What is a data-driven business?
In simple terms, a data-driven business prioritizes data in its decision-making processes.
This means that rather than relying on assumptions or intuition to guide business decisions, everything is driven by solid data analysis.
The benefits of becoming a data driven business are significant:
- Better clarity about your target audience
- Stronger connections with potential customers
- The ability to incorporate personalization into marketing communications
- Better insight into the most effective marketing channels and campaigns
- Better resilience to changes in market conditions
- The ability to make decisions more quickly and efficiently.
In terms of business performance, one study found that data-driven companies are 19 times more likely to stay profitable and almost 7 times more likely to retain customers compared to non-data-driven competitors.
Yet despite the benefits being clear, 81% of businesses consider the implementation of data-driven strategies to be ‘extremely complicated’.
The reality is that a business doesn’t become data-driven overnight. It's a step by step process. A business is likely to move through several different data maturity stages in the journey to becoming truly data driven.
What are the 5 stages of data maturity?
Understanding the 5 stages of data maturity is important to help businesses progress toward becoming truly data-driven.
The 5 stages are typically referred to as a ‘data maturity model’ and help businesses assess their current data approach and implement improvement strategies.
There are various interpretations and labels for each of the five stages, but perhaps the most user-friendly and easy-to-understand approach is as follows:
Stage 1: Manual chaos
At this early stage, it’s common to find that data is in data silos, with different teams across the business accessing data from different locations without clarity or consistency in how data is analyzed or interpreted.
The lack of standardization and data democratization at this stage can lead to a lot of confusion, time-consuming manual data operations, and a higher risk of human error impacting the quality and accuracy of data analysis.
Stage 2: We need more dashboards
The next stage a company typically goes through in the data maturity model is an enthusiasm to begin collecting data in BI tools and data visualization platforms.
This can often be the most dangerous stage of data maturity, as it can lead to a false sense of confidence and security that the company is data mature.
Centralizing data without the correct data governance strategy or an effective data integration approach means that the full potential of your business data isn't unlocked yet. Data democratization hasn’t yet been addressed, and there are still likely to be issues with the consistency, accuracy, and quality of data.
Stage 3: Single source of truth
At this stage, businesses become more organized and structured in the way they collect and analyze data. At this point, data governance strategies are likely to be put into place, and the business looks for the right ETL or data integration tools to help them more effectively manage their data.
There’s also commonly more focus and prioritization around data democratization, leading to stronger collaboration and more consistent decision-making across the company.
Stage 4: Predictive capabilities
By the time a company reaches stage four, a data-driven approach is well-established throughout the organization.
Marketing teams and decision-makers increasingly rely on data for everyday optimizations and strategic planning. This integration of data into all levels of decision-making enhances business efficiency, driving growth and enabling more predictive capabilities.
Stage 5: Strategic revenue driver
Data analysis is present in every aspect of strategic planning and decision-making for businesses in this final stage.
Data is likely to provide insights beyond analyzing past events, such as providing projections for future trends.
This advanced stage marks the evolution to a truly data-driven business model, where data is a key driver of revenue growth and strategic foresight.
How can you increase data maturity within your business?
After reading through the five stages of the data maturity model, you’ll probably have a fairly accurate understanding of where your business is at the current time. If you’re still unsure you can take our data maturity quiz here.
But how do you progress to the next stage of evolution? What key activities and processes do you need to put into place?
There's no one-size-fits-all answer, as every organization is unique and needs a tailored approach to data governance, data integration, and business data management.
However, it’s likely that some of the things you’re going to need to consider include the following:
Data and technology investment
A balanced approach to data and technology investment is important to evolve through the stages of data maturity effectively.
This means not only investing in the 'hard' aspects like hardware and data automation tools but also focusing on the 'soft' elements, such as upskilling your team and improving data literacy.
A focus solely on implementing tech without investing in the skills to use it can leave you with tools and systems that teams within your business are unable to use effectively.
On the other hand, a highly skilled team without the necessary data automation tools or infrastructure is unlikely to be able to harness the full potential of your data.
You can read more about how to find a balance between investing in technology and people in more detail in our article, The 3 Cornerstones of Marketing Data Maturity.
Historical evidence shows the importance of data and technology investment for becoming more efficient and resilient in the face of global financial challenges like we’re facing today.
Use of automation
Embracing data automation is a significant milestone in a company's journey towards digital maturity.
Choosing to work with the right data integration platform is one of the most significant steps businesses can take toward improving the quality and consistency of their data, improving data governance, and moving toward data democratization.
Automating the data integration process also removes the need for your data team to spend their time on time-consuming and repetitive manual data processing tasks, freeing them up to focus on more meaningful, strategic insight to help drive business performance.
Choosing the right data integration tool is a decision that requires a lot of thought and planning. It’s essential that your chosen solution is able to connect to all your important data sources, transform your data so it’s exactly how your business needs it, have the functionality to support your data governance efforts, and be able to load into your preferred data destination.
Businesses often face two primary internal challenges when trying to work towards a data-driven culture: resistance to change and skill gaps in talent.
Gartner's third annual CDO survey illustrates this, pinpointing "poor data literacy" and "cultural challenges to accepting change" as significant barriers to organizational success with data.
A further Gartner study found that 50% of businesses identified a shortfall in the data skills necessary to extract the true value from their data.
To transition and evolve into a truly data-driven organization, companies need to invest in continuous upskilling initiatives that enhance data literacy across all levels of their business, often starting at the top.
Culture and leadership
Successful digital transformation and the evolution of data maturity across a business must be driven from the top down.
Creating a data-driven culture requires an organization’s leadership team to evolve how it thinks about data and to adapt its business model accordingly.
There needs to be unity across the executive team that turning data into an asset is a top priority.
A clear indication of a commitment to becoming a data-driven organization is the appointment of a Chief Data Officer (CDO). The CDO would assume the responsibility of improving data governance and data literacy across the business and driving the vision to become more data-focused.
It’s not possible to become a data-driven business if you don’t focus on building the technology stack to enable it.
To become truly data-driven, businesses are likely to need technology solutions for storing and querying their data, running front- and back-end applications, using machine learning to run advanced analytical models, and creating data visualization dashboards.
But for any of these DataOps tasks to be successful, it’s important that data pipelines are properly flowing with clean and reliable data.
This is where investing in the right data integration platform is important, to help ensure consistency and standardization of data from all your important sources, enabling your business to implement an effective data governance strategy and loading data into any destination the business needs for analysis and visualization.
Improve data maturity in your business with Adverity
Becoming a data-driven business is essential for efficient and effective decision-making in today's digital world.
However, transforming into a truly data-driven business involves reaching the highest levels of data maturity, which is a process that doesn't happen overnight.
An important step in progressing through the stages of the data maturity model is selecting the right technology and tools to enhance your data management and data governance capabilities.
Adverity is a leading data integration platform with a wide range of features and benefits to help your business become more data mature:
- Connect to more than 600 marketing data sources
- Transform and standardize your data with ease
- Create more value from your data with powerful data enrichments
- Load data into any destination of your choosing
- Ensure a consistent understanding of data with an intuitive data dictionary
Elevate your data maturity with Adverity.
Book a demo today and take the first step towards a data-driven future.