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Monthly Archives: January 2015

Consider this: Hedge Funds are Evil

30 Friday Jan 2015

Posted by Martyn Jones in Banking, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, Strategy

≈ Leave a comment

Tags

Banking, finance, funds, Good Strat, Good Strategy, hedge funds, Martyn Jones, Martyn Richard Jones, money


Hedge Funds are evil, right? Go on, you know you want to say yes. Almost everyone has an opinion about them, but very few people can actually tell you what they are.

Indeed, there’s am awful lot of nonsense written about Hedge Funds, and this piece might just end up being a worthy addition to that body of baloney. But, the intention is somewhat different.

The objective behind this piece is to provide a quick look at where the modern hedge fund started; what they are; how they work; the mechanics of participation; and who traditionally has put their money into them.

Of course, this piece is a necessary simplification of what is a fascinating aspect of the alternative investment universe.

To begin at the beginning

In 1966 Carol J. Loomis[i] blew the lid on one of the best kept investment secrets of the 20th century.

In an article penned for Fortune titled “The Jones that nobody keeps up with”, Loomis revealed that over a five year period a fund run by Alfred Winslow Jones had consistently outperformed the Fidelity Fund, the most successful mutual fund of that time, by a remarkable 44%.

Not only that, but between 1956 and 1966 the Jones fund had outperformed the Dreyfus Fund, the best performing mutual fund of that decade, by a massive 87%.

Jones was born in Melbourne, Australia, but from the age of four he lived in the USA. He graduated from Harvard in 1923, and before becoming involved in the finance industry he toured the world working on steamships. He was to serve as a diplomat in Germany, and also worked as a journalist covering the Spanish civil war.

In 1941, with conflict raging in Europe, Jones returned to the USA. He then studied for, and obtained a doctorate in sociology at Columbia University, and became a reporter for Fortune.

His thesis, Life, Liberty and Property, is a reference text in sociology.

In 1949 Jones formed a company, A. W. Jones & Co., arguably the first modern Hedge Fund.

Robert A. Jaeger characterised the fund as “an opportunistic equity hedge fund”, that relied heavily on discerning stock picking abilities, combined with bets on long positions (rising prices) and short positions (falling prices).

In 1952 the fund was converted into a limited partnership, and during the 50s other such partnerships were set up, including the sage of Omaha’s Buffet Partners, and WJS Partners, founded by Walter Schloss.


What they are; how they work

Hedge funds are loosely regulated, exclusive and limited-membership investment clubs, usually run as partnerships or as corporations. They focus on absolute returns on investments, for themselves and their members, regardless of market conditions.

Direct participation in Hedge Funds is theoretically limited to between 100 or 500 investors, depending on the class of investor. Moreover, because of the unregulated status of most hedge funds, they are not allowed to actively market their products, so they have to use more exclusive means to attract investors, typically word of mouth.

Hedge Funds typically invest in traditional securities, such as stocks, bonds and commodities, but they can also invest in real estate, art, wine or any number of other non-traditional areas of investment. In fact, they are free to use virtually any pick and mix of strategies from the entire range of investment possibilities.

That said, a lot of Hedge Funds will opt for a specific investment strategy and will stick with that strategy for the life-time of the fund.

So what’s in it for the Hedge Fund managers and administrators?

Hedge Fund Managers typically charge a management fee of between 1and 3 percent of the value of the assets under management, regardless of performance. They may also – almost always in the past – charge a performance fee, which can start at around 20 percent of any fund gains above a certain minimum performance hurdle or target value. Managers also generally ‘eat their own dog food’, in that they will also invest in their own Hedge Fund.

Investors in Hedge Funds are informed of the value of their investments via a statement that shows the calculated value of shares in the fund, the Net Asset Value (the NAV). This can be calculated monthly, quarterly or even yearly, depending on the fund. In addition funds are free to choose if they wish to publicly disclose performance figures or not.

Some hedge funds may require additional fees and commissions, and may impose lock up periods, and strict and narrow redemption periods. They may also reject some applications for subscriptions without giving any reasons, and they may also forcibly redeem shares held, and without having to justify their actions.

In addition, some hedge funds use equalization methods – and there are a number of variants – to equitably distribute hedge fund fees amongst its partners, yet other hedge funds do not use equalization methods at all.

The mechanics of participation

So, briefly, how do you get to invest in a Hedge Fund (subscribe), how do you liquidate that participation (redeem), and what happens between ‘subscription’ and ‘redemption’.

Offer: A Hedge Fund details what’s involved in a particular offer in an ‘Offering Memorandum’, also known as a ‘private placement memorandum’. This is typically an ‘enriched’ business plan tied to a specific issuance of shares in a fund. It basically sets out the stall.

Subscription: In order to subscribe to a fund the potential participant in the fund signs up to a subscription agreement, and the conditions laid out in that agreement. Conditions may cover aspects such as minimum subscription amounts; minimum share increments, rules governing the liquidation of participation in the fund; management and performance fees; and, so on and so forth.

Redemption: This is the liquidation of shares in a fund. Typical redemption points can occur from anything from 15 days to up to 180 days, and sometimes more than this, depending on the fund and the rules related to lock-ups, redemption. In addition, redemption options may be linked to other financial charges, penalties and constraints.

So what happens between subscription and redemption?

Custody: A hedge fund subscriber may wish to use the services of a large and reputable financial service provider to act as custodian of their hedge fund shares. In addition to holding securities for safekeeping, most custodians also offer other services such as account administration, transaction settlements, collection of dividends and interest payments, tax support and foreign exchange. (Source: Investopedia).

Dividends: It is very uncommon for hedge funds to pay dividends, as any accruable earnings are realized upon redemption of the shares. However, some fundsdo incentivize the maintenance of subscriptions through the payment of dividends.

Calculating NAV: Periodically – or even on a real-time basis – a hedge fund will recalculate the Net Asset Value of the fund shares. This is done by dividing total value of all securities held by the number of shares. The NAV is more or less subjective in cases where the associated assets are more or less liquid. An extreme example of this may be the calculation of a NAV that has to take into account the theoretical market value of art.

Of course, the mechanics of participation is typically more involved and complex than this.

Who plays, who pays

Investors in Hedge Funds are individuals and institutions (such as foundations, endowments, family offices, pension funds, insurance companies, private banks and funds of funds).

One of the key criteria in the Hedge Fund business is that only people and institutions with money can invest in them. On face value this prerequisite seems a tad bizarre, but there are some very valid reasons for it.

In order to be able to invest in Hedge Funds an investor will need to meet certain legal requirements. They have to be either a credited investor or a qualified purchaser. The qualification is based on net worth and individual income. The qualified purchaser has a higher net worth than a credited investor.

In my opinion the practical rules of Hedge Funds are clear, albeit wrapped up in more indirect language. The biggest rule is: ‘do not put any money into a Hedge Fund that you are not prepared to lose’. The second big rule is: ‘only subscribe to a Hedge Fund with money that you can lose and without the risk of significantly and adversely affecting your lifestyle.’

Of course, this didn’t stop people from jumping on the Hedge Fund bandwagon with little or no clue about what they were getting themselves into.

That’s all folks

Hedge Funds are subject to a dire circle of misleading, banal and frequently reactionary published and public opinion. Which is unfortunate, because it ignores that almost all of the Hedge Funds reflect a culture and style of their managers and administrators, and that in the business there is a a lot of plurality and diversity.

Some Hedge Funds have been the epitome of sharp investment practice, hubris and good old fashioned albeit legal duplicity.

There are macho funds, and non-macho funds, high risk funds and risk-averse funds, highly leveraged funds and funds that use little or no leverage.

Some funds are discrete, some are ugly, some are charming, and some are boisterous and incredibly aggressive. Some are socially responsible, and others might ask “is social responsibility part of the NAV calculation?”

Some funds delight in planning raids on markets, mounting shark attacks on political systems and find ‘justifiable’ enchantment in destabilizing economies and currencies. Other Hedge Funds – in my opinion the vast majority – would never dream of doing such things.

Furthermore, some funds actively encourage responsible investment in developing countries and in other ethical investment strategies. Moreover, there are plenty of examples of funds that sit somewhere between the extremes, so there is no one size fits all when it comes to characterizing funds.

But whatever the style of the Hedge Fund, at the heart of each individual Hedge Fund culture is the culture of the team leaders and team players.

So, are Hedge Funds intrinsically evil?

No, I don’t think so. But that sort of headline grabs a lot of people’s attention, and frequently for all the wrong reasons.

Thank you so much for reading.

[i] Loomis, Caroll J. The Jones that nobody keeps up with. Fortune, April 1966


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

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

≈ Leave a comment

Tags

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


LinkedInHeader1

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: Does all data have value?

30 Friday Jan 2015

Posted by Martyn Jones in Assets, Consider this, Data governance, data science, Good Strat, Martyn Jones

≈ 1 Comment

Tags

corporate assets, data governance, DW 3.0, Good Strat, information supply framework, ISF, Martyn Jones, traditional assets


Header1

To begin at the beginning

You can use all the quantitative data you can get,

but you still have to distrust it and use your own intelligence and judgment.

Alvin Toffler

There is a touching belief that all markets are rational and that a company’s value is accurately reflected in the current share price multiplied by the total number of shares in circulation.

The thing is, this doesn’t square up with formal accounting practices and financial reporting, as they now stand. So, there is a sentiment that the gap between capitalisation and accounting valuation must be due to some less tangible factors, which is true, and for many years it was accepted that aspects such as good will, were not in fact measurable or even easily manageable.

In the nineties, Leif Edvinsson and a team of accounting and finance specialists at Skandia pioneered the accounting and reporting of non-tangible assets, which they called the Skandia Navigator, the focus was clearly on reporting the progress of the creation and use of Intellectual Capital (IC). What the Skandia IC team did, how they went about it, and how they presented their results, (as a supplement to their annual report,) made a lot of good sense.

Now there is a movement to measure, value and manage all data as if it were a highly tangible asset, because, according to some, IT has been incapable of managing data because they don’t know how to measure it and they aren’t particularly well equipped to do so, a claim that is paradoxically made by people who are very much in the IT camp.

But this is not about treating data as an asset but focuses on potential risks due to data loss, data corruption, quality issues, misinterpretation, misuse or whatever. Simply stated, a risk prevented from arising or a risk that is mitigated is not an asset, and risk management is not primarily about rigidly theoretical asset management and vice verse.

Now, I won’t argue that it’s not a good idea to have an idea of the value of business assets, from either a quantitative or qualitative perspective, but I think there are more moderate, coherent and less fundamentalist approaches to the understanding and valuation of data, approaches that are more aligned to contemporary views of the understanding of the value of intellectual capital assets – unstructured and structured. What follows is a brief and high-level view of some potential options.

Data Assets in MOSCOW

What follows is a simple explanatory technique that is part of the DW 3.0 Information Supply Framework approach. It focuses on qualitative aspects of data as an asset. The following diagram provides an overview of foundations of the first phase of this simplified risk, classification and prioritisation approach:

On the horizontal access we have used the MOSCOW classification to break the problem down into simple terms. Here is an example of its configurable application:

MUST – Must treat the data as an asset: If this data is compromised in anyway then it would possibly result in serious negative implications for all (or most) customers and severe financial, contractual and reputation impacts on the business

SHOULD – Should treat the data as an asset: If this data is compromised in significant ways it would result in intolerable negative financial implications for a number of customers and would also lead to negative financial impacts on business

COULD – Could treat the data as an asset: If this data is compromised in significant ways then it could result in financial consequences for the business, but without impacting customer churn

WON’T – Won’t treat the data as an asset: Data that if compromised would not result in financial consequences wither for customers or for the business

It is important to understand that these definitions are examples and that in practice the descriptions and parameters must be determined and aligned in cooperation between the distinct business stakeholders, including business IT.

Data Value Chains

Another aspect that is important to the understanding of ‘data as an asset’ is to be found in data value chains. The following diagram illustrates the DW 3.0 technique used to simplify the attainment of a consensual view of the understanding of this value.

DATA: “Data is a super-class of a modern representation of an arcane symbology.” –Anon

In order for data to be more than an operational necessity it requires context.

Providing valid data with valid context turns that data into information.

Data can be relevant and data can be irrelevant. That relevance or irrelevance of data may be permanent or temporary, continuous or episodic, qualitative or quantitative.

Some data is meaningless, and there are cases whereby nobody can remember why it was collected or what purpose it has.

Taking all this into account we can now ask the pragmatic question: what value does this data have? Which is sometimes answered with a: ‘no value whatsoever’.

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

In order for information to be created to drive organizational operations and tactics we must have valid interpretations of data.

Providing valid information with valid interpretations potentially turns that information into knowledge.

Information can be correct, partially correct or incorrect; it may be relevant or irrelevant. The validity of information may also be permanent or temporary, continuous or episodic, and qualitative, quantitative or both.

Some information may be relevant, irrelevant or misleading. Information derived from erroneous and incomplete data may be usable, but the outcomes of using that information may be unpredictable.

Taking all this into account we can now ask the pragmatic question: what value does this information have? A question which can sometimes be answered with an ‘it is probably too early to say’.

KNOWLEDGE: This necessarily refers to a subset of ‘business knowledge’ known as structured intellectual capital.

The adequate, appropriate and timely application of knowledge (structured intellectual) requires wisdom; knowledge alone will no longer be good enough.

Providing valid knowledge with valid interpretations potentially turns that knowledge into valid and executable strategies.

Knowledge may be useless or useless; it may be relevant or irrelevant; it may even be wrong, even if we have named it ‘knowledge’. The usefulness of knowledge may also be permanent or temporary, continuous or episodic, qualitative or quantitative.

Some knowledge may be relevant, irrelevant or misleading. Knowledge derived from erroneous and incomplete information may be usable, but the outcomes may be unpredictable. As they might say in Córdoba: ‘Give knowledge to a donkey and it will still remain a donkey’.

Taking all of this into account we can now ask the pragmatic question: what value does this knowledge have? A question which can sometimes be answered with: ‘Isn’t it getting rather hot in here’.

What does it all mean?

To paraphrase George S. Patton, for the purposes of business data, an imperfectly good pragmatic valuation of data executed today is better than a perfect theoretic valuation of data made in the next life.

The valuation of data, information and knowledge is complex and involves many intangibles, and although some may view the data, information and knowledge chain as a closed process, this is not in fact the case, as each step of the process is influenced by a number of factors that fall outside of a simplified view of any theoretical or practical progress from data to wisdom.

When I started in IT over 30 years ago I worked on some of the first large-scale OLTP projects in Europe. On one such project, the development, test and production the databases were continuously replicated. Every day, not one, but ten backups of the data were made, and then shipped to different physical locations.

Thirty years on and some people are saying that IT has never treated data as an asset?

The fact of the matter is that data is managed as an asset. In fact, some IT organizations may be taking exaggerated measures in order to protect the data and information in their care. That some people have issues with the contemporary management of data does not change the facts on the ground, and the perceived shortfall in the commercial exploitation of data is frequently and erroneously interpreted as the absence of asset appreciation and management.

Moreover, even with generally accepted accounting principles there are tangible assets that are either of dubious value or are cost-absorbing liabilities.

Finally, there may also be unintended consequences of an overzealous approach to the financial reporting of intangible assets such as data, and just as the value of tangible assets can be accidentally or deliberately overstated, so too could the value of data, which may well lead to a significant overvaluation of a business. Moreover, it’s not beyond the realms of possibility that big data lakes, data universes and dark data dungeons – the ones that apparently will drive trillion dollar economies – are in fact somewhat worthless.

In this respect I think there is in some quarters an unhealthy fixation on the supposed (and as yet mainly unproven) value accruable from masses of data.

That’s all folks

Steve Jobs didn’t turn Apple around by relying solely on data and information, it was knowledge, and more especially the wisdom of knowing where, when, why and how to apply that knowledge that made the significant difference.

So, until the next time, hold this thought: ‘Google market cap slashed and Facebook in freefall as accountants ask “where’s the big data beef?”… Breaking news!’

Many thanks for reading.

Consider this: Social Media Big Data killed Ad-land

21 Wednesday Jan 2015

Posted by Martyn Jones in Big Data, Consider this, Information Supply Frameowrk

≈ Leave a comment

Tags

admen, advertising, Big Data, dave trott, DW 3.0, social media


Hold this thought: Big Data is the future of online business and interactive advertising is its profit.

Much is being made of Big Data and its role in social media and online interactive advertising. The advertising industry itself has a “big crush” on Big Data, and it fuels the elevated revenues, profits and share prices of a number of online companies.

Continue reading →

Consider this: Big Data in Context

21 Wednesday Jan 2015

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

≈ Leave a comment

Tags

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 →

Consider this: Big Data and the Analytics Data Store

19 Monday Jan 2015

Posted by Martyn Jones in Analytics, Big Data, Consider this, statistics

≈ Leave a comment

Tags

Analytics, Big Data, Data Marts, enterprise data warehousing, statistics


To begin at the beginning

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. 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

≈ 4 Comments

Tags

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


Martyn Richard Jones

Continue reading →

Consider this: Aligning Big Data

13 Tuesday Jan 2015

Posted by Martyn Jones in Big Data, Consider this

≈ Leave a comment

Tags

Big Data


Martyn Richard Jones

Remastered for 2026

Intro

I aim to bring some semblance of simplicity to the Big Data debate. I also strive for coherence and integrity. Hence, I am sharing an evolving model for pervasive information architecture and management.

This is an overview of the realignment and placement of Big Data within a more generalised architectural framework, one that integrates data warehousing (DW 2.0), business intelligence, and statistical analysis.

The model is now referred to as the DW 3.0 Information Supply Framework, or DW 3.0 for short.

A recap

Continue reading →

My Experience with a Professional Who’s Who Scam

12 Monday Jan 2015

Posted by Martyn Jones in Condiser this

≈ Leave a comment

Tags

Business


Martyn Richard Joneswhoswho

A couple of day’s back I was looking through some really interesting LinkedIn articles published via Pulse, and my mind started to wonder.

On each article page, an advert for a professional Who’s Who service appeared, the same advert, again and again, just begging to be clicked.

Continue reading →

Big Data? Dopey Quotes?

10 Saturday Jan 2015

Posted by Martyn Jones in Big Data

≈ Leave a comment

Tags

1, 10, 2, 3, 4, 5, 6, 7, 8, 9, Big Data, CCC


Martyn Richard Jones
Remastered for 2026
Imagen1

I came across a blog post titled “The Best Big Data Quotes Of All Time”.

At first, I ignored it. I really like the author, but I’m generally not a fan of this type of ‘content’; of top 10 lists or cute collections or amazing pussy cat videos on YouTube, although I respect other people’s tastes.

Continue reading →

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