In a buzz-word heavy industry, there is a lot of confusion around the difference between Data Management and Information Management. After all, isn't data information? Well, yes and no.

What is Data?
Wikipedia defines data as: "The term data refers to qualitative or quantitative attributes of a variable or set of variables. "

Data is more than one such attribute value. Is data information? Yes, information is provided by data but only because data is always specified in some abstract setting. The setting includes:

• The class to which the attribute belongs
  
• The object which is a member of that class
  
• Some ideas about object operations or behavior, and relationships to other objects and classes.

Data alone and in the abstract therefore, does not provide information.

What is Information?
Wikipedia defines Information as: "A collection of facts from which conclusions may be drawn; "statistical data" or data as processed, stored, or transmitted." Information is data extracted, cleansed, or transformed and presented to draw insights.

Data is often viewed as the lowest level of abstraction from which information and then knowledge are derived.

Information Management vs. Data Management
Information Management is defined as a program that manages the people, processes and technology in an enterprise towards the control over the structure, processing, delivery and usage of information required for management and business intelligence purposes.

Information, as we know it today, includes both electronic and physical information. The organizational structure must be capable of managing this information throughout the information lifecycle regardless of source or format (data, paper documents, electronic documents, audio, video, etc.) for delivery through multiple channels that may include cell phones and web interfaces.

Data Management is a subset of Information Management. It comprises all the disciplines related to managing data as a valuable resource. Data management is the process involved in creating, obtaining, transforming, sharing, protecting, documenting and preserving data.

The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise."

Data Management includes everything from file naming conventions to policies and practices on creating metadata and documentation for the long term. Data Management ensures data that underlies an organization is available, accurate, complete, and secure. Architectures, policies, practices, and procedures that manage the full data lifecycle are developed and executed.

Information Management Components	Data Management Components
IM Strategy Business Intelligence and Performance Management Enterprise Data Management Information Asset Management Enterprise Content Management Content Delivery Architecture and Technology Enablement	Data Governance Data Quality Master Data Management Metadata Management Data Architecture Privacy/Security Data Retention and Archiving

Conclusion
Why is it important to delineate the difference between information management and data management? So what?

First, we get this question a lot from our clients. People want to be sure they are understanding the hot topics and determining the value to their organizations. Second, as organizations become aware that they need to treat data a corporate asset, make decisions based on facts, provide insight and therefore drive action and enhance their customer/client experiences, information and data strategies need to be comprehensive.

An understanding of information management vs. data management will allow organizations to identify gaps in their approaches and create a foundation that will drive to high quality data and therefore accurate information for decision-making. Having a broad, holistic view is paramount, even when addressing these issues incrementally.

Do you have both an Information Management and a Data Management program? How do you synchronize the two? How do you differentiate the two? I'd love to hear your input.

As I'm thinking about my goals for 2012, I realized how similar this process is to setting up a Data Governance Program. Like many people, every year over the holidays I take stock of what happened the year before, what I enjoyed, what I didn't, and what I'd like to do in the coming year. I resolved to exercise more (resolved to do this last year), create more work-life balance (also on last year's list), climb a mountain (yes, this one too), and write my blog more regularly (not exactly on last year's list - last year's was to start a blog). And like last year, it starts out with a lot of enthusiasm that eventually wanes by March. Needless-to-say, this is a repetitive and not-necessarily productive process.

This process is similar to what I hear from clients who resolve to start a Data Governance program, only to get distracted and make the same resolution over and over. "I need to set up a Data Governance Program. I started one last year, but also had to respond to a regulatory request, so it fell apart." And, "I know I need to better govern my data. It's on my list to do this year." Or, "I realized that many of our processes are redundant and we need to focus on streamlining in order to improve data quality and accuracy. This year, we are going to do governance!" So with the best of intentions, companies start a Data Governance Program that over time starts to disintegrate. Like a New Year's resolution, what can be done to continue the enthusiasm and make a Data Governance Program stick? Following are some thoughts on extending Data Governance beyond the launch and keeping your resolution.

Create a resolution that is personal and measurable. Saying I'll "exercise more" is nebulous and unclear when the goal is met. Is it more than last month? Last year? Or more than the neighbor who rides his bike more than he works? A more motivating goal would be "to exercise 3 times a week". It's flexible enough to accommodate business travel, but still a higher number of days than last year, and certainly easy to keep track. It's SMART.

Starting a Data Governance Program with the goal of "improving data" is similarly nebulous and unclear. Rather, identify a meaningful and measurable goal that can encourage success. What about creating a Data Governance Program to "be able to better identify whom to call about which types of data" (Ownership and Accountability)? Or, "to reduce the number of Customer data duplicates by 30% in six months" (Quality)? Or, "to ensure that new systems aren't implemented that create new data issues" (Process and Quality)? The good news is, like New Year's resolutions, there can be more than one goal for Data Governance. And also like a resolution, the goals can be revisited on a regular basis to ensure they still fit the demand for data and the strategy of the company.

Understand why it's meaningful. By understanding why that resolution is important, it's more likely to remember why that resolution was made in the first place. For example, creating more work-life balance may not be critical now, but come April, when baseball season starts, I'll want to have a process to get my work done so I can attend late afternoon games. Creating a habit of working smarter not harder or delegating more may not happen overnight, so it's important I stick with it and make it routine.

Similarly with Data Governance, understanding why it's important to start up and maintain Data Governance will help to keep the interest and involvement of key participants. Perhaps the organization is coming under greater regulatory scrutiny and needs to have better processes for tracking data creation and changes throughout the organization. As those new processes are identified and implemented, reminding everyone of the importance of data accuracy to compliance will help to prevent people from reverting to previous (bad) habits.

Be accountable. One of the biggest reasons that New Year's resolutions aren't kept is because it's easy to not keep them. After all, who knows if you fail? Other than yourself, of course. Most people don't publish their New Year's resolutions, so they don't have to explain to anyone why they are still carrying around 10 extra pounds. By involving others and having someone else help to hold you accountable, you are more likely to keep your resolutions.

This concept also works from a business perspective. If there is an announcement to the organization that a Data Governance Program is being launched and is going to improve the reporting efficiency by providing higher quality data, chances are those people who create the reports will come looking for those data improvements that improve productivity.

Get a coach. Many times we believe we know what it takes to accomplish that New Year's resolution, yet get discouraged when we are doing what we think is right and we still aren't accomplishing that goal. Using a trainer has worked well for me because not only do they keep me accountable, they also have several more ideas on what I could be doing to accomplish my goal.

Data Governance can seem daunting when embarking on the process. There is a lot of literature about "best practices", but it's also helpful to have an experienced partner to call on when something occurs that isn't printed in a whitepaper. Bringing in a coach, a partner, a trainer, whatever they are called, can provide creative ideas on how to implement Governance in the organization based on years of practical experience, not just reading literature. As well, they can be a third party that tracks stated goals and progress, and maintains accountability of the individuals and the program.

So having laid out a "4 Step Program to Resolution Success", the most important step is just getting started. What are your goals for 2012?

An analogous rule to The Power of Context is The Rule of 150. This rule says that close-knit groups have the power to magnify the epidemic potential of a message or idea. If we want groups to serve as incubators for contagious messages, then we have to keep groups below the 150 person tipping point. Above that point, there begins to be structural impediments to the ability of the group to agree and act with one voice.

Although we don't consider Data Governance Organizations as having 150 people, the number of people it influences in a company can be far greater than 150. It follows that the sphere of influence of the Data Governance group needs to be capped at 150 to ensure the company is agreeing and acting with one voice. This includes all the people explicitly allocated to the Data Governance program as well as the people who implement its policies.

A first step in doing this is to create an Operating Model that can scale without becoming too large. Ensure that you design a model that can grow and scale as you extend Data Governance across multiple data assets and domains, and multiple business processes.

Another approach is to separate decision-making structures that become too cumbersome by nature of being too large. It may be necessary to push decision making down to the Working Group rather than only being at the Steering Committee. Else subdivide the Working Group into smaller Working Sub-Groups to better tap into sets of expertise and facilitate better analysis and decision-making.

In order to create one contagious movement, you often have to create many small movements first. By starting with smaller projects with measurable tangible benefits, it's possible to better manage the vision and execution through unified thinking and acting.

We can learn many lessons from The Tipping Point about how to create word of mouth epidemics and start a "governance wave". One key takeaway is that there are unique aspects to epidemics that indicate a better return on your invested time and efforts so concentrate your resources on a few key areas:

• Identify the right stakeholders and messengers to be involved and focus your efforts on them.
• Structure the messaging in a way that is compelling, practical and gets people to act.
• Create a context and environment that encourages people to better govern data - don't rely on individuals alone to make the right decisions.
• Keep your sphere of influence at 150 or below to magnify the epidemic potential of Data Governance.

And consider how best to use those types of people that can help spread the word on the Data Governance and its benefits. Recruit Connectors and Mavens to spread the information and Salesmen to convince people to get on board and take action in order to improve the way data is created, managed and maintained. Why not leverage other people with unique skills and personalities to assist you in your cause? They may be able to cover much more ground than you ever could on your own.

Lastly, those who are successful at creating social epidemics don't just do what they think is right. They deliberately test their intuitions. Therefore, test your strategies, policies and processes to determine whether the impact is as anticipated and whether people adopt them. Test decision-making structures to be sure decisions are made quickly and are enforceable - and be willing to adapt if the results are less than ideal.

What other examples and ideas do you have that relate to the rules of epidemics applied to the adoption of Data Governance?

We looked at The Law of the Few and The Stickiness Factor rules that author Malcolm Gladwell describes in his book, The Tipping Point. The final rule is The Power of Context. How can this concept help in the process to create awareness, get people on board and communicate the value of Data Governance?

Gladwell explores the fact that human beings are a lot more sensitive to their environment than they may seem. Behavior is more of a function of social context, rather than individual drive. For example, when people are in a group, responsibility for acting is diffused - they assume that someone else will make the call, or in the case where nobody is acting, the apparent problem isn't really a problem.  An example of this is when an individual is walking down an alley and witnesses an elderly lady being mugged they are more likely to try to retrieve the purse from the thug. When this occurs on a busy street in Manhattan, nobody acts.

The Power of Context relates that the way a person responds to something is less about the kind of person, and more about their environment. Data Governance professionals generally focus on individual ownership and accountability, rather than the environment in which Data Governance takes place. Could the goals of ownership and accountability be improved by thinking about them in terms of group dynamics and context?<

Consider how company structures/hierarchies, environments and groups impact people's behavior. Are there cultural norms in your organization that may be influencing people not to adhere to the governance policies and processes? This is common where sales staff is responsible for customer data. The "norm" is that everyone keeps their contact information in the eMail messaging system rather than in the Salesforce Automation system. Changing this "norm" may entail more than instituting a new policy. Recognizing that this group of people will not readily change as a fully compliant group will impact the way you set new policies and how they are put into place.
 

At other times, it helps to create a context that encourages people to better govern data. This can be done by aligning with another company change initiative, such as a "One Company" program or a shift to a customer-centric organization. Here is a ripe opportunity where people are ready and willing to change based on the larger context and initiative - one that is highly visible to the organization. Sneaking in some Data Governance activities along with another corporate change initiative may prove more impactful.

Gladwell also informs that an epidemic can just as easily be reversed or tipped by tinkering with the smallest changes in the immediate environment. Identifying how the context and the environment impacts the adoption of Data Governance is just as important as the goals and activities that encourage individual accountability and ownership.

One of the biggest challenges in adopting and sustaining Data Governance is spreading ideas and creating action - creating the tipping point. The second rule author Malcolm Gladwell describes in the book, The Tipping Point, is The Stickiness Factor.

This rule relates that there are specific ways of making a contagious message memorable - making it stick. Typically, there are relatively simple changes in the presentation and structuring of information that can make a big difference in how much of an impact it makes. "Sticky" messages are personal and practical, and they engage people such that they can figure out how to best fit it into their lives.


Incorporating this concept into your Data Governance program can occur in several ways. Your communication plan should be crafted (or revised) to make the messaging more engaging, impactful and relevant. Does the method grab people's attention? Does the message make sense to them? Is it written and communicated in a way that is relevant to the recipients? This is a good time to solicit help and guidance from the marketing team, who are experts in messaging, to help create "sticky" communications.

Don't make the mistake of assuming people can make the leap to understand the benefits of Data Governance. Rather, explain clearly why governance is important to different groups of stakeholders by making the message both personal and practical. When rolling out a new policy, process or standard, help people understand how it fits into their existing role and function. If people feel these are incremental changes and improvements, they are more likely to adopt them - as compared to significant changes.

If we look at the idea that a small change can have a big impact, apply this to those aspects of your program that don't have as much traction as you'd like. When thinking about the first project to tackle to get Data Governance off the ground, identify a small change that can make a difference and also deliver a lot of value. For instance, a new way of entering customer or product information could greatly reduce the number of duplicate records if it is more controlled and executed by fewer people.

Another approach to consider is how to make a single change that is pervasive across your organization. An example of this is to create a milestone in the Project Management Office process so that the Data Governance team is involved in approval processes to ensure that new projects abide by the standards and guiding principles of the governance program. Putting your efforts into a single pervasive change could have a much greater impact than several smaller process changes.

In an epidemic, the messenger matters because messengers are what make something spread. But the content and quality of the message matters too. We are overwhelmed by people clamoring for our attention. Your Data Governance message needs to break through the clutter. The Stickiness Factor rule establishes that there is a more effective, simple way to package information that can make it irresistible.