Informatica Evangelizes Its Increased Business Relevance With Latest Release

By Rob Karel

Last week Informatica announced the release of Informatica 9, its data integration/data management platform that continues to evolve its flagship PowerCenter and PowerExchange data integration and access technologies into a much more comprehensive data management platform going well beyond the scope of traditional, batch-oriented ETL that remains Informatica’s bread and butter.  

The three main themes Informatica has pitched for this release include:
- Pervasive Data Quality
- Business-IT Collaboration
- SOA-based Data Services

While these themes and capabilities - reusability, SOA-compatibility, real-time, business engagement - are not necessarily new to the broader data integration or data quality software markets, few organizations have been effectively able to execute on them.   For the purposes of this blog post, I’d like to focus a bit more on the DQ and business/IT collaboration parts of the announcement.

With pervasive data quality, Informatica wants its customers to extend the use of data quality software well beyond the limits of data warehousing.   Informatica 9 provides role-specific tools that can be leveraged by both the IT and business stakeholders involved in defining, analyzing, testing, and implementing data quality logic.   Informatica has also included advanced postal address verification and global matching from its acquisitions of AddressDoctor and Identity Systems, respectively.   

I’m a huge fan of Informatica’s goal of centralizing the definition and management of data quality rules that can then be delivered as real-time data services to any part of the enterprise.  But as I discussed in my “It’s Time To Invest In Upstream Data Quality” research last year and most recently with Forrester BPM analyst Clay Richardson in “Warning: Don’t Assume Your Business Processes Use Master Data”, the biggest inhibitor to enabling this is not technical.  Business process owners responsible for the capture and update of critical enterprise information are too often more concerned with process efficiency than data quality issues that may impact downstream processes. 

Similarly, I think Informatica is on the right track with its Business-IT Collaboration theme, but has a lot more work to do from evolving this from vision to reality.   Informatica 9 provides some useful business-oriented capabilities including browser-based tools designed for business analysts and data stewards to participate more actively in the data profiling and DQ rule definition phase of a data quality initiative.   And from a collaboration standpoint, the shared metadata environment ensures both business and IT have access to the same information, and the ability to bookmark and share views of analysis and business rule output, along with comments, is a great feature that eliminates the old method of exporting results to a spreadsheet and attaching to an email.   My primary concern is that Informatica has not yet created a pre-packaged interactive collaborative environment.  For example, there are no predefined approval, escalation, and validation workflows.  

Interestingly, almost every major data quality and master data management vendor I speak with is investing quite heavily in what some are trying to define as the Data Governance Technology market. Or in other words, tools used to support both business and IT stewards in the data governance process.   Of course data governance is not a technology market, it is a business responsibility that must manage the people, process and organizational challenges required to get business value from data assets.   But there are enabling technologies that can support data stewards from both the business and IT side during certain steps of the data governance lifecycle, and the jury is still out on what approach to this technology will be the most effective. 

But the biggest challenge for enterprises trying to embrace data governance has nothing to do with the tools.  It’s getting the business to participate in the first place!   In a recent Forrester survey asking MDM and DQ professionals to rate their organization’s current level of data governance maturity, 79% said they did not have a data governance organization that included active participation from all the necessary business stakeholders. Unfortunately, there is very little that a software vendor can do to change this culture, so I’m not expecting widespread adoption from the business of Informatica’s or anyone else’s data governance tooling until that maturity improves.

Process-centric DQ Services and MDM among top technology trends to watch

By Rob Karel

For the past few years, master data management (MDM) has been a hot topic among enterprise architects and data management professionals. But many of these evangelists have felt like rebels without a cause because their MDM initiatives have been primarily IT driven with minimal business sponsorship and participation.   In other words, the business expects high quality data, but hasn’t taken much accountability in delivering it. 

Data quality (DQ) management, a more mature technology and competency than MDM - and a required core capability for any MDM solution - has also struggled to be embraced by the business as a top priority.  More often than not, DQ has been relegated as a supporting downstream batch application for ETL, data warehousing and business intelligence applications, but not for a broader cross-enterprise data architecture including upstream transactional systems and processes.

But Forrester now sees this trend changing for the better, as highlighted by my colleague, Alex Cullen, who recently published important research on “The Top 15 Technology Trends EA Should Watch.”  A key theme Alex identifies is around Process-Centric Data and Intelligence. Alex believes - and I agree with him - that both MDM and real time data quality services will become critical enablers to support real-time operational and decision-making processes.  

Organizational competency in the definition and implementation of DQ rules, processes and technologies that can effectively cleanse, standardize, enrich, match, and merge critical data is a foundational step to moving towards true MDM:  the real-time delivery and synchronization of a single, trusted view of enterprise reference data to both operational and analytical environments.  Why? Because you can’t bi-directionally synchronize master data if you haven’t implemented the DQ rules required to derive a master record.

Unfortunately in many instances the after-the-fact batch cleansing of data in a data warehouse is often too little too late in ensuring high quality data can be delivered via MDM or otherwise.   This is why we see real-time DQ Services as a critical evolution for data architects to embrace and evangelize: embed DQ validation and cleansing logic within the upstream transactional processes that capture and maintain this data in the first place.   Not only will this of course improve the trustworthiness of the data used for analysis later, but even more transformational – it will improve the efficiency and quality of the upstream transactional processes themselves. 

A simple example:
A “Ship To” postal address is typically captured as part of an order management process.  Before real-time DQ Services are implemented to ensure that the addresses captured meet formatting standards and are in fact deliverable, order fulfillment runs a very real risk of being delayed – leading to potential postage penalties and negative customer satisfaction due to the delays.  But DQ Service can significantly reduce this risk by recognizing these DQ issue while the customer is still engaged in the process and best able to respond.

As further evidence of this trend toward transactional data trustworthiness, in my just-published “Trends 2009: Master Data Management” research I share the results of a recent Forrester MDM readership survey where over 64% of respondents said their planned or implemented MDM solution must support real-time subscription requirements, and not just scheduled batch loads.   Therefore it’s not too surprising that almost 79% of those surveyed that have decided upon an architectural deployment style for their MDM solution selected a Hub, SOA, Federated, or Registry approach since all of these are optimized to support real-time, transactional MDM requirements.  

But don’t expect this transformation to real-time DQ and MD to happen overnight. The complexity and invasiveness of these data services to the streamlined transactions that are managed make many EA’s wary, especially since most organizations still have extremely immature data governance programs.   In the same readership survey, nearly 46% of companies rate their data governance maturity as low or very low, while another 29% rate their maturity average, seeing significant participation from just a few key business stakeholders.   Data governance – an organizational and cultural shift – is the first trend EA’s must follow and evangelize in order to deliver the process-centric data and intelligence their organizations are demanding.

Podcast: Metadata Management: Getting Your Business Partners To Listen

Our latest featured podcast is Rob Karel's "Metadata Management: Getting Your Business Partners To Listen".

In this podcast, BP&A Principal Analyst Rob Karel addresses best practices for driving the adoption of metadata management technologies.

We look forward to your questions and comments.

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Process Data Management: Like Your Brain And Your Heart, BPM and MDM Can’t Survive Independently

By Clay Richardson & Rob Karel

 It's an age old question:  Which came first:  the data or the process?  Okay, not an age old question, but an interesting one to ponder nonetheless!  Of course the answer depends on who you ask.  Ask most Business Process pros and the reply:  "Process existed even before computers. In fact I saw on The Discovery Channel where they found a process map in King Tut's tomb. No joke..."  Ask a Data Management pro and you're likely to hear: "Data was invented by the Pharaohs - you ever hear of a little thing called 'numbers'?"  

In upcoming research we tackle this vexing question and conclude that process and data are of course inseparable (shocking we know).  Unfortunately, most Business Process pros, Data Management pros, and vendors we interviewed couldn't see the forest for the trees.  In fact, in our recent MDM Survey, only 11% of respondents said that their MDM and BPM initiatives share the same cost center and team members that work together on a daily basis to develop solutions for the business.

In many ways, master data management (MDM) and business process management (BPM) represent two different sides of the same coin when it comes to business optimization and transformation.  So, why is there so little collaboration and interaction between Business Process and Data Management professionals?  There's plenty of blame to go around:

• Business Process pros don't find data sexy. While most process improvement initiatives pay lip service to data quality, only a small handful take data seriously and incorporate data modeling and data mapping into upfront discovery efforts. BPM teams typically focus on process first, user experience second, and data is an afterthought - no one ever asks if the data is clean until it’s too late!
  
• Data Management pros value clean data over process context.  "If we create clean data, then they will come" is often the mantra of Data Management pros. Unfortunately, the person coming is the CFO - asking "why are we spending so much money on something when we can’t articulate its business impact?"  In other words, Data Management pros often fail to provide cross-enterprise context for MDM.
  
• BPM Suite vendors conveniently MIA on the issue.  Some argue that SOA is the silver bullet to bridge the gap between MDM and BPM initiatives.  While SOA can definitely address reusability and architecture issues, many BPM teams come to realize the importance of master data too late into their initiatives.  While BPM suite vendors provide basic tools to define process variables, only a few (Lombardi/Siperian, Appian, and Polymita) accept some responsibility for keeping process data in sync with master data sources.
  
• MDM vendors focused on data governance and stewardship only.  The good news is these MDM vendors do in fact recognize that business process plays a major role in their success. The bad news is they are only actively addressing one side of the problem: the business processes and stewardship roles that govern the administrative workflows within the MDM tools themselves. The MDM vendors are not actively influencing the business processes that consume and depend upon the master data these vendors provide.

Although process and data professionals may not see it, like your brain and your heart, BPM and MDM are interconnected and one cannot survive for long without the other.  Our upcoming research, "Warning: Don't Assume Your Business Processes Use Master Data," highlights the fact that process improvement initiatives face a vicious cycle of deterioration and decline if master data issues are not addressed from the outset. And MDM initiatives face an uphill battle and certain extinction if they're not connected to cross-cutting business processes that feed and consume master data from different upstream and downstream activities.

Earlier this year, Forrester highlighted the emergence of "process data management" in our BPM Tech Radar report.  This new category in the business process landscape organically merges BPM and MDM disciplines and capabilities to provide the enterprise with one version of "process and data truth".  

Over the next several months, we will publish a body of research that scopes the process data management challenge, provides best practice for combining process improvement and data management initiatives, and presents case studies highlighting teams that have successfully integrated these two critical capabilities.

Why It Matters?

Call it what you like, the consolidation of process improvement and data improvement is inevitable.  Organizations looking to minimize operational risk and boost adoption on BPM initiatives must incorporate data modeling and data management activities for cross-functional process improvement projects.  Additionally, organizations looking to increase the visibility and importance of master data must identify and harness the most critical business processes that generate and consume mater data.  These are just starting points for bringing process and data closer together.  Most importantly, senior management must foster collaboration and provide cross-training between these two siloed disciplines to begin this long overdue paradigm shift...

What’s Your Take?

We want to hear from you.  Let us know whether you think there is a real need to synchronize MDM and BPM activities? Do you think BPM vendors should accept some responsibility for keeping process data synchronized with master data sources? Alternatively, do you think MDM vendors should be doing more to educate the marketplace? Also, let us know if you have a process data management success story, or lesson learned that you want to share.  Post your thoughts in the comment section or feel free to shoot us a quick e-mail at crichardson@forrester.com or rkarel@forrester.com.