TRILLIUM SOFTWARE INSIGHTS

Data Governance – Where are the Teeth?

 By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

I enjoy hearing the terms that so often describe the data governance discipline, especially those that represent unity and harmony. Words such as;

Collaboration, Cooperation, Joint ownership, Consensus, Partnership, Group effort, Teamwork, Alliance, Ambassador, Facilitation
 
I myself have used many of these terms because we all know that success in data governance requires strong cross-functional participation. What I don’t see and hear enough of are words such as authority and accountability.

Why? One reason may be because many organizations choose to build their data governance programs solely on the principle of collaboration rather than institutional authority. Ideally, you really need a good balance of both to make it work, but many companies are currently falling short on the balance thing.

Companies that error on the far side of collaboration and consensus are struggling to secure, implement, and keep control over fundamental areas of data governance essential to developing and implementing standards for data models, quality, business rules, process, etc. Yes, these overly collaborative and typically project based models can drive small incremental progress but are falling short of overall expectations. They are also more likely to fall apart over the course of time.

On the other hand, companies that invoke more institutional authority over data management are starting to see accelerated levels of success. By no means am I suggesting a highly autocratic bureaucracy, however I do endorse a model that clearly defines roles, responsibilities, ownership, authority, and accountability. In addition, one that provides for quick and effective issue resolution at a tactical, strategic, and political level.

So I ask the question, is your data governance program heavy on collaboration or institutional authority? Is it helping or hurting you? Where does it need to be? What do you need to do to get there?

Share your thoughts, as this is a critical subject for your company's future.

Data Governance – Growing and Expanding

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

By all indications, data governance continues to gain momentum and compete for top billing in the industry. The recent MDM Conference in San Francisco and the Data Governance Conference in San Diego are good barometers for this. In both cases, conference attendance was up and the topic of data governance was splashed everywhere.

While I found much of the messaging and presentations around data governance to be siloed and somewhat confusing, I can see where the industry is beginning to more clearly define this discipline at an administrative, technical, business, and tactical layer.

I also found companies to be more specific as to why they attended these events which is a sign that this discipline is maturing. In other words, it was less of a curiosity as in past years and more about how do I go about actually making this a reality within my organization.

Of interest was the number of vendors that are getting on board and touting a solution for data governance. Albeit, some are just jumping on the train with the same technologies others have announced some creative solutions. One that caught my eye was the data governance work flow management tool that Kalido unveiled at the Data Governance Conference. Certainly not a be-all end-all to data governance, it does begin to address the tactical aspects of work flow management.

I also heard some interesting back room dialogue from some of our industry leaders suggesting that the term data governance does not adequately describe this discipline. This is music to my ears! As I mentioned before here and elsewhere, data governance is really about Information Asset Management. It just happens to have various components to it, including the strategic and tactical management of data.

Does this ring true with you and your experiences?

We’re Not Even Speaking the Same Language Yet!

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

I’ve been traveling a lot lately and have had a number of really interesting and engaging conversations about data governance.  And you know what?  I’m not convinced that we all are even talking the same language yet.  That is to say, organizations continue to have vastly different perspectives on what data governance is, and equally diverse ways on how to approach it.  My notion is supported by the types of data governance RFPs that are being written.

Like anything else, it’s always good to carve things up into smaller, manageable pieces (read Jon Asprey’s blog). With this in mind I propose that you think of data governance in terms of 3 distinctly different yet fundamental categories:

1)    Administrative data governance
2)    Technical data governance
3)    Business data governance

Administrative data governance focuses on things like direction, prioritization, scope, structure, organizational alignment, business case, and funding.  It also includes other administrative issues like policy, procedure, roles and accountability.  This parcel of data governance requires executive and business leader support and participation. It also requires a distinct set of business leadership skills to formulate and implement.

Technical data governance relates to activities around technologies and systems that facilitate data management such as data models, metadata management, workflow management, data quality tools, CRM, and BI.  As you might expect, this segment of data governance should be IT-led

Business data governance pertains to business centric activities that influence data outcomes such as business process, business rules, data standards, stewardship, reporting, metrics, and other associated activities. All of which should be business led.

Now, am I advocating 3 separate data governance programs? Absolutely not! In fact one is dependent on the other with administrative governance serving as the overarching authority over the entire program. What I am saying is that by dividing these things up, organizations can better clarify their business requirements, develop better strategies and plans to solving their challenges, and ensure that the right skills, people, and groups are involved in the right activities at the right time and for the right reasons.

Does this ring true with you and your experiences?  Tell us how you’re dividing and conquering data governance.

Data Governance, Data Quality and MDM – How do they fit together?

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

Data governance, data quality, and MDM; just how do they fit together if at all? This question and others were put to the test at the recent TDWI Summit in Savannah, Georgia. I had the privilege to attend this invitation-only event that brought together leading companies, practitioners, and technology vendors – and focused on MDM, quality, and governance.

As evidenced by the number of great presentations, networking opportunities, and case studies covering several core topics, the overarching themes were clear:
•    MDM success requires data quality and data governance,
•    Enterprise-wide data quality success requires data governance at some level,
•    Data governance enables MDM and enterprise data quality

In different ways, the message was repeated over and over again, three disciplines, one real solution. In other words, these domains and initiatives rely heavily on each other for them to be successful. Furthermore the solution for effective data management revolves around an organization’s ability to align people, process, and technology.

While some people may have heard this before and ask “but what’s new?,” from my perspective, it was a great step forward for the industry to really see how people are talking about the co-dependence of these disciplines. By setting aside a forum on these three topics alone, it reinforces and further acknowledges the fact that MDM-type initiatives, data quality, and governance are all required to drive desired outcomes around a company’s information and data assets.

You really need to be thinking about how all these disciplines interact and integrate as you move towards any MDM or CRM implementation.

Did you attend the TDWI event? If so, what were you’re thoughts? If not, what have you heard about the event?

Data Governance – Where does Data Quality fit?

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

I’m often asked how data quality fits within data governance, or are they the same thing.  While both essentially strive for the same outcome of optimizing data and information results for business purposes, these disciplines address that goal from very different angles.  They are not the same, but complement each other for the good of your business.

From a high level, many companies may view data quality as a subset or part of data governance.  In other words, data quality focuses primarily at a data element layer where technologies, best practices, and standards are applied to understand, analyze, monitor, fix, and report data anomalies within the data itself.

Data governance deals primarily with orchestrating the efforts of people, process, technology, and lines of business in order to optimize outcomes around enterprise data assets. This includes, among other things, the broader cross-functional oversight of standards, architecture, business process, business integration, and risk & compliance.  In other words, most anything that can impact the integrity, quality, and security of company information.

While data quality is mission critical for any organization, it often is relegated to a single project or data source within the company. But, this approach could severely restrict a company’s bottom line and its return on investment for four primary reasons:

• Project or silo based data quality only, prevents the company from understanding and addressing upstream problems that impact data outcomes caused by people and processes and that cannot be fixed by technology alone.

• Project-based data quality often leads to independent or isolated data standards, business rules, and data models that cannot be fully leveraged across the company, therefore duplicating operational costs for future projects.

• Project-based data quality many times leads to the implementation of multiple technologies for the same purpose within the company, which raises the cost of software, training, and maintenance.

• Enterprise capable technologies are utilized in a single instance when they could be leveraged across multiple projects and data sources.

Data governance exposes these opportunities and risks, and provides the cross-functional platform necessary for organizations to address them accordingly. In other words, data governance enables enterprise data quality by breaking down the barriers associated with project based data quality. All of which further improves data outcomes, bottom line performance, and return on investment.

Does this ring true for your company? What data quality projects are you working on, and how will data governance play in a role in your company to tie it all together?

Data Governance 2010: A New Beginning

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

The great thing about a new year is that it rekindles hope and expectations for personal and professional development. And, for many of us, this fresh perspective can be extended to our data governance initiatives as well. While our challenges remain much the same as in 2009, this new year represents a great opportunity to reflect, adjust, prepare, and energize your data governance programs.

With this in mind, here are a few of my thoughts, trends, and projections for the new year based on talks with all kinds of organizations.

Drivers & Funding
Most data governance initiatives will continue to be driven and funded by your IT department through projects such as MDM, EDW, data quality, BI, and CRM. While companies will eventually establish data governance programs that can scale across all types of projects and business processes, most initial funding will come from traditional sources.

However, though projects are still king, the number of organizations that recognize the need for standalone data governance will continue to grow this year. Companies are beginning to understand the overall impact data governance can have on their finances, operations, and risk. This is true not only for the large, multi-national companies but also for the mid-tier businesses that are challenged with complex, fragmented technologies and operations.

Organizations that can fund data governance initially through a project but still position it as a sanctioned enterprise body for managing data assets will see the most benefit this year and beyond.

Opportunities
Many stakeholders continue to look at data governance tactically and technically, and miss out on the overall business opportunities that can help them sell, position, and secure executive support for data governance. Data governance will begin to move away from an all-IT focus, and assume its rightful position as a business solution for helping companies drive revenue, reduce and control costs, and avoid risk. Many of these opportunities will be found in areas such as;
•    Sales & Marketing
•    Customer care
•    Order-to-cash
•    Supply chain
•    Risk & Compliance
•    Operations
•    Manufacturing

Challenges
The primary challenges for data governance proponents will stay the same, with companies becoming more creative in trying to demonstrate value and the business case for a data governance program:
•    Creating business alignment (executive & business leader support)
•    Awareness (creating awareness across the organization)
•    Implementing effective structure (responsibility, authority, accountability)
•    Deploying (taking it from paper to reality)
•    Sustainability (keeping the momentum going)

The good news is that organizations are becoming more aware of these opportunities and challenges and how best to address them. As a result, the number of companies that will launch and achieve data governance success this year will exceed the growth we saw in 2009.

Happy New Year and good luck in 2010!

Data Governance – What do we call it and where is it headed?

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

While the data governance industry is rapidly maturing, there are times when it seems to be like shifting sand from an ocean breeze. In the last 60 days alone I’ve seen data governance actually defined in a number of ways. In other words, companies are talking about implementing data governance practices around initiatives, but they call it many different names. In actuality, they’re really trying to solve similar data governance challenges. 

As I’ve said in a past blog post, “no two solutions are ever the same,” but many times, companies are in fact trying to solve the same general dilemmas – aligning people, process, policy, standards, technology, and business units in order to drive the desired business value and outcomes from their data assets. Though the challenge may be the same across organizations though, it’s the scope of these projects that differ, which in turn, accounts for the various labels companies apply to common initiatives.

Among the most common terms that I am hearing these days include:

• IT Governance
• BI Governance
• MDM Governance
• Stewardship
• Risk & Compliance
• Data Management
• Data Asset Management
• Information Management

For example, when people speak with me about their IT/BI/MDM governance, they are simply trying to apply data governance practices at a project layer. In many cases the projects just happen to be large.

When people talk about stewardship, risk and compliance they are often talking about sub-domains that typically fall under the umbrella of data governance. It just so happens that some of these sub-domains play a significant role in the business such as risk and compliance within the financial industry.

Terms and practices I expect will evolve, survive and eventually converge are data management, data asset management, information management, and data governance itself. In reality, each of these has striking similarities. In addition, many people are asking the question, “why should we restrict data governance to structured data only? Can’t the same principles and practices be applied to all our company information? The answer is most certainly yes.

Today data governance is the predominant term and practice used to align people and processes and standards. However, I foresee that this too will continue to evolve and may at some point down the road incorporate unstructured data assets which in turn may lead to the next generation of data governance.

Data governance is complex, but don’t make it too hard. Many times, you’re simply solving the same issues on a larger or smaller scale. But keep in mind it takes a lot of planning. Just realize that many of the same data governance best practices can be applied to all of these types of projects or definitions, and number one is creating the business case or you won’t get budget, resources or buy-in to realize your data governance goals.

Data Governance – No Two Solutions are Ever the Same

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

The intriguing thing about data governance is that there never seems to be a right or wrong answer to anything you do. Rather than hold steadfast to the virtues of planning a data governance project via one standardized method, I instead, initially tout general best practices and methodologies that will increase your odds of success, knowing that one customer’s solution will be quite different from another. To some folks, this might seem too simplistic early on, but in the end, it really makes a lot of sense, especially for this dynamic discipline.  The key is asking a lot of questions about data governance goals and needs to learn more about the customer, instead of simply talking and trying to sell a solution that won’t help them.

For example, when it comes to data governance there are a variety of elements that determine where, when, why, and how a company should approach a project. Some of the most prevalent of these include:

• Company size and complexity
• Industry
• Business drivers
• Business processes and technology
• Regulations
• Data volumes and type of data
• Executive awareness and support
• Ability to execute
• Funding

To complicate matters, none of these attributes are identical from one company to the next. In fact, because companies are so different, there are no data governance programs that look remotely the same, even between organizations in the same industry!

Just this past week, I spoke to two very contrasting organizations.  One was a multi-billion dollar/multi-national insurance organization and the other a 75 person contractor overseeing government programs and grants. Both had legitimate and similar data governance needs, but each will have very different solutions with different results and final goals.

With that said you can understand my dilemma when I’m commonly asked to explain exactly how data governance will or should work in a prospective organization. I may have a general idea but the real answer is that I never know for sure until I’ve had an opportunity to understand the finer points of the company’s business.

Companies really should keep this in mind when researching and planning for data governance.  While it is important for an organization to hear about the success and case studies of similar companies in their industry, it is infinitely more important to look to the cross-industry best practices and methodologies that generated the success.  You need to then apply these best practices to your own individual organization to create your customized data governance program and process that will succeed and generate the results that you need.

Data Governance – Business Case, Fundamental to Success

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

I have long been an advocate of trying to help companies understand and communicate the business value behind data governance and data quality. I truly believe organizations that fully grasp the business benefit of their data quality initiatives make better investment decisions and are far more successful selling, positioning, implementing, and scaling their programs across the enterprise.

With that said, it’s amazing how many data quality technology projects are funded without a solid business case. Seems there’s a generally accepted practice of not developing a business case at the technology layer. I realize there are a number of reasonable, and not so reasonable, explanations for this, but a few stand out in my mind as to why in-depth business cases may not be the norm:
•    Project-based focus limits the need to sell others outside of IT,
•    Little cross-functional participation required,
•    Company assumption that technology alone will fix the problem,
•    Business case built around the automation of data management activities, not business benefit of the data itself.

On the other hand, data governance programs have polar opposite characteristics that subsequently raise the bar for business case requirements:
•    Enterprise-based across business and IT,
•    Significant cross-functional participation required,
•    People, process, policy, technology, and business alignment collectively solve the problem,
•    Business case built primarily around financial, operational, and risk implications to the company.

When we compare the two, we better understand why companies around the world are struggling to demonstrate a business case for data governance. In essence, a business case for data governance is far more complex to develop and requires a different approach, focus, role, and skill set than traditional IT projects.

This poses a unique set of challenges to IT professionals who are often tasked with the job of building a business case for data governance. IT folks are not always called upon for creating cross-functional business cases, and the demands of data governance frequently fall outside the realm of their experience and skills. Finally, it’s common to see traditional IT project-based methodologies applied to data governance initiatives when in fact they are very different.

The result rings clear when I hear so many companies talk about failed data governance initiatives and the challenges in securing executive support. If one has a compelling business case, then executive and organizational support almost always follows. It also carries over to the sustainability and growth of the program.

A business case is both fundamental and mission-critical to data governance. With that in mind, we leave you with two thoughts. One is to consider carving out the time in your plan to conduct a thorough, mid-to-high level business case. The other is to consider partnering with outside organizations that have completed this type of work in the past. Both are worthy investments that will contribute significantly to the success of your program.

“The success of your data governance program is proportional to the strength of your business case and ability to communicate it!”

Data Governance - Industry Maturity

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

In June I spoke in San Diego at the Data Governance Conference, which by the way is a real good event for those of you that are serious about the discipline. During one of the sessions I participated in I mentioned the relative immaturity of the data governance industry which to my surprise offended at least one person in the audience.

Seems there are those that have lived and breathed data governance for some time and those that have not. The individual I appeared to have offended was one of those professionals that had obviously been working in this space for some time and was probably very good at what they do. Unfortunately this is not the norm by any means nor is it what I witness as I interact with companies around the globe.

What I do see is a large number of companies struggling because they either don’t have a data governance program (vast majority), have a disjointed program that is not working, have a program that is not nearly as good as they tout, or have a program that barely scratches the surface of opportunity. At the same time I see a ground swell of interest in the subject matter with people wanting to learn as much as they can as fast as they can about it.

While interest is high budgets remain low in that most companies have not budgeted for this part of their project. In fact I’ll go out on a limb and estimate that over 90% of companies interested in data governance do not have a budget for it in their current or upcoming projects around data management (DW, MDM, CRM, CDH, etc.).

In addition, many companies are still trying to sort out what data governance is. To some it remains a technology, for others a mechanism to support standards development, and yet for others it is all about stewardship. Though the industry is starting to bring clarity to the definition (it’s actually all these things and more) it remains an anomaly to many companies.

To add to the confusion many technology companies are referring to their applications as data governance when in fact theses applications are simply supporting a process within the broader data governance arena.

Collectively these things point to an immature industry that is still very much in the awareness stage. However, the next 2-3 years should be an exciting time in the maturity of data governance as we should expect to see awareness transcend to budget which will give way to implementation of this dynamic discipline across the broader market.