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jerryking : small_data   8

Little metrics can make a big difference (and here’s how to use them) - The Globe and Mail
BRIAN SCUDAMORE
Special to The Globe and Mail
Published Thursday, Jun. 09, 2016

small businesses can concentrate on collecting different metrics that have an impressive impact on the bottom line. I call it little data. It’s easier to collect and it’s a great way to take the pulse of your company on a day-to-day basis.

Here’s how to find the little data that matters, so you can make impactful changes to your business without spending a fortune.

Sweat the small stuff

Looking at traditional metrics – sales revenue, cost of customer acquisition and overhead – is important, but it’s also worth tracking intangible elements that don’t make it onto a spreadsheet.

I like to look around the office and focus on the energy – is there a buzz or are people bored? – or I’ll look at notes from exit interviews to see who is leaving the company and why. Keeping this little data in mind has enabled us to make important changes to our culture when we need to.

External feedback is powerful, too. Whenever I’m in a new city, the first thing I ask my taxi driver is, “Who would you call if you needed your junk removed?” I’m not just making conversation or trying to name-drop one of our brands – I’m doing my own survey to see if our marketing efforts are sticking....you can’t run your business on anecdotes, focus on key numbers that provide meaningful insight and measure them consistently.... communicating these benchmarks, everyone in the company can understand and can react quickly to fluctuations.

Our key metrics are call volume, website traffic, and jobs completed. We also work on our “customer wow factor” by looking at our Net Promoter Score (NPS), asking every customer how likely they are to recommend our services to a friend.[aka delighting customers]
anecdotal  Brian_Scudamore  consistency  delighting_customers  feedback  Got_Junk?  Haier  insights  massive_data_sets  measurements  metrics  NPS  small_business  small_data  Wal-Mart  UPS 
june 2016 by jerryking
Big insights gleaned from small data - The Globe and Mail
HARVEY SCHACHTER
Special to The Globe and Mail
Published Tuesday, May 24, 2016
small_data  insights  Harvey_Schachter  Lego 
june 2016 by jerryking
When Big Data Isn’t an Option
May 19, 2014 / Summer 2014 / Strategy + Business | by David Meer
When Big Data Isn’t an Option
Companies that only have access to “little data” can still use that information to improve their business.

Many companies—probably most—work in relatively sparse data environments, without access to the abundant information needed for advanced analytics and data mining. For instance, point-of-sale register data is not standard in emerging markets. In most B2B industries, companies have access to their own sales and shipment data but have little visibility into overall market volumes or what their competitors are selling. Highly specialized or concentrated markets, such as parts suppliers to automakers, have only a handful of potential customers. These companies have to be content with what might be called little data—readily available information that companies can use to generate insights, even if it is sparse or of uneven quality....the beverage manufacturer developed an algorithm based on observable characteristics, then asked its sales professionals to classify all the bars and restaurants in their territories based on the algorithm. (This is a classic little data technique: filling in the data gaps internally.)

. Little data techniques, therefore, can include just about any method that gives a company more insight into its customers without breaking the bank. As the examples above illustrate, mining little data doesn’t mean investing in expensive data acquisition, hardware, software, or technology infrastructure. Rather, companies need three things:

• The commitment to become more fact-based in their decision making.

• The willingness to learn by doing.

• A bit of creativity. ...

The bottom line: Companies have to put in the extra effort required to capture and interpret data that is already being generated.
small_data  data  analytics  data_driven  market_segmentation  observations  call_centres  insights  data_quality  data_capture  interpretation  point-of-sale  mindsets  creativity 
september 2015 by jerryking
The Value of Bad Data - The Experts - WSJ
Apr 22, 2015 | WSJ | by Alexandra Samuel--technology researcher and the author of “Work Smarter with Social Media.”
*** Can I apply the idea of negative space towards evolving a dataset?

What do you do when you don’t have access to a large data set?...even without access to big data, you can still use some of the tools of data-driven decision-making to make all the other choices that arise in your day-to-day work.

Adopting and adapting the tools of quantitative analysis is crucial, because we often face decisions that can’t be guided by a large data set. Maybe you’re the founder of a small company, and you don’t yet have enough customers or transactions to provide a statistically significant sample size. Perhaps you’re working on a challenge for which you have no common data set, like evaluating the performance of different employees whose work has been tracked in different ways. Or maybe you’re facing a problem that feels like it can’t be quantified, like assessing the fit between your services and the needs of different potential clients.

None of these scenarios offers you the kind of big data that would make a data scientist happy. But you can still dig into your data scientist’s toolbox, and use a quasi-quantitative approach to get some of the benefits of statistical analysis… even in the absence of statistically valid data.
massive_data_sets  data  data_driven  small_business  data_scientists  books  hustle  statistics  quantitative  small_data  data_quality 
july 2015 by jerryking
How Not to Drown in Numbers - NYTimes.com
MAY 2, 2015| NYT |By ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ.

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”...So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data....For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of fivethirtyeight.com estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.

And data-driven teams found out the hard way that scouts were actually important...We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.

==============================================
From Market Research: Safety Not Always in Numbers | Qualtrics ☑
Author: Qualtrics|July 28, 2010

Albert Einstein once said, “Not everything that can be counted counts, and not everything that counts can be counted.” [Warning of the danger of overquantification) Although many market research experts would say that quantitative research is the safest bet when one has limited resources, it can be dangerous to assume that it is always the best option.
human_ingenuity  data  analytics  small_data  massive_data_sets  data_driven  information_overload  dark_data  measurements  creativity  judgment  intuition  Nate_Silver  expertise  datasets  information_gaps  unknowns  underestimation  infoliteracy  overlooked_opportunities  sense-making  easy-to-measure  Albert_Einstein  special_sauce  metrics  overlooked  defensive_tactics  emotional_intelligence  EQ  soft_skills  overquantification  false_confidence 
may 2015 by jerryking
Small Area Data
Environics Analytics 416.969.2733

"Due to changes in methodology, Statistics Canada is not officially releasing dissemination area (DA) data from the 2011 NHS. But we are"
neighbourhoods  data  analytics  Statistics_Canada  small_data  Environics 
may 2014 by jerryking
Small Data: Why Tinder-like apps are the way of the future — Medium
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The card-based UI updates the classic way in which we’ve always interacted with physical cards. When you think about it, cards are nothing more than bite-size presentations of concrete information. They’re the natural evolution of the newsfeed, which is useful for reading stories but not for making decisions.
++++++++++++++++++++++++
Cards are kind of natural choice for mobile screens because of their size and shape. But lay your cards on the table or put them on a board and they will also help you in revealing connections, understanding the topic and making decisions.
++++++++++++++++++++++++

every single interaction with card-swiping apps can affect the outcome.

We can call it small data. Imagine if every time you made a yes or no decision on Tinder, the app learned what kind of profiles you tended to like, and it showed you profiles based on this information in the future.

“With swipes on Tinder, the act of navigating through content is merged with inputting an action on that content,” says Rad. That means that every time a user browses profiles, it generates personal behavioral data.
bite-sized  Tinder  small_data  ux  design  decision_making  information_overload  behavioural_data  metadata  gestures  Snapchat  personal_data 
march 2014 by jerryking
Start-Ups Are Mining Hyperlocal Information for Global Insights - NYTimes.com
November 10, 2013 | WSJ | By QUENTIN HARDY

By analyzing the photos of prices and the placement of everyday items like piles of tomatoes and bottles of shampoo and matching that to other data, Premise is building a real-time inflation index to sell to companies and Wall Street traders, who are hungry for insightful data.... Collecting data from all sorts of odd places and analyzing it much faster than was possible even a couple of years ago has become one of the hottest areas of the technology industry. The idea is simple: With all that processing power and a little creativity, researchers should be able to find novel patterns and relationships among different kinds of information.

For the last few years, insiders have been calling this sort of analysis Big Data. Now Big Data is evolving, becoming more “hyper” and including all sorts of sources. Start-ups like Premise and ClearStory Data, as well as larger companies like General Electric, are getting into the act....General Electric, for example, which has over 200 sensors in a single jet engine, has worked with Accenture to build a business analyzing aircraft performance the moment the jet lands. G.E. also has software that looks at data collected from 100 places on a turbine every second, and combines it with power demand, weather forecasts and labor costs to plot maintenance schedules.
start_ups  data  data_driven  data_mining  data_scientists  inflation  indices  massive_data_sets  hyperlocal  Premise  Accenture  GE  ClearStory  real-time  insights  Quentin_Hardy  pattern_recognition  photography  sensors  maintenance  industrial_Internet  small_data 
november 2013 by jerryking

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