TRILLIUM SOFTWARE INSIGHTS

Managing Metadata = Managing Data – Is There a Difference?

By Michele Goetz, Director of Product Marketing, Harte-Hanks Trillium Software

I had the great experience of participating in Information Management’s DM Radio panel discussion on metadata this week.  Listen to the recording. Information Management’s Eric Kavanaugh and Jim Ericson asked myself, Rob Karel of Forrester Research, and other data quality providers, some pretty provocative questions that moved metadata from ‘data about data’ to defining its value in driving operational success in business processes and clarifying its role as the backbone to describing the business. 

As we talked about metadata in this context it really hit home that managing metadata is not unlike managing data.  Get your metadata wrong, and you will quickly feel the results of inconsistencies, gaps, and risks in your business.  Quite the data quality issue!

Why is that?  Jim Ericson brought up the point that metadata can be served up in a service that initiates an action.  Think about the status of a claim, the threshold for a stock trade, the instruction to call out to a cloud service.  These can all be metadata elements.  The implication of poor quality metadata in these instances can mean the rejection of reimbursement for healthcare services rendered, investment loss, or a breakdown or bottleneck in a business process.

When building out your data quality practices, don’t forget about metadata.  Profiling, and continuous profiling and monitoring, is key to maintaining alignment of metadata to your business.  As your business, market, and processes change, so too will your metadata.  Things to consider when determining how to define, measure, and monitor metadata are:

• Make sure your profiling solution is robust enough to account for the quantification of metadata conditions both as a way to check against defined standards or glossaries as well as help discover new metadata. 

• Your profiling solution should be available and easy to use by your business data stewards that understand how metadata supports and drives processes, reporting and linkage between their business applications

• There are reporting capabilities that address both context and data structure to ensure there is linkage between what the business needs and expects and how your system will support and serve to that expectation

Metadata is as much about providing context to the business as it is to help data fit within your infrastructure.  Keep in mind the quality of your metadata and apply the same methodologies for data governance and stewardship to be successful and drive value to the business as much as to your data management platform.

Proper Communication Leads to Innovation Breakthroughs

By Bernie LeCuyer, Sr. Vice President, Global Services, Harte-Hanks Trillium Software

Every so often, I’m fortunate enough to experience “breakthrough thinking” when working with a customer.  I see innovative ideas just about everyday but rarely see them become something of significant business value.  Too often, ideas never see the light of day because of something I was once told is called “the drift.” You know, statements such as, “that will never happen because the way it is around here is, blah, blah….”  You’ve heard it before.

Well, just a few years ago, a customer asked to me to review the design for automation of their data quality process. The solution, while innovative, is not the part I want to tell you about. What made this a true breakthrough in my eyes was how he got the project accepted by the organization as a whole – from top executives on down.

The organization that sprouted this project idea is one I’ve worked with for nearly a decade.  It’s full of bright people with innovative ideas for everything, it seems. Trouble is they have problems getting the ideas accepted within their organization, and consequently, these ideas are not funded.  This is a common problem in many companies. However, one IT management individual stepped up and took responsibility for this latest idea and effectively championed the value to both the business and IT.

Inadequate communication had been at the core of the organization’s inability to get this sort of thing done in the past.  Not specifically the verbal or written kind of communication, but the ability to articulate, understand and balance the needs and personal agendas across all departments/groups and at all levels of the organization.

Getting IT and the business to understand each other was a major breakthrough for this organization. To make it happen required the individual to acknowledge these points:
•    The business owns the data (but they may not know it);
•    You have to speak business benefits to business people (and it is not as easy as most people think);
•    You have to build and maintain a relationship with your stakeholders through continuous coaching.

Once the above points were acknowledged, then it was possible to do the following in order:

1) Change the thinking within the management of both IT and the business to accept responsibility for the entire process related to the data. This was done by identifying the most important aspect of the proposed changes as it pertained to each stakeholder and obtain their buy-in that the changes were good for each of them.

2) Educate both sides on the benefits (often confused with advantages) of the solution. The advantage may be that a process takes less time to complete but the benefit is cost savings. Don’t confuse the two.

3) Coach the stakeholders to assist with communicating the benefits to their connections, thereby creating a sort of “tipping point” for the solution throughout the organization. It was critical to create a groundswell needed for large, high-cost, and/or radically new ideas as was the case with this project.

4) Convey passion about the benefits of the solution, in turn, making others passionate and supportive of the project and its stakeholders, fueling momentum for the project.

So, sometimes, it’s the means to the end that is most important.  The breakthrough really was the ability of my customer to put himself in other people’s shoes throughout the organization, and sell them on what is important about this data quality project to each of them, individually.  Then, articulate the expected benefits in a way that they all understand to get them to buy in.  It was important for him to listen to the stakeholders first, understand what was important to them, and learn how to talk their talk to articulate the benefits and results.  That’s a breakthrough that all companies should strive for in order to ensure their data management or data governance projects move forward.

Wasted Talent and Lost Opportunity: Are Your MBAs Just Sifting Through Data?

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

It always amazes and gratifies me when clients realize the depth, impact and true costs of poor data quality within their organizations. 

In a recent customer meeting, one of the data stewards was diagramming the architecture and lifecycle process of their information needs within an order-to-cash system.  During the conversation, the steward discussed the types of data that each step in the process required, for example; names and addresses of customers for sales, marketing and shipping applications – product information for billing, inventory control, distribution and shipping – credit and/or payment for the finance department, etc.

In each scenario, data points were added or eliminated based on the contextual need for information for each department.  Representatives from MDM, Data Integration, and SCM all added value to the conversation in terms of completeness, accuracy and structure.

It was at that point when one of the business analysts described what her life was like trying to rectify and assimilate information from the various reports she received in order to answer two primary questions from senior leadership:

• How much new revenue was attributed to new customers last quarter?

• How many new licenses/products were sold to existing customers last quarter? 

She explained that because each business function defined customer, product, and yes, even revenue differently, she spent three of every ten days collecting, assimilating and analyzing reports from across the organization, and aggregating the information within Excel spreadsheets and reports.  Then, and only then, does she feel prepared to provide answers to senior managers. 

This was a simplistic boiling down of the facts in the meeting, but it demonstrated to all that a business analyst (an MBA level employee), whose job it is to analyze data in order to provide information for decision-making, was spending 30 % of her time performing data quality checks on information that previously was thought to have been high quality data.

Like Sonny said in the movie A Bronx Tale, “Wasted talent.”   Imagine the costs and wasted opportunity associated with this manual approach to ensuring high data quality.  Never mind the fact that she is paid to strategize about business processes and how to optimize business results and performance, not sift through data looking for data quality problems.  And, realistically, with new data coming in all the time, the data set may never be accurate enough to maximize business results because the manual process is never-ending. 

I know I’m preaching to the choir here, but this is a real live situation involving a company that really did not understand the impact of data quality ON THE BUSINESS.  And unfortunately, this lack of communication between IT and business happens all the time.  There is so much opportunity to utilize data quality tools to solve real, live business problems in so many companies.  But you have to communicate across business and IT lines, constantly measure results, and ensure data is analyzed, cleansed and reported on in real time to answer the big business and revenue questions that senior management will ask you. 

How are you breaking down the barriers between IT and business users to eliminate wasted opportunity?  Would love to hear about your ideas and anecdotes.

When it comes to data quality, it’s the last mile that counts - That last mile is Operational Data Quality (ODQ)

By Michele Goetz, Director of Product Marketing, Trillium Software

Today a significant gap exists between how IT managers or employees, and that of line-of-business executives, view the overall quality of data, according to a recent study by Forbes Insights.  This chasm exists because there is a missing link between what data quality projects are designed to achieve vs. what the business expects them to achieve. 

In light of this, alignment of data quality projects needs to provide tangible business benefits that are tracked through key performance indicators rather than though data quality indicators alone.  Both IT and business users must understand that data quality success is intimately tied to business success.  The context and use of data is required when approaching data quality.

In the past, the easiest route for most companies was to approach data quality within the data warehouse, installing the black box that runs on a server tucked away in a back room. While effective for a specific task, this approach leads to a lack of enablement of data quality policies across the enterprise. 

In fact, we hear from customers that instead of improving their data quality services within their quality solutions, they turn to quality procedures that are implemented within ETL processes. This approach, in many cases, conflicts with policies within the original quality services causing further degradation rather than improvement.  Lastly, the approach is also entirely reactive, causing business processes to be less effective and efficient.

The solution is to employ Operational Data Quality (ODQ).   ODQ practices happen within a business process deploying data quality services at the point of data entry, and usage within an application and between applications.  There are a number of benefits in this approach: 

•    Data quality policies are designed and aligned to business process improvement and results, providing transparency and immediate understanding of the linkage between data quality and business goals;

•    Data stewardship and standards are maintained and remedied by data stakeholders that are most impacted by degradation, as well as having the most expertise on how to improve the data according to business needs;

•    Improvements are made as they happen and quality of data migrates throughout the enterprise, allowing improvement beyond the immediate process;

•    Success in one business process provides a framework and justification for investment across the enterprise in data quality initiatives;

•    Data Governance initiatives benefit through Operational Data Quality practices by addressing data quality policies within the context of a business process that already has roles, responsibilities, and accountability.

The quality of data is no longer just an aspect to satisfy executive level reporting and analysis.  Companies are increasingly relying on their data to make decisions within processes such as customer on-boarding and off-boarding, risk assessment, and supply chain management.  This requires increased certainty that the data available supports next steps in a process and decision to make these steps.  Rolling out ODQ within your business is imperative to your business success.

The Language of Data Quality

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

The language of data quality is interesting, isn't it?  To truly interpret or understand it, it really depends on whom you are talking to and what their specific goals are for their unique business challenges.  During one of our most recent regional user conferences in Boston, it was really evident that there are distinct ways to refer to the challenges and enormous business benefits that improved data quality can bring to an organization. 

When you listen to IT professionals, they speak in terms of formats, structured and unstructured data types, metadata, etc.  Listen closely though, and they talk about challenges in terms of consolidating different data sources, completeness and accuracy of records, and ability to discover anomalies – both known and unknown.

While Data Stewards on the other hand refer to understanding business process break down, data governance, application integration and reusability of data quality processes in both upstream and downstream systems. 

Both of these are clear examples of how different data quality solutions and services users talk, but in most cases, and they’re referring to similar data quality pain points, and you can hear the features of their current tools and offerings coming through the vernacular.

On the other hand, when you listen to the business buyers of data quality solutions, they speak a completely different language.  I heard them speak in the following terms; "We purchased a solution to reduce our procurement cost across the organization by 3%, you guys at Trillium Software helped me better understand who our distributors are, and what we purchase from them." 

Also from an insurance executive said, "I wanted to better understand how many claims were being submitted for the same incident and which of our customers had multiple policies within our organization.  From a shipping executive, "We are being charged enormous fees from importers and exporters due to incorrect or incomplete shipping information on containers, how do I drastically reduce or eliminate those fees?  Another executive stated, "By better understanding the information assets of an intended acquired company we were able to reduce the acquisition price by 33%.” 

Lastly, an automotive executive opined, "My companies reputation is on the line every time an automobile leaves a distributor.  One of our most critical business processes is to ensure we know when a vehicle is returned for a warranted repair.  It is at that point when a customer is most vulnerable, so understanding exactly who the customer is, which vehicle, which part, what distributor and how long the vehicle was out of use is vital for our business.  Closing the gap on those data "unknowns" is the key for our success."

Now you may ask, aren't all of these people – the IT folks, Data Stewards and executives – the "buyers" of data quality solutions?  Yes and no. 

At the end of the day, the person putting their signature on the contract has a vested interest in, and an enormous responsibility for, seeing that the solution they are purchasing meets not only the needs of the IT professional who is primarily worried about the bits and bytes, the Data Steward attempting to bring line of business and IT professionals together in a collaborative way, and the business buyers who run campaigns and business processes.  The needs of all these buyers must be taken into account to ensure the success of a project, as well as ensuring they are solving a business challenge or need for the organization.

It is therefore essential that my staff and I understand and better communicate the benefits, as well as the challenges each buyer faces. 

What type of buyer are you?  What “language” do you speak and what are your most pressing business needs that can be addressed by improved data integrity?

From Location Intelligence to Business Intelligence

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

In my last blog on geocoding, I spoke about the increasing adoption of geocodes in a wide variety of consumer and business applications.  I mentioned how geocoding helps to enhance “Location Intelligence,” so, thought it would make sense to talk a little more about what I mean by Location Intelligence and why it’s increasingly becoming important.  

Check out the Wikipedia definition of Location Intelligence.  It is defined as “the capacity to organize and understand complex phenomena through the use of geographic relationships inherent in all information. By combining geographic- and location-related data with other business data, organizations can gain critical insights, make better decisions and optimize important processes and applications, and streamline business processes and customer relationships.”

I like this definition a lot.  It says so much about the value of information and how it can be applied with geographical elements, namely geocodes, to enhance business value and decision-making. With better insight into location relevant information, organizations can identify opportunities which otherwise may not be obvious.  There are many industry examples in which location intelligence is important.

For Insurance risk management, Insurers need to know precisely where properties are located in proximity to a flood zone or crime area or other potential catastrophe-prone region, or otherwise, suffer revenue and underwriting consequences.  In the retail industry, understanding property and population densities and demographics within commuting distances will help determine new store locations which could improve customer service and experiences.   Telecommunication suppliers also need location data to optimize cell mast positioning and maximum network coverage. 

We are also likely to see Location Intelligence play more of a role in Business Intelligence. As organizations mine and analyze their data to enable them to make better, more-informed decisions, visualization and mapping tools incorporating location data will increasingly be used. This will help identify location-specific market and business opportunities, and localized marketing and sales strategies.

Is your organization using geocoding and location intelligence in some innovative way to drive business efficiencies and improvements?  Send along your company success stories around geocoding.

Customers Telling Us Data Quality Impacts Business Processes

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

I spent all of last week with existing customers and interested prospects at our Trillium Software Regional User Conferences in St Louis and Columbus, OH. It’s always refreshing (and informative) to hear about the anecdotes and practical sides of data management implementations that are successful, as well as the ones that face challenges.

A common thread running through their experiences is the need to pull senior-level executives into their data quality world in order to better define how information quality impacts business processes.  Business processes can be simplistic – the need to identify when an insurance claim has been submitted on more than one occasion for the same incident. Or, they can be more complex issues like determining which systems are authoritative and what processes must be implemented during the lifecycle of master data entities.

More successful organizations, and those further down the information management maturity scale, fully understand that aligning data quality or information quality practices to definable business processes provides the best chance of improving their business results most quickly. Better stated, when you fully define how poor data quality manifests itself within a business process, you truly get the attention of those whose jobs are evaluated based on the revenue generated from those same strategic business processes. Boy, that will open some eyes, won’t it?  

These business process optimization challenges are the types of engagements that our customers have partnered with Trillium Software to help them solve and it is enormously rewarding to know that they trust our experience and expertise as well as our technology to make them successful. 

I am looking forward to our next set of customer conferences in Boston and Philadelphia to hear what our clients have to say. Will you be in Boston on June 15 or Philly on June 17? Come by and we can talk.

Eating the Elephant One Bite at a Time - MDM and Data Quality

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

How do you eat an elephant? One bite at a time of course. It’s a well known phrase and a philosophy often cited in management. Defining and implementing an MDM solution can be a risky and complex programme which is only partly dependent on technology to be successful.

From my experiences of engaging with our customers, those implementing MDM one bite at a time are riding the smoother path to success. Now this may sound an obvious way to proceed but some organisations are suffering by failing to adhere to the basic principle of understanding and meeting the (data) users’ needs.

I have seen many cases where a data “master” has been defined with little consideration for the quality of data held or the intended downstream uses of that data. To create a true “master” source that can serve a multitude of business needs and stakeholders, business analysis must be done first. This requires focusing on a defined set (or subset) of master data for the initial project and includes making a clear business case based on the improvements to be realised. By targeting efforts in this way, uncovering the true needs of the business becomes more manageable and a robust business case can be built.

Initially the focus of MDM is defined by data domain, with many organisations targeting customer data in their first project, although supplier, asset and product are also common. At this point it is often sensible to sub-divide data into smaller data sets. For example “customer” may be broken down into residential and commercial customers with one sub-set “mastered” in a pilot MDM project. The rationale for the break can vary, relating to a number of points that favour one sub-set over another:
•    Higher business priority/value
•    Smaller data volume (more manageable)
•    Less complex business process, therefore less stakeholders and less complex requirements

One point to note here is that residential customer data may have very different standards, matching (duplication) rules and business processes compared to a commercial customer. Therefore “customer” data must be subdivided to enable the business to have a sensible discussion on what standards and requirements the customer master must satisfy.

Geography can also prove to be an effective way to sub-divide data for a pilot, for example a business in the United Kingdom may have a customer base in the Republic of Ireland which is representative but much more manageable (in size) as a proof point. Of course there may be some regional differences in the customer data but the high level principles remain the same.

Breaking up the MDM elephant into manageable bites allows the implementation to gain momentum. A success in one domain, proven with return on investment figures and managerial commendations, does a great deal to win interest from leaders of other domains.

Of course, there’s no one route to success. It depends on your company and your people and culture. Perhaps you’d like to share what you’ve experienced? Have you already embarked on and MDM project? How did you approach it and what worked/didn’t work?

Location, Location, Location! - A look at the world of geocoding

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

Location intelligence is one of the fastest-growing technology trends in the world, enabling consumers and companies alike to make better, more informed business decisions. Widespread adoption and use of mapping and geospatial applications has resulted in more and more reliance on location data than ever before.

We now have satellite navigation systems to point our cars in the right direction, Internet mapping tools to show us where we need to be, mobile applications on our cell phones to locate restaurants, bars, shops.  And it all comes down to what are known as geocodes – latitude and longitude coordinates. Once the domain of geospatial and mapping professionals only, geocodes are now commonly used in a wide range of consumer, business and public sector applications.

For example, location is everything in the real estate business. Buying a property on the wrong side of town or outside the boundary of a particularly popular school could result in several thousand dollars difference in valuation.  Having accurate address data up front, before a purchasing decision is made, is important.  However, for certain business applications, an address is just not precise enough. 

Take postal services for instance, a correct address will ensure you receive mail promptly. But knowing whether a property is at one end of a street or another makes a big difference if the street is 5 miles long, and your business is a logistics company, delivering millions of packages a year around the globe. Knowing exactly where a property is located on a street, can save, time, fuel, labor and other related costs.  Aggregated across thousands of properties, it soon adds up. But its not just logistics operations, many businesses are exploiting geocodes for their location intelligence too.

Geocodes, simply put, are mapping coordinates, expressed as latitude and longitude that provide precise geospatial coordinates for a particular location.  Often, they are assigned based on address data – street, postcode, city, country information supplied. The more precise and accurate the address data, the more precise and accurate the geocodes assigned.

But, many geospatial applications do not validate, cleanse and enrich address data, prior to assigning a geocode, nor do they support international address data at all well.  Hence, we are now seeing wider adoption and use of data quality solutions tightly coupled with geocoding technologies to dynamically enhance location intelligence.  

There are many applications and uses of geocoding across all industries. In my next blog on this subject, I will provide some specific industry examples on the use and benefits of precise geocode data.   However, in the meantime, if you want to understand more about geocoding and geocodes, check out our white paper.  How are you using location intelligence in your business and what benefits are you achieving with it?

Why Don’t They Talk about Data from a Business Perspective?

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

I was just thinking about the Webinar Ed Wrazen conducted recently titled “Operational Data Quality” and how my friend David Loshin's recent blog post titled "How Do You Know What Data is Master Data"  were connected, yet with different outcomes. 

David remarked how difficult it was for some of the MDM vendors at the Enterprise Data World conference to really codify and define the term "data" as it applied to Master Data Management.   While not surprising that they all had slightly different takes on the term "data," what was really distressing to me was how none of them talked about it from a business perspective. 

In my view, that speaks to the heart of a serious data management problem.  Many of the leading MDM vendors are wedged into rigid stack solutions that only satisfy infrastructure needs rather than ensuring data is "fit for purpose," regardless of the business process or implementation type.

Ed Wrazen on the other hand approached the Operational Data Quality Webinar from a different perspective.  He focused on the business challenges that motivate data stewards to collaborate with their business and IT partners.  First, they must specifically define what "data" is relevant to each business process, and second, define the context of the data within the business driver – in this case, MDM. 

Ed clearly outlined the importance of understanding the types and usage of data that inform the decisions of sales and marketing, and how the contextual use of the data differs from other equally important, yet disparate systems, including finance, credit billing, shipping, inventory management and storage.

He was able to clearly demonstrate that when you focus on the business requirements and processes for the data, you can deliver the means to dynamically improve the value of your information on a unit level as well as an enterprise level.  And, this will work regardless of what project or initiative you’re talking about – MDM, MDM, Data Governance, Compliance, CRM or any other discipline. 

How do you think and talk about your data?  From a technology-only standpoint or from a business process perspective?  Ask your boss that question after you answer it yourself.