TRILLIUM SOFTWARE INSIGHTS

Data Management – it’s all about the data...and the people

By Jon Asprey, Director, Strategic Consulting EMEA, Harte-Hanks Trillium Software

“A cardinal principle of Total Quality escapes too many managers: you cannot continuously improve interdependent systems and processes until you progressively perfect interdependent, interpersonal relationships.”
- Stephen Covey

I’ve been very fortunate to work with a number of talented and inspirational clients on a variety of data driven programs. And I was musing on why some of these data quality programs have been more well-received within their organizations than others, and I came to the following conclusion. The initial, and continued, success of any information quality or data governance program is very much dependent on the qualities of the people involved in that initiative.  Now, that sounds like a no-brainer, but this is an important observation.

Furthermore, the ability to communicate effectively with senior management and the business functions within an organization will determine the success and the longevity of any data governance initiative.

So, there are some key areas where critical skills must be honed within any information management team to ensure credibility and business buy-in.

First, a consultative approach is a must.  The team should include people with keen personnel management and business analysis skills who understand business processes, can run internal workshops and collate requirements and feedback.

Next, focus on bottom-line impact.  Ensure that issues identified can be linked to a tangible business impact. Efficiency and time savings are good, but metrics relevant to the business, such as financial loss, risk or compliance, are most effective.  This will require individuals who can work with data analysts to “frame up” data issues in a way the wider business will understand.

Finally, internal communication and “PR” is critical.  Once tangible benefits are identified or realized, a senior representative must be able to communicate this across the wider organization. This will assist with momentum, increased participation and the ability to engender a culture change on the front line.

In many cases, these skills are not naturally present within the profiling or data quality management team and must be developed. Not to say the individuals are not capable, but it is simply a way of thinking that the team must become accustomed to and comfortable with.

I believe that, as with data quality improvements, the establishment of a data management strategy is an iterative process which improves and is enhanced over time. It’s the skills of the actual people involved, and not just the technology or partners that will ensure its ongoing success. So, as always in business, your people and your data are your greatest assets.

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 Arbitrage – a new way to manage risk for profit

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Arbitrage is one of those concepts that seem incredibly complex and arcane until you see it in action.  It was originally a practice that gained currency (literally) before the advent of internet-speed information exchanges. In an earlier time, the trading price of commodities could vary slightly in different markets. If you knew the price difference, it was possible to simultaneously buy and sell something and realize immediate gain. This concept can be applied to financial markets or even more recreational endeavors.

Spring is around the corner, and for those of you who equate that with the start of baseball season, you may also know that the concept of arbitrage was used in a fantastic baseball book by Michael Lewis titled Moneyball. In it, he explained how the principles of arbitrage were applied by the small-market Oakland Athletics professional baseball team to achieve a completely different goal – winning baseball games.
 
If you haven’t read the book, it highlights how Billy Beane, the team’s general manager, implemented a series of key metrics that could be applied to the player performance statistics captured throughout the major and minor league baseball system to find undervalued talent. Statistics are everywhere in baseball, but the idea that you could develop statistical metrics to spot and exploit something as seemingly arbitrary as performance on a baseball diamond to win major league baseball games was a stroke of genius. It enabled the Oakland A’s to consistently outperform teams in its division and across the major leagues every year.

One of the great pleasures of my work is that it gives me the opportunity to travel and meet the kind of people who have the same level of insight and intelligence as Billy Beane. Only for them, the challenge is to develop metrics that spot misalignments in financial and customer data that can be exploited to improve operational performance.
 
The same problem exists with data management as in baseball…statistics are everywhere, but you have to be creative and brilliant to develop the metrics that will bring out benefits and minimize risks, resulting in measurable ‘wins.’
 
That’s really what is happening in some of the leading financial services firms I’ve talked with.  Creative minds have begun to spot where data gaps and inaccuracies incur punitive risk reserves. They are finding ways to spot and resolve these gaps, enabling their organization to free up badly needed investment capital that can be pumped back into the investment economy.
 
These days, no available resource is more important than a firm’s maximum investment capital. It’s the equivalent of adding a number one starter to your pitching rotation or a potent number four hitter in the lineup. 
 
I am going to enjoy watching this ‘season’ unfold.

For Financial Institutions, the Devil is in the Details…and the Details are in the Data

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Now that the Financial Crisis Inquiry Commission (FCIC) has begun hearings in Washington, questions are being asked that will require firms to perform granular research and provide transparency:

Were the financial statements your firm issued covering performance throughout 2005-08 accurate?

What information did you disclose about your firm’s exposure to derivatives and nonprime mortgages? When did senior executives disclose that information to the markets? How did they measure the accuracy of that information?

What sources and documents were used to determine risk exposure and capital adequacy?

What measures and what actions were recommended, and what changes did the firm actually implement, in response to the identification of this risk?

Accuracy in capital adequacy and risk reserve data will play an important role in documenting the integrity of business practices employed by financial institutions when they are called on to answer these questions. More important, metrics about the key data used to support Value at Risk or other Key Performance/Risk Indicators will bring to light critical facts about the problems, inadequate measurements and poor practices of the firms involved in the catastrophic failure of the financial markets.  

Beneath all the spreadsheets and business intelligence reports used to tally profit and loss, value at risk, and risk reserve adequacy used by financial executives to make critical decisions, there are ‘stubborn facts’ that exist in the data.  And those facts will tell an important story about the business practices of the firms coming under scrutiny.

Answers to questions like the ones mentioned above will require firms to produce accurate documentation around communication, process and supporting data. Providing accurate responses to inquiries about the complex combination of information and data will be crucial to the reputation of a financial services firm and its officers – and may even determine who spends time in jail.

The devil is in the (data) details.  What does your financial data tell you?  What does it say about you?

Let me know your thoughts by offering comment.

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!

Customer Data Quality is Never Easy, Just Understood

By Ed Wrazen, Vice President, Product Marketing, Harte-Hanks Trillium Software

I often read that name and address cleansing is considered easy and a commodity compared to cleansing of other data domains such as product data, financial data, parts and materials. Boy, that annoys me, and I don’t get ruffled easily. 

Certainly name and address data is more understood and generally better-structured than other types of data (though this is not always the case). As you know, companies already have generally accepted rules for names and addresses. There are fewer, less diverse attributes (first name, surname, address1, address2, city, state, etc) than in other data domains, and these are more universally known.

We know first name abbreviations and synonyms (Peggy/Margaret, Mike/Michael), and titles such as Mr, Mrs, Miss, Doctor. We also understand how addresses are specified using rules, formats, postal codes and reference data for street names/city names. So there’s a lot of universal knowledge and data already available that describe how names and addresses should appear.

Yet, there are still many variables and degrees of sophistication required for name and address cleansing to consider. Large and mid-sized organizations and businesses, particularly B2C-focused companies, have more complex issues associated with customer data that extend beyond the capabilities of most name and address cleansing technologies. 

I would say there’s a big distinction between what name and address cleansing is and what I mean when I talk about “customer data quality.” Customer data is often much more complex, comprehensive and broader than first thought, and the requirements for a customer data quality solution go beyond basic name and address scrubbing.

Contact information may not always be as well structured as you would like. Unstructured name and address data present many challenges that basic cleansing solutions can’t cope with. The data may span several files or databases with different formats, structures, degrees of quality, standards and conventions. Multiple address variables may be held in one attribute in one file, but stored in several others, and data in multiple silos in multiple varying structures will require customizable transformation and merge logic before cleansing can commence. 

Also, you’ll need broader international data cleansing as your business and technology deployments expand into new geographies. Often a tick-in-the-box solution may be able to handle batch name and address cleansing for one or a few countries and only when that data is well-structured. But once more countries are introduced into the mix or more complex data issues such as the ones I’ve talked about are presented, these solutions fail to deliver, and require a lot of manual effort.

This is largely due to the fact that it’s not just about the technology but also the vendor's knowledge and capability to develop country-specific rules. This demands an understanding of country-specific name and address data and being able to create appropriate context-sensitive rules specific to each country. The data quality provider has to have access to relevant country data (street names, types, towns, cities, districts, regions, postal codes) to build the rules into the software rule sets for parsing, standardization matching and enrichment.

All this available information can be exploited by data quality software solutions, but often the amount of investment to create a truly global solution, fit for the most complex and demanding requirements can be enormous – even for name and address cleansing.

A simple cleansing tool may not be able to merge disparate data from multiple silos and may require users to perform extensive formatting, standardization and even cleansing to get it into a usable shape. Not really what you want or expect from a data quality solution is it?

So, it’s clear that you can’t simply assume your data quality plan or solution will handle your customer data cleansing projects, nor simply accept that if data quality is “bundled in” as part of a suite, that it will do the job.  Look deeper into your plan and requirements and evaluate your chosen vendors carefully. Ask for references and customer contacts that you can talk to for advice and guidance And, never assume that customer data is EASY!!

Want to Achieve Cross-Enterprise Data Quality? You are not Alone.

By Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte Hanks Trillium Software

I was just thinking…

…about the folks who came up to the Trillium Software booth at the recent MDM New York 2009 Conference. They all talked about a very similar problem they’re trying to solve – calculating the benefits derived from multiple and different data quality initiatives across their organizations. 

One of them remarked, "We have five or six completely-separate data quality implementations throughout the company, and I would like to:

1. Better understand the goals of each individual data quality initiative,
2. Determine "standardized" best practices in order to reduce work load amongst the groups, and,
3. Better understand where we are not capitalizing on a truly unified view of customers and products.” 

And, most important, he wants to attempt to drive a “coherent, overall data quality strategy throughout the organization.” Easier said than done as many of you know.

It was refreshing to hear and talk to a good handful of customers and prospects that have moved beyond a single point solution for data quality within their organization to a state of ongoing operational data quality in support of strategic business processes. 

And, like some kind of “Dr. Phil” of data quality, it was helpful for me to let them know they are not alone in their efforts to derive more business value from data quality. They are not the only ones feeling lost within their uncoordinated data quality efforts. Believe me!

If you find yourself in this situation, you can bet your colleagues on other data quality projects across the company feel the same way.  Take a step forward and ask your colleagues, “Do you feel the same way I do?”  If so, it’s important to know that there are real methodologies and best practices that have been established to help organize your goals, establish standards, and measure expected results for better return on investment.

Consultants can help, and software providers can assist as well.  But, technology should not come first.  You need to establish your internal goals and milestones for cross-enterprise operational data quality first, then bring in help to get it all together.

My friend and colleague Jim Orr blogs regularly about these first-step data governance and data quality best practices.  Check him out. 

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.

Is Data Quality just a Checkbox on an RFP? Well, it better not be.

By Len Dubois, Sr. Vice President, Marketing & Sales Support, Harte Hanks Trillium Software

When your company plans to spend $5 million for a strategic data management, warehousing or BI solution, you better be taking an extremely close look at the information and data that feeds that solution.  Take data quality lightly at your peril.

Data is one of the most important assets in your company, and its quality needs to be clearly understood at all times as it flows through your organization. Yet, some companies see data quality as simply a commodity to be “checked off” as part of some RFP, instead of a strategic, unique part of your business.  Data quality doesn’t lend itself to being a commodity, especially when evaluating the impact of data quality on your company’s operational performance.

Industry analyst firm Gartner says in research from August that companies are losing an average of $8 million annually because of data quality problems.  Do you think data quality is just a check box now?

Former customers of the “mega-vendors” tell me these software providers say they “understand data quality” and “we do data quality.”  But, chances are, they will not meet your expectations if you ask them to explain how their data quality solution will solve your unique business challenge.  They may think they do, but they’re simply treating data quality as a component of an ETL process and will fall negligently short of goal in an operational environment.  Their perception will hurt your business, so get under the hood, or better yet, make them get under the hood to define their data quality solution. You will be surprised by their lack of knowledge.

Our advice; be vigilant and advocate on your own behalf for the most comprehensive solution that meets your business needs. Demand a solution that:

• Solves the challenges inherent in multi-domain applications…. companies have data touching virtually every part of the business,
• Maps, both methodologically and physically, to your existing and future business processes,
• Demonstrates the ability to grow as your company changes and grows – it can handle 50 million records today, can it handle 150 million tomorrow?
• Is application-agnostic, and integrates with existing and future applications and infrastructure,
• Will be your data quality partner….one who is knowledgeable and understands all your inherent challenges and how they relate to your business.

If your data quality partner can’t explain to you how data and information changes at every stage of the data lifecycle – from the moment it enters your systems until it is archived – than you will have made the wrong choice.  Data changes and manifests itself in many different ways as it flows from point-of-entry through sales, marketing, billing, manufacturing and shipping departments.

Your data quality partner needs to be able to clearly tell you what data gets added, what gets taken away, what needs to be verified and how, and what must be reported on. 

So, just because you think you checked off that box on the initial RFP, does not mean you really solved your data quality problems?

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.