Big Data Maturity in EMEA: Identifying the Big Data Innovators

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The growing focus on Big Data and analytics solutions as a basis for competitive advantage is both an opportunity and a challenge for most organizations. To help organizations assess their current capabilities and to evaluate gaps in reaching higher levels of BDA maturity and competence, IDC has developed a BDA maturity framework that identifies five stages and five critical measures as well as the outcomes and actions required for organizations to effectively move through the maturity model stages.

The five maturity stages are ad hoc, opportunistic, repeatable, managed, and optimized. The five dimensions of maturity are intent, technology, data, people, and process. Click on each of the stages and dimensions below for a description.

Ad hoc Opportunistic Repeatable Managed Optimized

model: Ad Hoc

The primary BDA goal of organizations at the ad hoc stage is to provide decision makers with access to information. This can involve the use of query, reporting, dashboard, and search software simply to expose a defined data set to end users.

model: Opportunistic

Organizations at the opportunistic stage are mainly focused on providing data analysis, but the data will typically lack support from appropriate data preparation and management technology and will be based on incomplete historical data.

model: Repeatable

Organizations at the repeatable stage are involved in recurring, budgeted, and funded BDA projects with business–unit-level stakeholder buy-in. They are aiming to provide comprehensive insights based on data from multiple internal and external structured, semistructured, and unstructured sources.

model: Managed

Organizations at the managed stage experience the emergence of BDA program standards. Their primary BDA goal is to provide actionable insight to a range of decision makers within the organization. BDA capabilities are used to determine what happened and why it did.

model: Optimized

Organizations at the optimized stage ensure continuous and coordinated BDA process improvement and value realization. They have an enterprisewide, documented, and accepted BDA strategy; executive support; and budgeted as well as ad hoc funding (to address unforeseen opportunities). The system delivers the ability to provide foresight to decision makers throughout the enterprise and to relevant external stakeholders.

dimension: People

The people measure is about the technology and analytics skills that technical and business users have, the level of intra- and intergroup collaboration, as well as organizational structures, leadership, training, and cultural readiness.

dimension: Process

Process is about the processes of data collection, consolidation, integration, analysis, information dissemination and consumption, and decision making.

dimension: Technology

Technology concerns the appropriateness, applicability, integration, support for standards, and performance of technology and IT architecture to the relevant workloads.

dimension: Data

The measure of whether data is mature focuses on attributes such as the quality, relevance, availability, reliability, governance, security, and accessibility of multistructured data.

dimension: Intent

Intent is about attributes such as strategy, capital and operational budgets, performance metrics, sponsorship, and project and program justification.

Big Data Innovation and Maturity — How do the Subregions Compare?

With the goal of evaluating Big Data and analytics maturity across EMEA, IDC interviewed 978 organizations across the EMEA region that have adopted or intend to adopt some form of BDA technology. The interview questions covered all five dimensions of the model (people, process, technology, data, and intent) and the responses were translated into maturity "scores."

To understand the best practices from the most successful organizations, IDC extracted the 38 highest scoring respondents, and identified them as "Big Data Innovators." Responses from this group were considered separately to other responses in order to answer the question: "What do the Big Data Innovators do better?"

In assessing the data from survey respondents there are notable differences in terms of Big Data maturity across the subregions, particularly in terms of the geographic spread of the Big Data Innovators:

  • 33 of the 38 Big Data Innovators in EMEA were from northern Europe (France, Germany, the Netherlands, Norway, and the United Kingdom).
  • 5 were from southern Europe ((France, Italy, Portugal, and Spain).
  • There were no Big Data Innovators from either the Middle East (KSA, Kuwait, Oman, Qatar, and UAE) or Africa (Kenya, Nigeria, and South Africa).

click each region to view its Big Data Maturity
45% 39% 54% 50% 53% 47% 50% 42% 47% 47% 6% 11% 2% 0% 0% 1% 2% 3% 1% 0% 0% 0% 0% 0% 0% Ad Hoc Opportunistic Repeatable Managed Optimized Stage 1 Stage 2 Stage 3 Stage 4 Stage 5