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We address 3 questions: 1. What data do we really need answers for? 2. Why is a sound methodology critical? 3. Do metrics that focus on small but useful improvements make sense?

With business analytics, the toughest challenge is collecting data needed for questions one needs answered. My emphasis here is on:

– must-have answers, not – desired answers!

This is the third post in a series of entries about big data. Others so far are:

– Facebook mood study: Why we should be worried!
– Secrets of analytics 1: UPS or Apple?

New technqiues will not do

Often, we focus on predicting or forecasting the future. However, in management it is more important to understand the analytic HOWs and WHYs. These matter more than the promise of prediction. In the past we did not call things predictive analytics but forecasts instead. We used

– time series, as economists still often do, and – tried our luck with multivariate analysis (both part of what is called parametric statistics). These days, we still use the above methods. However, new ones have come to the fore, such as:

– k-means clusters, and – random graphs.

2014-09-08-EPFL-evolution-of-random-graph-Erdoes-Renyi

A random graph is obtained by randomly sampling from a collection of graphs.

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KEY INSIGHTS
Why little data mean a lot: Incremental innovation is key.
Google Trends shows a spike in searches – iPhone6: Remember the flu trends? Increased searches do not make something a fact…
Constant experimentation and rapid implementation: Strive for lots of small and frequent advances, because that is good enough.

We address three questions

1. What does it mean when Google Trends shows a spike in searches?
2. Should we aim for lots of small wins from ‘big data’ that add up to something big?
3. Do metrics that focus on small but useful improvements make sense?

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CLICK - Caution - things may not be as they appear. Check the methods.

1. ‘iPhone slow’ and Google Trends

There are three types of business analytics:

Descriptive analytics that look at historical data,
Predictive Analytics that try to determine what might happen, and
Prescriptive Analytics that focus on giving us different options, in which case we choose what we think suits us best, given time and money constraints.

The question remains whether we have the right data… To illustrate this challenge, we can look at the Google Flu Trends (GFT). Using search results from Google, the GFT supposedly indicates how the flu spreads and affects people in various countries.

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CLICK - Facebook Likes tell a lot about you, such as if you drink beer, have sex regularly and are happy.

Facebook engaged in a large study to see if users’ emotional states could be affected by their news feed content.
Consent of Human Subjects: Subjects not asked for permission first.
Findings: Extremely small effects.
Research methodology: Poor algorithms used, questionable findings.

Key finding: A reduction in negative content in a person’s newsfeed on Facebook increased positive content in users’ posting behavior by about 1/15 of one percent!

We address 3 questions

1. Why did some of the checks and balances possibly fail?
2. Should we worry about the study’s findings?
3. What benefits do Facebook users get out of this study?

Non-techie description of study: News feed: ‘Emotional contagion’ sweeps Facebook

1. Some checks and balances failed

Following the spirit as well as the letter of the law is the key to successful compliance. In turn, any governance depends upon the participants doing their job thoroughly and carefully.

In this case, the academics thought this was an important subject that could be nicely studied with Facebook users. They may not have considered how much it might upset users and the media.

Cornell University has a its procedure in place for getting approval for research with human subjects. As the image below illustrates, the researcher is expected to reflect on the project and if in doubt, ask for help.

CLICK - Why does the media not get the facts right about the Facebook study? #BigData

The university points out that it did not review the study. Specifically, it did not check whether it met university guidelines for doing research with human subjects. The reasons given were that its staff:

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