When organizations fail to utilize their data fully, they overlook business-critical insights, resulting in less informed decisions and missed opportunities.
With data maturity, you can transform every click, view, and interaction into important insights that inform your marketing strategies.
This article looks into the data maturity model’s various stages, offering insights to help businesses transition from data novices to experts.
Data maturity measures the sophistication and effectiveness of how a company collects, manages, analyzes, and uses data to guide decisions and strategies.
Rather than relying on gut decisions or defaulting to traditional strategies, data-mature companies use qualitative and quantitative data to make decisions about their business.
Example: Netflix collects viewing data from its subscribers using sophisticated algorithms to recommend shows and movies tailored to individual preferences.
Using data maturity in programmatic advertising, your company could analyze the purchasing behaviors of your high-spending customers and then target lookalike audiences with personalized ads. This optimizes ad spend and increases your potential conversion rate as you’re targeting not just any audience but the right one.
A company’s data maturity is characterized by five stages: initial, managed, defined, measured, and optimized. These stages help organizations assess where they stand in their data journey and the steps needed to progress further.
Let’s examine the typical stages of data maturity:
The first stage centers on discovering the customer data at your disposal, also called First-Party data. Data is collected and stored, but you may not understand its potential and have no standardized processes in place for data management.
Within many organizations, silos between teams and functions blur the road toward a unified approach to data maturity. Consequently, each team operates with its interpretation of data best practices, leading to disparities in the way different teams analyze disparate data points using various tools.
At this initial stage of data maturity, decisions are often made without data support, and initiatives lack proper measurement for assessing their impact.
As your data maturity journey progresses, you’ll establish basic data management practices. These processes include data collection, storage, and basic reporting. However, there might not be a comprehensive strategy for data utilization, and data remains primarily descriptive.
Your leadership recognizes the value of exploring data-driven tools, improving data utilization, and identifying existing data gaps. Leaders prioritize investments in data collection and management, establishing best practices for roadmap planning and post-launch analysis.
Success metrics are integrated into project briefs and campaigns, and post-mortem analyses become standard. Team members receive fundamental analytics training and access to self-service tools for real-time problem-solving.
You’ll formalize your data strategies during this phase, emphasizing data quality and governance. This involves establishing transparent data management processes and defined roles within the organization, with a central goal of enhancing the reliability and accessibility of your data.
Data takes center stage, driving the core of your strategies and operations. Each initiative is powered by a shared ambition to realize tangible business impact through the prism of data-driven decision-making. Teams are increasingly skilled at harmonizing digital experiences with critical business KPIs, ensuring data is pivotal in every facet of your organization’s endeavors.
The emphasis shifts from data collection to meticulous performance metrics tracking and optimization. It’s about making every piece of data count and extracting maximum value from your information resources.
You’re not just accumulating data; you’re harnessing its potential to unearth valuable insights. These insights become the cornerstone of your strategies, enabling you to make well-informed decisions that drive growth and innovation.
You’ve reached the pinnacle of data maturity when data becomes a strategic asset driving innovation, customer experiences, and business growth. Advanced analytics and machine learning extract deeper insights, leveraging data to make real-time decisions.
Everyone across your teams looks at the same numbers and dashboards. At this point, your focus shifts to fine-tuning your data strategy.
Organizations gather rich, direct, and valuable First-Party data from their audiences throughout their data maturity journey. Using this data is pivotal for successful data maturity.
Some common First-Party data types include:
Incorporating First-Party data into your data maturity journey ensures compliance with evolving data privacy norms and offers unparalleled insights into your customer base.
Data organization and cleansing are pivotal to accurate, reliable, and actionable data throughout the data maturity journey.
Let’s explore how these aspects relate to key areas within an organization:
Clean and organized data gives decision-makers the confidence that their choices are based on accurate information.
Example: A programmatic advertising agency utilizes clean data to make real-time decisions on ad placements. Accurate data ensures their bids are strategically placed, maximizing their client’s return on investment (ROI).
For marketing personalization segmentation and customer engagement, data must be organized to send the right message to the right audience.
Example: An e-commerce platform uses clean customer data to segment users based on their purchase history. This allows them to send personalized product recommendations and tailored ads, increasing customer engagement and sales.
A well-structured dataset allows organizations to track and analyze the entire customer journey, identifying pain points and opportunities for improvement.
Example: A travel booking platform uses programmatic advertising to re-engage users who abandoned the booking process. Clean data helps track user interactions across devices, allowing them to retarget potential customers with tailored ads and incentives.
Data cleansing ensures that sensitive customer information is handled in compliance with data protection regulations, reducing the risk of legal and financial repercussions.
Example: An ad tech company ensures that user data used for programmatic advertising complies with GDPR regulations. Clean data practices help them avoid legal issues and fines while maintaining user trust.
Clean data streamlines processes and reduces the time and effort required to access and analyze information.
Example: A manufacturing company centralizes its product data in a clean and structured database. This simplifies product information retrieval, streamlines production processes, and accelerates time-to-market.
Data accuracy and quality are paramount for organizations aiming to monetize their data.
Example: A media publisher leverages programmatic advertising to monetize its website traffic. Clean data on user demographics and behavior allows them to offer highly targeted ad inventory to advertisers, resulting in increased CPM rates and revenue.
The destination of the data maturity journey typically involves aggregating your data back into a centralized system, such as a Customer Relationship Management (CRM) platform or a dedicated database for easy organization. This aggregation is the foundation for further data analysis, visualization, and decision-making.
However, choosing between a CRM and a dedicated database depends on the organization’s complexity, expertise, and specific needs.
Aggregating data into a CRM system offers a comprehensive perspective on customer interactions and relationships. This approach is beneficial for organizations prioritizing customer engagement and relationship management.
CRM systems often feature integrated visualization and reporting tools, facilitating easier insight derivation.
Some Examples of CRMs:
Database aggregation is most suitable for organizations with intricate data demands or those desiring custom analytics. Dedicated databases support advanced analytics, machine learning, and bespoke visualization tools.
These databases allow for enhanced flexibility in terms of data storage and retrieval.
Some Examples of Databases:
Businesses often choose between CRM systems and dedicated databases, each presenting unique advantages and challenges.
Here’s how the two compare:
Understanding your current position on the data proficiency spectrum is crucial for charting the next steps in your data journey.
To gain clarity, engage with your team with these probing questions:
Reflecting on these questions will pinpoint your organization’s data maturity and foster a culture of continuous improvement and data-driven decision-making. Remember, the journey to data maturity is iterative and requires regular introspection.
Collecting, managing, and successfully using First-Party data can be a challenge for many organizations.
At KORTX, we can assist you in your data maturity journey. We offer a range of services and solutions to help you with your data needs, like helping assess your current data maturity, setting future data-driven goals, and helping bring together different data streams.
With Axon Audience Manager, our advanced customer data platform (CDP), we make it easier for you. Axon combines varied data sources into one cohesive dashboard, enabling you to maximize the full potential of your First-Party data easily.
As evidenced by leading companies, high data maturity translates to more informed decisions, heightened business outcomes, and a competitive edge in the market.
Therefore, businesses must assess, nurture, and optimize their data management practices to truly realize the immense data potential and confidently navigate the future. The journey from initial data collection to optimized utilization might be complex, but the rewards are undoubtedly transformative.
Eric Lee is the Co-Founder and COO at KORTX.
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