Currently viewing the tag: "literacy"
So far from making us more profligate with information, perhaps the Goddess of ‘big data’ will spur us to be smarter in data selection, and ensure more intelligence is embedded within our data extraction, transformation and reporting processes.
Greg Taylor‘s comments on the ‘Knowing what you’re missing‘ post are spot on. One of the clear implications of the big data explosion—technical challenges aside—is that manual analysis methods simply can’t scale to the volume and velocity at which potentially relevant data is being generated. As such, analytics (particularly of the machine learning variety) is ever more vital. One of my rules of thumb in consulting is that any OLAP cube is a standing business case for predictive modelling. As I put it in the ‘Advanced analytics and OLAP‘ post:
OLAP makes multidimensional data exploration about as fast and intuitive as it can be when a human is doing the driving. This means being able to arrange on screen, in two dimensions (perhaps taking advantage of colour and shape to visualise a third and fourth), relatively small subsets and arithmetical summaries of data. Advanced analytics, however, automates exploration. Only data mining methods can look at all dimensions simultaneously, at all levels, in combination. And they can do this in unsupervised (looking for natural structure in the data) or supervised (inferring input-outcome relationships) modes.
Greg elaborates:
- So as Analysts search more broadly for relevant data to meet the decision making requirements of management, perhaps they need to increasingly ask themselves: how will this piece of information fit within the network of predictive functions which explains the business?
- How might Analysts apply Occam’s razor to ensure only information which contributes predictive understanding is included, given the exponential growth in the potential data sources that could be used? One logical approach is for Analysts to undertake more experimental testing of variables (and transformations) for their explanatory power with respect to business outcomes.
As the earlier post reported, status quo electronic infrastructures aren’t ready for big data, and new technologies and disciplines are evolving rapidly to close the gap. But even more substantive changes are required of organisations’ human infrastructures. The key transition that business users of data need to make in the big data context is from the default of consuming more data to the practice of consuming data of higher value. This means becoming analytically literate and learning how to trust and leverage analysts. The key role that analysts must play in supporting decision makers is to understand what constitutes higher value, and to seek it out and communicate it. The key role for IT functions and BI managers is to enable analysts to enable decision makers.
Related Analyst First posts:
Further to recent initiatives in free online learning covered here is David Friedman’s course on ‘Analytic Methods for Lawyers’ from Santa Clara University. As he describes it:
The idea is that there are a number of subjects, such as statistics, accounting, and economics, that lawyers cannot expect to be competent in but should be familiar with. We spend a week or two on each.
Other methods covered in the course include decision theory, game theory, and ‘back of the envelope’ calculation. In essence, the classes (which are available for download here as recordings and whiteboard snapshots) provide ‘literacy primers’ for business professionals on each of these forms of reasoning. Analyst First maintains that analytics—over and above being a discipline, a set of techniques, and a profession (all of which it is)—is a literacy. As such, does analytical literacy draw on, live in parallel with, or subsume these?
All of the above. Business Analytics at a minimum fuses statistics (probabilistic reasoning) with accounting (the language of business). Some analytical techniques additionally integrate game theory (e.g. agent-based modelling). Others such as prediction markets bring in price theory from economics. All involve the scientific method, and therefore require empirical literacy.
David Friedman is a brilliant communicator and his lecture audio files are highly recommended. There is something new for everyone in the Analytic Methods course.
Related Analyst First posts:
- Want an intro to AI from Stanford?
- CORTEX MBAnalytics – Week 1, Day 1
- Analytics is… A Literacy – Parts 1 and 2
What are the defining attributes of the ideal Business Analytics sponsor? Over the years we have distilled them down to three, the ‘holy trinity’:
- Understanding: The sponsor needs to be analytically literate. Not a practitioner necessarily, but knowledgeable enough to know how to manage in both directions. Managing a team of analysts requires knowing how to tell whether they’re pursing relevant questions in technically appropriate ways and knowing how to validate what they produce. This means understanding the organisation’s data and its analysis objectives, knowing what analytical techniques exist and where they fit, which error measures are appropriate to each, and how to interpret results. Managing up means knowing how to communicate analytical results to non-analysts, particularly to senior management, and critically, how to translate executive demands back into analytically tractable questions.
- Empowered: The sponsor also needs to be given the appropriate mandate. The Business Analytics function needs to be resourced with human and electronic infrastructure, of course, but it also needs to be protected politically. Doing analytics means measuring things. Sometimes those things are people, and none of us likes to look bad. Sometimes those things are business functions, and none of us likes to be part of an ineffective team. Then there are the various organisational and perceptual barriers which make it difficult for Business Analytics functions to learn from mistakes and adapt.
- Motivated: Finally, the ideal sponsor needs to be motivated and incentivised enough to stay the course. Business Analytics initiatives typically take time to get going, and often their analyses don’t turn up the results people were wanting or expecting. Sometimes the result is ‘no result’, and this can be dispiriting. Other times it’s a mythbusting or counterintuitive finding, and this can be challenging. Almost always, the organisation’s data is in a more of a mess than expected. Analytical blind alleys are also inevitable, and exploration and discovery aren’t assessed via a mature set of measures. Nor is Business Analytics an IT project that can be communicated and monitored using standard KPIs. The Business Analytics sponsor, simply put, needs to be on a perpetual ‘internal sales’ mission.
Related Analyst First posts:
- Analytics is… A Literacy – Parts 1 and 2
- Barriers to entry and exit
- Analytics is… A Lean Startup Enterprise
- Assume bad data
- Forrester on the need for agility
- IT support
The modern knowledge worker has indeed progressed far past the illiterate, innumerate businessmen of ancient Sumer. They can do their own reading and counting, and many other things besides. They know the rudiments of double entry bookkeeping, though they may not be accountants. They are familiar with laws pertaining to their business, industry and work area, though they are probably not lawyers. They probably know the basics of project management, marketing, human resources or event management, without being an expert in any of those fields.
Thus, most modern knowledge workers can perform basic functions in any of these areas. Where their expertise is stretched, they would usually know how to recruit, retain or collaborate with an expert in any of these areas, brief them on requirements, and understand any advice or directions given.
This laundry list of capabilities is a core of the checklist of skills one would expect from a business course. While one need not be an expert or accredited in any of these areas, we can say that a modern knowledge worker is literate in all of them.
Closer to the topic at hand, the modern knowledge worker is expected to be computer literate, which is to say able to use a computer productively, often in the service of the professional literacies outlined above. Again, they would hopefully know their limitations, and know when to call an expert to repair faults, enable new capabilities or create new tools.
One interesting thing about literacies is that they are often unspoken: few job interviews ask explicitly if one can actually read. Not many more executive interviews ask if one can surf the Web, read a balance sheet, instruct a lawyer or define what a “marketing campaign” is. These things are tacit, assumed knowledge.
While there are indeed islands of expertise in law, IT, accounting, HR, marketing and many other areas in the modern business, these would be crippled if the rest of the business, particularly senior management lacked the minimal literacy required to engage these expert functions, to cooperate with them, instruct them and act on their advice.
Most crucially, a minimal degree of literacy is required to determine if the expert has done a good job, added value or created risk. Again, these processes are largely tacit.
It would be untrue to suggest that these literacies exist perfectly in all businesses. Indeed, one way to assess the effectiveness of knowledge workers, particularly middle and senior managers, is the degree to which they really have a truly literate grasp of the business functions that they interact with on a regular basis.
The Dilbertesque world all too familiar to so many of us exists due to an epidemic of false literacy in some organisations. What is false literacy? It is the ability to impersonate a literacy to another illiterate person. In business, it can be seen as a minimal, inadequate level of literacy, usually consisting of nothing more than buzzwords. To thrive it usually requires a critical mass of absent or false literacy, a lack of influential, literate people, and poor performance measurement. Usually arbitrary politicisation, poor accountability and poor literacy work together. This situation is rarer in smaller, privately owned organizations with majority shareholders. They are more common at the other extreme of the ownership spectrum.
False literacy is often sufficient, or deemed to be so, in some business areas such as recruiting or sales. Here an explicity “laundry list” of features, skills or other factors can be exchanged between buyer and seller without either party actually knowing what any of the terms mean. And perhaps this is enough in recruiting an IT developer with “C++, Java and backend systems”, but more of an issue when it is the way a CEO runs an insurance company.
Another way of looking at false literacy can be found here. The idea of a “cargo cult” helps to define the culture of an organisation where false literacy is the norm.
And now we are finally ready to talk about modern Business Analytics.
Today’s discussion begins with a key information technology underpinning all business today. This toolset provided novel forms of data storage, access, retrieval and analysis, many of which are used to this day.
The new technology in turn led to significant financial innovation, enabled new forms of exchange, and in particular facilitated the creation of new, tradable derivative products. It also led to improved military, industrial and agricultural production, as well as enhancing emerging communications networks.
The technological breakthrough required developments on a number of fronts, including hardware developments, especially storage media, along with innovations in data encoding techniques, and an extensive training program for the rigorously skilled technicians required to operate the new technology, which was not initially as user friendly as it could be.
Happily, the technology was adopted by business executives who were only glad to hire technological experts to drive these new systems, resulting in growing wealth and influence for those regions where the technology took root.
The technology in question is, of course, writing in its crudest, cuneiform pictographic form. It was closely coupled with its cousin accounting, which is the basic arithmetic of business in recorded form. The substrates were initially clay tablets, which required baking to give them any degree of permanence.
Much has changed since then. Clay tablets have been replaced by LCD screens, and pictographic cuneiform with a Roman phonetic alphabet and zero-bearing, decimal Indo-Arabic numerals. Nevertheless, it can be argued that the Sumerians covered more conceptual ground than what lies between them and many of today’s users of Analytics.
How can this be? One side of the argument is the sheer conceptual distance between Sumer and the pre-literate societies that predated it. This is a historical digression, where it may be instructive to compare Sumer with pre-literate civilizations such as those of the pre-Columbian Americas.
The other side of the argument is more relevant to the issue of Analytics today. Namely, for many users of Analytics, there has been one, and only one significant innovation since the Sumerians: modern, post-Sumerian business users of Analytics no longer require a technical specialist scribe to read, write or count for them. They can do it themselves.
And there is the second great conceptual revolution: the form of literacy invented by the Sumerians has become universal. What is more, it became apparent somewhere between then and now (perhaps around New Testament times) that perhaps business people would require a basic level of literacy and numeracy to conduct their own affairs, that understanding of such elementary concepts was not something to outsource.
Two thousand years later and it is inconceivable for a modern business executive, manager or clerical worker to lack literacy (in the sense of being able to read and write) and numeracy (in the sense of being able to count, add, subtract, multiply, divide).
Thus, in four thousand years, a technology that was the province of technical specialists has become an essential and fundamental part of the business toolkit. Lacking it is not a disadvantage: it is inconceivable.
The reader may have objected much earlier in this piece, noting a cornucopia of conceptual and technological innovations: Syllogisms, geometry, Algebra, calculus, statistics, Cartesian coordinates, symbolic logic, 3D animation, relational databases, OLAP, machine learning…and so many others.
The problem is, basic literacy and arithmetic numeracy is pretty much where it appears to have stopped for all but a new technological elite of scribes. This includes way too many people whose job it is to develop strategy, see “the big picture”, produce “evidence based policy”, hear the arguments of quantitatively skilled advisors or in many other ways interact with, and manage a data-rich world, of changing, poorly understood circumstances, vast uncertainty and with powerful analysis tools just a click away.
This is basically the condition of most people interacting with data in the modern world. These are the people who think that BI=Analytics=Reporting. These are the people who cannot read an XY graph, or trust any data summary more complex than an average. These are the people who when shown any kind of report, dashboard or graph ask to see the raw numbers because they are on firmer ground there, even if the numbers are millions of transactions and no useful inference can be drawn from eyeballing them.
There are many other literacies in the modern world, and most of these remain “unknown unknowns” for most of the people interacting with Analytics. What are these literacies, and how do the deficiencies affect business?
Like all good things in a business function not directly supervised by a real owner with a real stake, a good outcome is defined as:
“What appears to be a solution to what is understood to be the right interpretation of what is understood to be a problem”
Performance management can happily coexist with this definition as long as it does not measure anything actually useful, which is to say controversial and relevant.
It helps a lot if the task, its outcome or success criteria are beyond the capacities of those managing it.
This definition works both ways.
The obvious interpretation is that complete nonsense can pass for good Analytics.
The converse is that the success of good Analytics can be easily lost.
The general theme is that the sociology of Analytics can sometimes read like Dilbert.
A postscript to ponder:
While it may true that “if you can’t measure it, you can’t manage it”,
Just because you are measuring something does not mean that you are measuring “it”.
Also, note that if you can measure “it”, it is still possible that perhaps you still cannot manage it, by elementary logic.
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