"Great knowledge and a wealth of experience" "Informative and entertaining as always" "Captivating!" "Very relevant information" "10 out of 7 actually!" "Extremely eloquent, knowledgeable and great at joining the topics and themes between presentations" "Informative, dynamic and engaging" "I'd work with Alan even if I didn't enjoy it so much." "The quintessential information and data management practitioner – passionate, evangelistic, experienced, intelligent, and knowledgeable" "An extraordinary thinker and strategist" "The best knowledgeable, enthusiastic and committed problem solver I have ever worked with" "His passion and depth of knowledge in Information Management Strategy and Governance is infectious" "Surprisingly entertaining..." "Feed him your most critical strategic challenges. They are his breakfast." "A rare gem - a pleasure to work with."

Thursday, 24 April 2014

Data Quality is Boring!


Is this the kind of response you get when you mention to people that you work in Data Quality?!

Let’s be honest here. Data Quality is good and worthy, but it can be a pretty dull affair at times. Information Management is something that “just happens”, and folks would rather not know the ins-and-outs of how the monthly Management Pack gets created.

Yet I’ll bet that they’ll be right on your case when the numbers are “wrong”.

Right?!

So here’s an idea. The next time you want to engage someone in a discussion about data quality, don’t start by discussing data quality. Don’t mention the processes of profiling, validating or cleansing data. Don’t talk about integration, storage or reporting. And don’t even think about metadata, lineage or auditability. Yaaaaaaaaawn!!!!

Instead of concentrating on telling people about the practitioner processes (which of course are vital, and fascinating no doubt if you happen to be a practitioner), think about engaging in a manner that is relevant to the business community, using language and examples that are business-oriented. Make it fun!

Once you’ve got the discussion flowing in terms of the impacts, challenges and inhibitors that get in the way of successful business operations, then you can start to drill into the underlying data issues and their root causes. More often than not, a data quality issue is symptomatic of a business process failure rather than being an end in itself. By fixing the process problem, the business user gains a benefit, and the data in enhanced as a by-product. Everyone wins (and you didn’t even have to mention the dreaded DQ phrase!)

Data Quality is a human thing – that’s why its hard. As practitioners, we need to be communicators. Lead the thinking, identify the impact and deliver the value.

Now, that’s interesting!


Tuesday, 15 April 2014

Rory Sutherland - lessons from an Ad Man.

On a number of occasions recently, I've had cause to refer colleagues and friends to the highly entertaining and thought-provoking TED Talks of Rory Sutherland, Vice-Chairman at Ogilvy and Mather marketing agency.

I don't want to steal Rory' thunder, except to say that he has some fascinating ideas about the benefits of lateral thinking and development of psychological solutions to real-world problems. His ideas are particularly applicable in areas where there is limited budget, significant architectural or technical constraints or general resistance to change.

In the world of Information Management and Data Governance, I think these translate into questions such as:

* How do we influence people's behaviour more to achieve better information-enabled outcomes?
* Can we shift focus to business outcomes, not technical outputs?
* Do we really need to build another solution, or can we just make better use of current capabilities?

There are three videos, all well worth watching in full:






Do you agree with Rory? Are there areas where this type of thinking could be of value within your organisation? Are we spending too much time "doing" and not enough time "thinking"?

I'd love to hear some of your stories.

Sunday, 30 March 2014

Information Management Quote of the Week 30/03/14



"What I Tell You Three Times Is True."

(The Bellman, from "The Hunting of the Snark by Lewis Carroll)






and 

Friday, 28 March 2014

2014 Data Quality Congress AsiaPacific: Top 10 Takeaways

This week, I was honoured to be invited chair the DataQuality Asia Pacific Congress, this year held in Melbourne.  (Special thank you to Sangita Rai and Katelyn McGee from ArkGroup for preparing such a well-run event.)

For me, the event is one of the highlights of the Information Management calendar and this year’s Congress featured two days of conference proceedings, together with an additional day of in-depth workshops (including my own half-day session on Managing for Effective Data Governance, presentation materials for which are available here.)

Participants in the conference came from commercial, public and not-for-profit sectors and featured contributions included: Melbourne Water, Telstra, National Australia Bank, Australia Post, Insurance Australia Group, ANZICS, Mater Healthcare Services, Australian Bureau of Statistics. Contributions for the vendor and service provider community included presentations from ExperienceMatters, Accenture and BigData-Startups.com.

Telstra: Winners of the DQAPAC Enterprise Award for 2014





In addition to the main conference, the Asia Pacific chapter of the InternationalAssociation for Information and Data Quality (IAIDQ) presented the 2014 Data Quality awards, with honours going to:

Individual Data Quality Champion Award: Grant Robinson.

Project Award: Winners: Mater Healthcare Services; Runner-up Telstra.

Enterprise Award: Winner: Telstra. Runner-up; SBI Insurance.




There was so much to take in throughout the two days that it’s pretty tough to try and summarise everything! (I will probably revisit some topics in more depth in future posts…) But for now, here are my “top 10” highlights:



1. Data Quality is boring!
Or at least, it can be perceived to be boring by people who aren’t actively engaged. Passionate people are needed if the data is doing to be cared for, so you need to engage in a manner that is relevant to the business community, not concentrate on the practitioner processes. Use language and examples that are business-oriented. Make it fun!

Branding is also a crucial element of the communication process – it raises the visibility of Data Quality & Governance, and makes it more engaging. (e.g. meet Deekew, the mascot for data quality at SBI Insurance.) 

2. Learn from you mistakes
Don’t repeat the failures of before. Projects should incorporate a “Long Hard Look” exercise at the start of a project phase as part of project initiation and planning, not at the end of a project (when the lessons will quickly get forgotten).

3. Plan for change, and expect to change your plans.
Having a good plan will enable you to adapt later. But don’t adhere rigidly to the plan, because reality never turns out the way you though it might. Be flexible, be adaptable, be responsive, and be available.

4. Identity Management and Federated MDM
The concepts of “Party” and “Roles” are critical to enabling a single consolidated understanding and unique identification of each “customer.” At some point, an individual could play any number of roles in interacting with the organisation (e.g. customer, member of staff, supplier, broker, agent, benefactor, consultant…. IAG currently recognise 37 valid roles that a Party may play.) The concepts of Party and Role require constant ongoing education to the business.

5. Customer-Centric = Information-Centric
To enable a customer-centric approach, an information-centric enterprise view is foundational; it enables both cross-application integration and analytic insight. System- and Process-centric architectures drive compromises (because of silo thinking being built-in by design). As a result, data quality suffers. We need to have an information-centric architecture to overcome this.

6. Data Quality Declaration
The Australian Bureau of Statistics (ABS) publishes a Data Quality Declaration statement (DQD) with each data set it issues. The DQD provides contextual and narrative guidance to data consumers as to the relative suitability of the data set within a given context. The consumer can then adjudge whether or not the data set is suitable for their purpose.

7. “The data is always right”
A data quality error indicates a failure in the process, the system or the people. Use the data to inform and drive process change.

8. Data Quality by Design
In manufacturing, the production line would stop if there was a failure in the quality of products. Why is it not the same with failures in data entry?

Note that the just because we have the ability to profile a feature doesn’t mean we should! 100% data quality is almost never necessary (at least for analytic decision-making). Pragmatism should be applied to prioritise profiling and remedial efforts.

9. Useful Reference Frameworks
Several public domain reference frameworks were identified as being useful to the Data Quality Practitioner community, including:

10. Data Quality with “Big Data”:
In “Big Data” environments, the velocity, volume and variety of data processing create issues for timely measurement of veracity. Compromises will be necessary and we therefore need to be very clear and pragmatic about applying our data quality checks:
  • When to check.
  • What to check.
  • What rules apply.
  • How frequently to check.
  • Where to check (which steps in the data processing flow).

Please do leave a note to let me know your thoughts, or to share any similar experiences.

Sunday, 23 March 2014

Information Management Quote of the week 22/03/14



"When it is obvious that the goals cannot be reached, don't adjust the goals, adjust the action steps.”

(Confucius) 

Thursday, 20 March 2014

Now that's magic!


You'll like this, but not a lot...

When I was a kid growing up in the UK, Paul Daniels was THE television magician. With a combination of slick high drama illusions, close-up trickery and cheeky end-of-the-pier humour, (plus a touch of glamour courtesy of The Lovely Debbie McGee TM), Paul had millions of viewers captivated on a weekly basis and his cheeky catch-phrases are still recognised to this day.

Of course. part of the fascination of watching a magician perform is to wonder how the trick works. "How the bloody hell did he do that?" my dad would splutter as Paul Daniels performed yet another goofy gag or hair-raising stunt (no mean feat, when you're as bald as a coot...) But most people don't REALLY want to know the inner secrets, and ever fewer of us are inspired to spray a riffle-shuffled a pack of cards all over granny's lunch, stick a coin up their nose or grab the family goldfish from its bowl and hide it in the folds of our nether-garments. (Um, yeah. Let's not go there...)

Penn and Teller are great of course, because they expose the basic techniques of really old, hackneyed tricks and force more innovation within the magician community. They're at their most engaging when they actually do something that you don't get to see the workings of. Illusion maintained, audience entertained. (Here is Paul Daniels doing the classic "ball and cup" routine, and Penn & Teller doing their expose of the same thing...)

As data practitioners, I think we can learn a few of these tricks. I often see us getting too hot-and-bothered about differentiating data, master data, reference data, metadata, classification scheme, taxonomy, dimensional vs relational vs data vault modelling etc. These concepts are certainly relevant to our practitioner world, but I don't necessarily believe they need to be exposed at the business-user level.

For example, I often hear business users talking about "creating the metadata" for an event or transaction, when they're talking about compiling the picklist of valid descriptive values and mapping these to the contextualising descriptive information for that event (which by my reckoning, really means compiling the reference data!). But I've found that business people really aren't all that bothered about the underlying structure or rigour of the modelling process.

That's our job.

There will always be exceptions. My good friend and colleague Ben Bor is something a special case and has the talent to combine data management and magic.

But for the rest of us mere mortals, I suggest that we keep the deep discussion of data techniques for the Data Magic Circle, and just let the paying customers enjoy the show....


Sunday, 16 March 2014