What I Read – McKinsey Quarterly, October 2016 Issue

Tags

analytics, big data, McKinsey Quarterly, organization

Here is what I noted from the article “Making data analytics work for you – instead of the other way around” by Helen Mayhew, Tamim Saleh and Simon Williams:

[…] Advanced data analytics is a means to an end. It’s a discriminating tool to identify, and then implement, a value-driving answer. And you’re much likelier to land on a meaningful one if you’re clear on the purpose of your data […] and the uses you’ll be putting your data to […]

[…] the insights unleashed by analytics should be at the core of your organisation’s approach to define and improve performance continually as competitive dynamics evolve […]

[…] Ask the right questions. Clarity is essential and so is focus.

[…] Think really small … and very big. […] Identify small points of difference to amplify and exploit. The impact of “big data” analytics is often manifested by thousands – or more – of incrementally small improvements […]

[…] Embrace taboos […] useful data points come in different shapes and sizes […] Too frequently, however, quantitative teams disregard inputs because the quality is poor, inconsistent, or dated and dismiss imperfect information because it doesn’t feel like “data” […] Recording the quality of data – and methodologies used to determine it – is not only a matter of transparency but also a form of risk management […]

[…] Connect the dots […] Too often, organizations drill down on a single data set in isolation but fail to consider what different data sets convey in conjunction […]

[…] Run loops, not lines […] Best-in-class organizations continually test their assumptions, processing new information more accurately and reacting to situations more quickly […] OODA loop – Observe, Orient, Decide, Act […] OODA plus data amplify the effect and accelerate the cycle time.

[…] Make your output usable – and beautiful […] Analytics should be consumable […] organization will respond better to interfaces that make key findings clear and draw users in.

[…] Build a multiskilled team […] Key team members include data scientists […] engineers […] cloud and data architects […] user interface developers […] You also need “translators” – men and women who connect the disciplines of IT and data analytics with business decisions and management.

[…] Make adoption your deliverable […] the best day-one indicator for a successful data-analytics program is not the quality of data at hand, or even the skill-level of personnel in-house, but the commitment of company leadership.

Article provides a nice overview of how-to with regards to organizational and organized approach to big data analytics with some good examples. Italics mean direct quotes from the article. Reach for it.

 

What I Read – “Thinking with Data” by Max Shron

Tags

argument, big data, knowledge, Max Shron, questions, Thinking With Data

This book provided excellent further reading proposals and good references to some definitions related to scientific argument. Here is what I marked in that book:

[…] There are four parts to a project scope […] the context of the project; the needs that the project is trying to meet; the vision of what success might look like; and finally what the outcome will be, in terms of how the organization will adopt the results and how its effects will be measured down the line […]

[…] A data science need is a problem that can be solved with knowledge, not a lack of particular tool.

[…] There are three groups of patterns we will explore. The first group of patterns are called categories of disputes, and provide a framework for understanding how to make a coherent argument. The next group of patterns are called general topics, which give general strategies for making arguments. The last group is called special topics, which are the strategies for making arguments specific to working with data […]

[…] A very powerful way to organize our thoughts is by classifying each point of dispute in our argument. A point of dispute is the part of an argument where the audience pushes back, the point where we actually need to make a case to win over the sceptical audience […]

[…] Ancient rhetoricians created a classification system for disputes. It has been adapted by successive generations of rhetoricians to fit modern needs. A point of dispute will fall into one of four categories: fact, definition, value, and policy. […]

[…] Once we have identified what kind of dispute we are dealing with, automatic help arrives in the form of stock issues. Stock issues tell us what we need to demonstrate in order to overcome the point of contention. Once we have classified what kind of thing it is that is under dispute, there are specific subclaims we can demonstrate in order to make our case. If some of the stock issues are already believed by the audience, then we can safely ignore those. Stock issues greatly simplify the process of making a coherent argument. […]

[…] A dispute of fact turns on what is true, or on what has occurred. Such disagreements arise when there are concrete statements that the audience is not likely to believe without an argument. […]

[…] The typical questions of science are disputes of fact. […]

[…] There are thus two stock issues for disputes of fact. They are: What is a reasonable truth condition? Is that truth condition satisfied? […]

[…] Disputes of definition occur when there is a particular way we want to label something, and we expect that that label will be contested. […]

[…] Definitions in a data context are about trying to make precise relationships in an imprecise world. […]

[…] There are three stock issues with disputes of definition: Does this definition make a meaningful distinction? How well does this definition fit with prior ideas? What, if any, are the reasonable alternatives, and why is this one better? We can briefly summarize these as Useful, Consistent, and Best. A good definition should be all three. […]

[…] When we are concerned with judging something, the dispute is one of value. […]

[…] For disputes of value, our two stock issues are: how do our goals determine which values are the most important for this argument? Has the value been properly applied in this situation? […]

[…] Our values are dictated by our goals. Teasing out the implications of that relationship requires an argument. […]

[…] Disputes of policy occur whenever we want to answer the question, “Is this the right course of action?” or “Is this the right way of doing things?” […]

[…] The four stock issues of disputes of policy are: Is there a problem? Where is credit or blame due? Will the proposal solve it? Will it be better on balance? David Zarefsky distils these down into Ill, Blame, Cure, and Cost. […]

[…] Discussions about patterns in reasoning often center around what Aristotle called general topics. General topics are patterns of argument that he saw repeatedly applied across every field. These are the “classic” varieties of arguments: specific-to-general, comparison, comparing things by degree, comparing sizes, considering the possible as opposed to the impossible, etc. […]

[…] A specific-to-general argument is one concerned with reasoning from examples in order to make a point about a larger pattern. The justification for such an argument is that specific examples are good examples of the whole. […]

[…] General-to-specific arguments occur when we use beliefs about general patterns to infer results for particular examples. […]

[…] Arguments by analogy come in two flavors: literal and figurative. In a literal analogy, two things are actually of similar types. […]

[…] The justification for argument by analogy is that if the things are alike in some ways, they will be alike in a new way under discussion. […]

[…] In a figurative analogy, we have two things that are not of the same type, but we argue that they should still be alike. […]

[…] behaviour in one domain (math) can be helpful in understanding behaviour in another domain (like the physical world, or human decision-making). Whenever we create mathematical models as an explanation, we are making a figurative analogy. […]

[…] Optimization, bounding cases, and cost/benefit analysis are three special arguments that deserve particular focus. […]

[…] An argument about optimization is an argument that we have figured the best way to do something, given certain constraints. […]

[…] There are two major ways to make an argument about bounding cases. The first is called sensitivity analysis. In sensitivity analysis, we vary the assumptions to best – or worst-case values and see what the resulting answers look like. […]

[…] A more sophisticated approach to determining bounding cases is through simulation or statistical sensitivity analysis. […]

[…] In a cost/benefit analysis, each possible outcome from a decision or group of decisions is put in terms of a common unit, like time, money, or lives saved. […]

[…] The goal of a causal analysis is to find and account for as many confounders as possible, observed and unobserved. In an ideal world, we would know everything we needed in order to pin down which states always preceded others. That knowledge is never available to us, and so we have to avail ourselves of certain ways of grouping and measuring to do the best we can. […]

[…] Data science, as a field, is overly concerned with the technical tools for executing problems and not nearly concerned enough with asking the right questions. […] YEAH 🙂

Good start for anyone who wants to dive into Big Data field.

Big Data

Tags

big data, Big Data @ Work, data ownership, Davenport, digital age, laws

I got it now – just finished my 100^th book (since August 2011, I wrote some more about that in my previous post). It was “Big Data @ Work” by T. H. Davenport and it is somewhat fitting as the topic of that book touches on subject which will be – maybe even is already – very important to all of us. Now, I didn’t choose that book on purpose, I usually read randomly, even up to three books at the same time. Many times books I read point me to the next book etc.

Big Data is something we all need to get used to quickly. We all leave “digital trace” now and we all, at least all using smart phones, email, laptop, online shopping and anything digital or online this century offers. All that generates our personal digital data, our preferences, places we went to, shops we paid with our credit card (or any other card) at, web search habits, sites we visited, our names and addresses (if you ever filed an application online), health history, account statements… All this sits somewhere, most likely in many different places at once, and tells a very complete story about us. This is already a lot of data. Add to it our images, GPS data from our cars, videos from security cameras, or usage data from sensors in our homes – such as electrical meters, gas meters, whatever. And this will still not be all of it.

All this can be used for or against us. Big companies use that data already to some extent to sell us more, which practically means that we are being exploited – and whichever company has a better model, there we go spending our money. The models get better with every second and we are becoming more predictive just as quickly. Insurance companies may refuse to sell us certain policies or raise our premiums – either based on some disease history, or for example on our driving habits supplied happily by sensors built into our cars.

Big Data has its good and bad side, like anything. But what I see – at least right now – as the worst side is the fact that despite some movement in that area we still do not own completely our data. Somebody does and there is many of those somebodies. We should have a choice who gets what data from us. We should be able to choose if we want to be targeted with better advertising in exchange for our shopping habits. Or get better movie suggestions based on our viewing history and preferences. Or get better medical treatment (or heighten chances of discovering one) in exchange for our medical records. But we do not have that choice now.

The first problem lies in numbers – data of a single person don’t matter, so for that single person fight to get it back is not worth it. But data of many people does matter, and it does matter for every single individual. Compare this principle to start-stop system in cars: one does not save that much gas. All of them do save a lot.

The second problem is that each one of us would need to have some means of identification which would be the same across each data collection point. Under that identification our data would be stored, each time. But you know what that means? We would just become unique numbers. But here is the thing: we are numbers already. Entries in some big fucking database.

I would like to own all that sits under my unique number and decide what do I want to do with it. Wouldn’t you?

Big Data

Tags

big data, consequences, development, era, future, K. Cukier, revolution, society, V. Mayer-Schönberger

I mentioned already that I am reading “Big Data” by V. Mayer-Schönberger and K. Cukier, now that I finished it, I wanted to share some thoughts.

The book itself should be read by all who have at least minimal interest in what surrounds us, or in direction where we, as humanity or as community, are going. The subject of the book is already reality for all, we cannot stop it anymore, same as we could not stop industrial age or progress in general. As in those previous revolutions, this one – although much quieter – will definitely affect us all, one way or the other. Authors try to present us with most of the good and bad things approach to big data may bring, they even try to present us with some legal frameworks to ensure that this new digital god will not become devil. Only time will tell if they will be right.

One thing is for sure: we, as individuals, will need to change with big data. If we will stop invasion of privacy of a single person, we will need to act in thousands, if not nations. If we will want to get cure for our particular genetic disease, or cancer, we will need to share our data with companies having necessary tools to provide us with cure. Right now, since we all don’t really know (or care) what happens with our “data exhaust”, we just create it merrily going about our lives clutching our smart phones or wearing sensors. The era of big data is already here – only we seem not to heave grasped its consequences yet.