight gained on one project will not be easily explainable or transferable to other areas of the business, and data gets siloed within teams.
  • Risk Aversion
    Staff can hesitate to experiment with data and assumptions that exist will be protected from challenge, and that stifles innovation.

Opportunities

What you can achieve at this stage:

  • Predictive Analytics

    Construct models from historical data to forecast what is likely to occur in the future, such as customer churn, campaign results, or sales forecasting.

  • Customer Personalization
    Achieve a deeper understanding of how customers act in order to create more targeted marketing campaigns and personalized product experiences.
  • Root Cause Analysis
    Utilize advanced analytics to quickly establish the underlying reasons for changes in business performance.
  • Data Storytelling
    Train staff to communicate insights in simple and compelling ways so they become more persuasive and actionable storytellers to everybody.

Dangers

What you need to be wary of at this stage:

  • Misreading Correlation for Causation

    Mistaking correlation for causation can lead to flawed strategies and wasted resources.

  • Ignoring Critical Data
    Teams can often focus only on the data with which they are comfortable, ignoring other critical data that might provide a more comprehensive picture.

"Having your data accessible for multiple teams and educating them on how to collect that data is really important."

Simme Volkers, Head of SEO, DPG
The Undiscovered Metric Season 3, Episode 7

Intelligence image

Stage 4: Intelligence

Proactive, Strategic Decision-Making.

Intelligence is not the end, but the beginning of a new way of operating. Here, your competitive advantage comes from constantly refining your models, ensuring AI decision-making aligns with business ethics, and having human oversight and creativity at the center of the process.

Challenges

What you need to address at this stage:

  • Heavy Investment and ROI

    It can be costly to integrate AI, machine learning, and other new technologies, and proving an obvious return on that investment is quite often rather difficult.

  • Model Governance and Maintenance
    AI and machine learning models must be continuously monitored, retrained, and governed in order to be accurate and unbiased.
  • Ethical Issues
    Automatically made decisions must be fair, transparent, and regulatory compliant, which means having well established ethical standards.
  • Organizational Adoption
    Establishing trust in AI-based recommendations calls for thorough and careful change management and ongoing communication.

Opportunities

What you can achieve at this stage:

  • Automated Decision-Making

    Use AI to handle everyday but critical decisions. E.g. dynamic pricing, campaign targeting, and recommendations.

  • Prescriptive Guidance
    Move from predicting outcomes to proposing the best next steps. E.g. identifying a churn-risk customer and recommending a targeted retention offer.
  • Monetization of Data
    Develop fresh revenue streams by turning data into a product, selling market insights or analytics as a service to customers and partners.
  • Long-term Competitive Advantage
    Utilize data capabilities to innovate quickly, disrupt industries, and establish competitive barriers that competitors find difficult to replicate.

Dangers

What you need to be wary of at this stage:

  • Algorithmic Bias

    If models are trained on biased data, they might perpetuate or increase those biases, producing unfair and harmful outcomes. 

  • Too Much AI Reliance
    Depending on AI output without human checks or critical review can lead to costly errors and reputational damage.
  • Data Privacy and Regulation
    Advanced data use can cause new privacy concerns and increase risks of non-compliance and of any consequential penalties or legal action.

"AI is the future, and we use AI nowadays to calculate the best way to optimize media across channels for different KPIs."

Sven Meijer, CEO, Objective Platform
The Undiscovered Metric, Season 1, Episode 6

7 Key Principles for the Journey from Data to Intelligence

Getting to the Intelligence stage is not about technology alone. It is governance, culture, and embedding data into the way your business runs. These principles are to help influence decisions from the outset of your journey.

 

1. Begin with the Business Challenge, Not the Technology

Do not fall for the "we have data, let's go find a problem for it" trap. Most successful initiatives are built on an urgent business issue. Having established the problem then you can identify which data and capability are needed to address it.

2. Handle Data as a Strategic Asset, Not a Cost Center

Data is not just a by-product of operations. It is an asset with the power to generate revenue, lower costs, and create competitive advantage. Turn the thinking on its head: "data storage is an expense" becomes "data is an investment with a return."

3. Give Data Governance Top Priority Right From Day One

Governance is not bureaucracy. It is the key to trust, scalability, and compliance. Without standards on quality, security, and ethical behaviour, data initiatives will fail to gain traction or add value.

4. Develop a Culture of Data Literacy and Curiosity across the Organisation

The data team alone will not succeed in isolation. Real intelligence occurs when all the people in the organisation are data-literate, so they are better at interrogation, asking questions, and challenging assumptions with proof.

5. Build an Integrated and Scalable Data Architecture

Disconnected systems will stifle growth and insight. Create a plan on how data will flow through the business from collection to analysis to action. Integration is vital and will combine marketing, sales, and customer data.

6. Adopt Incrementalism and Experimentation

Intelligence building is a sequence of steps, not leapfrogging the whole thing in one go. Create an environment where the teams are confident enough to test brand-new concepts, data sources, and analytical techniques all in a safe, controlled way.

7. Call for Ethical and Responsible Use of AI

As AI and machine learning begin to permeate your decision-making, you will need to ensure they are fair, transparent, and free of harmful bias. Ethical use of AI helps safeguard both your customers and reputation.

How to Get Started

1. Determine Your Stage

Assess where you are for each major data area in your organisation, accepting the fact that different teams or functions will often be at different stages.

2. Bring Stakeholders Together

Bring marketing, sales, and operational leaders to the table early on to achieve alignment on priorities, goals, and successful results metrics.

3. Determine One High-Benefit Focus Area

Pinpoint the stage or capability that will provide the most business value if improved.

4. Define the Problem Before the Solution

Be very clear on exactly the issue you're addressing, and the value in solving it, before investing in tools or processes.

How to Turn Disparate Data From your Marketing Channels into Actionable Insights

 

 

 

DMEXCO Masterclass | From Data to Intelligence: Building Future-Ready Marketing Analytics

This masterclass, led by Adverity and Barilla, delivers practical advice on transforming all the disparate data from your marketing channels into actionable insights. You’ll learn how to build a strong data foundation, apply effective governance, and leverage cutting-edge AI products to supercharge your analytics strategy. Through real-world examples and an interactive showcase, discover how to move from fragmented reporting to a future-ready, insight-driven marketing engine.

Contributors

Lee McCance

Lee McCance, CPO at Adverity, brings 20+ years of product leadership from roles at GroupM, Essence, and McAfee. He’s now spearheading Adverity’s expansion into AI-powered, customer-centric data analytics solutions.

Lily Johnson

Lily Johnson is a Content Manager at Adverity, where she leads the creation of research reports, long-form editorial, and thought leadership on topics ranging from data governance to retail media and AI in marketing. She also produces Adverity’s The Undiscovered Metric podcast, bringing expert voices into the conversation around data and marketing. With seven years’ experience in B2B content marketing, she’s helped shape content strategies across the SaaS, retail, and events sectors.

Tom Rennell

Tom Rennell is Head of Content & Communications at Adverity, where he leads the team responsible for all brand, editorial, and external messaging across the company’s owned channels. With over a decade of experience in content strategy, communications, and storytelling, Tom has shaped messaging for global organizations ranging from Alibaba to the United Nations.

Find out more about how Adverity can help you today.

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