The Importance of Context and Explicit Values (Part 3), by Rob DuMoulin
Here’s the final segment of Rob DuMoulin’s series on the dangers of implicit knowledge and the importance of context.
The Importance of Context and Explicit Values (Part 2), by Rob DuMoulin
In the next segment of Rob DuMoulin’s three part series, he continues to discuss the dangers of implicit knowledge and the importance of context.
The Importance of Context and Explicit Values (Part 1), by Rob DuMoulin
“I shot a cat wearing green pajamas.”
Before you report me for animal cruelty, did you paint a mental image of me or the cat in pajamas? Read more 
Information, Intelligence and Process (Part 4) by Julie Hunt
Here’s the final article in this great series by Julie Hunt, an accomplished software industry analyst. Read more 
Information, Intelligence and Process (Part 3) by Julie Hunt
Here’s the next article in the series by Julie Hunt, an accomplished software industry analyst. Read more 
Calendar and MDM
The Hub Designs Blog welcomes a guest post by Rob DuMoulin, an information architect with more than 26 years of IT experience, specializing in master data management, database administration and design, and business intelligence.
Most business intelligence architects are well versed in the value of the time dimension.
With query performance and the need to support complex analyses being the two most important considerations in BI, a flattened set of time dimensions provides a multitude of options to represent and standardize time with limited overhead.
It’s easy to see the value of having a flexible, consistent, and integrated representation of time when thinking of business activities. Aspects such as when a transaction or activity occurs in relationship to other transactions, activities, or even pre-defined thresholds form the basis of Business Process Management activities. And accounting departments group transactions into time periods every financial reporting period.
So, how valuable can this same time dimensions be to a Master Data Management solution? If you are well versed in MDM at this point, you’re probably saying “What you’ve talked about so far is useful for relating transactions but it doesn’t tie back to mastering business objects like customers, products, or locations”.
But remember that mastering those objects does require standardization during information acquisition and publishing and that the various inputs and outputs to an MDM system are often diverse. Also, don’t underestimate the value of mastering “Time Tables” themselves as a component in your MDM universe.
First, let’s define just what we mean by a set of time tables before we apply them to MDM. A typical implementation would have two distinct groups of tables to represent time: day, and time-of-day. At the lowest level of the day group is a day-level table with every imaginable way the business can identify a day, such as: by its day of year, week, month, quarter, advertising week (for retail), same day last year (in some special context), or special tags like holiday, weekend, season, positional sunrise/sunset times, or even astrological sign and full moon cycles. And that just covers the calendar view of the business. There is an equally important and extensive set of calendar hierarchies and attributes associated with the business fiscal reporting needs. Add to that every way you want to represent attributes like day of the week or month of the year (number, 3-letter abbreviation, full name) and ending up with over 100 attributes in the day-level table is not uncommon.
Related to the day-level table are hierarchy tables at levels such as: month, quarter, year (and their fiscal counterparts). Each of the hierarchy tables contains all the attributes that define that level and higher levels. For example, the calendar month table would contain attributes defining month of year, month of quarter, and month overall, in addition to quarter and year and all the ways to call the month. Primary keys for the higher level hierarchy tables, like month, would have child entries in the lower level tables, like day, for every entry that rolls into the higher level.
The same holds true for time of day, with hierarchies like hour, minute of hour, shift, peak time, off-peak time, and others.
Because all the higher-level attributes are repeated in the lower-levels, there is typically not a compelling need to join the two tables. The relationships are there for flexibility. Having the various hierarchy tables as stand-alone entities allows you to attach them to business tables at all of the levels you collect or report time values. These tables and hierarchy relationships allow you to easily merge data of different time grains.
The best thing about time is that time is constant. There are always sixty seconds to the minute, sixty minutes to the hour, twenty-four hours to the day (excluding Daylight Savings Time adjustments), seven days to the week, the number of days to the month is fixed, the number of days in a year is predictable. Except for adjustments to fiscal calendars and special events, most of the information related to time hierarchies is static.
BI uses these techniques to conform information allowing it to readily apply to many views of the business… which sounds a lot like the same business issues we try to solve when integrating data within an MDM solution.
Introducing a robust set of Master Time dimensions into an MDM architecture opens up flexibility in how you consolidate information and also how you can apply it to many business purposes. It’s a natural expansion of MDM to include a master version of the corporate calendar (particularly the fiscal calendar) using a common set of time-related identifiers complete with any time references relevant to business operations.
Please let us know what you think of mastering the Time dimension or other types of corporate reference data in the MDM hub by leaving a comment here.
“Just Call Oracle”
Oracle showed a funny video today in Thomas Kurian’s keynote address on Day 2 of Oracle OpenWorld.
Using a fictional company with lots of systems and applications issues, Thomas walked everyone through how Oracle would solve a lot of those problems.
There were some great customer cameos from companies like Ingersoll-Rand and Office Depot. It was a little on the sales-y side, as Oracle keynotes can sometimes be, but it was well done and wasn’t over the top.
This session was a good reminder of the breadth and depth of Oracle’s offerings in the technology and applications space, and frankly it made my head hurt. I’m glad that Hub Designs specializes in master data management – the Oracle universe has gotten so big, it’s a little overwhelming for most people.
I’ll write more later today on the MDM track sessions.
MDM and Operational Business Intelligence
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Editor’s Note: Another in the series of “MDM and …” guest posts by Joan Lawson, an enterprise architect who I’ve known since 2003.
For more details on Joan, please see her LinkedIn profile — Dan Power
There’s been a resurgence lately of writing about Operational Business Intelligence.
It’s a valuable concept. Operational BI gives people in the enterprise an up-to-date view of performance against the key metrics that drive the success of the organization, so they’re better informed and can more readily determine corrective actions when needed.
All the vendors and analysts identify success criteria to include Business Activity Monitoring (BAM) engines, business rules, and high end presentation capabilities.
But they’re missing the #1 criteria for success… high quality master data.
In Operational BI, the real time transactional data will most likely be sourced from two or more business applications.
The historical source of data, for comparison’s sake, can be the data warehouse.
But for the master data, where is the “source of truth”? If you’re going to fast track your business monitoring, be sure to include an architecture for real-time master data management to provide top quality dimensional data.
Please let us know your thoughts by commenting here or on the MDM Community on your experiences in using MDM in concert with Operational Business Intelligence.
Keynote at Oracle BI SIG Conference
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The Oracle Business Intelligence Special Interest Group, which is part of the Oracle Applications User Group, is hosting Desktop Conference 2008, its annual online conference, in mid-November.
Here’s a brief description:
“Join the Oracle Business Intelligence community in the only global, online business intelligence conference that addresses business intelligence and data warehousing topics related to the Oracle technology stack.”
The SIG president, Faun deHenry of FMT Systems, asked me to do one of the keynote sessions.
It’s titled “Master Data Management 101″ and will be covering:
- what is Master Data Management (MDM)?
- some useful MDM and Data Governance best practices
- what works and what doesn’t
- importance of a holistic approach to MDM
- how to get the political aspects right
- the relationship between MDM and Business Intelligence
The session will be held online on Wed. November 12th at 2:45 pm Eastern, 11:45 am Pacific. Click here to see the agenda and here to register.
Getting to the Single View
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If not Master Data Management, what?
Enterprise Resource Planning (ERP) – the “back office” – has been around forever, and the “customer master” function in most ERPs is adequate, but due to acquisitions, many companies have more than one ERP system, and some companies let major business units build their own separate technology architecture.
Customer Relationship Management (CRM) – the “front office” – was supposed to be a “silver bullet”, bringing businesses closer to their customers, delivering 1-to-1 marketing, and increasing sales.
And data warehousing and business intelligence were supposed to deliver performance management and analytics, enabling better decision-making and deep analyses, but have sometimes proven to be difficult to deliver and extend.
But to varying extents, all of the technologies failed to deliver on all of their promises.
So circa 2004, along came Customer Data Integration (CDI) and Master Data Management (MDM). I call it the “hole in the donut”. MDM takes information from source systems like CRM and ERP, and eventually passes it on to downstream applications like data warehousing and business intelligence. But a lot of magic happens in that “hole in the donut”.
Information is consolidated into an MDM hub, usually using service-oriented architecture based integration technology. It’s cleansed using data quality software and completed or enriched with third party information. And it’s managed by a data governance organization. For more details on the end-to-end MDM process, see our earlier post on the “Five Essential Elements of MDM“.
So that would give you the Single View of the Customer (or Product, or Supplier, or whatever data domain you were mastering).
And from there, most companies would, in fact, flow the consolidated / cleansed / completed information into a data warehouse or business intelligence application.
But if your MDM hub is missing, and you don’t have the data governance organization or processes, all of the above is going to be much more difficult, if not impossible.
Organizations are waking up to this, realizing that they’ve got “the donut” i.e. key pieces of the puzzle (plenty of source systems, decent integration technology, tons of third party data) but no data quality tools and no central MDM hub.
If you want the Single View (the “whole donut”), you need to invest in those missing pieces.
Next Week’s DIG Conference
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I’m really looking forward to speaking at next week’s Decisions, Information and Governance conference in Las Vegas, sponsored by The Palladium Group.
And I spoke earlier this week at the New England Oracle SOA Users Group, talking about Master Data Management as a foundation for Service-Oriented Architecture.
MDM initiatives seem to be getting linked to Service-Oriented Architecture or to advanced analytics and business intelligence programs.
I think there can be a problem (but also an opportunity) for MDM in inserting itself between two things that used to talk directly from one to the other (an ERP system to a data warehouse) or (b) asserting itself as a predecessor task to ensure a better outcome (for example, when MDM is used to consolidate and improve the quality of enterprise data before people try to use it in analytics or business intelligence).
While I think it’s true that MDM is in fact needed at most large organizations, having to coordinate with an already-underway SOA initiative, or step back from a planned BI initiative and first tackle MDM, does complicate things a bit. So that’s the “problem” part.
The “opportunity” part is that, for organizations that have the foresight or the luck to tackle MDM first, it makes implementing SOA or achieving business intelligence success that much easier. There’s already a centralized repository of information on customers or products (or whatever domains have been mastered), and that information is proactively managed so that it’s trusted to be accurate, complete, timely and consistent.
Whichever situation your organization is in (tackling MDM first or building it into something else like SOA or advanced analytics), spend the time to develop a workable MDM strategy, using a holistic approach that addresses people, process, technology and information. By all means include an MDM hub in your planning, but make sure you also plan for business process management or sophisticated integration, as well as built-in or bolted-on data quality and enrichment capabilities. And be sure to build a data governance framework around your MDM initiative.
I’ll write a trip report after next week’s DIG conference, to let you know what I thought of the conference itself and whether I got lucky at the tables!











