The story goes that Willy Sutton robbed banks because "that's where the money is."  While this attribution appears to be an urban legend, it's no myth that Oracle has a lion's share of databases - both transactional and analytic.

IBM started an advanced land grab for Oracle customer conversions by bringing a high compatibility of PL/SQL into the DB2 database.

Now, Teradata has invested resources in facilitating the migration away from Oracle.  With the Teradata Migration Accelerator (TMA). structure and SQL (PL/SQL) code can be converted to Teradata structures and code. This is a different philosophy from IBM, which requires few code changes for the move, but also doesn't immediately optimize that code for DB2.

While data definition language (DDL) has only minor changes from DBMS to DBMS, such as putting quotes around keywords, Teradata's key activity and opportunity in the migration is to change Oracle cursors to Teradata set-based SQL. 

"Rule sets" - for how to do conversions - can be applied selectivity across the structure and code in the migration.  TMA supports selective data movement, if desired, with WHERE clauses for the data.  TMA also supports multiple users doing a coordinated migration effort.

TMA also works for DB2 migrations.

While it will not do the trick on its own, having these tools, which convinces a shop that the move could be more pain-free than originally thought, will support DBMS migrations.  

Teradata Aster demonstrates its graphical "pathing" capabilities very nicely by showing the relationships between tweeters and their tweets at events, like the Teradata Third-Party Influencers Event I attended last week. 

The demonstration shows how to produce some sentiment of the event, but more importantly demonstrates relationships and influence power.  Customer relationships and influence power are becoming part of the set of derived data needed to fully understand a company's customers.  This leads to identifying engagement models and the early identification of patterns of activity that lead to certain events - desired or otherwise.

One important point noted by Stephanie McReynolds, Director of Product Marketing, at Teradata Aster, was that the sphere of relevant influence depends on the situation.  You can retweet hundreds of tweets, many for which you do not even know the tweeter.  However, when buying a car, those who would influence you would be only a handful.

One would need to take some more heed of an influencer's opinion - or that of someone with a relationships to the influencer.  It can become quite a layered analysis and influence power is hard to measure.  Grabbing various digital breadcrumbs is relatively easy, but is it indicative of influence?  Likewise, is a tweetstream indicative of the sentiment of an event?  I'm not sure.  It may not even be indicative of the sentiment of the tweeters.  Digital is all a start.  The worlds of third-party data, real sentiment analysis and possibly sensor data are coming together.   

Teradata rolled out Teradata Data Labs (TDL) in Teradata 14.  Though it is not a high-profile enhancement, it is worth understanding for not only Teradata data warehouse customers, but also for all data warehouse programs as a signal for how program architectures now look. Teradata Data Labs supports how customers are actually playing with their resource allocations in production environments in an effort to support more agile practices under more control by business users.

TDL is part of Teradata Viewpoint, a portal-based system management solution.  TDL is used to manage "analytic sandboxes" by these non-traditional builders of data systems.  Companies can allocate a percentage of overall disk and other resources to the lab area and the authorities can be managed with the TDL.  By creating "data labs" and assigning them to requesting business users, TDL minimizes the potential dangers of the "can of worms" that has long been opened, supporting production create, alter and delete activity - not just select activity - by business users.

These sandboxes must be managed since resources are limited.  Queries can be limited, various defaults set and, obviously, disk space is limited for each lab.  Expiration dates can be placed on the labs, which is not dissimilar to how a public library works.  Timeframes will span a week through a year.  The users may also send a "promotion" request to the labs manager, requesting the entities within the lab be moved out of labs and into production.

Data labs can be joined to data in the regular data warehouse.  One Teradata customer has 25% of the data warehouse space allocated to TDL.

TDL can support temporary processing needs with strong resources - not what is usually found in development environments.  I can also see TDL supporting normal IT development.  Look into TDL, or home-grow the idea within your non-Teradata data warehouse environment.  It's an idea whose time has come.

TDL is backward-compatible to Teradata 13.

I have completed teaching the Master Data Management Course in Sydney.  Thank you to my wonderful students.  Some memorable learning the last 2 days was done around some of these points:

• Master data, with MDM, can be left where it is or, more commonly, placed in a separate hub
• Product MDM tends to be more Governance-heavy than Customer
• In a ragged hierarchy, a node can belong to multiple parents
• Be selective about the fields you apply change management to
• Customer lifetime value should ideally look forward, not behind, and should use profit instead of spend
• Customer analytics can be calculated in MDM or CRM, the debate continues
• Complex subject areas require multiple group input
• Critical elements in MDM data security include confidentiality, integrity, non-repudiation, authentication and authorization
• Syndicated data is becoming increasingly important and MDM is the most leveragable place to put that data
• The web is also a source of syndicated data
• Data quality is a value proposition
• Do you have a data problem or a customer data problem or a product data problem?  It affects your tool selection
• Care about what matters to your shop when you evaluate vendors
• The program methodology should be balanced between rigor and creativity
• In the design phase, you develop your test strategy, data migration plan, non-functional requirements, functional design, interface specifications, workflow design and logical data model
• Don't mess up by staffing the team with only technicians
• The purpose of the data conversion maps is to document the requirements for transforming source data into target data
• Organizational change management is highly correlated to project success
• Stakeholder management is not a one-time activity

Day 1 of The 3-day Master Data Management course is in the books here in beautiful Sydney, Australia.  It's been an outstanding day of learning and sharing about the emerging, important discipline of master data management.

Here are my most vivid recollections from today:

• MDM is highly misunderstood due to the wide range of benefits provided
• MDM is part of major changes in how we handle data and to information chaos, which will get more complex before it gets less complex
• MDM can and should support Hadoop data and all manner of data marts
• Lack of a subject-area orientation in the culture is a challenge for MDM
• Some MDM is analytical, most is operational
• MDM subject areas can mix or hybrid across factors of analytical/operational, physical/virtual and the degree of governance needed
• Often many systems build components of a master record, few work on the same attributes
• MDM returns are in the improved efficacy of projects targeting business objectives
• To do a return on investment justification, all project benefits must be converted to cash flow
• MDM should be tightly aligned with successful projects, creating benefits for the MDM program
• Personal motivators must be understood and are important in building an MDM roadmap
• Vendor solutions may be subject area-focused or support multiple subject areas
• Tactical MDM supports an individual project, enterprise MDM supports the organization for the subject area
• Strong project management discipline can be more important in that role than MDM domain knowledge
• The data warehouse will remain relevant in organizations, but many of its functions are moving operational, such as those to MDM
• You can mix a subject are with the hub persisting frequently used data elements and pointing to source systems with the rest of the data
• Do not count on the data warehouse for what MDM provides
• Governance workflows provide the ability to escalate if actions are not taken in a timely manner
• External sources like EPCID are becoming relevant in the product subject area

More to come on days 2 and 3.