Chief Insights & Analytics Officers at the world’s leading brands are seamlessly integrating sources and channels with a structured roadmap that makes their data work harder:

Defining Objectives to Match Data Sets to Outcomes

Relevant consumer insights aren’t born from every available data set, but from data sets that are most appropriate, accurate and reliable.

Mapping out your purchase funnel with the analysis and insights you need at each stage will uncover the value of each data point in consumer understanding, and focus the integration on your end goal of better decision-making. Every data source, type and format can be assessed against the outputs your CMI teams are striving for. This transparent view will determine which data is truly necessary and useful to take forward to integration, and which you can eliminate to save valuable time, cost and resources.

James recommends, ‘In a world with so much data at our fingertips, we must be very clear about the outcomes we want. You can buy a million tools – there’s always someone trying to sell you something – but you can quite easily blow the bank on features you don’t need. When I worked for a large retail bank, a vendor approached us to build an AI voice search tool into our app, but we had no data to suggest consumer need. In reality, our consumers didn’t want to share private information over their speaker phone when mobile banking – we were trying to solve a problem that didn’t exist.

‘As a buyer, be really clear about what your problem is, and be quite cynical about how you solve it with a partner. Third-party restrictions and internal governance bureaucracy can delay experimentation. Start small and simple: get something up and running, then isolate out what is and isn’t working. Build a sandbox to experiment on your data, enabling your team to co-create innovatively with partners in a controlled, compliant way. Operating with a clear use case avoids overspending and identifies which features you really need, and then data analysis and insights that will provide unlock.’

Auditing to Reveal the Most Valuable Data Sources

Although the average organization collects data from over 400 sources, much data is rarely or never used to inform relevant decision-making. Mapping all your current data sources will reveal redundant platforms and opportunities to better leverage partnerships for greater return. The volume, types and complexities of your sources will determine the scale of your integration project and the timelines and resources involved.

James expands, ‘The fusion of multiple data sources means complexity is inherent in the integration process. However, sufficient auditing can translate that complexity into the power to deliver clear, simplified insights. Pulling together different data sources not only reveals how much you’re spending across TV, social media, OOH and other channels, but the most important data and sources that will help identify the efficiency and impact of your cross-channel investment.’

Choosing Extraction Methods and Tools to Combine Data Sources

• Batch Extract, Transform, Load – Cost-effective ETL cleans and standardizes before loading, but a lack of real-time data will delay decision-making, and its complexity makes ETL difficult to scale
• Batch Extract, Load, Transform – Highly compliant for sensitive data, ELT can move large complex data sets but requires significant maintenance and resources to implement and adapt over time
• Middleware – Flexible and scalable systems can support and transform large volumes of complex data at any moment, but guaranteeing accuracy an prove time- and resource-intensive
• Real-Time Integration – Always up-to-date for accurate decision-making, real-time data improves efficiency and visibility but struggles with high volume and complexity
• Virtualization – Mimicking a single source without the need to physically move any data, virtualization instantly democratizes data across all teams but must be closely supervised to protect security, and can involve commitment to a single vendor
• Application-Based – Highly customizable with the ability to build in new systems and processes as a company grows, APIs are also maintenance-heavy as integrations must be adapted at the pace of technology developments.

James advises, ‘The chronic shortage of tech talent should be front of mind when selecting data extraction tools. In the past, we could count on standard development skills coming out of Universities, and usually defaulted to Python as everyone knew how to use it. The evolution of emerging technologies is seeing demand for specific skills rapidly outpace supply. Selecting data extraction and management tools that are as mainstream as possible, with low-code and no-code options, will mitigate against the skills shortage whilst we’re building the wide range of tech capabilities we need for the future.’

Storage & Management Options for Application and Efficiency

The right data storage and management solution for your CMI team and business will depend on multiple factors. Considering access requirements, existing technical skill sets and resource capacity will help to identify the most appropriate methods to store and manage your data:

• Data Lakes – Storing structured, semi-structured and unstructured data in raw native formats, data lakes are flexible and scalable, able to train machine learning models on large complex data sets and aid pattern identification and predictive analytics. However, the vast complexity of the stored data means these systems require close governance and continued maintenance investment from skilled data specialists.
• Data Warehouses – Immediate access to data stored in optimum formats for quality and consistency, data warehouses best serve accurate analysis and relevant decision-making. Flexibility and scalability are compromised in the name of structure and organization.
• Central Data Hubs – Cloud-based solutions, especially those built bespoke for your organization, can centralize data for seamless sharing to multiple destinations for use by multiple teams. This type of storage system also requires investment to implement and continual close management to maintain data quality.

James divulges, ‘One of the biggest oversights in data integration is knowing not just where your data comes from, but where it’s going. Many third parties operate with an intelligent front-end that passes data through an Open AI (or similar); guidelines are not always clear about where data travels to and from, particularly concerning AI-powered services. Licensing agreements from some subscription providers will also involve stringent access and transfer restrictions.

Owning your data is vital to store and manage it for maximum efficiency and application. Up-front transparency of ownership, permissions and third-party restrictions will aid in sharing your data with other partners and collaborating with integrated data long-term.’

Checking and Guaranteeing Data Quality

70% of businesses are not maximizing the usage of their existing data, hindering innovation and growth. Better use of data begins with better data.

Delineate’s data quality scoring process is built on four elements: Fair, Representative, Efficient and Real. Every research participant should be treated with respect, with compensation tailored fairly by country and context to maintain respondent attention and value their time appropriately. Sampling techniques and diverse recruitment strategies reduce the opportunity for bias against underrepresented groups.

Advanced verification measures ensure data is collected from authentic respondents, distinguish between real human and synthetic data, and effectively detect fraud and bots.

User-friendly surveys with clear and concise questions, logical survey flow and mobile-friendly formats provide a superior respondent experience and streamline the data collection process. We ensure data consistency by standardizing formats, reconciling identifiers and applying validation rules to detect anomalies, inconsistent patterns, duplicates and missing values. These checks are critical to maintaining data integrity and enabling robust downstream analysis. Embedding these principles and utilizing efficient data collection methods produce actionable and insightful data.

Creating a shared data taxonomy with naming conventions and standardized labels for consumer segments identifies where data types will need to be converted to maintain consistency. A combination of both field-level and cross-field validation can identify patterns, outliers and relationships between data sets and guarantee that only high quality data is taken forward for integration.

James elaborates, ‘We protected data consistency and quality for one of our global clients with the introduction of a clear taxonomy against all their brand and campaign assets. The client can instantly find an asset’s type, campaign, local market and sub-brand, and then use LLM-powered conversational AI agents to meaningfully analyze asset performance. By integrating asset data with surveys, diagnostics, likeability and outcome metrics, the company has revealed the magic formula behind which assets perform best, for which occasions, and in combination with other assets and factors.

‘The power of integrated brand and campaign data is the consumer understanding to build the next killer creative. The right data, structured and stored properly, and indexed and used in the right way, informs the creative that not only gets consumers talking but also encourages them to buy.’

Democratizing Consumer Data to Inform Department-Level Decisions

Mapping all teams across your organization and outlining each department’s KPIs will reveal the extent of direct data access, hands-on analysis and level of insights that your connected data strategy needs to deliver. API integrations between ResTech tools, CRMs and analytics platforms can eliminate the need for manual data entry and batch uploads, reducing errors and synchronizing data sets for faster decision-making across teams.

Integration must balance self-serve empowerment with protecting quality and accuracy. Defining clear roles and responsibilities for data management incorporates privacy and security at the heart of data usage, whilst equipping teams to perform their own analysis compliantly. The right match between AI-powered reporting and expert CMI analysis enables relevant interpretation and communication of insights at the right cadences for department-level decision-making.

James proposes, ‘When assessing potential data partners, prioritizing simple, clear solutions will help identify the right vendor to meet your needs. The best providers will offer agility rather than restrictiveness, for example updating your campaign data within 48 hours rather than weeks, as we practise at Delineate. Making sure you have the right team in place – both internal CMI specialists and external partners – is the recipe for truly understanding data and actioning insights.

‘Democratization is a vital feature of any tracker: rather than relying on reporting, your users need to get hold of the information they need, at the moment when they need it. Once you have the number you’re looking for, your partner can work with you to peel back the layers and find the meaning behind that number. Suppliers should work around their clients. The right provider will both empower and support you to continually develop the capabilities of your insights team and their impact on the wider business.’

When building a holistic view of the consumer, the choice of integration partner is as vital as the data itself.

Data validation vendor Truthset reports that 51% of the data used for ad targeting and audience measurement is inaccurate. Chief Insights Officers know that the wrong data leads to the wrong recommendations, but third-party data accuracy cannot be improved without truly collaborative vendor partnerships.

Insights leaders are creating symbiotic partner relationships that improve data quality by:

• Clearly defining roles and responsibilities. Setting goals, KPIs and metrics outlines the tangible deliverables and standards suppliers will be expected to meet. Scheduled review meetings and regular updates continually assess supplier performance against their commitments to maintain data accuracy and produce relevant analysis.
• Aligning agency activity with business decision-making needs through a comprehensive partnership plan. Sharing the questions your business needs its third-party data to answer and the relevant context helps vendors understand how they can provide the most valuable data and analysis for you.
• Ensuring a consumer-first approach to integration by balancing privacy needs with tracking consumer behaviour. Due diligence reviews and minimum security requirements guide compliant data collection that protects sensitive information and maintains consumer trust.
• Mitigating errors with Standard Operating Procedures that promote consistency of data collection and analysis across channels, formats and levels of granularity. Requiring vendors to demonstrate their data sources, collection processes, testing and validation methods confirms the legitimacy of data acquisition.

Genuine partnerships that create real value are built on trust and mutual understanding. An environment of mutual information sharing is the foundation for risk identification and crisis management. Transparency established through proactive communication keeps vendors and brands consistently aligned to initially agreed goals.

Delineate tracks brand and campaign data across 100 markets, integrating data sources and types for fast, accurate insights. Our flexible technology is underpinned by industry expertise, to serve as the insights partner that drives competitive advantage.

We share all consumer data to help Insights & Analytics teams understand the live and long-term impact of brand and campaigns on consumer behavior. Our transparent partnerships are revealing previously hidden insights for the world’s most valuable CPG brands.

Find out how Delineate can help your CMI team make smarter, faster decisions.