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Getting Started With Data Governance

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Implement Processes And Controls To Support The Data Governance Framework

An Introduction to Data Governance (Part 1 of 2)

After developing a framework for how data will be governed, it is time to put it into action. This includes implementing processes and controls to enforce the framework, such as data quality assurance processes or access control measures. If youre looking to truly capitalize on the value of your data and extend it to multiple parts of your organization, its important to ensure the tools in your data stack give you the ability to manage these down to the finest level, at every row of your data, for every user.

How Do You Build A Data Governance Framework To Support Your Data Governance Program

Here are a few steps you can take in creating a data governance framework when approaching a more democratized data governance program:

When building a data governance framework, remember to develop policies and standards, create a business glossary, establish data governance processes, and apply governance metrics.

Develop Policies And Standards

To effectively communicate your data governance program and get buy-in from the organization, you need to establish policies and standards for data management from the beginning and assign data stewards whose role it is to ensure that the policies and standards turn into practice across the organization. Often two of the most urgent things to address are data quality and data security.

Establish and maintain thresholds for data quality metrics and identify and create rules for sensitive data and regulatory compliance. Democratizing your data still requires security, its just not as governed. Take inventory of policies and standards already in place, identify gaps, and add initiatives to a backlog to address the gaps. Define user roles and responsibilities for who will manage and monitor data quality and data security. While data quality and data security are critical, setting the prioritization and direction of ongoing business initiatives is an often-over-looked area of data governance.

Data governance can better ensure the long-term success of data and analytics initiatives by establishing the stakeholders and data stewards to approve priorities and own the definition of metrics and SLAs.

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Grow Up Kid: The Maturity Model

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.

Establish A Data Governance Council

8 Steps to Start Your Data Governance Program &  Framework

Data governance is not an IT function. Its an institutional mandate that requires cross-functional collaboration and stewardship.

One of the first things I help clients do is establish a formal data governance council. It should be comprised of senior staff who oversee the operations and strategic direction of all areas of the business. Thats because complying with regulations, setting and enforcing policies about data usage and integration, and resolving conflicts all require cross-departmental collaboration at the highest level.

Successful data governance requires a massive culture shift and implementation of new technology and workflows. Council members ensure buy-in and reinforce the legitimacy, importance, and careful management of each major change.

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Build A Business Case

Getting buy-in and sponsorship from leaders who will be part of the process is key when building a data governance practice, but buy-in alone wont fully support the effort and ensure success. Build a strong business case by identifying the benefits and opportunities that data quality will bring to the organization and show the improvements that can be gained, like an increase in revenue, better customer experience, and efficiency. Help everyone involved see and understand both the energy required and the eventual benefits to be successful. Most leaders can be convinced that poor data quality and poor data management is a problem, but data governance plans can fall short if leadership isnt committed to driving change.

Why Is A Data Governance Strategy Needed

Theres an old saying that bad facts make bad law. And in the world of analytics, bad data makes for bad decisions. A data governance strategy helps prevent your organization from having bad data and the poor decisions that may result!

Heres why organizations need a governance strategy:

  • Makes data available: So people can easily find and use both structured and unstructured data.
  • Maintains data consistency: Standardizing data fields across databases and departments makes data easy to manipulate and navigate, .
  • Maintains data accuracy: Deleting, updating, or correcting stale or irrelevant data is important to maintain the integrity and value of analytics.
  • Supports data security: To pass compliance audits, companies must ensure sensitive data is defined and protected across all locations. This includes where the organization stores, processes, and transmits it

A data governance strategy helps organizations gain greater value from data science and business intelligence tools, as well as the analysts and scientists utilising them. At the same time, it enhances data security and compliance programs.

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Implementing A Data Governance Framework Is An Iterative Process

Data governance is not a project that can be done in a weeks timeand not just because theres too much work to do! Sure, the work needs to be broken up into executable chunks, but data governance is also a practice that needs to be cultivated and kept alive and vital. Its a perpetual cycle.Whether youre starting a data governance program from scratch or wanting to improve your existing program, start here. And review these items yearly to keep your program fresh:

  • Conduct data security and risk assessments: What is important for my business via our risk profile?
  • Typical Data Governance Questions

    Data Governance Tutorial

    When considering your organizations data, its natural to ask many questions. Those questions may run along these lines:

    • Can my data be trusted?
    • Who understands this data?
    • I see this code in this data, what does that mean?
    • Who does what in terms of data governance?
    • Who should be able to change the data?
    • What happens after changes are made?

    All these types of questions generally open a Pandoras box of processes and standards that are absent from the enterprise. A solid data governance framework will address these and other data concerns.

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    Why Do Organizations Need Data Governance

    More and more, in-house information is finding a new life as a valued asset across the entire organization rather than simply as the property of individual departments. In fact, many data governance initiatives originate as attempts to improve data as it becomes actionable across the organization. Data is now used to develop organizational efficiencies, identify profit opportunities, enhance customer experiences, and improve or develop new products.

    However, two of the primary reasons for data governance are regulatory mandates and risk assessments that rely on high-quality data. In particular, many regulations focus on an organizations data to show proof of compliance, especially in the area of data security. According to the 2013 Rand Secure Archive Data Governance Survey, 82 percent of respondents know they face external regulatory requirements, but 44 percent of those respondents still dont have a defined data governance policy.

    Areas that benefit from data governance include those that require regulatory reporting data to meet guidelines for Sarbanes-Oxley Basel I, II, and III COBIT Dodd-Frank cGMP ISO/IEC 38500 and elements of the Health Insurance Portability and Accountability Act .

    How To Develop A Data Governance Strategy

    What does the business seek to do with data? Your data strategy should be clear and easy to communicate in simple language. If only a data scientist can understand the strategy, its unlikely that strategy will be successful, if everyone is to get onboard. Governance plays a key role in supporting that strategy at every step.

    Where does your business sit? For companies in highly regulated industries, like financial services or healthcare, data strategies are most often compliance focused: defensive. They may set a data strategy focused on protecting private health data and passing compliance audits. In this case, governance ensures key processes are documented for future audits. Increasingly, even across these industries, the CDOs success is determined by the positive value delivered. CDOs are increasingly de-prioritizing defensive strategies as a result.

    Conversely, the rise of personal data regulations is causing these industries to re-prioritize defense. These industries are having to increase their scope to cover their defensive needs, in addition to the value-add initiatives that support a competitive edge.

    But before governance can support a strategy, data governance must be implemented. Lets take a look at the seven key steps for implementing data governance:

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    You Cannot Governance Your Way Out Of A Bad Plan

    In the next few blog posts, we will look into the different components and design questions relevant to building robust and democratic data governance models. But before we do, theres one more issue I would like to get out of the way: All the data governance in the world will not turn a bad idea into a good one.

    Before you get into the nitty-gritty of designing the rules of the data game, you need to ask yourself the hardest question of all: should I even be doing this? You cannot governance your way out of a bad plan, and even the best intentioned plans carry risks. This is especially true when your plan involves the collection and use of data. As we have witnessed time and time again, data collected for the best of reasons, might still end up in the wrong hands or end up being used in ways that harm vulnerable populations. Therefore, before you get started, you need to answer these two crucial questions:

    Shouldanyone do this?

    Some ideas are net negative. Facial recognition is a great example of this. Its a technology that has few positive impacts and lots of negative ones in the form of increased surveillance. It is for this reason that various governments now ban its use in public spaces.

    Shouldwe do this?

    Want to know more?

    In the next installment, we will address the core components of a robust data governance system. Well look at decision-making, transparency around common agreements and how to build accountability mechanisms.

    Develop A Framework For How Data Will Be Managed And Governed

    Take control! How to get started with a data governance programme ...

    Once you have defined the goals and objectives of your data governance initiative, and identified the stakeholders who will be involved, you need to develop a framework for how data will be managed and governed. This should include processes and controls for things like data quality, security, access, and retention.

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    Key Performance Indicators And Metrics

    The effectiveness of the data governance program depends on establishing business metrics and KPIs for monitoring and measuring the programs overall business impact. Metrics and KPIs must be quantifiable, easily traceable over time, and measured in the same manner year after year.

    Many organizations make the error of failing to conduct a baseline measurement and then attempting to measure the performance of their data governance program years later. In these circumstances, determining an appropriate measure of success is extremely challenging.

    Connection To Master Data Management

    Data Governance is the strategic approach. Master Data Management is the tactical execution. Thats it. Were good. You can go home now.

    Not convinced? Ok. Dont take our word for it. As promised, were back with Scott Taylor of MetaMeta Consulting. He has forgotten more about master data than most of us will ever know, so were happy to give him the last word.

    All enterprise systems need master data management, Scott said at our Profisee 2019 kickoff event. Marketing, sales, finance, operations. There is benefit everywhere, in enterprises of any size, in every industry, across the globe, at any point in their data journey.

    Master data is the most important data because it is the data in charge, Scott said. Its about the business nouns the essential elements of your business. Customers, partners, products, services. Whatever your business is, thats where master data lives and breathes. You may have the best governance plan on the planet. Well-governed bad data is still bad data. Its not going to help your business.

    Everybody is in the data business, whether they realize it or not, Scott said. Everything we touch turns to data. Business is transforming from analog to digital. No matter what your product is, data is your product. Business is changing because of data, and data is power. With the right tools, you can harness that power right now.

    We could not have said it better ourselves.

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    The Final Task: Enforcement And Communication

    All of the topics above cover key focus areas to be considered when building a data governance framework. Building the framework is important but enforcing it is key. A data governance process must be created on the heels of the framework to ensure success. Some organizations create a data governance council a single department that controls everything. Smaller organizations may appoint a data steward to manage the data governance processes. Once your data governance framework is in place and is being rolled out, it needs to be communicated to all areas of the business. Make it resonate with employees by demonstrating how this framework will help carry out your companys vision for the future. As with any new policy or standard, employee awareness and buy-in is important to success and this holds true with the data governance framework.

    There is certainly no shortage of data, and building a data governance framework is a challenge many enterprises face. With Talend Cloud, you can build a systematic path to get to the data you can trust. A path that is repeatable and scalable to handle the seemingly unending flow of data to make it your most trusted asset.

    Tips For Learning Data Governance

    Data Governance Explained in 5 Minutes

    Here are three tips for you:

    1. First: Set a learning schedule for yourself. I feel it’s much better than leaving your learning to chance and just learning when you have some free time. Free time gets booked fast. I find that setting an hour aside each day to learn data governance would give me better results quicker than not reserving that time.

    2. Second: Set goals for yourself. Some say that goals are overrated, but I think that if we set goals and we share those goals with others makes us more accountable to keeping to a learning schedule and accomplishing our goals. For example a goal could be the get your certificate of completion from one of my courses in two weeks time. Not only you would have learned something new that you could apply at work the next day, but now you have a certificate that you can showcase around to your colleagues, employer, and even job recruiters.

    3. Third: I wish I would have had a study group or at least a studying partner. I feel it makes the process easier as you can share what you’ve learned and even teach each other. Plus, it enforces that accountability. Thank you for reading this article and I look forward to seeing you become a data governance professional or just learn a bit more about data governance as it’s important.

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    Establish A Single Source Of Truth

    Is a single source of truth a myth? We hope not. If you can make your team aware of a SSOT, you can eliminate a lot of frustration and build trust. Answer these questions about the dimensions and metrics you are sharing to help identify your datas source.

    • Where did it come from?
    • What does it mean?
    • What is the context?
    • Can we trust the data?

    Once you can identify the source for data, keep these points in mind for communicating that source to your team:

    • Acknowledge that information may be in multiple places and multiple meanings
    • Knowing where to go to get that information is important.
    • Context can change the meaning of the metric.

    Aligning on source is a trust exercise that happens over time, so its important to document, document, and continue to document to make sure everyone is clear on where your data is coming from. Documenting the metrics used in databases, reports, and BI dashboards help people understand the meaning and context.

    For example, if you have a dashboard with various metrics, you should create a definitions section or page that lays out the rules for interpretation. Include information about where it came from, how frequently the datas updated, and what it means. In some cases, it may be important to include instructions on how to interpret the data, along with including the formula that gives it context.

    Best Practices For Data Governance

    When looking for data governance best practices, you can learn a lot from others who have worked through the various processes and templates. While each organization is different and you will need to adapt your data governance practices to your process, there is no need to completely reinvent the wheel. When applying an agile development mindset to data governance, start small with a minimum viable deployment, and then iterate and grow from there. This can yield greater long-term benefits and bring the rest of the organization on the journey with you. First, it is important to understand what data governance is and what it can bring to your organization.

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