big data – Looking Forward

September 19, 2017

Eating Our Own Cooking

What do a doctor suggesting a patient diet, an IT person asking a fellow worker to start using a new application, a mom asking a child to eat something or an analytics expert asking a business person to trust statistical insights all have in common? They are trying to get other people to do things.

But as the old saying goes, too often, this suggestion comes across as “do as I say, not as I do.” We’ve heard other variations on this theme, such as “doctor, heal thyself” or my favorite, “eat your own cooking”.

When I was a CIO, I used to tease some of the staff by pointing out they were all too willing to tell everyone else that they should use some new technology to do their jobs, but when it came to new technology in the IT world, the IT staff was the most resistant to change.

Among the most important changes that need to happen in many organizations today are those based on new insights about the business from analytics. But in big organizations, it is difficult to know how well those necessary changes are being adopted.

Analytics can help with this problem too. Analytics tools can help to figure out what message about a change is getting across and how well is the change being adopted? In which offices, regions, kinds of people?

Yet, it is rare for analytics folks to use their tools to help guide their own success in getting analytics to be adopted. Here, though, are two examples, the first about individuals and the second about organizations as a whole.

Individual Willingness To Change

A couple of years ago, the Netherlands branch of Deloitte created a Change Adoption Profiler (CAP) model of their clients’ employees based on the willingness to adopt changes. As they describe it:

“Imagine being able to predict who will adopt change, and how they will adopt it before the change has even occurred. At Deloitte, we have developed a data driven decision making method called the Change Adoption Profiler – it provides insights into your company’s attitude toward change and allows you to address it head on.

“The CAP uses a diagnostic survey based on personal characteristic and change attitudes. Unlike traditional questionnaires CAP combines this with behavioral data to understand the profiles that exist within the organization. The CAP provides reliable, fact-based analytics – provides client insights that support smart decision making, reveals risks and signals how to approach change at an early stage.”

There is a nice little video that summarizes its work at https://www.youtube.com/watch?v=l12MQFCLoOs

Sadly, so far as I can tell from the public web, no other office of Deloitte is using this model in its work.

Organizational Analysis

Network analysis, especially social network analysis, is not a new focus of those in analytics. But, again, they don’t normally use network analysis to understand how well changes are being spread through an organization or business ecosystem.

One of the exceptions is the Danish change management consulting firm, Innovisor. They put particular emphasis on understanding the real organization – how and why people interact with each other – instead of relying solely or mostly on the official organization chart.

This little video explains their perspective – https://www.youtube.com/watch?v=ncXcvuSwXFM

In his blog post, Henry Ward, CEO of eShares, writes at some length about his company’s use of this network analysis to determine who were the real influencers in the organization. They ended up identifying 9 employees, not necessarily executives, who influenced 70% of all employees directly and 100% through a second connection. A detailed presentation can be found at https://esharesinc.box.com/shared/static/8rrdq4diy3kkbsyxq730ry8sdhfnep60.pdf

Given the value of these kinds of examples, the problem of not eating your own lunch is especially interesting in the analytics field. Perhaps it is new enough that many of its advocates still have the zeal of being part of an early, growing religion and can’t see why others might resist their insights. But they would be convincing if they could show how, with analytics, they did their own jobs better – the job of getting their organizations to achieve the potential of their insights.

September 13, 2017

Libraries As Platforms For Big Data

The yearlong theme of the New York State Regents Technology Policy and Practice Council (TPPC) is data. Given the Regents’ responsibility for education, the council’s focus is on data in education, but not just data arising from schools. Beyond education, they are thinking about data that is or could be offered through libraries, museums, libraries, public broadcasting, and the like.

With this background, Nate Hill, Executive Director of the Metropolitan New York Library Council and I (in my role at METRO’s board president) have been asked to make a presentation on this subject when the group meets today. That is partly because of METRO’s role as the umbrella organization for all kinds of libraries, museums, archives and, more generally, information professionals in the New York area.

They also want to know about METRO’s leading role in working on data and digital content, even open data. (And Nate Hill’s work on an open data platform at the Chattanooga Public Library, before he came to New York, is also relevant.)

Of course, this is not a new subject to me either as I wrote more than three years ago in “What Is The Role Of Libraries In Open Government?”

Here in a nutshell are some of the main ideas that we are presenting today:

-> There has already been the start of big data and analytics in K-12 education. Unfortunately, all of the tests that kids take is one manifestation of this application of analytics. But there are other good sources of data for the classroom, like that supplied by NOAA.

Data has another use, however. It can motivate students and encourage them to be curious. How? If instead of using the standard, remote examples in texts for most subjects, the examples were drawn from data collected and about their own community, where they live.

Drawing on themes from my Beyond Data talk in Europe, “Is Open Data Good Enough?”, it’s important not to just depend upon the data that some governments publish on their websites. There is a world of data that is of public interest, but is not collected by governments. And data alone isn’t insightful – for that, analytics and human inquiry are necessary, both of which students and older scholars can provide.

Libraries have been the curators of digital content and increasingly can be the creators, as well. Whether this is through mashups or linked data or the application of their own analytics skills, libraries will be extending and making more useful the raw data that has already been made public.

Libraries have historically been community centers where issues could be discussed in an objective manner. But when so many people are not satisfied with merely being consumers of content and instead act as producer-consumer, libraries can offer the intellectual resources, the tools and the platform for citizens to play a role in investigating data on public issues and in co-creating the solutions.

Our hope is that METRO can help to show the future paths for the open data movement in all of its venues and, maybe even provide the platform we envision in our talk today. If you’d like to join in this effort, please contact Nate Hill or myself.

June 1, 2017

Books That Link Analytics, Big Data And Leading Change

Last week, at the end of my class in Analytics and Leading Change, one of the required courses in Columbia University’s Masters Program in Applied Analytics, my students asked for books I’d recommend that provide more detail than we could cover in the course. It turns out that others are also interested in a good library of books about analytics from the viewpoint of an organization’s leaders.

You’ll see that these are not textbooks about analytics or machine learning techniques – there are plenty of those. Instead, this reading list is the next step for those folks who understand the techniques and now want the insights from their work to have an impact on and provide value to their world.

Although most of these books were published in the last decade, there are also some classics on the list going back fifty years. And I’ve chosen mostly popular books because frankly they are written in a compelling way that is accessible to all leaders.

With that introduction, here are my recommendations.

1. On the experience of doing analytics and seeing its impact:

Moneyball by Michael Lewis

The movie, Moneyball, starred Brad Pitt as the hero of the first and most storied use of analytics in professional baseball. For people in the field of analytics, what could be better than a movie about your skills helping the underdog. But like all movies, it tended to gloss over or exaggerate situations for the benefit of a good, simple plot.

The book that Lewis wrote originally is subtler and is a good case study of the human side of introducing analytics in a tradition-bound field. Tying it all up, his more recent book, The Undoing Project: A Friendship that Changed Our Minds, is the story of the collaboration between Kahneman (see below) and Tversky.

The Signal and The Noise: Why So Many Predictions Fail — But Some Don’t by Nate Silver

Nate Silver is probably the best-known analytics practitioner by those not in the business themselves, due to his work over the years, especially for the New York Times and in relation to high visibility elections. This is his review of the ups and downs in using analytics, offering lessons especially from sports and politics.

Victory Lab: The Secret Science of Winning Campaigns by Sasha Issenberg

Although sometimes a bit over the top and now five years old, it is a thorough description of the use of analytics in election campaigns. Election campaigns are good examples of analytics because they are both well-known and there is a huge amount of data concerning elections and the voters who determine their outcomes.

Dataclysm: Love, Sex, Race, and Identity — What Our Online Lives Tell Us about Our Offline Selves by Christian Rudder

The author is the co-founder and former analytics lead for OkCupid. Not surprisingly, much of the book is about dating choices, but he goes way beyond that to uncover insights about various social attitudes, including racism, from the large amount of data he had in his hands both at his former company and elsewhere.

How Not To Be Wrong: The Power Of Mathematical Thinking by Jordan Ellenberg

Since analytics is essentially a mathematical art, Ellenberg’s book about mathematical thinking is important preparation for the field. It also provides numerous examples of how to present quantitative insights in a way that non-experts would understand.

2. On expanding the normal range of analytics:

Unobtrusive Measures: Nonreactive Research in the Social Sciences by Eugene Webb, et al

I’ve added this fifty year old classic to the list because even in a world of big data we don’t necessarily have all the data we need, either in our computer systems or in the physical world. This book reminds us to observe indications of phenomenon that are not already available – such as the influence of an individual measured by the wear and tear on the entry to his/her office space. It also points out the need to always include metadata in our analysis since that is often revealing.

How to Measure Anything: Finding the Value of Intangibles in Business by Douglas Hubbard

Somewhat picking up the same theme, this book helps both the business executive and the analytics practitioner to be more creative in measurement, especially when it comes to things that people haven’t so far been able to offer good metrics for.

Connected: The Surprising Power of Social Networks and How They Shape Our Lives by Nicholas A. Christakis and James H. Fowler

This is a book about how social networks influence us in ways we hadn’t considered before. As they say: “How your friends’ friends’ friends affect everything you think, feel and do.” I suppose a good example is how their observation that you’ll gain weight by being connected to overweight people in a social network has itself become a meme. In its own way, this book is an interesting work of analytics.

Just as important is its elaboration of how to study social networks since an understanding of the network of influencers in any organization is essential to anyone who wants to change the behavior of the people in that organization.

Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

The author was part of Google’s analytics team, which is the analytics equivalent of working at the Vatican if you’re a Roman Catholic theologian. Her emphasis in on how to show the insights of analytics work and to tell a story about those insights. In a world of all kinds of data visualization tools and fads, her advice is clear and evidence-based.

3. On the way that the human mind perceives the insights of analytics and might or might change as a result:

Payoff: The Hidden Logic That Shapes Our Motivations by Dan Ariely

Professor Ariely, formerly of MIT and now at Duke, is one of the more creative experimenters in psychology and he quickly reviews both his own and others’ research results. The theme of this short book is that the payoff which often makes a difference in human behavior is not necessarily a financial reward and that sometimes financial incentives even backfire. This is important for leaders of change in organizations, particularly big corporations, to understand.

Thinking, Fast And Slow by Daniel Kahneman

I’ve written about the work of Nobel Prize winner and Princeton Professor Kahneman before, most recently in “What Do We Know About Change”. This describes what Kahneman has learned from a lifetime of research about thinking and decision making. His work on how people process – distort – quantitative statements is especially relevant to analytics experts who need understand the cognitive biases he describes.

Switch: How to Change Things When Change Is Hard by Chip Heath and Dan Heath

The Heath brothers, popular business writers, have done a good job in this book of explaining what’s been learned in recent psychological research – see Kahneman and Ariely, for instance – without dumbing it down so much that the key points are lost. In doing that well, they also provide the leader of change and analytics some good ideas on how to present their own results and getting their organizations to switch to a more analytics-oriented outlook.

4. On the strategic linkage between leading change and analytics

The Dance of Change by Peter Senge, et al

This is another classic that goes beyond the usual cookbook approach found in most books on “change management”. Yet, Senge and his colleagues anticipated the more recent approaches to change management which is about something more than just getting a single project done. For Senge, the goal he established was to help create learning organizations. While he does not focus on analytics, this book should particularly resonate with analytics professionals since they now have the tools to take that learning to new and more useful levels than in the past.

I could easily expand this list, as could many others, but this “baker’s dozen” books will provide a good rounded education to start.

October 27, 2016

Resistance To Change?!

As I’ve been going through articles and books for the course on Analytics and Leading Change that I’ll be teaching soon at Columbia University, I frequently read how leaders and other change agents need to overcome resistance to change. Whenever we aim to get things done and they don’t happen immediately, this is often the first explanation for the difficulty.

Resistance to change is a frequent complaint of anyone introducing a new technology or especially something as fundamental as the use of analytics in an organization.

The conflict that it implies can be compelling. You could make a best seller or popular movie out of that conflict, like that great story about baseball, analytics and change “Moneyball”.

There have been cartoons and skits about resistance to change — https://www.youtube.com/watch?v=XTLyXamRvk4

This is an idea that goes very far back. Even Machiavelli, describing Renaissance politics, is often quoted on the subject:

“There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things. For the reformer has enemies in all those who profit by the old order, and only lukewarm defenders in all those who would profit by the new order, this lukewarmness arising partly from fear of their adversaries … and partly from the incredulity of mankind, who do not truly believe in anything new until they have had actual experience of it.”

It’s all awful if you’re the one trying to introduce the change and many have written about the problems they saw.

But is that word “resistance” misleading change agents? Going beyond the perspectives and anecdotes of change agents and business consultants, there has been over the last two decades some solid academic research on this subject. And, as often happens when we learn more, there have been some important subtleties lost in that phrase “resistance to change”.

In perhaps a refutation or an elaboration on Machiavelli’s famous quote, Dent and Goldberg report in “Challenging ‘Resistance to Change’” that:

“People do not resist change, per se. People may resist loss of status, loss of pay, or loss of comfort, but these are not the same as resisting change … Employees may resist the unknown, being dictated to, or management ideas that do not seem feasible from the employees’ standpoint. However, in our research, we have found few or no instances of employees resisting change … The belief that people do resist change causes all kinds of unproductive actions within organizations.”

Is what looks like resistance something more or something else?

More recently, University of Montreal Professor Céline Bareil wrote about the “Two Paradigms about Resistance to Change” in which she compared “the enemy of change” (traditional paradigm) to “a resource” (modern paradigm). She noted that:

“Instead of being interpreted as a threat, and the enemy of change, resistance to change can also be considered as a resource, and even a type of commitment on the part of change recipients.”

Making this shift in perspective is likely harder for change agents than the changes they expect of others. The three authors of “Resistance to Change: The Rest of the Story” describe the various ways that change agents themselves have biased perceptions. They say that blaming difficulties on resistance to change may be a self-serving and “potentially self-fulfilling label, given by change agents attempting to make sense of change recipients’ reactions to change initiatives, rather than a literal description of an objective reality.”

Indeed, they observe that the actions of change agents may not be merely unsuccessful, but counter-productive.

“Change agents may contribute to the occurrence of the very reactions they label as resistance through their own actions and inactions, such as communications breakdowns, the breach of agreements and failure to restore trust” as well as not listening to what is being said and learning from it.

There is, of course, a lot more to this story, which you can start to get into by looking at some of the links in this post. But hopefully this post has offered enough to encourage those of us who are leading change to take a step back, look at the situation differently and thus be able to succeed.

[http://njacknis.tumblr.com/post/152378476173/resistance-to-change]

April 27, 2016

Tech Frontiers On The Farm

Farming is a remote, not well understood, occupation for most people
who live in cites. So the technology frontiers being pursued by farmers
is one of the most interesting and unreported stories. But I’ve only
touched on this topic before, especially in my report about very
innovative areas of rural Netherlands.

In this post, I’m writing
about some things on the agricultural tech frontier that have caught my
eye. But this only is a sample – one that doesn’t even cover biological
engineering on the farm. There is so much going on in ag tech that a
single blog post cannot capture it all, even if it were limited to the
US which is certainly not the only place this technology is developing.

As Cory Reed, vice president of John Deere – a company most of us associate with traditional tractors – has said:

“We are on the cusp of the next innovation wave of digital agriculture.”

The Tech Products

The
various tech products cover everything from sensors and drones to
assess the condition of soil and crops to sensors and locators on
livestock to robotic farm machinery that does what was once back
breaking work.

More diverse farm robots may emerge from the program that the National Institute of Food and Agriculture (NIFA) US Department of Agriculture announced a few months ago.

The
app phenomenon has also come to agriculture. LambTracker is a
smartphone app to track sheep. ThermalAid measures heat stress on
cattle.

You don’t even need to have a large farm to benefit from this developing technology. For example, there’s the Edyn Smart Garden System with its sensor stick.

And for more urban farmers, there is technology for vertical, indoor farms from a completely automated one to one that cuts out any transportation costs by being placed in a store.

Big Data On The Farm

With
all the data from sensors and drones collected on farms, it was only a
matter of time before the big data movement hit the world of
agriculture. As an example, Farmobile, has opened up its Data Store in Minnesota, where “farmers now have the ability to sell their agronomic and machine data to vetted third parties.”

Another company, the Farmers Business Network,
hopes to help farmers by enabling them to share their data. In that
way, FBN proposes to “access agriculture’s largest database of real
world seed performance” and thus “unlock profitable, actionable insights
from all your data”.

Startups & Investments

If you’re not
involved in agriculture or rural development, you might nevertheless be
thinking that this might be a good undiscovered market to invest in.
Sorry, you’ll have to get in line. Other investors are ahead of you
already, even in places where these investors are often hidden – for
example, in San Francisco where AgTech2050 held its World Agri-Tech Investment Summit last month, in Silicon Valley where the Third Annual 2016 Silicon Valley AgTech Conference will be held next month and in New York City’s Waldorf-Astoria hotel which is hosting the Global AgInvesting 2016 conference today.

One
recent estimate points to $4.6 billion in investments in ag tech
startups last year, a doubling from the previous year. Just last week, one such company, PrecisionHawk, raised $18 million in funding from Verizon, Yamaha and NTT Docomo.

While
there will always be new investment opportunities, the more positive
part of this story is that this helps to ensure that the billions of us
on earth will not go hungry. For the future of the countryside, this
new technology adds to the attractiveness of rural life and the strength
of the farm economy.

[http://njacknis.tumblr.com/post/143481039969/tech-frontiers-on-the-farm]

April 20, 2016

Number Sense & Nonsense

As
you will know from the news media, business executives and the techno-sphere,
we are in the age of big data and analytics.
(Disclosure: I too am part of this trend with my forthcoming course on
leading change in the Applied Analytics Master’s program at Columbia University.)

For
those of us who have been practitioners of analytics, this attention is long
overdue. But there is a certain naiveté
in the breathless stories we have all read and in many of the uses – really misuses
– of analytics that we see now.

Partly to provide a more mature understanding of analytics, Kaiser Fung,
the director of the Columbia program, has written an insightful book
titled “NumberSense”.

Filled with compelling examples, the book is a
general call for more sophistication in this age of big data. I like to
think of it as a warning that a superficial look at the numbers you
first see will not necessarily provide the most accurate picture, any
more than the first thing you see about an unpeeled onion tells you as
much as you can see once it is cut.

Continuing this theme in his recent book, “The End Of Average”, Todd
Rose has popularized the story of the Air Force’s misuse of averages and
rankings after World War II. He describes how the Air Force was faced
with an inexplicable series of accidents despite nothing being wrong
with the equipment or seemingly with the training of the pilots. The
Air Force had even gone to the effort of designing the cockpits to fit
the exact dimensions of the average pilot!

As Rose reports in a recent article:

“In
the early 1950s, the U.S. air force measured more than 4,000 pilots on
140 dimensions of size, in order to tailor cockpit design to the
‘average’ pilot … [But] Out of 4,063 pilots, not a single airman fit
within the average range on all 10 dimensions. One pilot might have a
longer-than-average arm length, but a shorter-than-average leg length.
Another pilot might have a big chest but small hips. Even more
astonishing, Daniels discovered that if you picked out just three of the
ten dimensions of size — say, neck circumference, thigh circumference
and wrist circumference — less than 3.5 per cent of pilots would be
average sized on all three dimensions. Daniels’s findings were clear
and incontrovertible. There was no such thing as an average pilot.
If you’ve designed a cockpit to fit the average pilot, you’ve actually
designed it to fit no one.”

Rose criticizes the very popular
one-dimension rankings and calls for an understanding of the full
complexity, the multi-dimensional nature of human behavior and
performance. As a Harvard Professor of Education, he puts special
emphasis on the misleading rankings that every student faces.

He shows three ways that these averages can mislead by not recognizing that:

The
one number used to rank someone actually represents multiple dimensions
of skills, personality and the like. Two people can have the same
score, but actually have a very different set of attributes.
Behavior and skill change depending upon context.
The
path to even the same endpoint can be different for two people. While
they may look the same when they get there, watching their progress
shows a different picture. He provides, as an example, the various
patterns of infants learning to walk. Eventually, they all do learn,
but many babies do not follow any standard pattern of doing so.

It
is not too difficult to take this argument back to Michael Lewis’s
portrayal in Moneyball of the way that the Oakland A’s put together a
successful roster by not selecting those who looked like star baseball
athletes – a uni-dimensional, if very subjective, ranking.

Let’s
hope that as big data analytics mature, there are more instances of
Moneyball sophistication and less of the academic rankings that Rose
criticizes.

[http://njacknis.tumblr.com/post/143113012009/number-sense-nonsense]

January 5, 2016

Analytics And Organizational Change

The accumulation of data is increasing fast – from wearables, the widespread deployment of sensors in physical locations and the ever increasing use of the Internet by people.

And someone somehow has to figure it all out. As a result, data scientists are in demand and analytics is a hot new field of study. On top of long standing statistical methods, there has been impressive progress recently in machine learning, artificial intelligence and new computer system architectures.

Yet, the use of analytics itself has not had as great an impact on many organizations as the data scientists have hoped. Some of the failures of analytics were really failures of implementation.

Perhaps the most public of these was the great Netflix million-dollar prize for a new recommendation engine. From a purely technical viewpoint, the winning team did exactly what was asked for – create a significantly better engine than the one Netflix had been using. Nevertheless, Netflix ended up not using their work. That’s an issue of implementation and integrating the product of analytics into an organization.

Being able to predict behavior or even generate new insights from all this data is one thing. As with Netflix, having people and organizations actually use that knowledge is another. Like many other new technologies, adoption is as much a question of managing change as it is developing the technology itself.

This surely bothers some data scientists. After all, they have a better mousetrap – why aren’t people using it? Being able to think quantitatively, they can prepare quite convincing business cases with impressive ROI statistics and yet even that isn’t enough to get executives to budge. But changing an organization isn’t simple no matter how good your arguments.

Despite this background, there has been very little overlap between the courses that prepare data scientists and the courses that prepare change agents in organizations.

Later this year, I’ll be doing something to help align these two fields to improve the success of both. I’ll be teaching an online course on analytics and leading change. It will be part of Columbia University’s new executive graduate program in Applied Analytics.

We’ll be reviewing what is known about successfully introducing changes into an organization from the classics on the subject that were written as much as twenty years ago to more recent research. The course will, of course, help its students understand how to get an analytics initiative started. More important, it will focus on how to sustain, over the long run, both analytics and the changes it informs.

Thinking about the long run, there are three facets of the relationship between analytics and organizational change.

The use of analytics as a part of everyday decision making and the rules of operation in a business – this is the obvious first thing everyone thinks of.
The use of analytics to help better implement the changes that its insights imply – a kind of a meta-analysis.
The continuing interaction between analytics and change to finally achieve the long desired goal of an organization that learns how to continually optimize itself – this is something of great strategic value to any business.

As the course develops, I’ll be posting more about each topic.

April 22, 2015

Is Open Data Good Enough?

Last week, on April 16th, the Knowledge Society Forum of the Eurocities group held its Beyond Data event in Eindhoven, the Netherlands. The members of the KSF consists out of more than 50 policy makers focused on Open Data, from Europe. They were joined by many other open data experts and advocates.

I led off with the keynote presentation. The theme was simple: we need to go beyond merely opening (i.e., releasing) public data and there are a variety of new technologies that will make the Open Data movement more useful to the general public.

Since I was speaking in my role as Senior Fellow of the Intelligent Community Forum (ICF), I drew a parallel between that work and the current status of Open Data. I pointed out that ICF has emphasized that an “intelligent city” is much more than a “smart city” with technology controlling its infrastructure. What makes a community intelligent is if and how it uses that technology foundation to improve the experience of living there.

Similarly, to make the open data movement relevant to citizens, we need to go beyond merely releasing public data. Even Hackathons and the encouragement of app developers has its limits in part because developers in private companies will try to find some way to monetize their work, but not all useful public problems have profit potential.

To create this value means focusing on data of importance to people (not just what’s easy to deliver), undertaking data analytics, following up with actions that have real impact on policies and programs and especially, engaging citizen in every step of the open data initiative.

I pointed out how future technology trends will improve every city’s use of its data in three ways:

1. Data collection, integration and quality

2. Visualization, anywhere it is needed

3. Analytics of the data to improve public policies and programs

For example, the inclusion of social data (like sentiment analysis) and the Internet of Things can be combined with data already collected by the government to paint a much richer picture of what is going on in a city. In addition to drones, iBeacon, visual analyzers (like Placemeter), there are now also inexpensive, often open source, sensor devices that the public can purchase and use for more data collection.

Of course, all this data needs a different kind of management than businesses have used in the past. So I pointed out NoSQL database management systems and Dat for real time data flow. Some of the most interesting analytics is based on the merger of data from multiple sources, which poses additional difficulties that are beginning to be overcome through linked data and the new geospatial extension of the semantic web, GeoSPARQL.

If this data – and the results of its analysis – are to be useful, especially in real time, then data visualization needs to be everywhere. That includes using augmented reality and even projecting results on surfaces, much like TransitScreen does.

And if all this data is to be useful, it must be analyzed so I discussed the key role of predictive analytics in going beyond merely releasing data. But I emphasized the way that residents of a city can help in this task and cited the many people already involved in Zooniverse. There are even tools to help people overcome their statistical immaturity, as you can see on Public Health Ontario.

Finally, the data can also be used by people to help envision – or re-envision – their cities through tools like Betaville.

Public officials have to go beyond merely congratulating themselves on being transparent by releasing data. They need to take advantage of these technological developments and shift their focus to making the data useful to their residents – all in the overriding goal of improving the quality of life for their residents.

[http://njacknis.tumblr.com/post/117084058588/is-open-data-good-enough]

February 4, 2015

Big Data, Big Egos?

By now, lots of
people have heard about Big Data, but the message often comes across as another
corporate marketing phrase and a message with multiple meanings. That may be because people also hear from
corporate executives who eagerly anticipate big new revenues from the Big Data
world.

However, I
suspect that most people don’t know what Big Data experts are talking about,
what they’re doing, what they believe about the world, and the issues arising
from their work.

Although it was
originally published in 2013, the book “Big Data: A Revolution That Will
Transform How We Live, Work, And Think” by Viktor Mayer-Schönberger and Kenneth Cukier is perhaps the best recent
in-depth description of the world of Big Data.

For people like
me, with an insatiable curiosity and good analytical skills, having access to
lots of data is a treat. So I’m very
sympathetic to the movement. But like
all such movements, the early advocates can get carried away with their
enthusiasm. After all, it makes you feel
so powerful as I recall some bad sci fi movies.

Here then is a
summary of some key elements of Big Data thinking – and some limits to that
thinking.

Causation and
Correlation

When
presented with the result of some analysis, we’ve often been reminded that
“correlation is not causation”, implying we know less than we think if all we
have is a correlation.

For
many Big Data gurus, correlation is better than causation – or at least finding
correlations is quicker and easier than testing a causal model, so it’s not
worth putting the effort into building that model of the world. They say that causal models may be an outmoded
idea or as Mayer-Schönberger
and Cukier say, “God is dead”. They add
that “Knowing what, rather than why, is good enough” – good enough, at least,
to try to predict things.

This
isn’t the place for a graduate school seminar on the philosophy of science, but
there are strong arguments that models are still needed whether we live in a
world of big data or not.

All The Data, Not Just
Samples

Much
of traditional statistics dealt with the issue of how to draw conclusions about
the whole world when you could only afford to take a sample. Big data experts say that traditional
statistics’ focus is a reflection of an outmoded era of limited data.

Indeed, an example is a 1975 textbook that
was titled “Data Reduction: Analysing and Interpreting Statistical Data”. While
Big Data provides lots more opportunity for analysis, it doesn’t overcome all
the weaknesses that have been associated with statistical analysis and
sampling. There can still be measurement
error. Big Data advocates say the sheer
volume of data reduces the necessity of being careful about measurement error,
but can’t there still systematic error?

Big Data gurus say that they include all the data, not just a sample. But, in a way, that’s clearly an
overstatement. For example, you can
gather all the internal records a company has about the behavior and breakdowns
of even millions of devices it is trying to keep track of. But, in fact, you may not have collected all
the relevant data. It may also be a
mistake to assume that what is observed about even all people today will
necessarily be the case in the future – since even the biggest data set today
isn’t using tomorrow’s data.

More Perfect Predictions

The
Big Data proposition is that massive volumes of data allows for almost perfect
predictions, fine grain analysis and can almost automatically provide new
insights. While these fine grain
predictions may indicate connections between variables/factors that we hadn’t
thought of, some of those connections may be spurious. This is an extension of the issue of
correlation versus causation because there is likely an increase in spurious
correlations as the size of the data set increases.

If
Netflix recommends movies you don’t like, this isn’t a big problem. You just ignore them. In the public sector, when this approach to
predicting behavior leads to something like racial profiling, it raises legal
issues.

It
has actually been hard to find models that achieve even close to perfect
predictions – even the well-known stories about how Farecast predicted the best
time to buy air travel tickets or how Google searches predicted flu outbreaks. For a more general review of these
imperfections, read Kaiser Fung’s “Why
Websites Still Can’t Predict Exactly What You Want” published in Harvard
Business Review last year.

Giving It All Away

Much
of the Big Data movement depends upon the use of data from millions – billions?
– of people who are making it available unknowingly, unintentionally or at
least without much consideration.

Slowly,
but surely, though, there is a developing public policy issue around who has
rights to that data and who owns it.
This past November’s Harvard
Business Review – hardly a radical fringe journal – had an article that
noted the problems if companies continue to assume that they own the
information about consumers’ lives. In
that article, MIT Professor Alex Pentland proposes a “New Deal On Data”.

So Where Does This Leave
Us?

Are
we much better off and learning much more with the availability of Big Data,
instead of samples of data, and the related ability of inexpensive computers
and software to handle this data?
Absolutely, yes!

As
some of the big egos of Big Data claim, is Big Data perfect enough to withhold
some skepticism about its results? Has Big Data become the omniscient god? – Not
quite yet.

[http://njacknis.tumblr.com/post/110070952204/big-data-big-egos]

May 28, 2014

Too Many Metrics?

The New York Times Sunday Style Section – of all places – recently contained a report, titled “The United States of Metrics”, about how every area of life now is dominated by numbers and statistics. As its author, Bruce Feiler, put it:

In the last few years, there has been a revolution so profound that it’s sometimes hard to miss its significance. We are awash in numbers. Data is everywhere. Old-fashioned things like words are in retreat; numbers are on the rise. Unquantifiable arenas like history, literature, religion and the arts are receding from public life, replaced by technology, statistics, science and math. Even the most elemental form of communication, the story, is being pushed aside by the list.

After reviewing the use of analytics in fields as diverse as sports, health, lifestyle, etc., Feiler ends the story with Einstein’s time-worn warning, “Not everything that can be counted counts and not everything that counts can be counted.”

A couple of months ago, Zachary Karabell’s book, titled “The Leading Indicators: A Short History of the Numbers That Rule Our World”, was published. Karabell goes into this subject in much more depth and with a lot more historical context.

(By the way, Karabell is a lively writer and brings all this to life in a more engaging way than the average reader would expect of a book about economic statistics.)

Despite their prominent role in politics and business planning, he notes that the statistics we all hear reported about – GDP, trade deficits, unemployment rates, etc. – are misleading, inaccurate to varying degrees and mostly fairly new. Nevertheless many are already outdated by changes in the economy and the ways that people make a living.

He discusses various ways that these economic statistics can be updated. However, he also points out that no single measure alone will be able to provide a good picture of something as large and complex as a national and changing economy. So maybe we need more metrics to round out the picture.

Karabell thinks the metrics are good and useful, but that we need to be more sophisticated in our handling of them.

That’s something that makes sense. In a world that increasingly needs and demands the kind of data-driven knowledge that all these measurements can provide, our understanding and literacy in using quantitative methods also needs to improve.

In a way, this is not all that different from the argument that is made by those in the visual arts, who also call for more visual literacy in a world that is also increasingly visual, rather than textual. See my post “Visual Images And Text” from about a year ago at http://njacknis.tumblr.com/post/60268577982/visual-images-and-text .

(Come to think of it, these last two paragraphs do pose an ironic challenge to a blogger who writes using words – as traditional text gets diminished in a world of numbers and images 🙂

[http://njacknis.tumblr.com/post/87101098190/too-many-metrics]