In some cases, the Data Stewardship group within an organization is composed of the Data Stakeholders themselves.  These Data Stewards ensure that data-related decisions are carried out in a way that doesn’t conflict with another person or entity within the organization.   Aside from technical skills, a Data Steward should also have a clear, crisp way of communicating issues and ideas and will be responsible for ensuring that any ambiguities in the data are removed.

Responsibilities of a Data Steward include:

1. Data Stewards must see to it that the data being carried out doesn’t overlap any existing, contradicting data within the organization
2. Data Stewards are always on the look out for possible errors in the structure. 
3. Data Stewards must help ensure that the data is error-free.
4. Data warehousing is one of the key roles of a Data Steward.
5. Data Stewards ensure consistency of data. They maybe one level below the Data Governance board, but these stewards also play a big role in data decision-making.

In a large enterprise, it is not sufficient that a single Data Steward is employed.   It usually takes a team of experts in their respective fields to come up with a successful Data Stewardship council.

One issue that must be reviewed is deciding on just exactly who has the power to decide. Likewise, questions regarding when and how decisions can be made and conceptualized should also be settled.  Therefore, to avoid any inconsistencies, the Data Governance program must clearly define and document all decision rights. This documentation should also include those detailed and further information on all decisions made in regards to data. These decision rights must be defined very well to avoid any conflict, specifically when it comes to settling issues.

On the other hand, decision-rights for programs related to compliance are quite easy to identify. The executive level of an organization has the power to choose if they will follow a certain standard, law or regulation; of course, these choices should be in relation to the organization’s mission and visions. Although the executive level can identify which rule to follow, it is the duty of different Data Stakeholders to discuss and come up with a general decision on how their group will comply and follow these certain rules.

Other than deciding on which rule to follow, other decisions related to data-processes need constant concession and analysis from one organization to the next. In line with this, different sectors within an organization have a specific right to decide but should be a result of thorough discussion between different data stakeholders. For example, Data Architecture has the right to decide on how long the data field will be in the new system as a result of different ideas and opinions from its stakeholders.

Data governance operates like this kind of government with its three separate branches: the executive branch, legislative branch and the judicial branch- all having their own duties, functions, and checks & balances. For background, the legislative passes, makes and even changes laws, the judicial branch interprets these laws specifically in terms of settling and resolving issues and the executive branch makes sure that these laws are followed and reinforced while providing all services to its constituents.

On the other hand, Data Stakeholders are like citizens particularly in terms on their rights, privileges and obligations. Just like a citizen whose actions is governed by laws, Data Stakeholders should be aware that there are rules governing their actions when it comes to traveling to different data domains. And they are also liable and can be sanctioned according to the rules set by the Data Governance office.

Those in the lower level can have their own mission specifically for their type of program and data. However, these specific missions must still be in accordance or in response to the general three missions of Data Governance.

Along with mission is vision, which should also be concise and comprehensible. When crafting your program’s visions, use inspiring words that can entice and persuade Data Stakeholders to set their own data-related goals.

In order to achieve utmost data quality, it has to start from the bottom-up. An enterprise needs to solidify and integrate this information into one silo and create processes and profiles to collect the data upstream.  Effective Data Governance requires that customer information be available where need be at the most opportune time when it is needed. Data should be centralized in a way that it would be easy for the Data Stewards to look for information and make necessary changes where applicable.

We need to gain a complete and timely understanding of our customers in order to effectively gather information. We need to know which information is needed at a given time. When all the needed information has already been collected, it should be compiled and consolidated into one big effective structure that can easily be accessed by people within the organization.  To reduce operational risks, and to lessen situations where customers get irate because of wrong information given to them, Data Quality should (as always) be observed. This regulating body ensures that various processes are being met in order to provide a flawless data structure.

A unified front end system must be employed by every enterprise if they want to maintain the integrity of their systems and their data.  With the overwhelming amount of information from both internal and external customers, Global organizations should start to see Data Governance not only as a single entity in an enterprise but its vital importance to the welfare of the enterprise as a whole.

An effective Data Governance team must know which Data Stakeholders to get in contact with depending on what type of data they need or are working on.  For example, if a company needs to know the current market trends and behavior, the Data Governance team should be able to identify at first brush that they need to get a hold of a representative from the Marketing department. If data about providing quality customer service is needed, then they need to get in contact with someone from the Quality Assurance department and so on and so forth.

Since this is the case, it is the responsibility of the Data Governance board to identify specific Points-of-Contact (POCs) from each of these departments for when there is a need to change and update people across the board. It is tedious to speak to everyone in the department to find the right POC, so it is extremely beneficial that an identified representative from each department be present when there is a Data Governance meeting.

Data Stakeholders can take either an active or a passive stand when it comes to these matters but it is best to get them involved in the decision-making process in some capacity.  We have to make these people understand how the business works, how the data within the business works and how it affects the department and the company as a whole if the data calls don’t go to the correct channels.

WHO ARE THE PEOPLE BEHIND DATA GOVERNANCE?

Data Governance is all about accumulating, gathering, storing and integrating raw customer data and making this information available when needed.  The people who are behind this process are called “Data Stakeholders” and they are composed of a mixed line-up of people from various departments in an enterprise – some coming from IT, Marketing, Human Resources, etc.

WHAT IS DATA GOVERNANCE AND HOW DOES IT WORK?

Data Governance is the discipline used by companies to ensure that accuracy, consistency and flow of data are stream-lined depending on the focus of the business.  The use of Data Governance ensures a smooth collection and dissemination of data information that are of quality, thereby solidifying the company’s integrity.

WHEN IS THE BEST TIME TO MAKE USE OF FORMAL DATA GOVERNANCE?

The local data management of an enterprise can only take you so far. When there is more data that needs to be managed than is practical for ad-hoc management, the best bet is to implement the use of a formal Data Governance initiative.  Here are situations that call for such big transition:

1. When there is a bigger volume of data that needs to be spread out within an organization.
2. When the local data management system is so primordial that it cannot efficiently store and show data when needed.  Formal Data Governance uses a more sophisticated system to ensure the quality of data being used in the enterprise.
3. The Data Architects in the organization needs to look at the enterprise from a macro-perspective in order to understand how data should be managed.
4. A compliance system is needed to manage risk, legal obligations, or other regulations.

WHAT DEPARTMENTS ARE INVOLVED IN DATA GOVERNANCE?

Data Governance is mostly seen on a larger scale than most programs in an organization.  Its participants vary, but these resources generally represent IT, Human Capital, Marketing and Finance and Operations and Management.  It looks at information from a global scale and inputs from people from these departments are highly valued and taken into consideration.

WHY IS THERE A NEED FOR DATA GOVERNANCE?

Most companies create an elaborate Data Management System because it is important that different methods and procedures are aligned with this set-up.  Data Governance guarantees that information is properly carried out within an enterprise.  In doing so, data is disseminated to the right people at the time it is needed and, in return, makes the whole process efficient and cost-effective. Data Governance is generally a win-win situation for companies that choose to integrate it into their system.

HOW TO IMPLEMENT DATA GOVERNANCE FROM THE GROUND UP?

Do you notice how most companies develop a mission/vision statement? The same holds true for Data Governance. In order to fully maximize its use, the program manager needs to sit down with its people first and talk about where they want to be heading and what areas or key drivers they want to put focus on.  Identifying these things will help keep the program organized and will help steer it into the direction that executive management ultimately wants it to go in.

HOW MUCH DATA GOVERNANCE DO WE NEED?

It is extremely helpful to incorporate data governance when the data is not yet complex. This is usually a utopian-like thought, however, because most organizations have very complex data before they ever begin thinking about introducing data governance.

If you do start early, however, it becomes very easy to scale the program;  just add data as the need arises and put more structure into the program.  In order for data governance to work cohesively, departments should make use of common variables and terminologies and use them correctly and efficiently at all times.  It is wise to use it in a localized setting but with a global mindset.

WHAT KEY AREAS DO WE NEED TO PUT FOCUS ON WHEN IMPLEMENTING DATA GOVERNANCE?

We have been stressing that Data Governance is all about the accuracy and effectiveness of data accumulation and dissemination, hence, people who implement it should have the ability to communicate their thoughts effectively – both in action and in writing.  They need to be able to translate complex issues in a way that will be easily understood no matter which line of business utilizes the information.  The people behind Data Governance should have the ability to create, influence and assertively implement the data framework within the enterprise.

Proper training, effective training articles and materials can help the Data Governance team stay up to date in the ever-changing world of data management.

To avoid this situation, many large companies now embed a set of IT personnel and software developers within their Data Governance team as dedicate resources. Their main duty is to ensure that they have a list of qualified individuals to develop new software that can monitor Data Governance and Quality. By having this dedicated team, they understand the program itself better and get exposure to the enterprise – all while better understanding the needs and goals of the Data Governance program itself.

There are 3^rd party companies who can provide services to help an enterprise build a Data Governance software. These services include, but are not limited to the following:

• DATA QUALITY WORKSHOP – this is a form of immersion wherein a team of 3^rd party data governance analysts and developers visit an enterprise and monitor how data is managed. They will observe and check for processes that can be best handled by implementing software.
• STRATEGIC PLANNING SERVICES – because of the complexity of data within an organization, there is a need to employ 3^rd party companies to help an enterprise formulate ideas and solutions  to guarantee a flawless execution of a Data Governance software
• DATA GOVERNANCE PLANNING – aids in identifying areas that needs to be improved. Every part needs to be accurate in order for the whole system to be deemed as credible.

Data Governance systems can get down into the nitty-gritty of an enterprise. It involves a lot of risks, it calls for a lot of improvements, and it needs a stable structure for it to work. Data Governance software can only do so much. Software can only do half the job. At the end of the day, it is the people behind the Data Governance team that really matters.

Some of the responsibilities of the DGO (Data Government Office) include:

1. Standardize processes and methods to align with the corporate culture of the organization
2. Identify a point-of-contact from each department that is affected, either directly or indirectly, by the data that is being standardized
3. Build the Data Stewardship council
4. Communicate effectively with the different lines of business within an organizational structure
5. Monitors  trends and drivers and report them to the departments concerned
6. Counter-check existing policies and procedures being carried out to the stakeholders
7. Collect metrics and best practices and reports them to the departments concerned
8. Provide communication updates to all departments concerned, including the Stakeholder council
9. Come up with activities that will help ensure cohesiveness within departments
10. Show the relevance of Data Governance in relation to the organization as a whole
11. Keep a list/spreadsheet/database of all Data Governance issues

On the other hand, there are small scale companies that do not employ the use of a Formal DGO framework.  What they have is one that is relatively similar in concept, but is much smaller in terms of staffing. These are your jack-of-all-trade Data Analysts and Data Architects. However, for as long as tasks and responsibilities are aligned, it is not so much of a big deal if a formal DGO structure is not utilized.

Another idea often bounced around at conferences is that only a short period of time is needed to make the guidelines and exercises that these data governance rules control. On the other hand, the process of understanding the choices, arriving at an agreement, assisting in decision-making, agreeing on deliverables, confirming areas or responsibility, etc etc etc can taken an excruciatingly long period of time.  While I agree with the sentiment that the basic tenants of Data Governance can be agreed upon quickly…. As they say, “the devil is in the details” and these don’t tend to work themselves out very quickly. 

The success of any program on Data Governance relies on the utmost capacity of a Data Governance worker to effectively work with and coordinate with Data Stakeholders and Data Stewards. How can these Data Governance workers do this task effectively? First, they have to create a very effective Communication Plans. They have to construct fail-proof mediums of communications like Elevator Speeches, Impact Statements, Presentations, Governance Status Reports, emails to and from the Stakeholders and many more. Without these plans, there is no way that a Data Governance worker can fulfill his/her duties very well.

In order to make these processes efficient and effective, there are a number of best practices that an enterprise can incorporate. Please note that this is not the end-all and be-all of Data Governance practices but it is informative and educational enough to apply to your own organization.  You have to remember that the concept of Data Governance is the same for all, it is only the system and how the data is needed by the various departments that changes depending on the enterprise.

• There should be transparency of goals within the organization. Each representative must be able to identify how the data can affect their department.
• Goals should be achievable, measurable and quantifiable
• A solid reporting system should be made; intervals on when these reports need to be generated should also be identified
• Proper education and authority should be assigned to everyone involved
• There should be a clear cut process of escalating data updates/disputes/variances
• There should be a clear work-around time for when these data updates/disputes/variances will be worked on
• People who will carry on Data Governance tasks should be broken down so that they can be easily identified as to who will do what
• There should be a regular touch-up on Data Governance so that the people are updated of new processes/systems
• Proper training needs to be given on a regular basis (monthly, quarterly, yearly, depending on the organization)
• There should be a set of individuals from the Data Governance team that will regularly attend enterprise meetings to ensure that Data Governance goals are aligned with the goals of the organization as a whole
• Metadata structure should be given importance and this provides key understanding of how data should be used
• Good governance metrics should be put in place and good governance output should be properly recognized
• There should be an executive responsible for monitoring and measuring the success of Data Governance