Recognize This Is Change
Among the best practices of data governance is recognizing that working toward a data governance program is cultural change. This kind of change can be HARD. People get comfortable in their routines and asking them to change can feel daunting. Think Who Moved My Cheese? Change is inevitable, but it can feel frightening, and there will likely be people who actively resist or who need to be reminded often and nudged in the right direction.
Because this is cultural change, it is important to manage the change by using recognized methods. One of the most important parts of change management is to understand why the change is necessary and to communicate this sense of urgency. Have this understanding firmly in mind before initiating conversations to get the ball rolling.
Whenever possible, start small. While it might be possible to have an over-arching strategy that reaches all the data systems in an organization, most strategies are going to be tailored to each system. It is best to start with one system, and then radiate your efforts outward to the systems and departments it touches.
Create A Team Dedicated To Your Program
If you want your data governance to be effective, then you’ll need to create roles dedicated to your program. The leader of the program should have strong communication skills and be able to communicate its importance to the rest of your company. Each person on the team should have clear responsibilities and ensure each data governance initiative runs smoothly and quickly.
Your data governance office should consist of employees who know how to best manage your customer data. These people are capable of meeting on their own and determining the most ideal process for organizing information. As a business owner, it may be tempting to add to the decision-making, but it’s important to give your team space when designing the framework. This will allow them to optimize the process and personalize it for your business’s needs.
Sap Master Data Governance
Best for: Enterprise companies
SAP Master Data Governance allows you to govern data in two ways: one, you can decentrally own and consolidate it across your enterprise system landscape or, two, you can centrally create, change, and distribute master data across that landscape. With this tool, you can consolidate databases, make changes in bulk, analyze the impact of your data governance processes, and define, validate, and monitor rules for data quality.
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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.
What Businesses Need Data Governance
Most businesses benefit from strong data governance, but industries with heavy regulatory burdens such as banking, finance, and health care have a greater need for formalized governance initiatives and are particularly focused on activities that put them at regulatory risk. Adherence to the regulatory challenges directly impact how they manage, report, and protect their sensitive information. Noncompliance can lead to fines, brand damage, or even jail time.
However, it should be noted that any organization that collects sensitive data, such as financial information, Social Security numbers, or medical records, is also subject to regulatory compliance mandates. Strong data governance first validates and promotes quality data, and then puts in place policies, controls, and management to meet internal and external expectations.
And although banking, finance, and healthcare are some of the most highly regulated industries, their governance structures can provide advantages that go beyond information security. In health care, for example, knowledge acquisition can open up opportunities for better patient outcomes.
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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.
Ensure Data Governance By Design
Governance is hard to do well when its tacked on to peoples jobs. Think about it: Everyone in your firm is already working hard, perhaps at or over capacity. Now you want to add a new data governance program and increase the responsibilities of some key staff.
Its unfortunately common in IT to launch an initiative intended to revamp how IT works. Its a sensible goal, since IT serves the business and needs to ensure its optimized for that purpose. However, great plans go awry when IT underestimates the level of involvement needed from business partners. Or even worse, things go wrong when IT needs business partners to act or behave differently, yet IT begins its transformation without the necessary conversations and commitments.
With these twin challenges in mind, lets look at how to apply data governance by design to maximize your chances of success.
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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.
Consistently Refine Your Data Governance Framework
Your data governance framework should be a consistent process for data collection and distribution. But, as your business grows and develops, it’s important to adapt your strategy to account for organizational changes. If you don’t update it, you may overlook customer data or accidentally leak sensitive information.
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Reality And Your Vision
Once youve got your team in place, the next step is to figure out where you are. This is the time to evaluate what is working and what is not, what data you have and what data is missing. At the same time you are doing this, it is likely you will start to formulate a vision and a plan for where you want to be.
Start by assessing your current state. Youll do this by:
- Talking to key stakeholders.
- Evaluating or defining master data.
- Studying applicable federal and state regulations surrounding the data in question and what, if anything, is in place to ensure your organization is in compliance.
As this evaluation is occurring, it is likely certain pain points will start to become apparent. Employees will note frustrations about missing data, problems with workflow, confusion around regulations, and other sorts of obvious problems.
At the same time, certain goals or possibilities are likely to present themselves either as solutions to the problems studied or as entirely new ways to explore the data. For example, if there are multiple departments currently managing the data, but all using different guidelines, merging the guidelines might be a solution. Or an employee might note that the visitor management system could be used to follow up with job candidates, prospective vendors or clients, if only it was integrated with the CRM.
How To Build A Business Case To Start A Data Governance Program
The optimal approach to starting a data governance program should include building a strong business case to identify the value and benefits of a data governance initiative
When it comes to executing a data governance strategy, there is no standard approach. Of course, there are common methods and tools, but its up to each company to decide how best to implement data governance initiatives to achieve the optimum business value, based on business requirements and challenges.
Some business leaders will prefer to go all in and implement governance initiatives in every department. Others will take a more measured approach, slowly introducing programs as staff become moredata literate and data management solutions are finalized. The most important step to deciding which approach an organization should take is to determine the business case for data governance.
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Governance Activities That Support Program Management
The governance of the program is primarily done through the program board and the program manager. However, there may be other activities or functions within the organization that also help the program manager in achieving these goals. In this section, we will look at five of these activities or functions. These are:
- Program management office
- Program management audit support
- Program management education and training.
There are a variety of PMO roles in organizations, consisting of different shades of roles and responsibilities. So what the PMO does in an organization may be very different from what PMI intends the PMOs role to be.
One of the best ways to express the role of a PMO is to call it the Center of Excellence for program management in an organization. This means that the best of the program management expertise, knowledge, and skills lie within the PMO.
Organizational Context For Governance
The organization pursues its work through multiple means.
One way of classifying this work is operations-like and project-like, i.e., projects, programs, and operations. Operations look to generate, preserve, and protect value by providing services that are consistent and repeatable. Projects look to add value by delivering changed or new products and services.
Both these worlds, i.e., management of functions and operations as well as management of projects, programs, and portfolios need governance, in the form of processes, tools, and metrics.
However, because of the inherent differences in the nature of work in operations and projects, the governance structure tends to be different for both.
For example, processes in operations tend to be well-defined, almost rigid. Processes in the project world, on the other hand, have to evolve based on the needs of the project and as they go through progressive elaboration.
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Stage One: Building The Value Driver For Data Governance
The first step to build a business case for data governanceis to evaluate all existing data initiatives and establish the organizations data goals. At this stage, recognize the likely use cases of the data in the organization before committing to an investment. How effectively is data used now? Does it contribute to decision-making, or is it only used for operations?
A mature organization can quantify the role and effectiveness of a data governance strategy by establishing how it could increase the data-related efficiency of existing data initiatives.
A mature organization will have formulated a business case before rolling out a series of data actions, like data warehousing, while a fledgling organization must evaluate the potential value of these initiatives first.
The next step for a mature organization is to ask whether it has hit the defined targets, and if it did not it needs to establish the reasons for missing the goals.
Some users operating in a mature organization will find it challenging to develop a comprehensive business case for data governance because so many data-focused processes are running. The most important thing for mature organizations, is to catalog all established business cases noting whether they have achieved their objectives. Fledgling organizations should identify the possible business cases based on current state and desired goals for data-related processes.
Discovering An Organizations Maturity
When it comes to data governance readiness, there are two types of organization: mature and fledgling. Building a business case for data governance cannot start before understanding the organizations current state of maturity.
To discover the organizations data maturity, start with identifying any existing data processes. Then, assess how data is used. If the organization uses data for analysis and making important business decisions based on these results, this is a mature organization.
However, if there is no data warehousing / business intelligence technology and the organization has not achieved any data-driven growth, it is likely a fledgling organization.
So, how does an organization determine its data management maturitylevel?
Mature organizations will use one or more data warehouses and large data stacks. Often, they will have a complex reporting system, with business intelligence capabilities.
A fledgling organization may not have a data warehouse system or use business intelligence / analytics but may be committed to launching a data-driven initiative.
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Data Governance Program Overview
A data governance program really has one clear goalto disappear. That may seem a bit enigmatic, especially since this book is about making data governance real. Nevertheless, it is true. Remember, you are deploying a new set of principles for treating a valuable asset in a much-improved manner. At the end of the day, the true mark of success is the organization treating its information as it treats its factories, supply chains, vendors, and customers. In the twenty-first century, no manager argues with standards for material handling, depreciation rules, or customer privacy. These are accepted business practices. There is no debate over whether you should have standards or controls. Yet it is easy to spread data all over an organization to the point that it is excessively expensive to manage, and you cannot find it, make sense of it, or agree on its meaning.
Ensuring a good understanding of how a data governance program looks and works is essential to getting participants engaged. Every time we kick off a new governance council or team to design a DG program, we always hear one person say, I don’t get the big picture. What does this look like? The concept of assimilating data governance into everyday corporate life adds additional challenge, since you are not only defining and implementing a discrete program you are also attempting to alter behavior to a point that the long-term program is visible only through verification and adjustment.
Assess Projects After Completion
After each data governance project is completed, you shouldn’t merely pat yourself on the back and move on. After all, if the project wasn’t successful in achieving your goals, it will need to be adapted for the next initiative. Run some tests on your data to note changes and discuss with your team what processes should be streamlined and which ones need to be readjusted.
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How Do You Set Up A Good Data Governance And Privacy Strategy
Several bricks are needed to enforce data management
- Data Architecture
Before even talking about data governance framework, a company needs the basis: a good infrastructure to begin with. Based on business needs and the company’s data maturity, the nature of the data architecture framework can change a lot. Regarding storage, do you go for: on-premise or cloud? data warehouse or data lake? Regarding modeling: Spark or DBT? in data warehouse or in BI tool? real-time or batch? Regarding visualization: do you allow anyone to build dashboards or data teams only? etc.
- Search and Discovery
The first level of any data governance strategy is making sure relevant people can find the relevant datasets to do their analysis or build their AI model. If you don’t implement this step, companies end up with a lot of questions on Slack and useless meetings with the engineering teams. The company ends up with a lot of duplicate tables, analyses and dashboards. It takes valuable time to engineering resources that are needed to perform the next steps.
- Metadata and Documentation
- Data Quality
- Security and Access Rights
- Compliance and Regulation
Identify Roles And Responsibilities
Determine who touches this report and why. Who creates it? Who approves it? Who uses it? What do those people use it for and what makes it relevant to them? Who provides the data? Who owns those systems? Who owns the processes?
Answering those questions provides you with the broad strokes of a data governance operating modela framework to help the producers and consumers of those reports collaborate more easily and securely.
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Data Governance Has A Direct Business Impact
Data governance isn’t just that rusty process that companies have to deploy in order to comply to regulation. Of course, part of it is a legal obligation, and thank god, but clean governance strategy can have key business outcomes.
Here are the main goals and benefits of a data governance program:
Data Governance Challenges Are Not The Same For Everyone
Diverse governance’s use-cases based on industry needs and organizations size
There are two main drivers for data governance programs:
- Level of regulation needed in the industry
Data regulation push the minimum bar of data governance processes higher. It requires business to add controls, security, reporting and documentation. Organizations set up a governance program to ensure transparency over sometimes unclear processes.
- Level of complexity of the data assets
Having a strong governance become increasingly important with the exponential growth of data resources, tools and people in a company.
The level of complexity increases with the scope of business operations , the velocity of data creation or the level of automation based on data.
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