Selling the Wheel . . .

At a recent meeting of the Data Governance Society meeting, someone said that a major part of the role of a Data Governance professional is selling. This could not be more true. Data Governance is a composite of activities some of which have been around for decades and some are brand new. In most cases, the difference between what we call Data Governance and the historical functions leading up to today is a simple nuance of difference in our approach. The "old" way of mastering data is close but not quite right. In a modern DG program, we strive to make it just right. This means that we spend a lot of our time convincing people that this program is different from the ones they've seen in the past. This is the hardest sell of all.

I would add that they key to all of this selling is to NOT SELL Data Governance. Sell the value of DG. Sell what's in it for your business or customer. This may seem obvious to many reading this but I can't tell you how many people I speak with that ask my, "how do you get them to understand data governance?" Simple answer. You don't! You get them to see how they will spend less time struggling with the data or how they will be able to generate more revenue or save money.

We all know that we are all sales-people and we sell every single day. It's a question of what we're selling. Successful sales-people know it's not the product, nor is it what the product does. It's how the product makes the customer's life better.

Final thought, when the first person to invent the wheel came calling, s/he didn't sell wheels, s/he sold carts and things that use the wheel. After people saw how much better their lives were because of the things wheels made possible, they started asking about the wheel and they started finding new way to use the wheel to make other parts of their lives better. Data Governance is like that. Strive to find ways to make people's lives better using data governance and they will thank you. Then, they will ask you, "how did you do that?" That's the magic question. Then, and only, then, can you sell Data Governance.

If this looks familar, it's because I originally posted it on the Data Governance Society LinkedIn group, found here:  http://tinyurl.com/kvbo47v

Redefining the Business for Success

Commonly, a business is described as an organization designed to exchange goods and services with customers for profit. While this definition provides a simple way to understand the purpose of a business, it leaves quite a bit to be desired when it comes to executing on the promise of profit. It doesn’t really give us any guidance on how to produce those goods and exchange them for profit.

Today, processes are a well understood concept for most business executives as is the value they provide. Processes are simply algorithms for success. They have inputs, actions and outputs. When the actions are consistent, a process will produce a consistent output from a given set of inputs. Thus, if we want to create a controllable business, we need to start by defining our business in terms that are controllable – in terms of process.

So, let’s redefine our business as a process. Business is the process of generating a profit from the exchange of goods and services for money. While this sounds similar – it uses some of the same words – it is quite a bit different. With this new definition, we can really start looking at the components of the business and how to best make them work to optimally achieve our goals and objectives – the main one of which is to generate a profit.

Processes, at their heart, are the most elegant way to look at the world. They only have three core components: Inputs, actions and outputs. In theory, any stable process can be optimized to perfection.

Let’s take a look at the process of business more closely. We take as our inputs customer requirements or desired outcomes. These are the criteria against which our customers will judge the value of our goods and services. We combine them with all other inputs such as products, factories and sales channels to produce our output – profit.

• Input: Customer requirements, customers, raw materials, vendors, factories, products, sales and distribution channels, employees, territories, countries, business units, etc…
• Actions: Convert customer requirements into goods and services, produce/obtain goods and services, exchange goods and services for money, and provide goods and services to customers
• Output: Profit

When we look at our business in this way, we see that in the calamity of corporate America, there really is a simple stream of calm running throughout. The process, no matter how big or small, should always be this simple. As we said earlier, processes are little algorithms for success. So, if you want your business to be successful, follow this simple formula:

Take deliberate, effective and efficient actions on high-quality inputs to produce valuable goods and services for which your customers will pay more than your costs.

To most, this seems obvious. In fact, corporations today build entire departments focused on nothing but improving internal business processes – the actions. Six Sigma teams, business Ninja warriors toting green belts and black belts, storm the offices promising to deliver high quality. Clearly this is very important. Business Process Management has been around for many years but, to date, it has not been able to rid the business world of bad outputs. Why? In our rush to execute these great processes, we often overlook one of the keys to our formula – high-quality inputs. Remember the phrase “Garbage in garbage out?” This is where that phrase comes from. To solve this problem, we introduce a new concept called Data Governance. Actually, it’s not that new, it’s been around for quite a while. However, until recently, it hasn’t gotten much attention because it requires that we look at our business in a whole new way. It requires that we see our customers, products, vendors, etc… as the building blocks of our organization. It requires that we see our business as a process and that we recognize the formula for success lies within the quality of both our Corporate Assets and the processes that string them together.

Data Governance is the formal process by which an organization ensures high-quality data used to create optimal business processes necessary to generate maximized output. In other words, we can rewrite our formula for successful business as:

 

Figure 1: "The" Business Process

In future posts, we will discuss how defining business in this new way allows us to more clearly see the building blocks of our organization – our Corporate Assets. We will further discuss how those building blocks, as the inputs to our mega process, need to be of the highest quality in order to optimize our success. We will then go on to discuss how this revolutionary way of looking at our business combined with the right process controls and technologies can create a sustainable engine for building profit for any company.

Data Governance - IT or not IT, part 1

One of the most difficult questions to answer with regard to Data Governance, after, “What is it?” is, “Where should it exist within the organization?” There are two parts to this question. The first is should it sit in the IT department? If not, in which department should it reside? Second, should it be in its own department? These are two excellent questions. Each organization has to decide for itself. In an upcoming article, I will tackle the questions to ask and how to use the answers to make a decision that works for you.

 

This article, however, assumes that you have chosen to put the Data Governance Office in the IT department. It also assumes your IT department is a shared service for the entire organization.

 

Many times, when a company builds a Data Governance Office, it starts in the IT department and, as often as not, it begins its life in the applications practice. This is a natural and understandable starting point. However, when we look at Data Governance and its overall mission, we can see that this is not a good long term strategy.

 

When you have finished reading the discussion below, ask yourself one question, “Would you include infrastructure or security under applications?” If the answer is, “No,” then I ask you, “Why put Data there?” Ultimately, all three support applications. However, they are all very distinctive perspectives to solving problems for customers.

 

Discussion:

In 2008, NASCIO, an organization the represents state CIO’s throughout the United States, released a study conducted across the Fortune 500 to uncover the value and importance to the organization of Data Governance. Among the key findings were:

1. Given the importance of data as the “currency” of the enterprise, it must be treated as a highly valuable Enterprise Asset.
2. Data must be maintained at some level of quality if it is to be trusted or relied upon for decision making. Again, data must be managed as an Enterprise Asset similar to finance and physical assets. Data Governance provides the means for properly managing this asset.
3. Information must be integrated across the enterprise.
4. Decision making relies on Business Intelligence that is derived from Enterprise Data. Data resources must be properly managed within a Data Governance operating discipline to enable effective decision making.
5. Data Governance allows the organization to act as a single enterprise – enabling faster response to environmental threats and opportunities. The circumstances driving those threats and opportunities must be recognized as interrelated furthering the need for “cross line of business” and “cross enterprise decision making” capabilities founded on accurate, timely, reliable, integrated and available information.

The study further concluded that the most important factor for success was having ownership from the “business side.” Data Governance must be viewed at the Enterprise Level. “Data Governance is a business concern that can be supported by information technology. It must be viewed as an Enterprise Asset Management Program.”

 

Further support that Data Governance does not belong within an applications practice comes from the following objectives of a typical Data Governance program:

• Instilling confidence and consistency in our decision making processes
• Reducing the risk of regulatory fines and impediments
• Increased security for our data, protecting it from both internal and external threats
• Maximizing our earning potential by optimally leveraging our data assets
• Ultimately, creating accountability for the quality of our data

These initiatives ensure the enterprise is as effective as possible. More specifically, they are designed to maximize both revenue generation and recognition. After all, that’s why companies are in business?

 

In summary, when you listen to those who have conducted extensive research into the objectives and challenges associated with implementing Data Governance, it becomes clear that the scope of Data Governance is not the same as that of an Applications practice – they are related, but they are not the same. Data Governance is about effectively managing the Enterprise Asset we call data. In fact, I suggest you consider creating a practice called, Enterprise Information Management. Within this practice, you could place similar services, such as:

1. Data Governance
2. Data Stewardship
3. Master Data Management
4. Data Architecture
5. Business Intelligence
6. Database Administration
7. Data Integration

In the end, it all comes down to accountability. If Data Governance is kept within the Applications practice, its overarching mission will be applications-centric and not data-centric. Data is the life blood of applications. However, that data will outlast the applications – requiring distinct management of data and knowledge assets through time as those applications, and even business processes, come and go.

 

Data becomes information and is transformed into knowledge – eventually being rationalized into the wisdom used to make decisions. Safeguarding the quality of that data needs to be at the same level – at least – as that of the applications that are used to interact with it. Process, Business Intelligence and Data are the key success factors for successfully solving business problems. Applications are designed to support our customers’ ability to interface with our data and business intelligence and to help execute our processes. Data Governance and Applications practices have separate missions. They should be separate organizations.

 

Now ask yourself the question from the beginning of this article. Would you place your infrastructure and security practices in the same office as your applications team simply because there is a relationship? No, you would not. Data Governance needs to have a face to the business if your business is to have good data upon which you can make trusted decisions and maximize your revenue potential.

 

In an upcoming article, I will take the next step in this discussion and help walk you through the process of deciding if the Data Governance Office belongs in your IT department or not. Stay tuned.

Data Governance - Value Proposition

There are many definitions for data governance. One of the more commonly used definitions comes from the Data Governance Institute.

A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods
- Data Governance Institute

 That’s a mouthful, isn’t it? Simply put, Data Governance is:

The formal process of managing data within an organization – John Eisenhauer

That’s it. Whatever we do to control our data – good or bad – is Data Governance.

But the real question is, “Why?” Why create a formal program for managing our data?

To answer the question, we must realize that, at its core, Data Governance is a process. Processes provide both consistency and repeatability. Without a data governance process, our data quickly degrades; undermining the fundamental value data provides to the organization:

TRUST

STABILITY

COMPLIANCE

Trust – without trust, we have chaos. Every decision we make is based on data. If our data isn’t trustworthy, how confident are we in our ability to make decisions?

Stability is the ability of an object to maintain equilibrium after displacement. Accurate, consistent data enables consistent processes, which, in turn, ensures business stability. Our ability to react to change is based primarily in our data and the processes by which it is maintained.

Compliance is conforming to rules, specifications and policies. Implementing Data Governance starts with Policies, rules and definitions followed by processes to enforce and automate them.

Ultimately, Data Governance can drive significant benefit to the organization along the following dimensions:

• Decreased costs
• Decreased time & effort
• Decreased risk of compliance failures
• Increased trust in corporate data
• Improved decision support capabilities
• Better organizational collaboration

Data Governance - An Executive Imperative

Trust! Kings have built empires on it. Generals have won un-winnable battles with it. CEO’s build their companies on it. You base Billion dollar decisions on the trusted input of your leadership teams. You make huge investments in marketing and public relations campaigns designed solely for the purpose of creating customer trust. It’s a powerful force. It can be the cornerstone of your empire or the Achilles heel of failure!

How do you decide where to place your trust, though? What do you do when you’re not sure? Do you guess? Do you go with your gut instinct? Many CEO’s report basing a significant number of key decisions on gut feel or a small number of factors because they don’t have sufficient trust; not in their advisors of course, but in the information used to make critical decisions. The human mind can only process seven to ten pieces of information at any one time. Most decisions CEO’s make require much more than seven to ten critical factors. As a CEO, you must trust your advisors to help process information and make good decisions. Success depends upon it.

However, with trust comes risk. Your advisors are using the same information you are. Do they all have the same perspective on the problem you’re trying to solve? Do they all have the same tools at their disposal? When you say “Net Revenue,” do they share your definition? How about the people they trust? How do they see the problem? Lurking behind every decision is a sea of facts, information and past experiences. We absorb that information to the best of our ability, draw conclusions and inferences and, eventually, make decisions. Unfortunately, facts are not always what they appear to be and while you trust those around you, do you trust the data they’re using to provide advice and guidance? Do they? Are they providing guidance based on their gut feel which is based on the gut feel of their trusted advisors?

Trust in your organization’s ability to provide you with good accurate facts on which to base decisions is a critical factor in your success. It must become an Executive Imperative to ensure that the information on which you base decisions is of the highest quality possible. We call this Data Governance and it’s not something CEO’s talk about. However, it is a core competency that every organization must develop if they are to maximize their competitive advantage in today’s market place. CEO’s sailing on instinct are likely to find themselves rounding Cape Horne in the middle of a perfect storm.

Every day your organization relegates the conversation of Data Governance to the IT break room, is a day of sailing without a solid grip on the rudder. The benefits of data governance transcend operational efficiencies and cost savings. Protecting and improving the integrity of the information on which you make key decisions will propel your organization to new levels of trust and agile decision making. It will allow you to transform the anarchy of disconnected operational functions into a cohesive trust-based team capable of delivering facts and analysis on which you can plot the fastest safest route to success. Data Governance creates trust. Trust creates success! Trust builds empires!

Data Governance - An Abbreviated Glossary of Terms

As I write more articles for you, I realize that I use a lot of terms for which you might find definitions valuable. I will not stand on my head and scream and shout if anyone disagrees with the definitions I’ve provided. This is not mean to be the be-all and end-all Data Governance glossary, although I might tackle that one in the near future since it sounds interesting (well . . . as interesting as Data Governance topics go, right?)

As always, please feel free to let me know if and how you think I’ve gone awry with anything I’ve listed here. I’m always interested in your feedback.

Data Governance:
The formal processes by which Key Corporate Data Assets are protected and maintained in alignment with the vision and mission of the business.

Data Stewardship:
The processes, policies and workflows implemented to ensure that Key Corporate Data Assets are managed in a consistent compliance manner in accordance with the Policies and Procedures of the Data Governance Office.

Data Governance Office:
The DGO is the office sponsored by the DG Governance Board and is accountable for the data quality of Key Corporate Data Assets, ensuring standardization of nomenclature related to those assets and generally governing the data in an active manner.

The DGO is responsible for ensuring that the guidelines policies and procedures established with the business and approved by the DG Governance Board are properly implemented and followed to ensure high-quality consistent data that is relevant and available when required to make critical decisions about and for the business.

Master Data Management:
The program(s) implemented to ensure Key Corporate Master Data Assets are managed consistently and appropriately.

Conditional Master Data:  Originates as a transaction then becomes relatively static meeting the rules for master data; it is a key corporate asset or is used to create other transactions. Examples include Contracted Products, Quotes and Contracts

Master Reference Data:  Data used to populate attributes associated with master data. Often called lookup/reference tables or list of values tables. This data directly affects the quality & integrity of the master data therefore it is managed & controlled in the same manner as master data. Examples: Cost centers, Industry Class/sub-class, profit centers, etc…

Meta-data:  Data about data.  Meta data is used to describe data and provide additional information about the data itself. Meta data defines such aspects as:

• Creation date, time and user – used to keep track of relevance
• Ownership, or other responsible parties – used for stewardship
• Purpose of the data – what system(s) use it, when, how often, for what. This is often documented in a data dictionary
• Business rules that apply or some level of scope of use for the data, etc…

Key Corporate Data Asset:
Assets within an organization which provide substantial value to the organization, its mission and its success in the industry. These can be Master Data Assets (see below)

Master Data Assets:
Data sets used to describe objects or entities that are part of the organization. (I.e. customers, products, materials, vendors, employees, regions, markets, etc…) Other properties include:

• Considered a key corporate asset
• Changes infrequently.
• Used to categorize & define hierarchical & referential relationships between transactions.

Enterprise Data:
"Enterprise" data includes, but is not limited to - shared (or potentially shared) data about managed entities, interests, finances, employees, resources, customers, providers, business affiliates, best practices, operating procedures, etc.

Data Integrity:
Business requirement that data in a file or message traversing the network remain unchanged and/or that the data that was received matches exactly that which was sent.

Data Integrity is the process of preventing malicious or accidental changes to data or message content as it is moved and/or updated throughout its life cycle.

Business Intelligence:
The processes by which data throughout an organization is brought together to provide support for making business decisions.

BI provides a company with the ability to glean both tactical and strategic insights into its operations to create a competitive advantage in the market or event to help define new more lucrative or otherwise advantageous markets in which to compete.

Data Governance – Does your BIER have a good head?

BIER is the German spelling of Beer. In the context of this article, however, it refers to Business Intelligence and Enterprise Reporting. Good beer head is also a good analogy for good BIER. When a fresh beer is poured from the tap into an icy cold frosted mug, the carbon dioxide inside comes out and forms frothy foam on top of the beer known as the head. Many people agree that a thicker more aromatic head is the best indicator of the quality of that beer. Data Governance and BIER are much the same in that regard. The output of all of our Data Governance efforts is our BIER. The more accurate and relevant the BIER output the better the quality of our data underneath.

I have been working with data for quite some time and I can tell you that difficulties managing business operations most often stem from bad or poorly managed data. In fact, tracking down, documenting and negotiating fixes for source data issues is probably the single most time consuming part of a BI teams day. This effort often led BI teams to become more pro-active. Over the years, BI data governance has been one of the biggest contributing factors in the formalization of Data Governance programs and Data Governance offices. As the most visible measure of success, then, it is critically important that Data Governors ask themselves, “Does our BIER have a good head?” If the answer is anything other than a resounding, “Yes!” get out there and focus your attention on BIER governance.

The truth is, if your BIER is in bad shape, it’s because your source data is in poor condition. This can be either a process and quality management issues associated with your source systems or, as often as not, it’s because you’re not managing your master data consistently – if at all. However, before you can address the data quality issues, you should put some governance around your BIER. This will do several things:

1. Give accountability for prioritization to the appropriate parties
2. Ensure consensus on reporting scope, definitions, metrics, etc…
3. Align/coordinate efforts between business units and functional departments
4. Create transparency

All of these benefits are valuable to both the BIER team and the DGO. When accountability for the output of our BIER efforts is properly placed, then we can more easily align our focus with theirs which, in turn, ensures alignment with the business. Understanding your organization’s BI needs is one of the surest ways to ensure alignment with the overall business focus.

Often, it is not necessary to separate the BIER governance and the Data Governance bodies. In another article, I will talk about governance models for both BIER and Data Governance where I will illustrate this.

In summary, BIER is the most visible and tangible evidence that your Data Governance program is working properly. When the head is good, the beer is sure to be as well.

Data Governance - Scope

When it comes to data governance we generally focus on the definitions, organizational structures and policies. However, it's important to make sure that we understand our scope. Scope tells us what we're going to govern. It tells us the limits of our governance for any particular asset to be managed. Most importantly, it tells others when they should consider working with us and when they can expect to be on their own.

The most common data assets that are formally governed are Customer and Product - in that order. However, some other common data assets that are often under the scope of the data governance office include:

• Employees
• Vendors 
• Sites, Locations, Plants, Warehouses, etc...
• General Ledger

These data assets are generally considered master data in the true (non-ERP) sense of the word. They are Key Corporate Assets. There are many questions to ask in a formal Master Data Management initiative to determine if a data asset should be considered "master data." The set of questions is the same but the answer is going to be different for every organization. In fact, it could be different for the same organization over a long enough period of time (although that would be well outside the scope of my conversation). The simplest way to describe the summation of questions to determine if something is master data or not is to ask, "Is this entity a core part of my business?" Customers are definitely a core part of most any business. Furniture is less likely for most business. Properties might be for some and not for others. The second question to ask is, "What happens to my business if this data were to disappear tomorrow?" If your answer is, "My business would shut down," or "My business would be severely impacted," then you may very well be dealing with master data.

Some other data assets that often come under the scope of data governance include:

• Industry classes
• Quotes and Contracts
• Install base
• Market segments (if complex and hierarchical)
• Routes (in ERP)
• Warehouses
• etc...

These are examples of reference data. if that reference data is sufficiently important that it could cause an critical system such as your main ERP system to become severely corrupted, then governing that data is important. However, it should not be confused with Master Data. This is often referred to as Master Reference Data and, while important, it is not Master Data.

One other important type of key data is Conditional master data. This is data that, under the right set of circumstances, may be considered master data. Under another set, however, it might not. Another time you may want to classify data as Conditional Master Data is when dealing with data that changes frequently at first and then, over time becomes stable. This data would also have to be critically important to the organization's well being and/or be integral in the operational processing of key transactions such as sales orders and invoices. Contracts might be a good example of this type of Conditional Master Data. This data is critically important. These contracts probably represent the totality of your customer's financial obligations. If you rely on those contracts for enforcing payment, this is a critically important data asset. however, it changes much more than most master data assets when it's first being created. However, when completed and signed, they usually change very little over time.

In conclusion, the scope document is one of the most important documents the Data Governance Office can have. It tells us and others what we’re supposed to be working to improve. Without defined scope, communications, processes and buy-in will not be consistent across the organization. There will always be argument and debate over which assets should and which should not be governed. Departments and business units may “opt-out” making data quality inconsistent. Without defined scope, you may not be managing all of the data assets that the business considers critical to its mission. If you don’t know that, you can’t show management that you are delivering reliable consistent value; they will lose confidence in you and in the data you govern. Know your scope!

Data Governance - A Lack of Executive Visibility

Data Governance (DG) lacks Executive Visibility.  Information Security, Privacy and Information Lifecycle Management get some play at the executive levels but not true Data Governance - that which encompasses all areas associated with safeguarding data and ensuring it is fit for purpose and available when we need it.  I believe that elevating the level of DG conversations from the IT Breakroom to the Executive Conference Room will allow companies to solve problems long thought un-solvable or too complex to address.

Many of the issues that face C-level executives today stem from a lack of governance at all levels of their organization - in particular in more ambiguous areas that cross functional boundaries.  It makes sense that organizations struggle with this.  Governance is often aligned with organizational structures.  This makes it much easier to understand and to implement.  However, in the case of Data Governance, those boundaries need to be crossed in order to be effective. 

Data Governance is a matter of protecting business data assets through policy and procedure - most often implemented in a manner which utilizes technology.  If you look at my Services Integration Model (previous post) you can see that some of the services are technical such as Data Architecture and Data Integration while others are functional such as Data Governance and Master Data Management.  Then, there are still others that are less easily defined.  Data Stewardship, for instance, can go either way - technical or functional, depending on the nature of the data being managed, the scope of that data and the specifics of the organizational structures in place for any given company.

At the end of the day, Data Governance is a cross-functional office that serves many masters and provides an array of services at all levels.  For the maximum effect, data should be governed at the highest levels of the organization.  To achieve this we, as professionals, must elevate the discussion beyond data stewarship, master data management and, even beyond "data."  We must all align ourselves to the mission of focusing on the organizational benefits we can deliver and bring the conversation from the IT Breakroom to the Executive Boardroom.

Data Governance – Three key Aspects for Success

I’m of the opinion that there are three key aspects to a good data governance program. While there certainly are others that could be added, the three listed here represent the core aspects and should be considered for any Data Governance program or Data Governance Office.

1. Data Management
• Master Data
• Transactional Data
• Conditional Master Data
• Master Reference Data
• Unstructured Data
2. Data Quality Management
• Data Architecture
• Data Integration
3. Policy – Governance proper

Another key to a successful data governance program is to be plugged into the corporate strategy. This ensures that governance is tied to execution from strategic planning through execution.

The following services integration diagram illustrates this point:

This illustration shows the relationship between the key aspects of data governance – data management, data architecture & policy. Surrounding those key aspects are the services they provide. Additional services such as PMO and EAI are shown in yellow to show how other services can integrate into the services offered by the DGO.

Data Governance - What's your Focus?

In this segment, we’re going to start moving from “what is” to “how to.” It is important to note that while a generic definition like the one we provided in our last segment is important, it is just as important to re-define data governance in terms that are specifically relevant to your organization’s needs. However, before you can write that definition, you must first identify the perspective from which you’re going to build your data governance program. This is called your focus.

The focus is most easily identified when you know the burning imperative that led you to embark on this journey in the first place. Remember, this only the starting focus. Over time, you will expand you focus to include other issues as they arise or as you have capacity to address them. Knowing your focus allows you to create a much more specific “tailored” definition of data governance that is immediately understood and recognizable. We say “recognized” because while you may know all of the benefits of DG, your customers and senior leadership probably don’t share that understanding. Remember, you may like talking DG over beer and pretzels, but you are definitely in the minority there. Identify a critical issue that you can help solve with Data Governance and build your program around that. This will help with communication, strategy and buy-in.

Some of the more common reasons company’s implement Data Governance include:

1. Business Intelligence
   Business intelligence and enterprise reporting depend on quality data. Without quality master data, for instance, consolidated reporting across IT systems, business units and regions becomes cumbersome at best and impossible at worst.
2. Master Data Management (MDM)
   Organizations are constantly struggling with managing the quality of their master data. They often throw technology at the problem only to find out within a short time (6 to 12 months at the far outside) that technology, while important, is not the solution. Process is what is most often missing or incorrect. Data Governance creates the foundation for good process management. In fact, as often as not, the processes associated with MDM are managed within the DGO.
3. Data Stewardship
   Data Stewardship is an aspect of Data Governance and it is a common approach for implementing the DG program. Data Stewardship is the execution side of DG and is a critical element in any successful DG program. Frequency or requests and response time for data requests often require that a host of different people be able to manage different data elements and/or their attributes. Data Stewardship provides them a controlled means to do this with process, workflow and, often, technology.
4. Risk Management/Compliance
   Regulated organizations are always concerned about compliance with the requirements of their particular regulators. Unfortunately, it is only after a serious audit failure that some organizations turn their attention to Data Governance.
5. Enterprise Application Integration (EAI) / Service Oriented Architecture (SOA)
   Those interested in creating SOA’s will quickly find that they must first clean up their data and the processes associated with managing it (i.e. keeping it clean and organized). They will also find that the policies regulating the creation, consumption and management of their data are as critical as the technology and development standards set forth by SOA programs. In short, SOA cannot be successful on a large scale without DG.

So, what is your focus? You should stop and think about this. Maybe it’s on the list above. Maybe it’s not. Whatever it is, write it down and repeat it often when you work with your customers. Make sure that everyone knows and agrees with it. It will become the cornerstone of your initial campaign to secure buy-in and understanding. When you have a good focus – one with which everyone agrees – buy-in will be easy and people will want to help you build your program.

Now, I know it’s tempting to say, “Ike, I choose all of them.” At first glance, this sounds like a sure win, right? If your business has said that all of these issues exist and that Data Governance will solve them, then you’re sure to be a winner. Unfortunately, that’s not always the case. While it may be true that you have all of those problems – and most of you do if you don’t have a Data Governance program – you can’t solve them all at once. Addressing too many problem areas will cause you to lose of focus and fail. So, pick one (or two) and focus on those to start. Once you establish credibility, you will soon find people knocking at your door asking to work with you. Build rapport and credibility through small successes viewed by senior leaders and influencers as vitally important. Your organization will thank you for it and you will be successful because of it.

What is Data Governance?

There are many definitions used to tell us what data governance is. Some of them include:

"A system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods" - Data Governance Institute

"Data Governance: The execution and enforcement of authority over the management of data assets and the performance of data functions.
Data Stewardship: The formalization of accountability for the management of data resources." - Robert S. Seiner

"Data governance (DG) is usually manifested as an executive-level data governance board, committee, or other organizational structure that creates and enforces policies and procedures for the business use and technical management of data across the entire organization. In a nutshell, data governance is an organizational structure that oversees the broad use and usability of data as an enterprise asset." – TDWI

However, the definition I have come up with over the years is simple and does the trick for me. "Data Governance is the formal process by which an organization manages its data." – John A. Eisenhauer

As important as these definitions are, it is also critical to keep in mind what Data Governance is not. Data Governance is not:

• Data Management (Master, Meta or otherwise)
• Data Stewardship
• Data Architecture
• Data Modeling
• IT Governance
• Corporate Governance

Some of these are common problem areas that might well be the reason a data governance program was initiative in the first place. Of course, many of these are important elements of a working Data Governance program. However, they should not be confused for Data Governance.

In summary, Data Governance is defined many ways by many people. These definitions are neither good nor bad. It is important, though, that you define it for your organization and your particular needs at any given time. You will find that your requirements change over time. Consequently, your definition will need to be updated periodically. This is a good thing. It means that you are progressing and that you’re achieving your mission.

Data Governance & BI . . .

Data Governance has been a rising concept for the past 20 years - or more. In recent years, however, it has reached a new high as the key ingredient to efforts such as implementating Enterprise Resource Planning (ERP) systems, decreasing overall IT spend and successfull M&A strategies.

But, how did this come to pass? Well, obviously, there was a big push by SAP, Siebel, Oracle and the like. However, I find that most companies start looking at Data Governance because their reporting is off. Once again, it all "starts" with Business Intelligence. It doesn't "end" there. When the reports are off, the BI folks have to scramble to figure out why. Almost always, it comes down to the source data and more often than not, it's the master data that's not being managed correctly.

But before you jump to conclusions, let's make one thing clear. This is NOT a technology problem. This IS a Data Governance problem. Let me restate that, this is a BUSINESS issue.

I can hear you now, "OK, Ike, I'll bite. What is Data Governance?" We'll, I'm glad you asked. It's many things to many people, however, the definition I came up with is both relevant and simple.

"Data Governance is the formal process of managing data assets within an organization."

That's it? Yup. That's it.

Obviously, it requires technology, process and people as does everything to do with business today. But at it's core, that's about all there is. But it's not as simple as it first appears. Let's really take a look at it. The most important part is the word "formal." A formal program is what sets Data Goverance apart from simple data management. By putting policy and procedure around our data management program (assuming we have one) we create the foundation for a Governance program. When we tie it to corporate and IT strategy, we've completed the loop and we're well on our way to Data Governance proper.

For the next few posts, I will be focusing on Data Governance and it's role in an organization (large or small). I will be describing the advantages of implementing data governance at various levels of the organization and I will walk you through the process of improving your business through data governance.

One of the key issues I will address is elevating the discussions of BI and Data Governance from the IT break room to the more appropriate corporate leadership conference room. In this disucssion, I will show you why data governance often fails to reach its true potential and why executives seem to "ignore" the topic. I will also show you - my executive readers - why lack of proper Data Governance may be the real reason you can't sleep at night.

Thank you for reading and I look forward to hearing from you as we move through this very important topic.