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The data asset/3

Once defined the business case that should lead execs to sponsor a data quality project, it’s time to understand what you have to do to improve the data governance within your company.

Tony Fisher presents a detailed “Data Governance Maturity Model” (to be more precise it is the Data Flux one, other models are available out there; I’ve found at least another one: the IBM data Governance Council Maturity Model. )

The Data Flux Data Governance Maturity Model classify companies in four categories, that represent the four stages of maturity:

1)    Undisciplined (more or less 35% of the company)
2)    Reactive (50%)
3)    Proactive (10%)
4)    Governed (5%)

Where’s the point in introducing this DG Maturity Model? It is not just in being able to classify a company and give it a note. To say Company XY is better than ZW. Following the Tony Fisher words it is more to build the roadmap for effective data governance. You cannot jump from 0 to the topgun data governance in one step.

You need a Maturity model to

• First: asses the maturity of your company to understand where to start
• Second: plan your next move, which will be to reach the next stage. You cannot go straight to the top.

“The Data Asset” book has many pages describing each of the four maturity stages, trough numerous examples, you should recognise where your company sits and for each stage there is a description about how to get to the next stage.

The IT Kingdom

I have got this one still from the Tony Fisher’s book.

The IT Kingdom “a mystical dimension where IT lives, but no one else in the organisation dares to enter”

This is about organisations with very few cross functional teams consisting of IT and business users that are tasked with addressing data issues.  In these organisations, when no one is allowed to enter the IT kingdom, requests for data and information are passed back to IT without any business input. And that’s the issue…

The data asset/2

It takes lot of pages to Tony Fisher to reach the conclusion: “Data governance is not a program, Data Governance is a mindset”.  (page 66, while introducing the Data Governance Maturity Model) this would be enough to put the book back on the shelf “interesting and motivating books but now let’s get back to reality”.
In this particular case it still makes sense, because before getting to such exotic even if meaningful statement the author spends three chapters building the business case for improving data governance. An he does build a rich business case with tens of real world examples about how a better data governance –think about it as enterprise wide data management- improves the three main areas of interest for executives:

Risk mitigation (like fighting frauds by enabling controls, reducing the risks in mergers and acquisitions by getting a proper view on benefits and profits that may come from the merger, etc…)
Controlling cost (reducing the amount of time necessary to analyse and understand corporate data by installing a data quality program, make the HR able to do some strategic planning by creating a master record for each employee within the company over 120 disparate existing HR systems, one for each country, etc… )
Optimizing revenue (by enabling effective use of CRM systems, because most of them get derailed by inadequate data quality processes, by introducing an household view that makes possible to optimise special offers, etc. )

Getting trough these chapters is sometime boring, many examples sound pretty obvious and lack of implementation details: how they implemented the solution to the data quality problem. But nonetheless this analysis builds the correlation between the quality of your data and the health of your business. So that at the end data governance looks less as a buzzword and more as an interesting path to follow and you can state “Data governance is a mindset” still giving the impression you are focused on your business.

The data asset

I’ve finally got this very appealing Tony Fisher’s book from Amazon : The Data Asset. This brings us back talking about BI, in particular about Data Governance.

This first thing I retain from reading the first pages is a definition of Data Governance as “Enterprise wide data management”

And so the author’s mantra: “Data quality and data governance should never be considered a one time project. A quality culture must be established as an ongoing, continuous process”

In the first chapter there is an interesting discussion about how to make understand the executive that the reason for better data management is to improve your business. You need to build a clear and compelling business case detailing these three major benefits:

• Risk mitigation
• Cost control
• Revenue Optimisation

Finally two more statements from chapter 1:
“In advanced organisations, the Chief Information Officer (cIO) is more business focused, understanding the needs of the other executives, helping align IT and business” is all about alignment between business and IT!

And the final analogy: “Achieving quality data is not like the process of buyng the car. But it is like the process of maintaining the car”.

Resilience Engineering Concepts

Thanks to this article’s bibliography I discovered some more interesting papers about Resilience Engineering. I’ve particularly appreciated this one:

Prologue: Resilience Engineering Concepts, David D. Woods & Erik Hollnagel

I copy and past “straight from the paper” the most interesting bits:

A broad and cleat definition of Resilience Engineering
“Resilience engineering is a paradigm for safety management that
focuses on how to help people cope with complexity under pressure to
achieve success.”

An example about how to learn from the daily practice:
“workers are struggling to anticipate paths that may lead to failure,
actively creating and sustaining failure-sensitive strategies, and working
to maintain margins in the face of pressures to do more and to do it
faster”

Again about the difference between be resilient and be reactive
“People in their different roles within an organisation are
aware of potential paths to failure and therefore develop failure sensitive
strategies to forestall these possibilities. Failing to do that
brings them into a reactive mode, a condition of constant fire-fighting.”

About success and resiliency
“Success belongs to organisations, groups and individuals who are resilient in the
sense that they recognise, adapt to and absorb variations, changes,
disturbances, disruptions, and surprises – especially disruptions that fall
outside of the set of disturbances the system is designed to handle”

Resilience engineering and non linear systems.

Here I’ve found an interesting contribution about Resilience engineering and the Nonlinear dynamic system theory. The author shows a couple of examples to make clear that Resilience engineering has come NDS properties.  Personally I’ve really appreciated the rich bibliography.

Resilience engineering

One of the subjects I’ve been on recently is the resilience engineering.  It is all about controlling risk, with a particular approach that is well resumed by the following statement:

“safety is an emergent rather than resultant property of a system”

(got this from this article)

“Safety is something a system does, rather than something a system has.”

Back on tracks

Hi,
I’ve been away from this blog for a while, since I’ve shifted my interest to some other IT areas. Now I’m back and I’ll try to integrate these other bits to the blog.

I am talking in particular about: Resilience Engineering, IT services management, Project Management standards.