Big Data Maturity Model

Big Data Maturity Models (BDMM) are the artifacts used to measure Big Data maturity. [1] These models help organizations to create a structure around their Big Data capabilities and to identify where to start. [2] They provide tools that assist organizations to define their data and their organizations. BDMMs also provide a methodology for measuring the state of a company’s big data capability, the effort required to complete their current internship or phase of progress and progress to the next stage. Additionally, BDMMs measure and manage the speed of both the progress and the adoption of big data programs in the organization. [1]

The goals of BDMMs are:

  1. To provide a capability assessment tool
  2. To help guide development milestones
  3. To avoid pitfalls in establishing and building big data capabilities

Key topics to “People, Process and Technology” and the subcomponents include [3] alignment, architecture, data, data governance , delivery, development, measurement, program governance, scope, skills, sponsorship, statistical modeling , technology, value and visualization.

The stages or phases in BDMMs can be used in many organizations and organizations. [4] [5]

An underlying assumption is that a high level of data correlates with an increase in revenue and reduction in operational costs. However, reaching the highest level of maturity involves major investments over many years. [6] Mature internship of big data and analytics. These include internet-based companies (such as LinkedIn , Facebook , and Amazon ) and other non-internet-based companies, including financial institutions (real-time customer messaging and behavioral modeling) and retail organizations ( click-stream analytics together with self-service analytics for teams). [6]

Categories of Big Data Maturity Models

Big data maturity models can be broken down into: [1]

  1. descriptive
  2. comparative
  3. Prescriptive models.

Descriptive Models

Descriptive models assess the current firm maturity through the qualitative positioning of the firm in various stages or phases. The model does not provide any recommendations as to how to improve their big data maturity.

Big Data & Analytics Maturity Model (IBM model) [7]

This descriptive model aims to assess the value generated from big data investments.

Maturity Levels

The model consists of the following levels:

  • Ad hoc
  • Foundational
  • Competitive Differentiating
  • Break Away.

Assessment Areas

Subject Matter: Business Strategy, Information, Analytics, Culture and Execution, Architecture and Governance.

Knowledgent Big Data Maturity Assessment [8]

Consensus of an assessment survey, this big data maturity model assesses an organization’s readiness to execute big data initiatives. Furthermore, the model aims to identify the steps and appropriate technologies that will lead to organization towards big data maturity.

Comparative Models

Comparative big data maturity models aim to benchmark an organization in relation to its industry peers and norms of quantitative and qualitative information.

CSC Big Data Maturity Tool [9]

The CSC Big Data maturity tool acts as a comparative tool to benchmark an organization’s big data maturity. A survey is undertaken and the results are compared to other organizations within a specific industry and within the wider market.

TDWI Big Data Maturity Model [6]

The TDWI Big Data Maturity Model is a model of a large data maturity area and a significant body of knowledge.

Maturity Stages

The different stages of maturity in the TDWI BDMM can be summarized as follows:

Stage 1: Nascent

The nascent stage has a pre-big data environment. During this stage:

  • The organization has a low awareness of big data or its value;
  • There is little to no executive support for the effort and only some people in the organization are interested in potential value of big data;
  • The Organization understands the benefits of analytics and may have a data warehouse
  • An organization’s governance strategy is typically more IT-centric rather than being an integrative business-and-IT centric.

Stage 2: Pre-Adoption

During the pre-adoption stage:

  • The organization starts to research big data analytics.

Stage 3: Early Adoption The Chasm There is a series of hurdles it needs to overcome. These hurdles include:

  • Obtaining the right skill set to support the capability, including Hadoop and advanced analytical skills;
  • Political issues, ie, big data projects, led to the development of the environment and to the control of the government.

Stage 4: Corporate Adoption

The corporate adoption stage is characterized by the involvement of end-users, an organization gains insight and the way of conducting business is transformed. During this stage:

  • End-users might have started operationalizing big data analytics or change their decision making processes;
  • Most organizations would already have some gaps in their infrastructure, data management, governance and analytics.

Stage 5: Mature / Visionary.

Only a few organizations can be considered as visionary in terms of big data and big data analytics. During this stage an organization:

  • Is able to run a big machine with a highly mature infrastructure
  • Has a good-established big data program and big data governance strategies.
  • Executes its big data program as a budgeted and planned initiative from an organization-wide perspective.
  • Employees share a level of excitement and energy around big data and big data analytics.

Research Findings

TDWI [6] did an assessment on 600 organizations and found that the majority of organizations are in Pre-Adoption (50%) or Early Adoption (36%) internships. Additionally, only 8% of the sample has been managed to move towards corporate adoption or being mature / visionary.

Prescriptive Models

The majority of prescriptive BDMMs follow a similar modus operandi in the current situation. Examples are as follows:

Info-Tech Big Data Maturity Assessment Tool [10]

This maturity model is prescriptive in the sense that the model consists of four distinct phases that each plot path to Big Data Maturity. Phases are:

  • Phase 1, Undergo Big Data Education
  • Phase 2, Assess Big Data Readiness
  • Phase 3, Pinpoint to Killer Big Data Use Case
  • Phase 4, Big Data Proof-of-Concept Project.

Radcliffe Big Data Maturity Model [5]

The Radcliffe Big Data Maturity Model, as other models

  • 0 – In the Dark
  • 1 – Catching up
  • 2 – First Pilot
  • 3 – Tactical Value
  • 4 – Strategic Leverage
  • 5 – Optimize & Extend

Booz & Company’s Model [4]

This BDMM provides a framework for not only the ability of organizations to view their current maturity, but also to identify goals and opportunities for growth in big data maturity. The model consists of four stages,

  • Stage 1: Performance Management
  • Stage 2: Functional Area Excellence
  • Stage 3: Value Proposition enhancement
  • Stage 4: Business model transformation

Van Veenstra’s Model [11]

The prescriptive model proposed by Van Veenstra aims at firstly explores the existing big data environment of the organization followed by exploitation opportunities and a growth path toward big data maturity. The model makes use of four phases namely:

  • Efficiency
  • Effectiveness
  • New Solutions
  • Transformation.

Critical Evaluation

Current BDMMs have been evaluated under the following criteria: [1]

  • Completeness of the model structure (completeness, consistency)
  • The quality of model development and evaluation (trustworthiness, stability)
  • Ease of application (ease of use, comprehensibility)
  • Big data value creation (actuality, relevancy, performance)

The TDWI and CSC have the strongest overall performance with steady scores in each of the criteria groups. The overall results are well-documented, they are extensive, well-documented, easy to use, and they are used in business value creation. The models of Booz & Company and Knowledge are close seconds and these mid-performers of big data value creation in a commendable manner, but fall short when examining the completeness of the models and the ease of application. Knowledgable suffers from poor quality of development, having barely documented any of its development processes. The rest of the models, ie Infotech, Radcliffe, Van Veenstra and IBM, have been categorized as low performers. Their ability to behave with business value creation and big data capabilities, they all lack quality of development, ease of application and extensiveness. Lowest scores were awarded to IBM and Van Veenstra, both of them being providing a low level of guidance for the respective maturity model, and they completely lacking in documentation, resulting from poor quality of development and evaluation.[1]

See also

  • Big data
  • Analytics
  • Maturity model
  • Data management
  • Capability Maturity Model

References

  1. ^ Jump up to:e Braun, Henrik (2015). “Evaluation of Big Data Maturity Models: A benchmarking study to support big data assessment in organizations”. Masters Thesis – Tampere University of Technology .
  2. Jump up^ Halper, F., & Krishnan, K. (2014). TDWI Big Data Maturity Model Guide. TDWI Research.
  3. Jump up^ Krishnan (2014). “Measuring maturity of big data initiatives” .
  4. ^ Jump up to:b El-Darwish and. al. (2014). “Big Data Maturity: An Action Plan for Policymakers and Executives”. World Economic Forum .
  5. ^ Jump up to:b “Leverage a Big Data Maturity Model to build your big data roadmap”(PDF) . 2014.
  6. ^ Jump up to:d Halper Fern (2016). “A Guide to Achieving Big Data Analytics Maturity”. TDWI Benchmark guide .
  7. Jump up^ “Big Data & Analytics Maturity Model” . IBM Big Data & Analytics Hub . Retrieved 2017-05-21 .
  8. Jump up^ “Home | Big Data Maturity Assessment” . bigdatamaturity.knowledgent.com . Retrieved 2017-05-21 .
  9. Jump up^ Inc., Creative Services by Cyclone Interactive Multimedia Group, Inc. (www.cycloneinteractive.com) Site designed and hosted by Cyclone Interactive Multimedia Group. “CSC Big Data Maturity Tool: Business Value, Drivers, and Challenges” . csc.bigdatamaturity.com . Retrieved 2017-05-21.
  10. Jump up^ “Big Data Maturity Assessment Tool” . www.infotech.com . Retrieved 2017-05-21 .
  11. Jump up^ van Veenstra, Anne Fleur. “Big Data in Small Steps: Assessing the Value of Data” (PDF) . White paper .