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Too much information

16 Wednesday Mar 2016

Posted by Martyn Jones in 4th generation Data Warehousing, All Data, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, Good Strat, Good Strategy, goodstrat, IT strategy, Marty does, Martyn does, Martyn Jones, Martyn Richard Jones, pig data, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians

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Big Data, Business Enablement, business intelligence, Business Management, Data Warehouse, Good Strat, Information Technology, Martyn Jones, Martyn Richard Jones, Organisational Autism, Strategy

Martyn Richard Jones

I have questions about data.

Most of us who have more than a cursory knowledge of the English language have heard of the phrase ‘too much information’. We know what it means, even if we don’t always know when to apply it.

For those who don’t know, or are unsure, the Urban Dictionary describes ‘too much information’ as “An expression of exasperation and disgust when a person is divulging personal details of his sex life, toilet habits, or anything the listener finds disgusting, uninteresting, and unwelcome.”[1]

Sum, sum. Just because we know it, doesn’t mean we should share it or even try and remember it, never mind go about analysing the hell out of it.

This is where Big Data comes in. Continue reading →

All Data: It’s about statistics

30 Friday Jan 2015

Posted by Martyn Jones in All Data, Consider this, DW 3.0, Good Strat, Good Strategy, Information Supply Frameowrk, Martyn Jones, Martyn Richard Jones, statistics

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All Data, Big Data, business intelligence, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, statistics

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A big computer, a complex algorithm and a long time does not equal science.

Robert Gentleman

To begin at the beginning

Fueled by the new fashions on the block, principally Big Data, the Internet of Things, and to a lesser extent Cloud computing, there’s a debate quietly taking please over what statistics is and is not, and where it fits in the whole new brave world of data architecture and management. For this piece I would like to put aspects of this discussion into context, by asking what ‘Core Statistics’ means in the context of the DW 3.0 Information Supply Framework.

Core Statistics on the DW 3.0 Landscape

The following diagram illustrates the overall DW 3.0 framework:

There are three main concepts in this diagram: Data Sources; Core Data Warehousing; and, Core Statistics.

Data Sources: All current sources, varieties, velocities and volumes of data available.

Core Data Warehousing: All required content, including data, information and outcomes derived from statistical analysis.

Core Statistics: This is the body of statistical competence, and the data used by that competence. A key data component of Core Statistics is the Analytics Data Store, which is designed to support the requirements of statisticians.

The focus of this piece is on Core Statistics. It briefly looks at the aspect of demand driven data provisioning for statistical analysis and what ‘statistics’ means in the context of the DW 3.0 framework.

Demand Driven Data Provisioning

The DW 3.0 Information Supply Framework isn’t primarily about statistics it’s about data supply. However, the provision of adequate, appropriate and timely demand-driven data to statisticians for statistical analysis is very much an integral part of the DW 3.0 philosophy, framework and architecture.

Within DW 3.0 there are a number of key activities and artifacts that support the effective functioning of all associated processes. Here are some examples:

All Data Investigation: An activity centre that carries out research into potential new sources of data and analyses the effectiveness of existing sources of data and its usage. It is also responsible for identifying markets for data owned by the organization.

All Data Brokerage: An activity that focuses on all aspects of matching data demand to data supply, including negotiating supply, service levels and quality agreements with data suppliers and data users. It also deals with contractual and technical arrangements to supply data to corporate subsidiaries and external data customers.

All Data Quality: Much of the requirements for clean and useable data, regardless of data volumes, variety and velocity, have been addressed by methods, tools and techniques developed over the last four decades. Data migration, data conversion, data integration, and data warehousing have all brought about advances in the field of data quality. The All Data Quality function focuses on providing quality in all aspects of information supply, including data quality, data suitability, quality and appropriateness of data structures, and data use.

All Data Catalogue: The creation and maintenance of a catalogue of internal and external sources of data, its provenance, quality, format, etc. It is compiled based on explicit demand and implicit anticipation of demand, and is the result of an active scanning of the ‘data markets’, ‘potential new sources’ of data and existing and emerging data suppliers.

All Data Inventory: This is a subset of the All Data Catalogue. It identifies, describes and quantifies the data in terms of a full range of metadata elements, including provenance, quality, and transformation rules. It encompasses business, management and technical metadata; usage data; and, qualitative and quantitative contribution data.

Of course there are many more activities and artifacts involved in the overall DW 3.0 framework.

Yes, but is it all statistics?

Statistics, it is said, is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments; learning from data, and of measuring, controlling, and communicating uncertainty; and it provides the navigation essential for controlling the course of scientific and societal advances[i]. It is also about applying statistical thinking and methods to a wide variety of scientific, social, and business endeavors in such areas as astronomy, biology, education, economics, engineering, genetics, marketing, medicine, psychology, public health, sports, among many.

Core Statistics supports micro and macro oriented statistical data, and metadata for syntactical projection (representation-orientation); semantic projection (content-orientation); and, pragmatic projection (purpose-orientation).

The Core Statistics approach provides a full range of data artifacts, logistics and controls to meet an ever growing and varied demand for data to support the statistician, including the areas of data mining and predictive analytics. Moreover, and this is going to be tough for some people to accept, the focus of Core Statistics is on professional statistical analysis of all relevant data of all varieties, volumes and velocities, and not, for example, on the fanciful and unsubstantiated data requirements of amateur ‘analysts’ and ‘scientists’ dedicated to finding causation free correlations and interesting shapes in clouds.

That’s all folks

This has been a brief look at the role of DW 3.0 in supplying data to statisticians.

One key aspect of the Core Statistics element of the DW 3.0 framework is that it renders irrelevant the hyperbolic claims that statisticians are not equipped to deal with data variety, volumes and velocity.

Even with the advent of Big Data alchemy is still alchemy, and data analysis is still about statistics.

If you have any questions about this aspect of the framework then please feel free to contact me, or to leave a comment below.

Many thanks for reading.

Catalogue under: #bigdata #technology

[i] Davidian, M. and Louis, T. A., 10.1126/science.1218685


File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Consider this: Big Data in Context

21 Wednesday Jan 2015

Posted by Martyn Jones in Big Data, Consider this, Data Warehouse, Data Warehousing

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Big Data, business intelligence, Core Statistics, DW 3.0, enterprise data warehousing, information management, information supply framework, statistics

Big Data, together with Cloud computing and the Internet of Things, are topics that are very much to the fore in contemporary trends in Information Management. Continue reading →

The World’s Best Data Quotes… Including Big Data quotes

17 Saturday Jan 2015

Posted by Martyn Jones in Analytics, Architecture, Big Data, Business Intelligence, Consider this, Data Warehousing, statistics

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Analytics, aspiring tendencies in IM, Big Data, business intelligence, Core Statistics, enterprise data warehousing, Quotes

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A Random walk down Data Street

If you enjoy, abhor or are simply bored with the massive surfeit of hype surrounding Big Data, Data Warehousing and Analytics, then you might just hate these less than faithful quotes as well.

If you enjoy one or two of the quotes, well, then that’s an acceptable bonus too.

So, to begin at the beginning…xHound

Data Sources

“My data sources are unreliable, but their information is fascinating.” – Ashleigh Brilliant

“I give no data sources, because it is indifferent to me whether what data I have sourced has already been sourced before me by another.” – Ludwig Wittgenstein

“In the kitchen of a great Data Warehouse, the data source chef is a soloist.” – Fernand Point

“It is better to be hated for what data sources you have than to be loved for what data sources you do not have.” – André Gide

“In England, there are sixty different types of Data Warehouse and only one data source.” – Attributed to Voltaire

“It is a capital mistake to theorize before one has data sources. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” – Arthur Conan Doyle, Sherlock Holmes

“From such a gentle thing, from such a source of all data, my every pain is born.” –Michelangelo

“Noise free data is a source of great strength.” – Lao Tzu

“In three words I can sum up everything I’ve learned about data: it goes on.” – Robert Frost

“Data enrichment improves a mighty fine data source” – Anonymous

xButcherBig Data

“Junk food, empty calories and carbs are the Big Data of the masses” – Karl Marx

“We live, I regret to say, in an age of Big Data hype.” – Oscar Wilde

“We are not rich by the Big Data we possess but by what Big Data we can do without.” – Immanuel Kant

“He who has Big Data hype on his side has no need of proof.” – Theodor Adorno

“The religion of Big Data sets itself the goal of fulfilling man’s unattainable desires, but for that very reason ignores her attainable needs.” – Ludwig Feuerbach

“The flesh endures the storms of the present alone; the mind in our social network interactions, those of the past and future as well as the present. Big Data is a covetousness of the mind.” – Thomas Hobbes

“Big Data is negative and dialectical, because it resolves the determinations of the understanding of things into nothings.” – Georg Wilhelm Friedrich Hegel

“I am trapped in this Big Data, and there is nothing I can do about it.” – Dudley Moore

“And remember, never take the ruby case off your iPad for a moment, or you will be at the mercy of the Big Data Witch of the West.” – The Wizard of Oz

“Imagine there’s no Big Data…” – John Lennon

Abacus3Data Transformation

“Analysis does not transform data.” – Jiddu Krishnamurtu

“I live in a data landscape, which every single day of my life is enriching data.” – Daniel Day-Lewis

“Data opportunities multiply as the data is transformed” – Sun Tzu

“He who integrates data badly is lost.” – Theodor Adorno

“Today we transform the data; tomorrow, the whole enchilada” – Leon Trotsky

“Well, it’s all about the ETL law of the transformation of data quantity into data quality, and vice versa. Innit!” – Friedrich Engels

“The management consultants have only interpreted the business data, in various ways. The point, however, is to transform it.” – Karl Marx

“Hey! What’s going down here in the Hollyweird of data?” – Joe McCarthy

“The Big Data alchemists in their transformational search for gold discovered much data of greater value.” – Arthur Schopenhauer

“That Schopenhauer yolk was a bit of an old Big Data ‘procurer’ wasn’t he now Rodge?” – Pádraig Judas O’Leprosy

IMGQBusiness Intelligence

“The trouble with the world is that the cocksure have Big Data and that Data Science and Business Intelligence are all sexed up.” – Bertrand Russell

If people never did silly things no Business Intelligence would ever get done.” – Ludwig Wittgenstein

“The best Business Intelligence user is intelligent, well-educated and a little drunk.” –Alben W Barkley

“The Master said, “If your conduct is determined solely by considerations of Business Intelligence and profit you will arouse great resentment.” ― Confucius

“That’s cricket, Harry, you get these sort of things in Business Intelligence” – Frank Bruno

“Business Intelligence without ambition is a bird without wings.” – Salvador Dali

“I would prefer a Business Intelligence hell to a Big Data paradise.” – Blaise Pascal

“Many much-learned business men have no Business Intelligence.” – Democritus

“We should not only use the brains we have, but all that we can borrow.” – Woodrow Wilson

“The reason we have Business Intelligence is so we don’t have to think all the time” –Homer Simpson

P3160034Data Warehousing

“The study of Data Warehousing, like the Nile, begins in Inmon and ends in magnificence.” – Charles Caleb Colton

“Big Data wins games, but Data Warehousing wins championships.” – Michael Jordan

“Big Data is no substitute for Data Warehousing.” – Frank Herbert

“It’s in me blood, Clive, without Data Warehousing I’d be nothing,” – Alan Latchley

“The trouble with the world is that the cocksure have Big Data and that Data Science is all sexed up.” – Bertrand Russell

If people never did silly things no Business Intelligence would ever get done.” – Ludwig Wittgenstein

“The best Business Intelligence user is intelligent, well-educated and a little drunk.” –Alben W Barkley

“You can catch all the whales in the ocean and stack them together and they still do not make a minnow.” – Ralph Wiggum

“Well, the smarter I practice Inmon Data Warehousing, the luckier I get.” – Gary Player

“Well, I’ve cleaned up facts and dimensions in a star-schema ‘data warehouse’. That was pretty terrible. But I can’t complain because I’m sure other people have done worse.” – Cee Lo Green

“You can give a person a bowl of Big Data Gruel and feed them for a day, or teach them Inmon Data Warehousing and feed them for a lifetime.” – Proverb

“A Data Warehouse is like a tea bag; you never know how strong it is until you are in hot water.” – Eleanor Roosevelt

” οἶδα δ᾽ ἐγὼ ψάμμου τ᾽ ἀριθμὸν καὶ μέτρα θαλάσσης, καὶ κωφοῦ συνίημι, καὶ οὐ φωνεῦντος ἀκούω. ὀδμή μ᾽ ἐς φρένας ἦλθε κραταιρίνοιο χελώνης ἑψομένης ἐν χαλκῷ ἅμ᾽ ἀρνείοισι κρέεσσιν, ᾗ χαλκὸς μὲν ὑπέστρωται, χαλκὸν δ᾽ ἐπιέσται.” – An Oracle to Croesus of Lydia

IMGThat’s all folks!

Well, now that that’s done I can always ask for forgiveness. Not that I will of course.

 Many thanks for reading.

abfab111

Martyn Jones

Founder and CEO, Cambriano Energy


File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Continue reading →

Big Data 7s: Talking Points #1

09 Friday Jan 2015

Posted by Martyn Jones in Big Data, Big Data 7s, Consider this, Data Warehouse

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Big Data, business intelligence, data analytics, Data Warehouse

JPR-Williams_181887k

To begin at the beginning

This is the first in a series of collections of talking points on the processing of very large data sets by non-relational or pseudo-relational means, speculative data analytics with these large data sets which is typically non-operational data and social media data obtained from internet sources, and how usable outcomes, if any are derived, can be integrated into strategic, tactical and operational decision support.

Currently this area is parked under the misleadingly named ‘Big Data’ umbrella, but in the near future I predict that this niche will be merged into the more recognisable and business oriented areas of data warehousing, data architecture and business intelligence, and rebadged to avoid even further confusion.

Each number of this series will be addressing 7 talking points.

Here are the first seven talking points that deal with aspects of primal mass data processing, speculative analytics and outcome and result persistence and association.

Keep it simple

The leading and continuous mantra for all ‘Big Data’ initiatives should be simplicity.

Simplicity means identifying a well -bounded speculative opportunity and then focussing on it, whilst not allowing for scope creep until the work is done and a following iteration is defined.

Simplicity means taking the data that is needed, along with the useless baggage data that it is unfortunately bundled with, and then reducing the data to the essentials at the earliest possible moment.

Simplicity means trying to move the data reduction problem up stream, preferably to the point where it is actually generated and stored.

Simplicity means not flannelling business people about the supposed benefits of ‘Big Data’. It means about avoiding patronising language akin to “Just do Big Data, because everyone will have to be doing it, and don’t worry your pretty little head about what it’s actually doing under the bonnet”. It means being frank, open and earnest about ‘Big Data’.

Hold this thought: You cannot bullshit simplicity.

Appropriate is good

The great economist John Kenneth Galbraith once observed that “The real accomplishment of modern science and technology consists in taking ordinary men, informing them narrowly and deeply and then, through appropriate organization, arranging to have their knowledge combined with that of other specialized but equally ordinary men. This dispenses with the need for genius. The resulting performance, though less inspiring, is far more predictable.”

Appropriateness is one of the more important aspects of supplying data for strategic, tactical and operational decision support, and it is data that must by its very nature be appropriate.

Appropriateness addresses the need for the right data.

Hold this thought: Appropriateness is good

Adequate is sufficient

Another important aspect of Adequacy means that there is enough data supplied to adequately meet the requirements for that data. Adequacy addresses the need for the right volume of the right data and at the right levels of abstraction.

I know that people in IT find it tempting to second guess requirements and to pile up unasked for feature additions like they were going out of fashion, but in the lean and iterative age of agile we can no longer afford to be so reckless in how we manage requirements, projects and resources, especially those assigned to ‘Big Data’ projects.

Just hold this thought: Adequate really is enough

Timeliness kills the competition

Another important aspect of this Big Data field is found in the timely provision of data and the fast delivery of usable outcomes.  But this not only requires ‘Big Data’ but also big data management smarts.

Timeliness addresses the need to get appropriate and adequate data to decision makers on time and every time, in order to maximise the possibilities for its use and therefore to increase the chances of it having some business value.

Hold this thought: Speed kills the competition.

Integration makes sense

If after running speculative analysis (diagnostic or predictive, etc.) and you are lucky enough to actually end up with something tangible and useful, you may also want to consider linking this or integrating the outcomes into mainstream and quality assured strategic and tactical decision support and analysis data.

This is where the Data Warehouse concept of Bill Inmon comes into its own. Because Enterprise Data Warehousing (and especially DW 3.0) provides a conceptual data architecture and data management protocols to support the adequate, appropriate and timely scaling of data set sizes from gigabytes to terabytes and then to petabytes – and beyond, if that is really what is needed.

Hold this thought: Integrate without losing essence

Big Data Science name change

There has been so much misleading, unreliable and unrepresentative puff built up around Big Data that it seems like an appropriate time to give it a ‘legal, decent and honest’ makeover, and to also change its name to something more appropriate such as Janus Data Analytics (JDA for short) or New Wave Punk Data.

I believe that Janus Data Analytics may be a good name for this niche technology field because it accurately reflects what it is and at the same time it is intrinsically linked to beginnings and transitions, to gates, doors, doorways, passages and endings. Janus Data Analytics looks into the future and into the past, and presides over the beginning and ending of conflict, war and peace.

There is also a certain attraction in the term New Wave Punk Data. It sends a strong and uncompromising signal to business. It deftly and simply describes the two key aspects of what is being currently touted as ‘Big Data’. New Wave Punk Data reflects the rapid, sharp edged and primal slicing, dicing and reduction of very large data sets, together with short term speculation, stripped-down analytics, with often opinionated and alternative drivers. It embraces a DIY ethic; many businesses that lead the movement (Yahoo, Google, Facebook, etc.) started with self-developed ‘Big Data’ tools (often initially as simple variations on the Unix power-chord themes of parallel grep, awk and cat) and shared them through open source channels.

The third option is to simply place the data aspect of ‘Big Data’ under the data architecture and data management umbrella as a facet of Data Warehousing and to place the ‘data science’ aspect of ‘Big Data’ under the statistics and data analytics umbrella, with a close association with the sub-class known as business intelligence. The true data mining and machine learning aspects of ‘Big Data’ can sensibly continue under the umbrella of Artificial Intelligence.

Hold this thought: A rose by any other name

Keep it legal, decent and honest

Potentially there are methods, technologies and techniques under the ‘Big Data’ big-top that could be used to accrue real business value; however, those benefits are being put at risk by the quality and quantity of puff in the environment, which was alluded to in the previous talking point.

The point is this. Banging on about the same nebulous futures of ‘Big Data’ rather than being specific, clear and verifiable about what is really going on is, to state it simply, is going to ‘queer the pitch’ for everyone; the good, the bad and the ugly… but especially the good.

Therefore I would suggest that we all take an additional New Year’s resolution on ‘Big Data’, and in future only refer to the application and benefits of ‘Big Data’ and ‘Big Data’ analytics in terms that could only be construed as legal, decent and honest.

Hold this thought: “If you are not a better person tomorrow than you are today, what need have you for a tomorrow?” – Rebbe Nachman of Breslov

That’s all folks

So, that is all from me in the first of what I hope will be many issues in the series Big Data 7s.

I would like to leave you with this fabulous quote from James Carville… just because.

“Sometimes the right thing gets done for the wrong reason and sometimes, unfortunately, the wrong thing gets done for the right reason”.

As always, many thanks for reading.


File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Consider this: Business Process Intelligence

30 Sunday Nov 2014

Posted by Martyn Jones in accountability, Consider this

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business intelligence, business process, business process intelligence

Business process intelligence

As for the future, your task is not to foresee it, but to enable it.

Antoine de Saint-Exupery

The allure of future happiness

Companies the world over have been busily moving away from the more traditional function-based business structures, with their attendant silos of competence, overlapping roles and artificially limited responsibilities, to highly focused business-driven process models.

Well-bounded business process reengineering has often been a critical success factor in contemporary business strategies. So, new ways of looking at processes are introduced in order to bring about far greater levels of simplicity, marked improvements in service and product quality, new-found process robustness, greater customer intensity and intimacy.

This is accompanied by sea-change improvements in the ways that forward-looking organizations do business.

As part of this Information trend, businesses are working towards service-oriented business-process operating platforms, characteristically these new platforms are easily configured to capture and store a wide range of additional data, way above and beyond that which has been associated with more traditional in-house application developments and 3rd party Enterprise Resource Planning (ERP) applications.

This new level of data intensity and process abstraction means that businesses can essentially record every step, state and decision point in a business process flow, from initiation to closure. Which notionally allows businesses to closely monitor service levels and key performance indicators right down to the finest level of granularity, and across the entire business organization.

Businesses can now look at who does what, when, where, and how, which allows for the accurate pinpointing of process hotspots and process bottlenecks, and the rapid initiation of corrective measures.

Excitement, elation and enthusiasm

The separation of operational support and strategic decision support, using a combination of data warehousing and business intelligence, is being blurred. Suddenly senior management can really have their finger on the business pulse.

The digital nervous system now promises to become a reality.

Ae we are moving boldly forward into the realms of business intelligence that will not only tell companies what they have done wrong in the past but will assist decision-makers to formulating winning strategies for the future of organizations.

What senior executive could possibly turn down the opportunity to be on top of all aspects of the business that they are ultimately responsible for?

Benefits and features

Industry experts and pundits alike are heralding a significant shift in the usefulness of Business Intelligence, a premonition which frequently leads to assurances that we are witnessing the beginning of the end of Data Warehousing as we know it, the removal of a major capital expenditure burden from corporate IT budgets, and the unchaining and liberation of Business Intelligence, from the terrible drug-like dependency on the Data Warehouse paradigm. The promise of new-age BI is compelling and simple to comprehend, and assures accruable benefits far surpassing anything witnessed in the data warehousing and business intelligence world to date; for example:

  1. Business intelligence will become an integral part of business process applications and on-line transactional processing.
  2. Management will be able to see, at any point in time, exactly how the business is doing.
  3. Management will be able to plan ahead using the rich set of information that the new synergistic integration between BI and business process applications.
  4. There will no longer be a need to constrain the freedom of business intelligence by tying it to data warehousing.
  5. Far more sophisticated BI tools, technologies and techniques will appear in the coming twelve to eighteen months, driving a wedge between bleeding edge information systems and more traditional approaches to information, analytics and their management.
  6. BI will support embedded data analytics, real-time data visualization, and universal use – anyone within an organization will be able to use future-proofed BI.
  7. Data warehousing in business organizations will become redundant. (It’s daft, but it needed to be stated).

Hitches, glitches and biting reality

There are a number of issues that may affect the usefulness of the new-age business process oriented business intelligence:

  1. The newly reengineered business processes may not in fact be well designed, appropriate, or workable.
  2. The performance of operational application platforms may well seriously deteriorate if the same platforms are also used to collect large volumes of disparate process data and at the same time also be used to support unpredictable ad-hoc querying by sophisticated new-age business intelligence tools.
  3. There might be difficulties in coming to agreement on service level and key performance indicator measurements, especially if the process paths are complex and full of many decision points, process activities and tasks, and parallel operations.

Failure, despondency and desolation

Someway down the road with your newly found faith in new-age business intelligence you might start to question your belief.

Typically this will come about for one or all of the following reasons:

  1. Your business intelligence isn’t giving you an accurate picture of what happened in the past.
  2. The quality of data is such that no one in their right mind would use it to try and predict the future, never mind analyse the past.
  3. The future-proof promises of BI and claims about being able to predict the future are not turning out as planned.

Bottom-line comments

 The only way you can predict the future is to build it.

Alan Kay

Okay, let’s be frank and earnest here, and try and keep it simple without making it brainless. Let’s assume for one moment that all that has been claimed for new-age business intelligence is possible, and maybe it is, we may intuitively feel a sense of déjà vu, and dismiss the position out of hand as so much frivolous nonsense and sentimental belief, but let’s take a more balanced view, and instead pose some questions:

  1. Which organizations can afford to hire the required number of staff in order to be able to effectively visualize and analyse all of the sheer volumes of data and the new wealth of rich business process data that is being produced day in and day out, by businesses all over the globe?
  2. Who is really going to use all the data collected by the business processes?
  3. Who is going to trust the largely unverifiable new data?
  4. Who is going to get the real benefits, if any, which might be accruable from the analysis of the data?

There are some who might think that the claim that business intelligence can happily exist without data warehousing is the biggest load of nonsense ever conceived in the field of information management, and that it beggars belief that “expert” BI consultants seem to confuse “finger on the pulse” with “finger on the trigger of the gun held to the head of business”. The actuality of the real business world is at odds with so much of the BI hype and hyperbole being spun so crudely, so freely and so easily, with scant regard for the business consequences of sowing so much opinion and speculation and spreading the nonsense around like a happy farmer with a truck load of bullshit.

On reading the comments of some business intelligence experts, one could be forgiven for thinking that the intention is to convert commercial businesses into experiments in the creation of technology based busy-work. Let’s be down to earth now, do BI “experts” really think that businesses can afford the luxury of having teams of business analysts dedicated to looking at process data via business intelligence tools, all day and every day?

So here is a roundup of our position with regard to “data warehousing without a data warehouse” and new-age business intelligence:

  1. The problem still isn’t lack of data, companies have more data coming out of their “processes” than most business executives have time to shake a stick at. People are virtually drowning in the damn stuff. So, the problem is not lack of data, or lack of data richness, the problem is still lack of appropriate, adequate and timely information.
  2. As the father of data warehousing might say, the recurrent idea that you can have a successful data warehousing process without a real DW crops up like the flu virus; it comes around each and every year, and the same damn thing happens year in and year out, some people catch it, quite frankly far too many people – and then don’t know how to let go of it, even if it makes them, and everyone who comes into contact with them, pretty sick and as dumb as rocks.
  3. Monitoring business processes, collecting a humungous expanse of data, and then pushing it through a BI tool, will tell you less about future market directions, client behaviour, next year’s fashions and fads, and your real customer satisfaction levels, than a session with, for example, Madame Molotova, the flamboyantly extravagant eastern European clairvoyant.
  4. Fourthly, the only blurring of the boundaries between operational applications and data warehousing is in people’s heads, this fuzzy reasoning has led to fuzzy practice, often called pragmatism, in an effort to compound the ill-informed stupidity, but at the end of the day, they are still two sides of the same coin, but they are certainly not the same sides of the same.
  5. Next generation business intelligence requires next generation business process applications, based on a comprehensive mix of service oriented architectures, message brokerage, enhanced metadata, and intelligent information interchange (extended mark-up languages etc.) as well as comprehensive process monitoring technology, otherwise you will only reap a relatively very small benefit from the exercise.

The Bullet:

The BI “experts” are at it again, the little sods – trying to bury the principles of well-engineered data warehousing and complementary business intelligence, and denouncing the well proven approaches as being passé, anachronistic, or simply inappropriate.

Business Intelligence doesn’t fail because of data warehousing, this is a load of old nonsense spread by mischievous marketers and useful idiots – useful for helping to sell crap that no one really needs; it fails either because of an absence of a well-engineered data warehouse, carefully aligned to business wants, or because of the adoption of the “pragmatic approach” to data warehousing, which usually means a collection of what are euphemistically termed “data marts” – an approach that seems to have more adherents amongst the technologically light-weight and principle free, both in terms of vendors, and in terms of competence in your everyday business organisations.

Tip for today:

Trying to do comprehensive Business Intelligence without a well architected data warehouse (see W. H. Inmon: DW 2.0, and the Corporate Information Factory,) is simply stated, the strategic, tactical and operational decision support approach of fools.

BI implementations are complex and expensive, and trying to successfully implement BI without the use of a Data Warehouse is like taking on the challenge of diving from a airplane, one mile high, without the cumbersome overhead of having a parachute to slow you down.

BI “experts” who are focusing on the vague and peripheral crapola on the margins of BI, and to the detriment of core information management issues, are not adding any value, au contraire, they are rehashing naïve and sentimental approaches that should have been put to bed a long time ago.


File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Responsible Use of Corporate Data

03 Monday Nov 2014

Posted by Martyn Jones in Ask Martyn, Best principles, Data governance

≈ Leave a comment

Tags

Big Data, Business, business intelligence, data governance, data management, Data protection, Data Warehouse, privacy, Strategy

IMGThere was a time, when absolute discretion was an important maxim in the relationship between a liberal professional (doctor, banker, solicitor, architect etc.) and their clients, but times have changed, and are continuing to transform at an ever increasing pace. Continue reading →

The Great Information Struggle: Us and Them

14 Tuesday Oct 2014

Posted by Martyn Jones in Architecture, Assets, business, Business Intelligence, Data Warehousing, Management, Value

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Tags

Big Data, Business, business intelligence, Data Marts, enterprise data warehousing, hadoop, information management, knowledge, knowledge management, Risk Management

In the dim and distant past most organisations struggled along with what they euphemistically referred to as Information Systems.

They were Information Systems with no overall design, no elements of management and no architecture.

Information Systems were built to show how the company had been performing in the immediate past, and that was it. Continue reading →

Silly Season! Data Warehousing is Hadoop is Big Data?

12 Sunday Oct 2014

Posted by Martyn Jones in Architecture, Ask Martyn, Banking, Best principles, Big Data, Business Intelligence, Creativity, Data Warehouse, Dogma, Knowledge, Peeves

≈ Leave a comment

Tags

Banking, Behavioural Economics, Big Data, Bill Inmon, business intelligence, data integration, Data Marts, Demagogism, Dogma, enterprise data warehousing, hadoop, Information and Technology, information management

Let’s get this baby off the ground

This weekend I read a piece on the Information Management website by Steve Miller with the title of Big Data vs. the Data Warehouse. It’s an old piece, from March 2014.

It was in response to a piece penned by Bill Inmon, titled Big Data or Data Warehouse? Turbocharge Your Porsche – Buy an Elephant, in which he singled out for criticism the ad campaign of a big-data and Hadoop promoter.

Continue reading →

The Awkward Squad – Big data informs

11 Saturday Oct 2014

Posted by Martyn Jones in Big Data, BS, Data Warehouse, disinformation, information, Knowledge, wisdom

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Tags

Analytics, Big Data, business intelligence, cloud, Data Warehouse, virtualisation

I am a sceptic. Part of the awkward squad of troublemakers.

People who ask questions and who won’t stop asking.

People who won’t take bullshit for an answer.

People who are not preprogramed to follow certain paths, unquestioningly.

But to question everything.

Such as, “What the feck’s that all about then?”

I’m in good company. Many people who have made a difference have been fully paid up members of the awkward squad.

People in the awkward squad might sell dog food, but we know that we shouldn’t eat it ourselves.

I used to tell people.

“If you must exaggerate, try and remember this one thing”

“What’s that then, Marty?”

“Never, ever, believe your own bullshit or you’ll ending up having to eat it”.

I remember when Cloud first appeared on the horizon, a marketing idea that was to popularise the expression “put it on the cloud”.

I vaguely remember Larry Ellison being asked about Cloud.

If I recall rightly his reply was along the lines of ‘Cloud? Oh, you mean connected mainframes and data centres?”

He saw it, others saw it, and I saw it. It had been done many times before.

But cloud was new, exciting, vibrant and well, vague enough for the market.

Only that it wasn’t and isn’t new.

The only thing is, a handful of stylists and hacks were let loose on what already existed, and they came up with a new idea, that wasn’t new, creative or innovative.

It was just repackaged. Old wine in new bottles.

I have the same issues with marketing terms such as business intelligence, virtualisation and big data.

Can you imagine Steve Jobs peddling such rebranded and rebadged crap?

I can’t.

Big data brings all the promise of being better informed by having access to far more data.

But for most things in the commercial world quantity of data has never been the issue.

If anything, we’ve had too much of it and for far too long.

We’ve been doing big data for years.

Previously we called it Very Large Data Bases.

We have been handling some forms of highly structured data for years.

We used to call it things like text management, document management and knowledge management.

Not that it matters too much.

We are still looking for real insight, but most of us are overwhelmed by countless gigabytes, terabytes and petabytes of data, and much of what we get is recycled, repackaged and ultimately repetitive.

We are drowned in data, low-utility information and marketing hype.

For all the good that the information we receive does us, we may as well be more dog.

As Ad superman Dave Trott asked, what the feck does ‘be more dog’ mean?

Exactly…

It doesn’t mean anything.

We could write a whole litany of the endless succession of IT snake-oil merchants that have passed through techy-tinsel-town flogging yet another dead-horse as the latest and greatest Kentucky Derby favourite.

But that’s just hokie. Even the media and the presses are in on it, up-close and intimate collaborators in keeping reality from us, by burying us in shit.

Remember the joke about the mushrooms?

That’s right.

“Keep them in the dark, feed them bullshit, and watch them grow”

Well, it came true.

Like life reflecting comic art.

Actually, most of us are still starved of knowledge and insight.

And, as for wisdom?

What’s that then?

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