A Deloitte study on 50 news and media companies proved that reaching optimal data maturity allows organizations to grow their bottom-line and market share.
Compared to their counterparts, more mature companies were able to drive up to 20% more subscriptions and increase conversion rates by 30%.
How does data maturity drive business growth? In this article, we’ll talk about what it is, and how to measure and improve it.
Let’s dive in.
Enterprise Growth and Data Maturity: Table of Contents
What is Data Maturity?
A company’s data maturity refers to its ability to manage and use its data assets to maximize business value. With high-quality insights, there’s no need to rely on intuition. Employees –technical or not – can easily analyze data to better understand customer behavior and detect industry trends.
A mature data environment typically exhibits the following characteristics:
- Data Governance: Clear policies, procedures, and roles governing data management.
- Data Quality: High-quality, accurate, complete, and reliable data.
- Analytics: Advanced capabilities to gather and analyze data to derive actionable insights.
- A Data-driven Culture: Understanding business data and maximizing its value across the organization.
Data maturity measures how well an organization invests in its enterprise data. But, how can organizations measure their maturity? To evaluate this, they rely on data maturity models.
What is A Data Maturity Model?
A data maturity model identifies a systematic way to assess an organization’s ability to manage its data.
Their main purpose is to evaluate an organization’s current state, identify improvements, and formulate a roadmap to reach a higher level of maturity.
Typically, maturity levels correspond to different degrees of data management capability. A common data maturity model might have stages such as:
- Initial/Ad-hoc: Data management is reactive and fragmented.
- Managed: Basic data management practices are established.
- Defined: Data management processes are documented and standardized.
- Measured: Data quality and performance metrics are tracked and managed.
- Optimized: Continuous improvement and innovation in data management practices.
Data maturity models also measure the flow of data, and how well or poorly data is used. This allows organizations to gauge how effective its data governance practices are, and to see how they stack up against competitors.
Unfortunately, a 2022 HPE research showed that the average enterprise data maturity level was only 2.6 out of 5. Furthermore, only 3% achieved maximum maturity. These figures indicate that many organizations still have a long way to go before fully leveraging their data assets.
How to Achieve Data Maturity
Achieving data maturity requires a tailored approach based on specific business needs. However, there are key steps and considerations every organization should follow:
1. Conduct a Comprehensive Data Assessment
Using data maturity models is the first order of business because doing so helps evaluate existing data practices. It involves identifying gaps in data management processes and understanding current data utilization. This step allows businesses to establish a benchmark for future improvement.
2. Map Out a Data Strategy
Developing a business-driven data strategy allows organizations to prioritize data initiatives based on business impact. For example, an effective data strategy can streamline inventory management and improve demand forecasting in the distribution industry.
Pairing this with clear, measurable objectives can help track the journey to improving data maturity.
3. Implement Robust Data Governance Frameworks
Data governance sets the rules for managing data, which includes policies to ensure the quality, security, and compliance of data. It also establishes data ownership and accountability protocols.
This framework identifies data stewards and governance committees that can reinforce these policies. They put the processes and procedures in place, monitor data quality, and solve data-related issues as they arise.
4. Establish and Maintain High Data Quality Standards
High data quality enables businesses to make better decisions based on better insights. To get to a higher level of data maturity, businesses need to establish steps to maintain it.
This includes developing data cleansing and validation processes, performing regular audits, and creating metrics to measure and track data quality.
5. Invest in Advanced Data Infrastructure and Tools
Investing in the right infrastructure and analytics platforms can support data management and remove data silos. Both are key to improving an organization’s level of data maturity.
Companies need to choose the right data warehouses and ETL (Extract, Transform, Load) tools, according to their specific needs. Implementing analytics platforms and cloud solutions, on the other hand, allows for flexibility, scalability, and cost efficiency.
Additionally, leveraging AI and machine learning tools increases insight and enhances decision-making.
6. Enhance Team Skills and Provide Ongoing Training
93% of surveyed employees believe a lack of data skills is hindering their workforce’s productivity.
Handling data can be overwhelming for non-technical workers. An organization’s job is to ensure their confidence through proper training and continuous guidance.
Companies need to evaluate data skill gaps, and create programs to fill them in. This also includes providing up-to-date data management, and analytics certification programs benefit data professionals as well. For example, training programs on advanced analytics can help employees better understand market trends and risks for financial institutions.
Fostering a learning culture with continuous skill development opportunities can also support employee retention. Data shows that 94% of employees would stay longer at a company that invested in their development.
7. Drive Organizational Change for a Data-Driven Culture
To create a data-driven organization, adequate training needs to go hand-in-hand with Organizational Change Management (OCM). As part of OCM, employees are guided through the whole process of data transformation, addressing concerns and providing continuous support.
Data-driven decision-making should be placed front and center, with leaders instilling it in every team member. To make this work, organizations need to reward and recognize data-centric behavior and success.
8. Commit to Continuous Data Improvement
To attain the highest level of data maturity, companies need to work on sharpening their data strategies over time. This process requires speed and flexibility, so they should lean on agile methodologies to adapt quickly.
The best way to ensure proactive data management is to conduct regular reviews and updates of the practices. Creating a feedback loop allows data users to report issues and suggest improvements, allowing data processes to be continuously refined.
Enterprises Grow with Data Maturity
Understanding data maturity is the first step to improving it within your organization. Unlocking the value of enterprise data requires measuring your current state and building on it.
Infoverity can assist with your data strategy, data quality, change management, and implementing the right tools along the way. Contact us today.
