17 Thursday Mar 2016
Why buy when you can get it for free?
Back at you! Here is the fifth fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
09 Wednesday Mar 2016
Posted in 4th generation Data Warehousing, All Data, Analytics, Ask Martyn, Big Data, Big Data 7s, Big Data Analytics, business strategy, dark data, data architecture, 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, statistics, Strategy, The Amazing Big Data Challenge, The Big Data Contrarians
Why buy when you can get it for free?
Back at you! Here is the fourth fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
05 Saturday Mar 2016
Posted in 4th generation Data Warehousing, All Data, Analytics, Big Data, Big Data 7s, Big Data Analytics, dark data, data architecture, Data governance, Data Lake, data management, data science, Data Supply Framework, Data Warehouse, Data Warehousing, pig data, statistics, The Amazing Big Data Challenge, The Big Data Contrarians
Why buy when you can get it for free?
Here is the first fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you. Continue reading
11 Wednesday Nov 2015
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In a city centre office block, somewhere in Scotland, the conversation between the IT Business Manager (Bill) and the Information Management Manager (Richie) is in full swing,, Bob is irate because his successfully delivered data mart has been derided as unusable rubbish by the business people it was meant to serve.
Let’s join the conversation:
Bill: I hate this job. Every time we try and help the business all we get back are complaints. Complaints because it’s not what they want, complaints because it’s in the wrong format, complaints because of the cost, or the performance, or the availability. All we get are complaints, complaints and complaints.
Richie: Well, to be fair, Little Bill, this was one clearly avoidable situation. We didn’t have to build the data mart.
Bill: I know what you’re thinking, but you are wrong. We had to do something. Anything.
Richie: I don’t agree, Little Bill, we always had the option of doing nothing.
Bill: And why would we do nothing?
Richie: Because. as I said at the time, Little Bill, without demand you don’t create supply, and at this level and on this scale, if you want to create supply, you first encourage demand. But it’s still fundamentally about meeting demand.
Bill: But, things don’t work like that in this organisation.
Richie: I think you will find that in fact that approach works remarkably well, Little Bill, and in almost any type of organisation. The problem is one of perception, if it has never been tried before there is no internal reference to whether it works or not, and of course repeating the mistakes of the past with absolute security, if better than doing something correct, but unproven in this setting.
Bill: No, I still think you fail to understand the nuances of this business.
Richie: You may well be right, Little Bill, but clearly if we really understood the even the nuances of the business, then we wouldn’t have wasted time on this effort, an effort that one of the business executive described as the expensive manifestation of an abject failure to understand the fundamentals of the business.
Bill: They said that?
Richie: Yes, they certainly did, Little Bill.
Bill: Well, if that’s the case then they clearly don’t know what they are talking about.
Richie: As may be the case, but that doesn’t help us either.
Bill: So, you with all of your ‘knowledge and experience’, what do you suggest?
Richie: I suggest that we take a proactive approach to encouraging demand.
Bill: Such as?
Richie: Well, I would revisit the recommendations that I made when I first joined this department.
Bill: Okay, just remind me of the key points.
Richie: We need part of IT to understand business process, and our business processes; in effect we need people who know the business of the business. These should be people who talk to the business in language the business understands, has a good grasp of a vast array of issues, and who can be confident in their everyday dealings with business.
Bill: But, the business always thinks it knows best, how will these people succeed where we have almost always failed in the past? They think we overly complicate things; they virtually try and tell us how to do our jobs.
Richie: That’s why we need people who can communicate with authority, persuasively and with ease, not from a basis of mistrust, lack of empathy and even disdain. We need people who can sell ideas, can frame discussions and articulate coherent and realisable proposals for business IT solutions using language the business grasps the first time. We need people who understand what is said, can lead discussion and can capture requirements in a way that IT can also understand.
Bill: But the refuse to talk to us.
Richie: Well, that’s perhaps rather unsurprising from people who seem to think they have articulated the same requirements to us, and repeatedly, over an extended period of time. The problem is that we have very rarely documented those requirements, and when it has happened it has not been in a way that business can understand and verify, they can’t take any of our requirements and actually understand them without resorting to a translator, so they don’t do it.
Bill: Okay, so apart from blaming IT, what do you suggest?
Richie: The first hurdle seems to be simple. We need to convince the business that we actually have something worth listening to, that we aren’t going back to waste their time, yet again.
Bill: And?
Richie: So, what I suggest is this. Part of my team will spend time on investigating existing and new technologies, methods and approaches and how these are applied in similar industries or even dissimilar settings, but with certain synergies. They will have a good grasp of the business but their focus will be on understanding technology and relating it to project opportunities within our business. They will then work with our Business Consultants to actually articulate, explain and sell the benefits of these ideas to the business.
Bill: This, as I have repeatedly told you, is what we do now.
Richie: I don’t think so, Little Bill. There is a marked difference between what we do now, with the “look what a marvellous data mart we have made for you, it has data and lots of menu options, and graphics and stuff” versus the “we would like you to that allows you to be able to identify tangible cross-selling opportunities between various lines of business and with a high degree of certainty, this driving increased revenue, and increased customer intensity, and therefore loyalty… and repeat business”
Bill: So, we go begging the business for projects, with silver-tongued rhetoric.
Richie: No, Little Bill, we give the business what it wants. They are our customers, and as any business person should know, giving the customers what they want is a sure fire route to success.
Bill: Yes, but it would never work here. We are a very conservative company.
Richie: If the rest of the organisation was that conservative, we wouldn’t even be in business.
Bill: So what happens if the business says yes?
Richie: The business consultants and the research consultants work with the architecture consultants in socialising the business requirements and in developing solutions architecture (or a domain architecture), and as part of this they will also interact with the enterprise architecture consultant. So at some point, we will have a Business Requirements document, an IT requirements document, and an IT / Business Process Architecture document, and a Project Proposal document. Then the Business Requirements document – including detailed financials, together with the Business Project Proposal are socialised with the business, and submitted to them for review and approval. We then negotiate.
Bill: You make it sound easy.
Richie: You have to know what you’re doing, and there is logic to it all, but it’s far more rewarding than working on projects that invariably fail to satisfy.
Bill: So, when do we get started on all of this…?
Richie: As soon as you want, Little Bill.
So as we leave Bill and Richie to hammer out the details of the new approach, what can we take-away from this piece of business voyeurism?
I sincerely believe that the hardest job of any data warehousing professional, at least one worthy of the name, is in convincing sometimes even senior IT management of the need for doing the right thing right, of the possibility of doing the right thing right, and of the dangers of confusing ignorance and wishful-thinking with pragmatism.
So, make sure you get an expert, that your expert really is a professional, is absolutely ethical and that they really know their stuff, and when they don’t know, will not try and pretend that they do know, then trust in their professionalism, judgement and expertise, even if you then verify what you are told – and please, don’t verify this knowledge with a charlatan, spend more money and get the verification of a trusted and proven expert. Do this, and in this way and you won’t go far wrong.
Many thanks for reading.
Oh, and one last thng…
According to SAP “Big Data is the ocean of information we swim in every day”. I disagree; Big Data hype is the ocean of crap that we have to navigate through every second of every day.
Moreover, SAP contribute to that shite.
In a big way.
So, not only do we have Software aus Polen, we have Big Data aus Polen. With apologies to the great Polish IT professionals I know and respect.
Champion!
SAP! Mend your ways.
The choice is yours.
Many thanks for reading.
11 Wednesday Nov 2015
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“In fact men will fight for a superstition quite as quickly as for a living truth – often more so, since a superstition is so intangible you cannot get at it to refute it, but truth is a point of view, and so is changeable.”
Hypatia
On the 1st of July, I decided to set up a professional group on LinkedIn in order to create a hype free Agora for Big Data dialogue. I called the group The Big Data Contrarians and although it is a closed group, all those with interest in an open, informed and honest exchange of ideas on data, from whatever angle they are coming from, are very welcome to join in. (URL: http://www.linkedin.com/grp/home?gid=8338976)
So, why is the group called The Big Data Contrarians and not something more generic, such as The Data Contrarians?
It was a hard call, but in the end, I decided to go with the spirit of the times, for as overhyped and laden down with tripe as it might be, and the name stuck. This doesn’t mean that the group is limited to airing views, opinion and speculation just about Big Data. There’s a wealth of related data out there, and it’s not all as big as Google’s stash. Indeed, one of my pet projects for the world of Big Data and the Internet of Things is in discovering, designing and developing ways to reduce the Big Data footprint, early and often, without losing the essence of what the data might be able to tell us.
I believe that The Big Data Contrarians group fulfils a number of useful functions:
Of course, these are simply suggestions, and what may not be false today, may be true sometime in the future, and vice versa. Nevertheless, that encapsulates the general spirit.
As with other groups, we can all contribute discussions, comments, links to other material advertise jobs and promote what our businesses, and what we ourselves are doing.
In particular, I would be interested in hearing the facts about real-life Big Data success stories. I want to see things that have some value, not just ambiguous references to what might work or allusions to success stories that are just too successful to talk about in anything but the vaguest or the most venal, pompous or preposterous of terms. I want simple, clear and coherent, and I’m sure I’m not alone in this desire. I want to hear:
I like these seven bullet-points, because they are essentially the types of things I can talk about when it comes to Data Warehousing and Risk Management and Reporting. I also think that the time has come for us to move on to less prosaic and woolly allusions to Big Data successes, and indeed share the skinny on both real Big Data successes and real Big Data failures.
Because that’s how we all progress, right?
Many thanks for reading, and don’t forget, please join The Big Data Contrarians.
14 Saturday Feb 2015
A helpful slideset that is used to explain the purposes, positions and roles of Knowledge Management.
Enjoy! Please tell me what you think about this slide deck. Many thanks for viewing.
30 Friday Jan 2015
Tags
All Data, Big Data, business intelligence, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, statistics
A big computer, a complex algorithm and a long time does not equal science.
Robert Gentleman
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.
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.
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.
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.
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
19 Monday Jan 2015
Posted in Analytics, Big Data, Consider this, statistics
Hold this thought: If Data Warehousing was Tesco then Big Data would be the “try something different”.
Since the publication of the article Aligning Big Data, which basically laid out a draft view of DW 3.0 Information Supply Framework and placed Big Data within a larger framework, I have been asked on a number of occasions recently to go into a little more detail with regards to the Analytics Data Store (ADS) component. This is an initial response to those requests.
To recap, the overall architecture consists of 3 major components: Data Sources; Core Data Warehousing; and, Core Analytics.
Data Sources – This element covers all the current sources, varieties and volumes of data available which may be used to support processes of ‘challenge identification’, ‘option definition’, decision making, including statistical analysis and scenario generation.
Core Data Warehousing – This is a suggested evolution path of the DW 2.0 model. It faithfully extends the Inmon paradigm to not only include unstructured and complex data but also the information and outcomes derived from statistical analysis performed outside of the Core Data Warehousing landscape.
Core Statistics – This element covers the core body of statistical competence, especially but not only with regards to evolving data volumes, data velocity and speed, data quality and data variety.
Fig.1 – 3 components of the Information Supply Framework
This piece will focus on the Core Statistics segment and in particular the Analytics Data Store, which is specifically designed to support professional statistical analysis and at the same time to support the speculative use of data.
Fig.2 – Core Statistics – Analytics Data Store
Daniel Keys Moran once stated that “You can have data without information, but you cannot have information without data.“, we’ll deal with that nonsense at another time.
The Analytics Data Store is the reference data store collection for the entire Core Statistics segment.
The following is a high-level diagram of the Analytics Data Store together some of its major option features:
Fig.3 –Inside the Analytics Data Store
Operating System Platform – Typically the operating system platform will be a flavor of UNIX (Linux or some other flavor).
The standard UNIX distributions can support parallel file manipulation commands, for mapping and reducing data in files that can be theoretically in the order of zebibytes.
Additionally, Hadoop Distributed File System can be overlaid on the UNIX platform to leverage the underlying UNIX primitives giving it access and control over the underlying devices, whether that device is a file, disk, cluster, node or anything else (but these files cannot be manipulated using regular UNIX primitives unless using something like FUSE)..
Hadoop – Hadoop is a set of algorithms (an organised collection of code) for distributed storage and processing of data sets on clusters of commodity computer hardware. The modules in Hadoop are designed with the idea that hardware failures are commonplace and should be automatically handled by the software. This is not however unique to Hadoop as there are UNIX distributions that also fulfill these functions, and then some. However, the attraction of open source software running on commodity hardware cannot be dismissed lightly.
Relational DBMS – This is the database model that most people who know anything about databases are familiar with. RDBMS is based on the relational data model. The relational data model provides an uncomplicated view of data to all users by representing data in two-dimensional tables of rows and columns. These tables are called relational tables. A relational database is a collection of relational tables. RDBMS is the data manager for relational databases.
Relational DBMS users use Structured Query Language (SQL), the industry-standard relational database management language, and with typically some extensions to SQL, to interact with the databases.
Document DBMS – This is a class of database management system oriented towards the management of unstructured, semi-structured and complexly structured documents, primarily digital textual documents. Examples of what might be labeled Document-oriented DBMS include Documentum EDMS and MongoDB.
Graph DBMS – Also known as Semantic Data Model Databases (back in the day). According to Wikipedia “a graph database is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.” One of the features of some of the early Graph DBMS (my first contact with this technology was at Unisys in the late eighties with a product called InfoExec,) was that the query languages allowed for structured queries to be stated in more business-like terms.
Key-Value DBMS – One can either view this type of database as an innovative reuse of the design of simple programmatic ‘collections’ (trust Microsoft to be the only ones to name a simple thing with a simple name,) used to structure data, then applied to the realm of database management, or as a mental aberration invented by bodgers and hackers. At the end of the day Key Value DBMS simply provides a simple means to store in memory ‘associative array stores’ on disk. If there is more to it than that then please let me know?
Object DBMS – Object-oriented database management system stores information is represented in the form of objects – as used in object-oriented programming.
Object-relational databases are a hybrid of both the object oriented and relational approaches. I have found use for object-relational in operational applications, but never in MIS reporting, OLAP, Data Warehousing, Business Intelligence or Statistics. Does anyone have an alternative perspective?
Column Oriented DBMS – This refers to how data is stored. Typically we now view data as being stored in rows or records, but it’s not the only way of storing data.
Column Oriented DBMS store data first by values in ‘columns’, hence the name.
Examples of this type of database implementation go from Apache HBase as a distributed NoSQL column-oriented store built on top of HDFS, to EXASOL, currently the world’s fastest in-memory database management system.
As you see, the Analytics Data Store is fast becoming a super-fantastic mix of artefacts, gadgets and toys which should satisfy everyone; from the most experienced and knowledgeable statisticians, passing by the data ‘creatives’, the data scientists and the data data-users to the most game oriented of data plumbers and punters.
The ADS is above all about quality over quantity, the now over the mañana, and the ‘just do it’ over the ‘can we?”.
But, also remember these words from Colin Powell: “Experts often possess more data than judgment.” So, be forewarned and forearmed.
What are the applications that the Analytics Data Store might be used to support?
Here is a non-exhaustive list (first described in the mid eighties) of the potential applications:
Interpretation – Inferring situation descriptions from the analysis of a variety of data.
Prediction – Inferring likely consequences based on situational data.
Diagnosis – Inferring deviations and malfunctions from observables – from data.
Design – Analysing data and configuring objects under constraints.
Planning – Designing actions based on data feedback and analysis.
Monitoring – Comparing observations to known plan vulnerabilities.
Debugging – Prescribing remedies for malfunctions based on the analysis of data.
Repair – Devising and executing a plan to administer a prescribed remedy.
Instruction – Diagnosing, debugging and repairing behavioural patterns captured in data.
Control – Interpreting, predicting, repairing and monitoring systems behaviour.
Given the availability and quality of data to support the activities listed above, the Analytics Data Store can provide a sound source of data for a wide range of statistical analysis, forensic and speculative activities.
The Analytics Data Store is developed iteratively to support the data needs of a range of activities, from main stream statistical analysis, and formal data mining to creative and eclectic exercises in speculative analytics and non-traditional data correlation. This will ensure that business value can be assessed sooner rather than later.
The Analytics Data Store is essentially technology implementation agnostic and that it has a clear mission and business objectives within an overall Information Supply Framework.
The choices of technology products are based on best fit criteria, so the use of technology should not be driven by the old commercial approach of ‘solutions in search of problems’ approach, which failed so miserably time after time again, but on ‘what are the most appropriate artefacts, resources and technologies to use in approaching this problem or testing this hypothesis?’
The description of the Analytics Data Store has been necessarily terse. But I hope that it gives a flavour of where the ADS fits into an overall Information Supply Framework that extends the enterprise Data Warehousing paradigm (DW 2.0) without disrupting ‘business as usual’ or by destructively distorting the purpose, architecture and management principles of Data Warehousing.
The Analytics Data Store and the much broader DW 3.0 Information Supply Framework are also aligned with the much longer term objective of addressing the knowledge, information and data needs of organisations.
What follows is a highly synthesised view of the long term. Which I will leave for now without further comment.
Fig.4 – The Iniciativa Knowledge Management Pyramid
Thank you so much for reading.
File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro
17 Saturday Jan 2015
Tags
Analytics, aspiring tendencies in IM, Big Data, business intelligence, Core Statistics, enterprise data warehousing, Quotes
If you enjoy, abhor or are simply bored with the 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…
“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
“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
“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
“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
“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
Well, now that that’s done I can always ask for forgiveness. Not that I will of course.
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
03 Wednesday Dec 2014
Posted in consider, Consider this, data science, statistics
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Big Data, BS, Consider this, data analysts, data science, Data Warehouse, enterprise data warehousing, statisticians, statistics
Blue sky data
Statistics is a science. Some argue that it is the oldest of sciences. It can be traced back in history to the days of Augustus Caesar, and before.
In 1998, Lynn Billard, in a paper that laid out the role of the Statistician and Statistics, wrote that “no science began until man mastered the concepts and arts of counting, measuring, and weighting”.[1]
