Tuesday, September 13, 2022

Data Governance Maturity Assessment Questionnaire

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And What Is A Data Governance Maturity Model

Data Governance Explained in 5 Minutes

A data governance maturity model is a tool and methodology used to measure your organization’s data governance initiatives and communicate them simply to your entire organization. In a mature organization, all the processes to manage, access, and innovate using data assets are in place. Less advanced organizations can use the maturity model to achieve this objective.

There are a handful of well-known data governance maturity models, including examples from IBM, Stanford, Gartner, and Oracle. These models provide a method by which a business can learn how to manage data effectively, provide user access, ensure that data is of high quality, and make it possible for everyone in an organization to benefit from these advances.

When a company achieves the highest level of data governance maturity, it will see palpable results. Company-wide, data will be used to innovate and collaborate and make better business decisions, while these same organizations will avoid the huge fines that arise when data protection regulations are not observed.

Assessment: Main Pain Points

In the ad-hoc assessment you want to first understand the pain points. I personally like to note these pain points from a data perspective, but at the same to understand what’s the impact to the business. So looking at this from a data perspective I recommend categorizing your assessment by 3 streams:

  • Data acquisition/ creation
  • Data maintenance
  • Data dissemination
  • I recommend this order because at a high level this is the process that data goes through. Before I cover each one of these, please note that you can also include “data destruction” and data archival as a stream, but those pain points don’t tend to be as dire as the other streams.

    When to include “data destruction”

    I recommend including the 4th stream that of “data destruction/ data archival” if your main driver for your data governance program is a regulatory compliance. Most likely you would need to have your data governance program’s immediate focus on data retention policies and procedures, the right to be forgotten in the case of GDPR and so on.

    How to gather data for your assessment?

    I would start in an informal fashion through meetings and interviews, and this could be in person or via email or a Zoom/Teams session or a phone call. You can also run a survey which has a more formal connotation and a lot easier to analyze the data collected.

    Example of main pain points

    Here are some examples of main pain points, from a data perspective, that you might gather from your stakeholders:

    Data acquisition

    Data Governance Maturity Model Ibm

    Introduced in 2007, this data governance model addresses a total of 11 domains mentioned below:

  • Data risk management and compliance
  • Classification and metadata
  • Audit information, logging, and reporting
  • This model consists of a total of five levels. Lets take a quick look at the characteristics and the action items required for each level:

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    Research Method And Results

    A DSR approach is adopted here to develop and test our new artifact: a data governance maturity assessment tool. Specifically, the six steps of the DSR approach proposed by Peffers et al. were followed: Problem identification and motivation, Definition of the objectives for a solution, Design and development, Demonstration, Evaluation, and Communication. These steps were chosen since they are clear and simple and also because they cover the key requirements of DSR as stated by Hevners et al. . In addition, throughout steps 1, 4 and 5, members of the industry and experienced domain experts were consulted and asked to provide key suggestions, comments and feedback in order to help us develop our new artifact. More precisely, members of the industry and experienced domain experts were consulted to clarify the artifacts objectives and to assess its usefulness, relevancy, reliability, validity and effectiveness . They were identified via personal contacts and semi-structured interviews were used to collect their suggestions, comments and feedback during these steps. For each interview, research notes were taken and then transcribed immediately after each interview. The following paragraphs detail what was done and the key findings of each step.

    Define The Objectives Of A Solution

    Stanford Data Governance Maturity Model  LightsOnData

    During step 2, findings from our interviews conducted in Step 1 and the knowledge gathered during a review of the relevant literature was used to infer the objective of our artifact. In broad terms, we wanted to develop an artifact to help organizations assess their own level of data governance maturity. Specifically, our objective was threefold. First, our artifact should help organizations know, before the realization of their data governance initiatives, which data governance processes, policies, practices and/or structure should be developed and prioritized. Second, our artifact should also help organizations evaluate, after the implementation of data governance initiatives, if those initiatives have allowed them to evolve in terms of data governance maturity. Third, our artifact should be aligned to already existing data governance maturity frameworks and data governance methodologies . With this aim and objective in mind, we elected to design a data governance maturity assessment tool.

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    How To Do A Great Data Governance Assessment

    When starting a data governance program, one needs to do a data governance assessment and understand current status, challenges and priorities. This as-is analysis will help you put together a business case for data governance and understand what you should first tackle.

    In this data governance assessment we’ll uncover:

    • The main pain points
    • The as-is technical, information, and data landscape

    Full Toolkit: 4 Assess Maturity Level

  • Contact and find us
  • This step in the process uses the data capability assessment to determine your organisation’s current level of data management maturity.

    Assess the data which powers your institution. Consider only structured data, but not just student data. Its important to include finance, HR, estates, etc. Each question assesses one of four blocks of maturity: people and culture, business process, data activities and technology.

    Choose an answer from the assessment that best represents the overall institutional fit. The five answers represent the capability levels: chaotic, predictive, stable, proactive and predictive.

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    Data Governance Maturity Model Gartner

    First presented in 2008, this data maturity model looks at the enterprise information management system as one single unit. It has five primary goals, as follows:

  • Data integration across the entire IT portfolio.
  • Unification of content throughout the organization.
  • Integration of master data domains.
  • Smooth flow of information across the organization.
  • Metadata management and semantic reconciliation.
  • This maturity model has a total of six stages of maturity. Each stage has its own attributes and action items. Lets take a look at each stage in detail:

    Assessment: Technical Information And Data Environment

    Data Governance Explained

    This last part of the assessment aims to understand the technical, information, and data environment. It’s not very important to understand all the details at this point, but it’s a nice to have as it can help during your data governance program implementation. I recommend breaking this down by:

    • Data sources/ systems
    • Data management and data governance tools
    • Other artifacts

    Data sources/ systems

    Here is good to be aware of what systems and databases are currently in your environment. There are different asset management tools that can track all this, but if there isn’t one you can always track it in a spreadsheet and here is a template that I use.

    Besides this I also recommend a data flow. A high level diagram such as the one below could give you an understanding of the different data sources and systems in your ecosystem and how they are interacting with one another.

    If you don’t explore this at this point, this is an artifact that you can put together at a later date as it will help you identify some of the technical data stewards, system and business owners that should be engaged for various projects.

    Data management and data governance tools

    The next on the list are those data management and data governance tools such as the business glossary, data dictionaries, data catalog. Do they exist? For the most part, probably not, but is there anything on

    • Data visualization tools
    • Data security and so on

    Other artifacts

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    Problem Identification And Motivation

    During step 1, exploratory interviews were conducted with eleven members of the industry to assess whether data governance was a preoccupation for them, what were the challenges they faced regarding data governance, whether they knew what were their organizations strengths and weaknesses regarding their data governance processes, policies, practices and structures, if they knew which data governance initiatives they should prioritize and what each should address, and if an DGM assessment tool could help them develop and deploy a DGF tailored to their needs.

    To help them develop, deploy and improve the data governance processes, policies, practices and structures of their organization, respondents mentioned they used various data governance methodologies as a guide. However, respondents indicated that these methodologies did not provide any tool to assess the maturity level of their organization in terms of data governance. Respondents said that such a tool would be more then welcomed as it would help them pinpoint the strengths and weaknesses of the their actual DGF. As such, respondents saw our DSR effort as a way to obtain such a tool and a means by which they could adequately define and prioritize the data governance content to be develop and deployed within their organization.

    Data Management Maturity Assessment

  • 1. Topic: Data Management Maturity Assessment Making data based decisions makes instinctive sense, and evidence is mounting that it makesstrong commercial sense too. Whilst being aware of this kind of potential is undoubtedlyvaluable, knowing it and doing something about it are two very different things.So how do you go about becoming a data driven organization?And how does the Data Management Maturity Assessment help in achieving your datastrategy goals?Speaker: Firas HamdanData Management and Analytics Professional with more than 15 years experience working inthe Technology and Information industry, providing technical, management and consultingservices, and building and leading innovative teams in challenging complex projects.In Australia he has been working on big data projects with major organisations such asDeloitte, Cloudera, Caltex, Channel 7, Optus, CBA, AusNet and NBN, where he hasdemonstrated success at all functions as well as every aspect of data analytics and datamanagement.
  • 2. Data Management MaturityAssessmentDAMA Sydney – September 2019Firas Hamdan
  • 3. DAMA Sydney Firas HamdanA data strategy defines how an organization achieves specific business goals through the strategic use ofits data assets.
  • 8. DAMA Sydney Firas HamdanBusiness DriversOrganizations conduct capability maturity assessments for a number of reasons: Regulation Data Governance Organizational readiness for process improvement Organizational change New technology Data management issues
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    Data Governance Maturity Models Explained

    It is a good practice to assess the maturity of your organizations system periodically. Maturity is the quantification of an organizations ability and scope for improvement in a particular discipline.

    A high level of maturity implies higher chances of improvement after the occurrence of an error or any incidence for that discipline.

    These improvements could be either the quality or the use or implementation of the resources within the organization.

    Data maturity models help companies understand their data capabilities, identify vulnerabilities, and know in which particular areas, employees need to be trained for improvement.

    It also helps organizations compare their progress among their peers.

    With maturity assessment, there is never a one model fits all situation. Although individual models for different organizations and vendors do exist, most follow the Capability Maturity Model method.

    Here, we will go through two Data governance maturity models developed by two different vendors. Lets dive right in.

  • Level 5: Optimizing
  • Ibm Data Governance Maturity Model

    Table 5 from Assessment to COBIT 4.1 maturity model based on process ...

    The IBM data governance maturity model is one the most widely recognized. Developed in 2007, the model is designed to help you determine your progress across 11 core data governance areas. These include data awareness and organizational structure, data policy, data stewardship, data quality management, data lifecycle management, IT security and privacy, data architecture, data classification, compliance, value creation, and auditing.

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    Data Governance Maturity Models And How To Measure It

    Summary: A data governance maturity model is a tool and methodology used to measure your organization’s data governance initiatives and communicate them simply to your entire organization.

    Want to know your organizations data maturity? 

    The questionnaire includes an in-depth assessment of your organizations data maturity with immediate results.

    How To Assess Data Management Maturity

    A practical and pragmatic approach to the implementation of data management that delivers quick wins is one of the key challenges of any data management professional. Sooner or later, you will deal with this at one point in your career.

    In the series of presentations on Practical implementation or optimization of data management with the Orange model, I share with you my practical experience of the past 10 years. This experience has led me to develop a new model and practical method for the implementation and optimization of data management. This method is a collection of techniques and templates that can be used for performing various tasks related to the development and optimization of data management in your company. Today we discuss how to assess data management maturity.

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    Data Management Maturity Self

    Are YOU curious to find out how mature is the Data Management in your organization?

    If YES, we are pleased to invite you to leverage our self-assessment diagnostic tool.

    For us at Deloitte, Data Management is a topic of focus while staying at the forefront of global trends and anticipating our Clients needs.

    • According to Forrester Research, up to 73% of all data within an enterprise goes unused for analytics.
    • As per Gartner, Inc. 87% of organizations have low business intelligence and analytics maturity.
    • Recent Deloitte studies confirm that employees in digitally mature organizations have higher levels of interest and enthusiasm and are provided with breath of opportunities to innovate in their jobs.

    These findings highlight uncaptured potential for increase of data assets value.

    Filling in the questionnaire should take no more than 20-30 minutes and upon completion, your organization will be profiled, according to our Data Management Capability Framework.

    We shall provide you with a summary, outlining the data management maturity level of your organization and you will be classified in one of the five maturity levels, namely: late comers | adopters | smart followers | experts | champions. In addition, this pre-assessment would indicate how Deloitte could support you to harness the full potential and value of your data.

    If you choose to participate, it is required to fill in all the questions, in order to provide us with sufficient information for the assessment.

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    Making data based decisions makes instinctive sense, and evidence is mounting that it makes strong commercial sense too. Whilst being aware of this kind of potential is undoubtedly valuable, knowing it and doing something about it are two very different things.So how do you go about becoming a data driven organization?And how does the Data Management Maturity Assessment help in achieving your data strategy goals?

    Making data based decisions makes instinctive sense, and evidence is mounting that it makes strong commercial sense too. Whilst being aware of this kind of potential is undoubtedly valuable, knowing it and doing something about it are two very different things.So how do you go about becoming a data driven organization?And how does the Data Management Maturity Assessment help in achieving your data strategy goals?

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    Which Data Governance Maturity Model Should You Use

    Although there are several data governance maturity models out there, the best known were developed by OvalEdge, IBM and Gartner. As mentioned earlier in this blog, a maturity model is a tool for measuring the level of your data governance capabilities. So, you must ensure that when you adopt a maturity model, you also have in place a data governance framework and roadmap that follows the same methodology.

    When you set out to decide on a data governance maturity model you need to consider many factors. These include key business drivers, the budget required to implement the model, the existing data management and governance framework, and the industry you operate in.

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    What Exactly Is Data Governance Maturity

    Data governance maturity refers to the stage an organization has reached in the implementation and adoption of data governance initiatives. An immature organization will have a great deal of unorganized data and will not be using this data to drive growth. Alternatively, a mature organization will be well-aware of the importance of data as a key business asset and governing and managing it accordingly.

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    Assessing Data Management And Governance Maturity

    Data management and governance implementation can be viewed as a long term process of maturation. Several models and assessment tools are available to help agencies identify their current state, set goals for where they want to be, and create plans for moving up the maturity scale.

    There are several different assessment tools tailored to DOT data programs that can be used or adapted as needed. In addition, several DOTs have created their own tools. Most of these tools are based on a maturity model.

    A typical maturity model could include the following levels:

    • Level 1-Initial
    • Level 3-Defined and documented processes
    • Level 4-Measured and managed processes
    • Level 5-Optimizing processes

    TIPUse a maturity model to identify gaps, prioritize initiatives and track progress over time.

    For TAM information and systems, maturity levels can be assigned to different aspects of data management and governance. Assessments can also be conducted at different levels of the organization from the agency-wide level, to the level of individual information systems .

    Figure 7.4 Example Maturity Model

    Figure 7.5 Folio Describing the Transportation Agency Data Self-Assessment Process

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