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.

But research has put the validity of this data into question. For instance, media attention can result in a spike in searches. Moreover, there is strong evidence that GFT does not use all the information at its disposal to take accurate measurements of flu activity. Finally, Google Trends’ lack of transparency around how its algorithm works has long been challenged.

Lazer, David; Kennedy, Ryan; King, Gary and Vespignani, Allessandro (March 13, 2014). Google Flu Trends still appears sick: An evaluation of the 2013-2014 flu season. Retrieved August 2, 2014, from

CLICK - Your mobile phone has not gotten slower - Facebook app for instance, does far more and requires far more of the hardware now than when it was first released, as does the Twitter app. STRESS











Accordingly, just because Google Trends shows a spike in searches for ‘iPhone slow’ the moment Apple releases its latest model, does not mean that your old iPhone is getting slower.

Is Apple really using planned obsolescence in its iPhone sales strategy? Maybe, but Google search data is not confirmation of such a claim, is it?

Interesting reads

The Macalope (July 31, 2014). Obsolete arbuments. Macworld. Retrieved July 31, 2014, from

Mullainathan, Sendhil (July 26 2014). Hold the phone: A big-data conundrum. The New York Times, The Upshot. Retrieved July 31, 2014, from

Pfander, Matthias (July 31, 2014). Neue Mythen und Fakten zum iPhone (New myths and facts about the iPhone). Tages-Anzeiger, p. 31. Retrieved August 2, 2014, from

2. Aim for lots of small wins

Google Flu Trends

– look at historical data – descriptive analytics – and research, and
– try to develop a model – predictive analytics – from these data.

These findings will help us better understand how the flu spreads next winter. At least, that is the idea!

Constant experimentation and rapid implementation can help improve our understanding of why people search one way, and not the other, or order this book more than another on Amazon. It also helps us better understand why searches perform poorly in predicting flu trends.

But many small wins with the help of data gained from business analytics also help businesses to improve their products, and of course, save money.

For instance, business analytics technologies have enabled UPS to increase their US drivers’ efficiency. It helped raise the average number of drop-offs per mile from 1.9 when drivers devised their own routes, to 2.2 (according to Roger Hicks, UPS business manager for Louisville East).

Such small things add up to huge differences in your bottom line. In this case, plenty of savings for UPS for their express parcel operations and their separate supply chain business – in a highly competitive market, no less.

Interesting read: No author (July 19, 2014). Schumpeter. Little things that mean a lot. Business should aim for lots of small wins from “big data” that add up to something big. The Economist, p. 60. Retrieved August 10, 2014m from

Bottom line metrics

This allows us to draw several conclusions:

  1. You feel your iPhone suddenly gets slower as Apple releases iPhone 6 this Fall: But where are the benchmark tests to confirm this ‘feeling’? Stay calm, remain collected. Things might not be as you perceive them.
  2. Big data are great for measuring correlations. Unfortunately, they may not allow us to gain insight regarding:
    what factors could help explain a certain outcome, and / or
    – what caused something to happen.
  3. Little things mean a lot: Focus on small, but frequent and rapid improvements or changes that make your product better.

In an age where we tend to increasing buzz and hype, staying down-to-earth about “big data” and “business analytics” while showing a certain degree of humility still matters. What do you think?

Interesting read: Morgenthaler, Markus (July 4, 2014). Wer seine Umgebung nicht lesen kann, geht unter. (Those who cannot read the current drown). Der Bund, online. Retrieved August 2, 2014, from

Source: Secrets of analytics 1: UPS or Apple?

Do you feel data have helped you gain insights (e.g., about spending time on LinkedIn, Facebook, whether it was worth it)?
What small win from big data were you able to realize within the last 12 months?
When did you last examine the fine grains of your social media or business activities for small but useful improvements?

Thanks again for sharing your insights – I always appreciate your very helpful feedback.

Urs E. Gattiker, Ph.D. - CyTRAP Labs - ComMetrics.

Hooray – you read the whole post by author Urs E. Gattiker – aka DrKPI! Want to hang out more? Check out the news updates on Twitter, join our Social Media Monitoring discussion group on Xing, chat with us on Google+, and receive fortnightly updates and behind-the-scenes scoops through our newsletter.

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Extra Tidbit: Need to do an audit? Get Social Media Audit: Measure for Impact (Springer Science Publishers).

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  1. […] – Data analytics: Lessons learned from Ebola – Scottish referendum: A false sense of precision? – Facebook mood study: Why we should be worried! – Secrets of analytics 1: UPS or Apple? […]

  2. […] tries to predict likely flu outbreaks based on how often people use key search terms, has been shown to be inaccurate. Other methods that make use of a much wider range of data sets are enjoying more […]

  3. […] Gattiker, Urs E. (August 17, 2014). Secrets of analytics 1: UPS or Apple? Retrieved September 8, 2014, from […]

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