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Entries in analytics (5)

Thursday
Jan072016

Move over, big data?

We thought this description by Deloitte of the digitization of industries was interesting, as it looks at the role of the consumer in the ongoing transformation to a more digital economy.

Big Data has been the big buzzword in recent years, but even it is seeing a disruption of sorts, as the focus turns to iData - data related to the individual. Deloitte believes that iData should be at the forefront of business operations. According to the consultancy group, technology adoption has reached a tipping point, where individuals are no longer considered passive spectators, but are becoming increasingly active participants in the industrial process, "becoming inseparable from ‘producers’ of content, data and even physical products." This is driving the personalization and customization of products and manufacturers are altering business models to benefit from the product-as-a-service concept, as is the case with companies like Airbnb and Uber. However, for most companies, the challenge with iData is how to source, organize and present it in a fashion that is acceptable to the individual.

Image source: Deloitte

 

Thursday
Aug132015

Beautiful data, beautiful purpose?

As big data powers a new generation of products and services that are revolutionizing every corner of the private sector, I think it's important to ask the question: What opportunities do we have to apply the principles of Mutuality to our use of data and analytics?

In an earlier post I talked about the excellent book on data and analytics from 2009, Beautiful Data, that explored the new universe of possibilities opened up by recent advances in data collection, storage and processing technologies.

One dark note sounded by the book's co-editor, Jeff Hammerbacher, in the wake of its publication was the priority being placed on commercial uses of big data over the development of data and analysis tools for the common good. He summarized his concerns this way: "The best minds of my generation are thinking about how to make people click ads. That sucks." Hammerbacher's response was to help found Cloudera, a data management company that would bring these tools beyond the consumer/marketing sphere to broader fields such as science, healthcare and "traditional" businesses.

Now, as we build a system of metrics and management tools as part of the Economics of Mutuality, I'd like to use the best and most powerful tools possible to support it. We can start by focusing on data related to human capital, social capital, and natural capital -- looking for those reservoirs of information that might already exist, and catalyzing new ones where they don't. Then building, testing, refining, and sharing analytical tools to visualize and understand what the data tells us.

Because as big data transforms every corner of the "traditional" economy, we want its capabilities to be built into the new Economics of Mutuality from the start.

Image source: Cloud Matters

-- Yassine El Ouarzazi

Tuesday
Jul142015

Beautiful Data

Beautiful Data (O’Reilly Media, 2009) is a follow-up to Beautiful Code, and, like the earlier book, it was envisioned to be a work of sharing and exploration rather than an argument or treatise.

We like the wide-ranging scope of this work and the inherent curiosity it both curates and fosters. The book includes 20 chapters from 39 contributors, and is loosely organized in an arc covering data collection through storage, organization, retrieval, visualization and analysis. Beautiful Data also includes examples of computer codes that readers are welcome to use for their own analysis (and encouraged to do so).

Data touches every business and an every increasing proportion of our daily, personal lives—this is not a recent development, but one that was building slowly and steadily for decades. Still, a number of relatively recent advances in collection, storage, and processing technologies have broken down barriers and torn open new areas for data collection and exploitation by businesses, governments and private individuals and groups.

The authors and editors showcase real-world examples, from the Mars Lander, a Radiohead video, Oakland crime mapping, to communities of individuals who obsessively track and analyze their own actions and behaviors.

More and more powerful tools enable us to visualize and gather crucial insights from this data—or, in some cases, capture incorrect insights and reach faulty conclusions. The authors also explore the human drive to build conclusions from data and the many ways that data analysis can lead us astray if we aren’t careful.

Another undercurrent of particular interest to us is the tension between commercial investment and exploitation of data versus initiatives to use the vast sea of information and powerful analytics tools for the common good. Marketing and business development can and do benefit tremendously from a rigorous approach to data and analytics, but there are strong arguments that open source data and analytics tools should be also be leveraged to improve science, medicine, education and other areas that will generate substantial benefits to society. Co-editor Jeff Hammerbacher delves into this issue, and his views on this in this discussion with Charlie Rose:

The best minds of my generation are thinking about how to make people click ads. That sucks.

It strikes us that there is an opportunity to apply the principles of Mutuality to this question in particular, and this is an area we will explore further in future posts.

In the six years since the book was published the technologies have advanced and the knowledge (and popularity) of big data and data analysis have increased as well. Yet this work is worth reading for the approach it takes and its explorations of “data philosophy.”

Image source: O’Reilly Media

-- Yassine El Ouarzazi

Monday
Feb092015

Customer social identity as an indicator of purchasing behavior

If you spend much time comparing market research and comparing that data to actual customer behavior, you know that the two can vary, and in certain circumstances they present significantly divergent pictures. Many theories have been put forward regarding the cause(s) for the discrepancies, but in this months Harvard Business Review, Guy Champniss, Hugh N. Wilson, and Emma K. Macdonald outline an interesting remedy in an article titled "Why Your Customers' Social Identities Matter."
 
The trio conducted research that suggests that consumers hold onto multiple social identities, and the social group that a customer identifies with when encountering a product or brand influences how he or she reacts to that product or brand.
 
This presents a challenge to traditional market research because a customers' social identity--the one they will take on at the moment of purchase--can’t be easily captured through questions on surveys. Champniss et. al. note:

 

Subtle shifts in social context can dramatically change what group we identify with at any instant."

The solution proposed is that market research should take social identity into account, shaping customer responses by reinforcing a particular identity, redefining what it means to have that identity, or finding a way to change the identity. This also applies to those who desing customer experiences, and companies are also advised to create new identities that will inspire desired customer behaviors.

-- Segundo Saenz

 

Thursday
Feb052015

Experimentation in business vs. prediction

The key theme in Michael Schrage's December article in the MIT Sloan Management Review is that data trumps intuition. He points to evidence that this is true even when experienced senior leaders and subject matter experts are involved, and this can have significant consequences for how companies approach the task of anticipating customer demand.

Organizations may be confident they know their customers, but they’re very likely to be overconfident."

-- Michael Schrage

  • I am particularly sensitive to his call to “embrace our ignorance”, that experts and managers are notoriously bad at predicting the outcome of experiments / decisions, that empathy and experience are very poor replacements for data and careful experiments.
  • Only humble and systematic experimentation is really “customer-centric”.
  • If big data can help us identify promising patterns and correlations, pervasive (and now cheap) experimentation can close the causality loop, creating "virtuous cycles of profitable insight between big data and small experiments”.

This view radically challenges our managerial hero complex (admit it, reader, you, like me, fall prey to it on a regular basis!). Imagine a world where the most valuable skills or personality traits we are looking to hire and retain are humility and curiosity instead of leadership and expertise?

-- Yassine El Ouarzazi