Thursday, June 16, 2022

Data Governance Process Flow Chart

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Data Management Process Description

Visualize key cloud governance data in your architecture diagram

Manual Transmittal

This transmits new IRM 2.120.11, Solution Engineering, Data Management Process Description.

Material Changes

This transmittal replaces IRM Part 2 Chapter 152 Data Engineering, Section 2 Data Management dated November 22, 2019 due to Solution Engineering IRM Restructure Project to reflect engineering process with projects, and incorporate into the IRM Engineering Chapter a new Section.

Change Catalog number from 59989Q to 74890K

Change IRM Chapter Number from 152 to 120

Change IRM Section Number from 2 to 11

Add new external process interface between Data Management Process and Naming Data Element/Object

Replaces Technical Solution external process by Solution Design, Interface, and Hardware external processes

Delete Data Engineering Policy in the Table of Control Section 2.120.2.1.5.1

Add Agile Vision, Scope and Architecture, and Simplified Design Specification Report to the Inputs, and Outputs Table and Data CollectionStep 1.9, and Step 1.10

Effect on Other Documents

Audience

Effective Date

2.120.11.1

Data Management And Data Governance Definitions

According to DAMA DMBOK®, data management is the business function of planning for, controlling and delivering data and information assets. Youll notice that this definition has a strong emphasis on a key principle: treating information and data as an asset.

A key sub-function of data management is data governance, which is defined by DAMA DMBOK® as the exercise of authority and control over the management of data assets. In other words, data governance is needed to ensure that the information assets of the organization are valued and managed appropriately.

Goals Of Data Governance

The goal is to establish the methods, set of responsibilities, and processes to standardize, integrate, protect, and store corporate data. According to BARC, an organizations key goals should be to:

  • Minimize risks
  • Improve internal and external communication
  • Increase the value of data
  • Facilitate the administration of the above
  • Reduce costs
  • Help to ensure the continued existence of the company through risk management and optimization

BARC notes that such programs always span the strategic, tactical, and operational levels in enterprises, and they must be treated as ongoing, iterative processes.

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Provides An Opportunity To Become More Efficient And Increase Profitability

Mapping out your processes in a workflow allows you to get a more precise, top-level view of your business. Even if you have a well-established set of business processes, do you know if they deliver you results? Are the processes as good as they could be? Think about how your business might benefit from following proven workflow diagram examples in order to streamline and optimize all your varied processes. Time is money, and efficient workflows save you time.

One of the biggest reasons why workflow is important is because it gives you greater insight into your processes. Using the insights from the workflows provides an opportunity to become efficient and improve its bottom line.

What Does A Data Governance Process Model Look Like

Data Governance Policy  Office for Information Technology

A data governance model is a framework that establishes and defines processes related to data creation, data-driven storage, maintenance, and data disposal. There are numerous different types of data governance process models that are built with the specific needs of industries and businesses in mind.

The four most common governance models include:

  • This model consists of individual business users that utilize, create, and manage their own data.
  • This model enables multiple businesses to use and share customer or vendor data from master data pools.
  • Centralized Data Governance Model This model comprises both single and multiple business units with centralized maintenance of master data.
  • Centralized Data Governance Model with Decentralized Execution This model provides a centralized data governance commodity that is responsible for defining the overall data governance framework and policies.
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    What Is A Data Governance Policy

    Before you implement data governance, you need a data governance policy: a set of rules for safeguarding an organizations data assets. Data governance policies center on establishing roles and responsibilities for data that include access, disposal, storage, backup, and protection.

    Data Governance Policy Template

    You can use this template to build your data governance policy. It will aid in establishing data quality and reliability standards, as well as standards associated with access, storage, and backup to promote data security.

    A Sample Data Management And Data Governance Strategic Roadmap

    Suppose you want to create a data management and data governance strategic roadmap. What would that look like and how can you go about building it? Were going to walk through a very simple capability-based approach to building a strategic roadmap, which we call the Jibility Steps.

    To build any roadmap, we need to start with understanding why were doing what were trying to do. Then we analyze what needs to change in order to visualize how we will ultimately achieve our strategic objectives.

    There are six Jibility Steps: Challenges and Objectives to understand why Capabilities and Courses of Action to analyze what then, finally, Initiatives and Roadmap to visualize how.

    For this example, were going to explore the data management and data governance roadmap for an example organization called RedYabber. RedYabber is a traditional wooden toy manufacturing and sales company that has made a strategic decision to undertake digital transformation. Some key pillars for this transformation include shifts from:

    • Traditional store front sales to online sales
    • Heavily labour-intensive manufacturing to automated manufacturing
    • Experiential and intuitive decision-making to data-driven decision-making

    For the purposes of this example, well be focusing on the third pillar.

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    How To Research And Plan The Process Flow

  • Define the scope of your process to be studied and what you hope to gain.

  • For an advanced process such as at an industrial plant, the research may be done through a project team, quality control group or a consultant. For a smaller, more basic process, you might do this yourself, perhaps even starting with sticky notes.

  • Study the equipment, activities and relationships through observation and interviews. If youâre modeling a brand-new process, study whatever data is available, including standards for whatever is being produced in the process.

  • Draw a draft diagram and confirm it with people involved in the process. Make any necessary changes, additions or deletions in collaboration with them.

  • Now the diagram can be used for its intended purpose of documentation, quality assurance, improvement or whatever other goal there might be.
  • Capability For Practical Analysis Of The Process Leading To Improvement Opportunities

    How to Conduct GDPR Data Flow Mapping

    Precise and up-to-date documentation of the current processes provides an opportunity for problem-solving when things do not run smoothly or when an analysis is required due to a change of policy or circumstances or application of new technologies.

    Unfortunately, because a business evolves, unnecessary and redundant tasks creep into the work practices that take place daily. Once you have more insights into your processes, you can determine what activities are essential while identifying and eliminating redundant, non-value-adding tasks and activities. This, of course, has countless benefits and creates value for your business.

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    Data Governance Framework Best Practices Definitions And Examples

    Inside Out Security Blog » Compliance & Regulation » Data Governance Framework Best Practices, Definitions and Examples

    Its 2020, do you know where your data is? If you answered yes to that rhetorical question, you have a decent grasp of data governance. If not, its time to start to figure that out. Either way, read on to learn more about data governance and how Varonis can help automate you out of a big hole.

    If you need more convincing about why you need data governance, check out the Varonis 2019 Data Risk Report. Its an eye-opener.

    Eric M Fair Cisa Cdpse Cbcla

    A critical component to understanding how an organizations data travels throughout its lifecycle is to develop business processes and data flow diagrams. Successful business process and data flow diagrams are achieved by conducting meetings with business unit representatives, as well as representatives from information technology . These meetings allow for both business unit and IT representatives to develop a baseline understanding of what data is collected and how it is used, processed, shared, stored and retained within and outside of the organization.

    The following components should surface throughout this exercise, to then determine how to protect and effectively control personal data:

    • Who collects, uses and maintains personal data relating to individuals, customers and employees?
    • What types of personal data are collected and what is the purpose of collection?
    • Where the data is physically stored?
    • To whom is the data transferred/shared?
    • When and how is the data collected?
    • How long is it retained and how is it deleted?

    Additional Schneider Downs Data Privacy Services

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    Making Flow Charts For Master Data Management Operations

    It can be very difficult to visualize the flow of master data work activities or the flow of organizational data itself. Flow charts will be useful in conceptualizing master data work activities or processes that are less tangible than other organizational workflows . Organizations can gain advantages over their competitors by creating sound data governance procedures and monitoring those procedures through flow charts and periodic data review audits. Here are a few ways:

    Grow Up Kid: The Maturity Model

    IT Governance Flow

    Measuring your organization up against a data governance maturity model can be a very useful element in making the roadmap and communicating the as-is and to-be part of the data governance initiative and the context for deploying a data governance framework.

    One example of such a maturity model is the Enterprise Information Management maturity model from Gartner, the analyst firm:

    Figure 2.

    Most organizations will before embarking on a data governance program find themselves in the lower phases of such a model.

    Phase 0 Unaware: This might be in the unaware phase, which often will mean that you may be more or less alone in your organization with your ideas about how data governance can enable better business outcomes. In that phase you might have a vision for what is required but need to focus on much humbler things as convincing the right people in the business and IT on smaller goals around awareness and small wins.

    Phase 1 Aware: In the aware phase where lack of ownership and sponsorship is recognized and the need for policies and standards is acknowledged there is room for launching a tailored data governance framework addressing obvious pain points within your organization.

    Phases 4 and 5 Managed & Effective: By reaching the managed and effective phases your data governance framework will be an integrated part of doing business.

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    Data Governance Operational Model

    To create an operations process you need to thoughtfully design three core areas: process, organization, and technology. For data governance and the Playbook, these areas are:

    Process: The day-to-day approach to conducting the core data governance jobs listed in Chapter 1. This includes the improvement process used to refine the Playbook.

    The Playbook provides a detailed description of the operations processes.

    Organization: The types and locations of human resources that are part of executing the data governance process. The organization also needs to include the oversight and management elements. Most organizations have found that since the data governance jobs and processes cut across functional groups, many data governance resources are part-time.

    The Playbook calls out the roles used in the process. Each role may be filled by one or more human resources in the organization. Taken together, the Playbook roles define the data governance organization.

    In addition to the core data governance process, organization, and technology areas, you will also need to ensure that the operational process is improved and tracked over time. Nearly all operational processes of significant scale are tracked through metrics. Some operational processes have extensive measures that are identified and reported through a scorecard. Many operational applications have built-in support to collect data for calculating the measures.

    David Loshin, in, 2009

    Deepening The Support For Data Governance

    Figure 19.4 enhances the previous value cycle diagram to address the needs of the companys EIM program by placing data governance at the center of the EDW subrelease development process. By doing so, we will guide the EDW team in focusing its contribution to the business-side of EIM, especially the data definition aspects of data governance.

    Figure 19.4. Agile EDW subrelease cycle showing support for data governance.

    In Figure 19.4, the dotted arrows pointing toward the center represent contributions that EDW team leaders can make during each step of the value cycle to a companys data governance process. Taken together, these flows will contribute a rich collection of target column definitions, which will be extremely reliable given the multiple validations they will receive from both business and the EDW developers during the many steps of the value cycle. The solid arrows indicate areas where referencing the data governance information collected during early steps of the subrelease assists either business or EDW team members in completing their work. These flows consist of the following:

    Lowell Fryman, … Dan Meers, in, 2017

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    Centralized Data Governance And Decentralized Execution

    The final model combines different aspects of the systems listed above. In this model, there’s an individual or team that controls the master data, but each team creates their own datasets to contribute information. This means that both management and team members are responsible for collecting and sharing internal data. This is great for larger businesses who are looking to streamline data to their management teams.

    Now that we’re familiar with what data governance is and how you can implement it, let’s talk about some best practices to consider when creating a data governance framework.

    Selecting The Advisory Committee

    Data Flow Diagram

    The advisory committee or the steering committee is responsible for implementing the data governance process across the organization.

    It also prioritizes the data governance programs as well as sponsors them.

    The committee has the power of making changes in the projects and recommends the projects along with approving the finance for the same.

    The advisory committee is expected to be a part of everything that is related to data governance.

    This committee consists of the following:

    • Senior vice president.
    • Vice president.
    • Executive sponsors in business as well as IT.

    Senior vice president and vice president should be from the field as that of data that is being governed.

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    What Are Flowchart Templates

    Flowcharts are great for describing business processes concisely without compromising on structure and detail. They allow users to summarize large amounts of information in a relatively little space. In addition, good flowcharts can improve the quality and accessibility of business reports. Below are four sample flowchart templates that should serve as a good starting point for an analyst looking to learn more information about some common flowcharts.

    How Are Gdpr And Data Governance Readiness Related

    The European Union General Data Protection Regulation requires companies to provide enhanced protection for the personal data of European consumersincluding businesses that are based outside the EU. Data governance is how your company achieves that goalit makes GDPR compliance feasible.

    Data governance enables key compliance actions, including:

    • Defining regulated data

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    Why Follow Data Governance Lifecycle

    Abiding by the data governance lifecycle can bring a lot of data related improvements in the organization. Some of them are discussed below:

    • Data security is enhanced as the main focus is on establishing data ownership.
    • Breaks down data silos in organizations.
    • Ensures compliance with privacy laws and other statutory regulations.
    • Data governance helps companies in understanding their growth journey by assisting them in evaluating the organized data.
    • It helps in simplifying data storage and data backup.

    Key Components Of The Compliance Function Process Flow Chart Analysis

    Five Essential Capabilities: Master Data Management

    Many organizations ramp up compliance efforts reactively after a noncompliance incident or a series of noncompliance incidents. Organizations should proactively invest in corporate controls, governance and compliance before any incidents occur to maintain the companys public image, avoid legal costs and remain trustworthy in the customers/clients eyes. The following areas can benefit from the use of flow charts and workflows in order to promote comprehensive compliance throughout the company:

  • Policy Creation: Developing a standardized process and flow chart for policy creation can help to ensure speedy implementation of new corporate guidelines and minimize the amount of time new policies spend behind red tape.

  • Policy Enforcement: Creating workflows, or flow charts, for the policy enforcement process can provide a number of benefits to the organization: decrease of issue resolution times and case backlog, decrease in compliance expenses and increase of trust from employees and shareholders. Step-by-step analysis and governance of the policy enforcement process will benefit employee relations, improve issue resolution operations and boost overall compliance efficiency.

  • Internal Audit: Since internal auditors main responsibility is to make sure corporate policies and processes are being adhered to, they too should have flow charts for their own auditing processes to make sure they are assessing and reporting the companys operations properly.

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    What Additional Documentation Is Available

    The following supporting documents provide additional information on the history of the Coordination Group and U.S. DOT Roadway Transportation Data Business Plan:

    • Data Capture and Management: Needs and Gaps in the Operation and Coordination of U.S. DOT Data Capture and Management Programs. This white paper examines current data capture and management activities across various U.S. DOT program areas and identified gaps and potential opportunities for filling the gaps to effectively and efficiently coordinate and manage the programsâ activities. The primary recommendation from the white paper was that the HOTM develop a Data Business Plan to address the gaps identified in the paper.
    • U.S. DOT Roadway Transportation Data Business Plan : Data Business Plan . This report documents the results of Phase 1 of the Data Business Plan, which serves to improve coordination among real-time data capture programs within U.S. DOT by clearly defining U.S. DOT needs for real-time data, address gaps and overlaps in program needs with respect to stakeholders, and ultimately result in cost savings for U.S. DOT. .
    • U.S. DOT Roadway Transportation Data Business Plan : Data Business Plan . This report documents the results of Phase 2 of the Data Business Plan, which includes execution of the Data Business Plan coordination, as well as conducting two data integration test pilots to demonstrate the benefits and value of the Data Business Plan. .

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