Data Professional Introspective: Comparative Data Management (Part 1)

Data-Introspective

Part 2    |     Part 3    |    Part 4 (Coming Soon)


Every now and then it’s useful to reflect on the progress of our industry over the last few decades. Many data management professionals who served time in the trenches of data-oblivious organizational cultures remember how it felt: preaching to deaf ears; a lone wolf howling at the moon; a prophet with no honor in her own land.

While we – the few, the proud, the Data Management Marines – never stopped trying to break down barriers and convince organizations that their data assets were essential components of the overall infrastructure, the acceptance of the need to treat data seriously has been a gradual process, which is now approaching critical mass. Some industries have made more progress than others in building, managing, and optimizing data assets, depending on mission criticality, the competitive landscape that they inhabit, and/or the degree of regulation with which they must comply – all primary motivators.

As with any other endeavor, where an organization focuses its time, talent, and treasure is typically where the most useful results are obtained. In assessing organizations’ data management capabilities against the Data Management Maturity (DMM)SM Model, both achievements and gaps are clearly pinpointed across 20 fundamental data management practice areas. Although each organization is unique and has followed its own path according to its primary motivators, its decisions about technologies and resources, and the drafting effect of major program implementations, trends in capabilities can be distinguished.

In Part 1, we’ll look at the score ranges and average scores of 12 organizations which have conducted comprehensive capability evaluations employing the DMM, explaining the capability levels and highlighting selected conclusions. In Part 2, we’ll examine results in each of the 20 process areas and provide examples of capability strengths and gaps.

The DMM consists of five core data management categories (groupings), with 3-5 process areas per category.

Data Management Strategy – capabilities needed to develop and sustain an organization-wide data management program, create appropriate organizational roles to manage data, and achieve aligned business cases and sustainable funding

  • Data Management Strategy
  • Communications
  • Data Management Function
  • Business Case
  • Program Funding

Data Governance – capabilities needed to support collaborative decisions, describe shared data, and capture knowledge about the data assets

  • Governance Management
  • Business Glossary
  • Metadata management

Data Quality – capabilities needed to establish and maintain timely, accurate and fit-for-purpose data by integrating line of business and information technology activities

  • Data Quality Strategy
  • Data Profiling
  • Data Quality Assessment
  • Data Cleansing

Data Operations – capabilities needed to define data requirements, establish authoritative data sources and data lineage, and manage internal and external data providers

  • Data Requirements Definition
  • Data Lifecycle Management
  • Provider Management

Platform and Architecture – capabilities needed to define a target architecture, enforce data standards, select appropriate technologies, integrate data, and manage historical data

  • Architectural Approach
  • Architectural Standards
  • Data Management Platform
  • Data Integration
  • Historical Data, Archiving, and Retention

The chart below represents the range of data management capabilities in 12 organizations from the financial, health care, technology, and Federal sectors. Each of the organizations received highly detailed results, based on internal consensus about the DMM’s 414 practice statements, and evidence in the form of work products from the 596 example work products that are often produced as a result of performing the practices.

The bars illustrate the composite score range in each process area, from the lowest score assigned to the highest, and the diamonds represent a rounded average score among all of the organizations, to the nearest quarter point.

Let’s talk about Levels. Within a DMM process area, practice statements are organized by best practice capabilities that represent a successive, abstracted path of capability growth for a typical organization. A brief Levels primer:

  • Level 1 – basic practices are being performed, usually project by project. Capabilities are not systematically shared across the organization – Project
  • Level 2 – capabilities are developing and extended across at least one line of business or major multi-business line program (such as a master data hub or a data warehouse) – Program
  • Level 3 – capabilities are well developed, integrated and extended across the organization – Level 3 represents the recommended target for all organizations, yielding the benefits of improved data assets, increased efficiency, and cost reduction – Organization – indicated by the blue horizontal line
  • Level 4 – capabilities are refined by employing metrics and statistical analysis techniques
  • Level 5 – capabilities receive senior management attention and are continuously improved.

Note the wide range of scores in each process area, showing the wide variance in achieved capabilities among these organizations, demonstrating that data management has a very broad scope – it’s difficult to develop strength in every discipline. Scores below Level 1 are indicative of organizational siloes, for example, in Business Glossary and Metadata Management, which is essentially a business problem preventing managing data as shared meaning. The lower scores in the Data Quality category illustrate that some organizations have not emphasized the importance of improving quality beyond specific project implementations. For data quality, effort = reward – if the scope of implementation is limited, the organization’s business processes are impacted.

MECCA1_APR

The higher score ranges, Level 3 and above, show that some organizations have mastered specific capabilities in a number of areas, achieving robust practices that are implemented organization-wide. Score ranges with higher floors, such as Business Case, Governance Management, and Data Integration, show that organizations have invested resources and attention into developing consistent practices. The green diamonds (average scores among all 12 organizations) collectively represent that while data management capabilities have indeed progressed, the average organization is still working primarily at the program level.

So how are we doing? Better, but we have more work to do. In Part 2, we’ll look at each of the process areas in more detail and synthesize situations frequently encountered within the organization.

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About Melanie Mecca

Melanie Mecca, Director of Data Management Products and Services, CMMI Institute, led development of the Data Management Maturity (DMM) SM Model. Her team created a highly interactive method for assessing an organization’s capabilities against the DMM, and she has led numerous assessments for organizations in the financial, Federal, and technology industries. She directed creation of the Building EDM Capabilities, Mastering EDM Capabilities, and Enterprise Data Management Expert (EDME) courses leading to DMM certification. In 30+ years solving enterprise data challenges, Ms. Mecca has architected and implemented data management programs and projects, data strategies and architectures, and designed enterprise data services. She is an active presenter of classes, seminars, webinars and case studies, and is a strong advocate for data management education, with a passion for assisting organizations to realize business value from their data management programs.

  • Chuck Walrad

    Would you be open to writing a chapter on Data Management for the IEEE Guide to the Enterprise IT Body of Knowledge?

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