Moving from data to information to insights to impact is rewarding, but it’s a multiyear journey requiring a holistic approach.
IDC has built the Data-Driven Intelligence Maturity Framework to give organizations a blueprint for success. The framework is built on IDC’s in-depth survey assessing the characteristics and attributes of data-driven intelligence to measure the maturity of EMEA organizations. The framework helps organizations identify their current status and their unique strengths and weaknesses, giving them actionable advice to invest in the right areas to deliver impactful results and make tangible progress in their data-driven journeys.
The three key dimensions that IDC has identified are:
- Exemplary data strategy
- Delivering data for business
- World-class data foundation
These core dimensions cut across technology, business, people, and processes. They are the fundamental building blocks and need to be aligned for success (see Figure 4).
IDC’s Data-Driven Intelligence Framework
Let’s delve into the attributes of each of these dimensions.
Data Strategy: What, Why, Success Factors, and EMEA Organizations‘ Current Maturity in This Dimension
Data exists in every business. But every business does not have a process or strategy to identify which data is valuable and turn that data into insights for business decisions. This is what differentiates highly mature data-driven organizations from the rest. Every business needs a data strategy to successfully transform data from „potential“ to „asset and capability.“
Organizations need a solid understanding and vision around the value of data for business opportunities and long-term digital innovation.
Only 16% of EMEA organizations IDC surveyed are Data Thrivers in the data strategy dimension. 35% are Beginners and 49% are Explorers in this category.
So, Why do Data Thrivers Excel in Data Strategy and What Are the Key Criteria?
81% of Data Thrivers in the data strategy dimension have operationalized their data strategy at scale, compared to just 6% of Data Beginners and 35% of Data Explorers.
Nearly 90% of Data Thrivers say their senior management is driving or are heavily involved in data strategy by driving data governance and compliance, data quality and access, culture and skills, and use of cloud platforms to execute on their data vision. At the same time, 38% of Data Beginners admit that data strategy receives minimal attention and support from senior management and the wider business.
Data Thrivers invest in core areas such as data-driven culture, skills development, data governance, breaking silos, and using next-generation tools to better leverage and visualize data in the long term.
59% of Thrivers in data strategy take a holistic view and focus on adopting new cloud platforms, BI tools, and data management that encompasses all the three dimensions. They demonstrate sharper acumen in data strategy and this helps them accelerate their data-driven journey faster than the market average.
At a Glance: Criteria for Maturity in Data Strategy
- Build a vision and outline clear, achievable objectives to leverage data companywide
- Develop a top-down vision to become data driven and an action plan developed by senior management to execute on this vision by including all stakeholders
- Operationalize a data strategy at scale
- Nurture and grow a data-driven culture, skills development, and data governance; identify and break silos; and promote the use of data-enabling technologies
- Identify gaps and plug those gaps organically or inorganically
- Build a proactive strategy to attract and retain the right data professionals
Benefits of a Data Strategy
A well-outlined data strategy is the first and most critical step to becoming an intelligent digital enterprise, as it enables organizations to turn data into valuable insights for business advantage.
Focusing on a data strategy helps organizations better articulate the data projects that will yield maximum results for the business. It also demonstrates commitment from all the key stakeholders and helps to drive an enterprise wide culture of leveraging data.
Data strategy helps organizations understand the limitations and better address core principles such as data governance, developing skills, breaking silos, determining data ownership, and outlining use cases right at the design point.
Data Beginners and Data Explorers should actively develop a strong data strategy that aligns with their digital vision and mission.
Data for Business: What, Why, Success Factors, and EMEA Organizations‘ Current Maturity in This Dimension
Data programs and initiatives need to be linked to business outcomes. Only one in six organizations can demonstrate quantitatively that data-driven decisions have a positive impact on business outcomes, however. The ability to consistently leverage data to positively impact business outcomes is what makes an organization intelligent. But only 8% of survey respondents said all business decisions are currently driven by data and analytics.
31% of EMEA organizations are Data Beginners in the data for business dimension; 50% are Data Explorers and 19% are Data Thrivers in this dimension.
So, Why do Data Thrivers Excel in Data for Business and What Are the Key Criteria?
86% of Thrivers are driving more than half of their business decisions with data, compared to 20% of Beginners and 51% of Data Explorers.
67% of Data Thrivers can demonstrate quantitatively the positive business impact of data-driven initiatives „to a great extent.“ In comparison, only 4% and 14% of Data Beginners and Data Explorers respectively can do this.
Lack of maturity in this dimension can also have a negative impact. For example, as seen in Figure 5, organisations with limited reliance on data for business decisions saw a higher decline in key business metrics compared to those that are highly data driven.
Just as higher reliance on data helped Data Thrivers improve their profits, revenues, and customer experiences (see Figure 2), the research also revealed that without data, these indicators can decline, especially when faced with unexpected market developments (see Figure 5).
Lack of Data-Driven Approach Results in Decline in Key Indicators
Q. How did your organization perform over the past 12 months on each of the following indicators?
Data Initiatives Driven by IT Alone Are Risky for Data for the Business Dimension
The survey revealed that IT still takes the lead in designing, implementing, and managing data-driven strategies and technologies. 6 in 10 organizations said IT (CIOs/CTOs/heads of technology) are the ones that sign off data/analytics initiatives. However, when it comes to data users, a diverse set of personas, including data teams, business teams, and IT teams, rely on data — suggesting that all expectations from data are not successfully met if IT solely holds the reins.
The strategies, approaches, and technologies preferred by IT are different from the approaches and technologies preferred by business, digital, or data. For example, the survey showed how non-IT personas valued openness of platforms and investment in better searches (federated searches), monitoring, and decentralized data platforms compared to IT users.
Data for business requires everyone to be empowered to use data in their decisions. Data Thrivers are shifting away from this IT-heavy approach to a balanced approach involving business teams. For Data Thrivers, personas such as CEOs, heads of operations, and chief digital officers are significant in signing off new data-driven initiatives. As line-of-business (LOB) users are increasingly deciding or at least influencing data investment decisions and use case requirements, tech leadership is in a unique position to coordinate the C-suite in driving an enterprisewide agenda.
At a Glance: Criteria for Maturity in Data for Business
- Identify and outline business use cases that can be enhanced with insights – better customer experiences or supply chains, for example, or better reporting and forecasting
- See, search, and use analytics data and measure the business outcomes – this requires a fundamental shift in how investments are made across business units, and align them to an enterprisewide strategy
- Develop AI/ML-driven data products and services or monetizing data
- Leverage data visualization, automation and search capabilities to find quick answers to business questions
- Communicate with the strategy and IT teams to align and collaborate
- Focus on skills and training among business users to improve interaction and exploit data for business advantage
- Reward and amplify the business success driven by data and encourage corporatewide use of data
Benefits of Advancing in the Data for Business Dimension
Having data projects that are not linked to business outcomes is like buying a new car without an engine. Linking data programs to business outcomes and clear use cases helps to better measure the benefits and attributes. This linkage also helps to maximize the value of data projects and to adapt programs better.
Ensuring maturity in the data for business dimension helps businesses use data to improve performance, optimize operations, generate new revenue streams, build relevant products and services, improve customer experiences, and build adaptability.
Data Foundation: What, Why, Success Factors, and EMEA Organizations‘ Current Maturity in This Dimension
Data foundation is the technology framework that helps organizations execute on their data strategy vision and meet their data for business objectives. It includes the technology pillars that help a business put into action its data-driven plans today and tomorrow.
The data foundation enables organizations to build a world-class technical foundation to address all the aspects of the data life cycle — from storing to analyzing and transforming the data foundation to meet modern data for business needs and tackle the bottlenecks and challenges that will determine an organization’s success in becoming data driven.
28% of organizations are Data Thrivers when it comes to architecting a solid data foundation. About 47% are Explorers and 25% are Beginners.
The higher number of Thrivers in data foundation compared to Thrivers in data strategy or data for business is a result of data strategies being heavily IT led. But it’s clear that organizations cannot be fully data driven without a solid data strategy and tight alignment with business outcomes.
So, Why do Data Thrivers Excel in Data Foundation and What Are the Key Criteria?
96% of Data Thrivers in data foundation use cloud platforms more robustly for data needs, compared to 74% of Data Beginners. Within the Thrivers, about 27% have cutting-edge modern cloud data infrastructure to explore newer data architectures, such as data mesh at scale. Only a handful of Data Beginners and Explorers are doing this.
As seen in Figure 6, Data Thrivers use public cloud platforms at scale for their data-driven initiatives a lot more than their less mature peers.
Current Use of Cloud Platforms to Become Data Driven
Data Thrivers are boosting their data foundation maturity further and faster across the board, including cloud platforms, BI tools, and other technology capabilities.
For example, 33% of Data Thrivers are enriching their cloud foundation with self-service capabilities, compared to just 12% and 23% of Data Beginners and Data Explorers respectively. Thrivers also exhibit higher maturity in using modern search and monitoring tools, focus on improving data visualization experience, and are ahead in using automation.
Figures 7 and 8 below also reveal how Data Thrivers lead in the use of BI technologies and data governance, management, and quality solutions to strengthen their data foundation dimension.
BI Solutions in Use
Data Governance, Management, and Quality Solutions in Use
Data Thrivers have the right building blocks to give businesses quick, data-driven answers to their questions. As a majority of data initiatives are currently IT led, this is what a best-in-class data foundation looks like for Data Thrivers:
- Data infrastructure at scale with strong tech pillars and data accelerators
- Access to a rich ecosystem of strategic data infrastructure partners
- Beyond infrastructure to modern data architecture capabilities such as modern analytics, BI, and visualization platforms
- Multiple analytics platform management for simplicity
- Expertise in data foundation modernization services, dynamic resource management for cost efficiency, cloud-native platforms, analytics at scale, and data ingestion at scale
At a Glance: Criteria for Maturity in Data Foundation
- Use agile, scalable and modern technologies such as public cloud platforms, cloud-native capabilities, and modern data aggregation, ingestion, and analytics tools
- Cope with the volume, velocity and variety of data and accommodating batch and real-time analytics needs
- Eliminate data silos, management complexities, security risks, and risking costs
- Pivot to coud-based data-warehousing, data lakes, and data management platforms
- Create data self-service capabilities
- Embrace modern BI, data visualization, and search and monitoring capabilities
- Leverage automation at scale for speed and efficiency of data operations
- Enable newer methodologies such as master data management, DataOps, data federation, and data hubs
Shining a Spotlight on Data Foundation as the Cornerstone to Becoming Data Driven
Data-driven organizations have a simple mantra — technology has to move at the speed and scale of data. Public cloud — with its highly scalable infrastructure and innovative data technologies — gives data professionals access to all the tools they need to drive value.
With cloud being everywhere for everything, organizations are not viewing it as an infrastructure to store data. The view is only a starting point — they want to leverage first-party data technologies, cloud databases, data engines, data lakes, and query tools in the public cloud to drive their data strategies at the speed and scale of data. IDC believes that successful organizations use modern compute tools such as serverless, containers, and microservices architectures in the cloud with modern data tools to speed up their innovation and intelligence journeys cohesively.
Forward-thinkers are leveraging public cloud capabilities and can deliver business value with greater impact than non-cloud users. 82% of Data Thrivers are looking to use cloud as their data foundation at scale, enrich their cloud foundation with modern cloud-friendly processes, or even add modern cloud data lake houses or data mesh architectures; only 27% of Data Explorers are doing the same.
Alongside cloud, data management platforms such as first-party cloud storage, container data services, backup platforms (that can ingest software as a service or SaaS data or historic data into data pipelines), and observability tools that help monitor microservices and mesh environments can help build resilient IT environments for data-driven outcomes.
One thing is clear — becoming data driven is an enterprisewide initiative and requires complete buy-in from all stakeholders, including management, business users, IT, governance, developers, and data science teams. It all starts with a data strategy, aligning data strategy with business objectives, and bringing the strategy to action with data foundation.