TRILLIUM SOFTWARE INSIGHTS

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!”

Wind River Achieves MDM and Data Quality with Trillium Software

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

It’s really fun when we can toot the horn of one of our customers! Information Management magazine recently wrote quite a positive review on how Wind River is achieving optimal order-processing performance using a combination of Oracle software applications and the Trillium Software System.  Check out the Information Management article. 

Wind River is a global leader in device software optimization, and they ship a lot of product. CIO Scott Fenton had faced a number of data quality issues that negatively impacted his order-to-cash flow processes and shipments.  Customer and product data tend to become inaccurate or faulty over time, and these inaccuracies contributed to delays in shipments and problems collecting cash.  Also, he was embarking on an extensive MDM project, and they didn’t want to move forward until the data was profiled and cleansed.

After a pretty extensive data quality provider evaluation, Scott and his team chose Trillium Software because of how we integrated directly into Oracle’s systems.  Scott says they were able to quickly get the data quality components working, with some experienced help from our professional services organization.  They began to actually analyze and cleanse data upstream as part of a data governance approach, before it passed to order-capture and shipping systems.  So, through everyday, ongoing real-time data cleansing, Scott could be sure the data was always correct before it even hit the MDM or business process systems. 

Scott was able to identify and standardize inaccurate data based on a number of pre-existing, and out-of-the-box rules, and has been cleansing data for about 3600 brand new customers per year.  As a result, Wind River has reduced shipping delays by 80%, improving order-to-cash flow and providing increased visibility into customer information to improve customer relationship management.  We’re also really happy to hear Scott say that our support before and after implementation was phenomenal, and that he sees Trillium Software as a strategic partner.

Congratulations to Scott and his data quality team at Wind River.  We’ll periodically keep you updated on what other customers are saying.  Do you have similar problems now within your organization?

The Growing Call for Enterprise-wide Data Quality

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

Organizations are always striving to differentiate their products from the competition.  Yet for all companies large or small, growing or downsizing, there’s this same direct correlation between relevant, timely and high-quality data and their ability to succeed in business.  In fact, we’re finding that data assets have actually become a primary differentiator for organizations that compete successfully and increase shareholder value, and those that don’t. 

There’s no clearer example than what we’ve seen in the global financial services market.  As we’ve witnessed over the past year, poor data quality severely impacts organizations, damages entire market sectors and can deteriorate the public’s faith and trust in the economy. 

But, the problem is, there’s so many types of data in today’s business systems.  It’s varied, ever-changing and more complex than ever.  Even sophisticated companies are having difficulty keeping pace with data quality.  At the end of the day though, its’ the responsibility of key business stakeholders to make sure data from all over the company is “fit” for the business purposes for which it was intended. 

So, it’s really exciting to see, more and more, that our customers’ business analysts are standing side-by-side and collaborating with their IT counterparts – implementing changes to data to ensure alignment with business processes – and always keeping in mind how these changes impact the data lifecycle and other processes downstream.  What we’re seeing now is the demand for a more comprehensive, enterprise approach to addressing an organization’s data quality needs. 

There’s this loud, growing call for an enterprise–wide approach to data quality, more closely aligned with strategic and operationally-oriented initiatives such as MDM, data governance, and Risk Management & Compliance.  It’s these types of engagements that help organizations move beyond typical vendor approaches to solving data quality challenges, to one that actually instills trust and confidence in the quality of data throughout the whole organization.

Now, organizations are demanding that data quality providers clearly demonstrate how poor data quality manifests across all the organizations’ operational applications, in addition to the more traditional approach of providing the ever-elusive “single view.”  They’re looking for a new approach to data quality – one that delivers not only best-in-class technology but a collaborative environment, enabling business analysts to instantly understand the impact of poor data quality through scores, ratings and KPIs.  Data quality vendors being asked to deliver the People, Process and Technology to overcome whatever enterprise data quality challenges an organization may face. 

What an exciting time for all us data quality lovers!

The Future Looks Brighter with Financial Data Intelligence & Governance

By Steve Bailey, Director, Financial Services Solutions, Harte-Hanks Trillium Software

As financial organizations look to brighter economic times, it’s a guarantee that governments and international regulatory agencies will introduce new legislation in the aftermath of the global freeze-up of credit markets.  If Sarbanes-Oxley is an example of legislation introduced in response to the collapse of Enron and Worldcom, then what will NEW legislation and reporting requirements look like?  Scary to think about!

Everyone knows that responding to the new demands of compliance will require complex combinations of legal experts, business analysts and implementation teams.  It could cost millions of dollars - and may eventually be perceived to have no appreciable business value at all.  Frightening! 

However, many of the world’s leading financial services organizations recognize that by taking steps TODAY to establish better information and data quality management processes, they can achieve returns with measurable business value immediately.  Even more important, they would position themselves to respond more quickly and at less cost than their industry peers to a new world of “oversight and transparency.”

Take one of my financial services clients for example, who revealed that due to a data quality error, the institution was forced to implement a compliance-mandated Risk Weighted Average, making it a policy requirement to set aside capital reserves of $800K on a $10 million loan.  But, through a Data Intelligence & Governance solution engagement, the data error was detected and corrected, resulting in an immediate $600K INCREASE in capital for use in other critical projects.  These types of real-world, tangible benefits can be experienced by any financial organization that really gets serious about data quality NOW.

The reality for most banks today is they’ve built webs of non-integrated compliance systems, each with extremely limited focus, typically centered on fulfilling a single compliance initiative at a time. Managing this hodge-podge of systems and data repositories adds multiple costly development steps to every business initiative, while creating silos of information and data that could defy data quality efforts.  And, consider the myriad of data that underpins KPI calculations, customer credit measurements, agency debt ratings, and accounts receivables, and you have an extremely difficult compliance challenge.  All these issues add up to hefty risk exposure.

This begs the question – how can firms address these data problems now in order to comply and protect their businesses from even greater risk exposure? 

I believe financial institutions need to consider a flexible, best-fit solution that leverages an enterprise technology platform and employs a consulting engagement model that empowers business owners to become proactive participants in the validation, measurement, and management of all data.  This type of solution would provide foundation processes for enterprise governance of risk and modeling data, and ensure data is matched and linked to enable business users such as GRC teams to correctly validate, aggregate and model risks as required by new regulation. 

What compliance and data quality challenges is your organization facing now?  Care to comment?  Would love to learn more.

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.