Monthly Archives: October 2014

The ‘Right’ Management Stuff: Lions ‘lead’ by donkeys

11 Saturday Oct 2014

Posted by in Management, project management

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Peter Drucker once stated that “There is nothing so useless as doing efficiently that which should not be done at all”.

That is one of the guiding principles in my professional role as strategist, leader and coach.

I work in business and IT.

With engineers, administrators, managers and executives.

I occasionally read blogs and forum posts related to my areas of interests.

A question appeared on a popular forum for Project Managers.

It asked, when it comes to successful Project Management, “what is more important, the right people or the right process?”

You get a lot of questions like that in IT.

It’s probably the same for other jobs.

A lot of the replies to the question were terse, mind-numbing and vacuous.

Other replies read like concatenations of fortune cookie quotes based on someone’s idealistic and flawed notion of management.

There were answers in favour of people over process, process over people and others that put “right process” and “right people” on an equal footing.

I didn’t get the impression that people were addressing the question from a position of knowledge and experience.

No one asked any questions.

No even the hint of one.

Though the obvious questions were there, staring at them in the face.

But no one asked.

  • What do you mean by “right”?
  • What do you mean by “right process”?
  • What do you mean be “right people”?
  • Why are you asking this question?
  • What do you hope to get out of this?

Everyone assumed that there was a common understanding about what “right”, “right people” and “right process” mean in a project context.

Because people didn’t ask the obvious questions, they couldn’t move on to the more subtle and substantial questions.

They couldn’t move upstream or downstream.

Wherever they stood their position was untenable.

They didn’t have the social skills, the creativity or the intelligence to step back from the question.

They were stuck in the trivial, the hackneyed and the simplistic.

They answered with clichés, vagaries and baloney.

So what we had, was a long-life thread of ill-informed responses to a vague question.

It was if you’d asked a group of unthinking patriots what was better for the country, “the right people” or “the right political system”.

But it goes deeper than that.

Politicians who are reduced to talking about rights and wrongs, without being able to pony up any rational explanations, are quite rightly derided for being shallow and removed.

In IT we think it’s a sign of considered professionalism.

But regurgitating motivational slogans that are well passed their use by date is not professionalism.

The unquestioning subservience to trite, populist and unrealistic management dogma is not professionalism.

Acting as if project management were some bizarre super-hero Hollweird invention is not professionalism.

Needing to break everything down into right and wrong, good and bad, black or white, etc. is the height of arrogant superciliousness.

What is worse than arrogance or ignorance, is when they go hand in hand.

It’s just not on.

If IT was an army, it wouldn’t be the professional modern army of today. But an army lead by well-meaning, socially inept and multiply-challenged incompetents. The sort of army that would march a battalion of the “right people” to their certain death, or the sort of people who would see instrumental reason as being the “right process”.

“Lions lead by donkeys”.

Students of European history – say from 1934 to 1945 – might make the connections.

If you can’t define what you mean by “right”, you may as well be discussing the sex of angels.

If some people can’t even ask the obvious questions, then what the feck are they doing managing projects?

Never mind, life is too short to fret the inadequacies and excesses of IT.

As Lucius Seneca was want to say “A physician is not angry at the intemperance of a mad patient, nor does he take it ill to be railed at by a man in fever. Just so should a wise man treat all mankind, as a physician does his patient, and look upon them only as sick and extravagant”.

Just who are you to tell us? Consultant Manager as Leader and Coach

10 Friday Oct 2014

Posted by in Big Data, Business Intelligence, Data Warehouse, Management

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Sometimes I get asked in to support the manager of a failing project.

Most of the time I get to take over a failing project, with or without the previous manager’s involvement.

Occasionally I am called in to act as a Project Manager for clients who have brought in Systems Integrators and Management Consultants to do most of the project work.

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The IT Circus and the Infinite Loop – Part 1

10 Friday Oct 2014

Posted by in Ask Martyn, awareness, Best principles, Big Data, business, Business Intelligence, Data Warehouse

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The IT business suffers a malaise, it also affects other businesses. In IT, like in other lines of business, much of what has been made is eventually side-lined and forgotten. If it was ever on the radar in the first place. Continue reading →

Strategy and Market forces – Get your ducks lined up

10 Friday Oct 2014

Posted by in Data Warehouse, market forces, nine competitive forces, Strategy

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Strategy and Market forces – Get your ducks lined up

Let’s now take a brief look at my ‘nine competitive dimensions’  model.

This model will be familiar to some who will readily connect with the inclusion of government as an environmental dimension.

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Main cause of IT project failure? – Big Data Informs…

09 Thursday Oct 2014

Posted by in Analytics, Best principles, Business Intelligence, Executive, Extract, Knowledge, Offshoring, Outsourcing

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We analysed all the big data and discovered that the biggest reason for IT project failure is people – Big Data Informs…

We had failed at Data Warehousing, Business Intelligence, Core Competence, and quite a few other things, so some bright spark decided to give Big Data a shot.

The first task was to identify the reasons for IT project failure, globally.

According to the techies, Big Data was helping to move things on quite a bit, especially considering a previous attempt to analyse IT project ended tragically when the Data Warehouse coal-face caved in.

Before we gathered together all of the data in the Good Big Data project, we didn’t have a clue as to what was causing so many frequent, costly and dramatic failures.

We have an idea of where the biggest problems may be, but the Big Data team are afraid to pony up.

So, instead of boiling the ocean of data again, we decide to narrow the scope to Data Warehousing and Business Intelligence projects.

We were three months into this project and we’d still not achieved anything to brag about. So I put on my Project Manager’s hat and diplomatically engage up with the Big Data team.

“What the feck are you guys playing at?” I ask “You’ve had three months to come up with findings, and you have found nothing”

So, one by one, out come all the perfectly reasonable excuses and justifications.

“We didn’t know”, “this is very complicated”, “you don’t understand”, “I have the flu”. It all came out. We dance around the issues for a while, and then I set some tasks.

“I want you to find out what the prime motivators are for working on Data Warehouse and Business Intelligence projects”

“Is it for the cache of working on such projects?”

“Is it to bring real technical knowledge and experience to the party?”

“Is it to learn a technology, new product or technique?”

“Is it solely for the money?”

“Is it to ensure that the project lasts for as long as it can?”

“Is it to milk the budget for all its worth’”

“Is it to achieve the business objectives?”

“Is it to create inertia?”

“Is it to be on the inside, to ensure that the project fails?”

“Go and find out just what motivates people to work on these projects”

“Do it now and report back to me this time next week”.

So, I set and assign the tasks, clarify and address every current doubt, and leave.

Next week I go back. The team has a delegated spokesperson.

He says “we have addressed the questions you posed, and the answer is yes”

“Go on” I reply.

“It seems that to a greater of lesser extent, the questions you posed last week are all relevant”

“Fine, now tell me more”

“Well, there is not much to say, apart from the fact that what motivate many people isn’t exactly in the best interests of the projects in question”.

What am I listening to? No shit Sherlock!

“Can you expand on that?” I ask “Let’s open this up to everyone”.

So we have another three hours of discussion.

In the end what emerges is a classic set of metaphors and analogies that clearly identify why so many Data Warehouse and Business Intelligence projects go wrong, and indeed why this particular project cannot really deliver.

So, I wind things up.

“This is how I see it”

“3rd party suppliers and vendors want to see these projects last for as long as possible”

“The more licences, consulting days and bodies they can bill for, the better for them”

“The longer they take our money, and the more of it they take, the better it is for them”

“The more that innocent glitches, hiccups, procrastination and prevarication can be fabricated, forced and imposed, the longer everything takes, and the more that is billed for”

“So, better to over-promise, over-reach and under-deliver, than do things on time and to spec”

“What’s more, many people working on such projects will take the sides of the supplier, to the detriment of the client’s interests”

“Money is being leeched from healthy corporations to pay for bullshit death-march projects that deliver no value, bring no insight and can actually be a risk to corporate health”

“Projects are being financed by us, and used by others, as training”

“Corporations are being used as reference sites, even though the fundamental premise is nonsense”

“We are paying to teach people, bad-practice, worst-practice and no-practice”

“We are creating private armies of artful mediocrity, banality and imbecility”

“And we are proclaiming it as the way that business should be done in the future”

“Well, feck that! I don’t need big data to inform me that we are being taken for a ride”.

So, after ten days of contemplation, I formally close the project.

It had become a meta-example of what we were ostensibly investigating, analysing.and reporting on.

Yours strategically, Martyn

Nothing achievable is impossible: Challenges, Self-worth and Strategies

09 Thursday Oct 2014

Posted by in Ask Martyn, Best principles, Creativity, Excellence, Methodology

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I came into IT at the tail end of the seventies when I joined one of the original computing pioneers.

It was a conservative company lead by veterans, engineers, accountants and sales, with many ties to the US administration, the Department of Defense and intelligence agencies.

My interests at the time were in philosophy, politics and economics. I liked to meet people and talk, and also liked to help people solve real-life business problems, so I was always engaged with the corporate staff and executive management rather than with the real hard-core technicians and engineers.

The thing is, I had no idea what constrained IT, so I never had that baggage when thinking about solutions.

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Operational awareness isn’t for wimps! So, get it right

08 Wednesday Oct 2014

Posted by in awareness, Big Data, business, Business Intelligence, Data Warehouse, operational, pig data, Strategy

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I frequently include the term Operational Awareness in talks.

I think it’s important for strategy.

So I wrote a piece that tried to convey what I mean by the term.

But first, a diagram:

Image1

Figure 1 – Operational awareness

This is a simplified high-level example of business data objects found in certain organisations. In the above diagram I have reused an industry example of nine business data objects to represent operational data[1].

For completeness and to maintain rationality in this section here follows a summary list of the nine key groups of business data needed to have a coherent and cohesive operational awareness (these data groups are also frequently referred to as business data objects):

  • A: Party embodies all of the participants that may have contact with the organisation or that are of interest to the organisation and about which the organisation maintains data. This includes data about the organisation itself; data about external organisations; data about external and internal individuals; and, data about the roles of involved parties.
  • B: Arrangement represents a prospective or existing agreement, between two or more individuals, organizations or organizational units that provides and affirms the rights, rules and obligations associated with a transaction between parties.
  • C: Condition describes the specific requirements that pertain to how the business is conducted and includes information such as prerequisite or qualification criteria and restrictions or limits associated with the requirements. Conditions can apply to various aspects of an enterprise’s operations, such as the operational parameters of a resource item, the sale and servicing of products, the determination of eligibility to purchase a product, the authority to perform business transactions, the assignment of specific general ledger accounts appropriate for different business transactions, the required file retention periods for various types of information kept by an enterprise and the selection criteria for a market segment.
  • D: Product/Service describes the services, merchandise or facilities that can be offered, sold or purchased by the enterprise, its competitors and other Involved Parties during the normal course of its business. This concept also includes goods and services that are of interest to the enterprise such as supplies for manufacture.
  • E: Location covers a place where something can be found, a destination of information or a bounded area, such as a country or state, about which the enterprise wishes to keep information.
  • F: Classification is used to organize and manage specific business information by defining structures that represent classification categories. Classification also organizes and manages groups of business concepts that apply to multiple concepts.
  • G: Business Direction/Organisation Direction refers to and records expressions of a party’s intent with regard to the manner and environments in which it wishes to carry out its business. Business direction items contains, keeps data about, and is used to support the enterprise’s business and financial plans, policies, procedures and schedules.
  • H: Events describe a happening about which the organisation wishes to keep information as a part of carrying out its mission and conducting its mission.
  • I: Resource object includes and describes any value item, either tangible or intangible, that is owned, managed or used by, or of specific interest to, the organisation in the course of accomplishing its mission.

The key facets of operational awareness detailed above constitute a potential of fundamental importance in the formulation of organizational strategy.

Timely, accurate and appropriate data at this level can temper ambition with the facts on the ground, with operation insight, and with the effectiveness of time and place utilisation.

But take care. In most organisations there will be a spread of attention to the key facets illustrated here, and they will be treated with varying degrees of intensity relative to their overall contribution to strategy formulation. In addition, organizational specific facets may also be introduced where needed in order to complement the overall set of operational awareness facets described here.

[1] IBM’s IFW/BDW.

What triggers a review of organisational strategy? An example

08 Wednesday Oct 2014

Posted by in Ask Martyn, Best principles, Business Intelligence, Strategy, tactics

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The way it is, and all that jazz

What triggers a review of organisational strategy?

Well, typically organisations usually shy away from major strategy reviews when things are just ticking over quite nicely. The old axiom of “if it isn’t broke don’t fix it”, has a lot of power of persuasion, even in cases where the logical and coherent thing to do would be to continually review strategy.

Many companies reach for a new strategy when one fine day they are rudely awoken to the fact that they are not doing as well as they once did.

Basically, organisations will seriously think about strategy at times when stakeholders and shareholders start to kick up an almighty ruckus.

With this incentive behind them, organisations embark on the tortuous journey to a new organisational vision and strategy. Or do they?

For the sake of brevity I will exclude all of those organisations who look at the strategy process as a great big exercise to produce an organisational wish list, for as warm and fuzzy and socially responsible it might be. For as laudable these initiatives might be, they just don’t count as strategy in this paper.

So, in the process of arriving at a strategy, we need to address the vision, the challenges to the vision and coherence of the executable strategy. In order to address these core elements effectively we need to be able to access as much relevant and actual information and data we can in order to generate, test and categorise our hypotheses.

So, where is this information and data and how can we get it?

I will take a brief look at three major areas of information and data, which roughly correspond to:

  1. Operational awareness. Data associated with key business data objects.
  2. Market forces. Competitive data taken from disparate data sources.
  3. Strategic fit. Data and information taken from disparate data sources, unstructured data and held in people’s heads, email servers, document archives laptops, tablets and mobile phones.

Now it must be made clear, upfront and clear, that data in an organisation is subject to the vagaries of data quality assurance. Therefore, in a far from perfect world, we will usually be looking at the following issues and will have to work with them, no matter:

  1. Incomplete and missing data.
  2. Erroneous and compromised data.
  3. Overlapping and contradictory data.
  4. Data as noise.

The mythical low hanging fruit – The bane of IT and Data Warehousing

08 Wednesday Oct 2014

Posted by in Big Data, Business Intelligence, Data Warehouse, Dogma, Peeves

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The problem with Data Warehousing is that at a superficially high level it is very easy to explain, and it’s quite amazing that this superficially high level is all that a lot of people need in order to do data warehousing their own way.

This is one of the reasons why many companies have been convinced that they could do Data Warehousing by simply building lots of “cost effective” independent Data Marts. Of course they were right, right?

Well, no. Companies found that the more independent data marts they had the more costly and complicated it became to maintain them and the more costly and complicated it was to add additional independent data marts into the mix. That and the absence of delivery on other promises – such as data integration and singular views of the organisation – ensured that this approach would rightly acquire bad press.

Why did this happen? Again we can go back to the beginning. People were told that data warehousing was about using a subject oriented, integrated, time variant and non-volatile data store in order to act as the source for all data mart development, and ultimately as the source of record for all strategic reporting. The trouble then came in two forms: people thought they could choose one or more characteristic and it would still deliver all of the benefits of data warehousing, and secondly, people were convinced by consultants that data warehousing could also be a collection of data marts.

As Bill Inmon put in 1998: “You can catch all the minnows in the ocean and stack them together and they still do not make a whale”.

It may be informative to take a step back. In the dark ages of data warehousing not all the technology vendors were equipped to cope with the burgeoning DW market, and some technologies just couldn’t hack it[1] when it came to building real Enterprise Data Warehouses. So, rather than miss out on all of the jolly cash that was finding its way into the fighting funds of DW initiatives, they decide to cash in on DW by muddying the waters and pulling the wool over people’s eyes, whilst effectively fleecing the gullible punters[2].

From the strategy of the small technical vendors came the idea that you could simply have data warehousing by building lots and lots of stovepipe data marts – hence Bill Inmon’s remark about the minnows and the whale. These marketing strategies were also supported by those who saw their own path to data warehouse glory passing through data silos rather than via well engineered end-to-end enterprise data warehousing. It may surprise some of the readers that the Kimball approach and the Inmon architecture were so different at one time, but they were. Yet over the years the Kimball camp has moved closer and closer to the Inmon approach, whilst at the same time seemingly[3] maintaining an aversion for anything that isn’t dimensional and explainable in terms of facts and measures. Although, if we are going to be frank and earnest (Frank in New Jersey, Earnest in Chicago), ‘conformed’ seems to be the most overused Information Management euphemism of recent times.

[1] Not appropriate or adequate.

[2] EDW customers.

[3] It’s my appreciation and I could be wrong.

Don’t fight the Bull, Isolate it – Deliberately avoiding the evident

07 Tuesday Oct 2014

Posted by in Banking, Best principles, Business Intelligence, Data Warehouse, Executive, Management, Methodology

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Much of my consulting work is done in the Financial Industry.

I was there when the crisis was prepared, when it was baked and when it was brought out of the oven.

There are many theories about what went wrong.

Most of them are misleading, misinformed or simply crap.

Mainly to protect the guilty.

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