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Can Analytics help fight Ebola?
The issue facing many countries that are both directly and indirectly effected is, how can we prevent the spread of Ebola?
The use of analytics in crisis and natural disasters is not a new phenomenon. In 2010 during the Haiti Earthquakes a research team made up of staff from Karolinska Institute in Sweden and Columbia University managed to map the spread of Cholera by mapping out mobile phone data.
What is happening in Africa?
Orange Telcom has handed over data from 150,000 mobile devices to a Swedish organisation in order to determine where people are moving. BBC found that this allowed authorities to see where to best place treatment centers and plan where to restrict, and prevent travel.
Nalini Joshi is a Professor in the School of Mathematics and Statistics at the University of Sydney. She stated during her appearance on Q&A that,
”The latest mathematical models from the CDC show that if you can isolate or hospitalise 70% of the infected patients by December, then the epidemic will be over in January. “So, it gives you a measure of what you can do to finish, to make sure that the epidemic doesn’t become a pandemic across the globe.”
She went on to say,
”It leads to a decision-making process, where you have to decide what resources you need to be able to hospitalise the 70% of infected patients that are expected by December. So it leads to all kinds of other branches, how many volunteers should you be sending, how many blankets and gowns and all of that should you send? So it gives you a measuring tool. It’s a ruler for deciding how to make the action happen.”
So, what does this all mean?
At the end of the day analytics within disaster control is a tool that empowers authorities to predict and properly plan. By providing quantitative analysis that is supported by data it reduces the need for spur of the moment gut feeling. This initiative and innovation used by authorities shows how analytics really can be used everywhere, and can help with disaster control.
Is it time for you to start using analytics?
Is your decision making based on Analytics or Gut feeling?
Despite the proliferation of data and the constant drive to adopt innovation to achieve competitive differentiation; many businesses continue to make critical business decisions that are a product of intuition and haste rather than fact and rigor.
40% of major decisions are still based on your Manager’s gut feeling
The question as to why companies continue to rely on ‘best-guesses’, despite the availability of advanced business analytics, quantitative models and optimisation methods is confounding. Using data and quantitative analysis to support decision making, removes ambiguity and improves speed and accuracy. Decision making is more likely to be correct and the process has more rigor due to the application of the scientific method.
Thoughts on Point Solutions
Lately I have been thinking a bit about the advantages of small, tightly focussed web apps (so-called “point solutions”) that scratch a single little itch, versus larger, more powerful and general web apps that tend to deliver more of a total body rub. This question is of utmost importance to a company like Biarri that needs to place its development time and effort into the best channels.
The question was highlighted by a real-world problem a colleague posed recently: how to assign foursomes in rounds of Golf so that all of the players got to play with each other player at least once. It is not trivial to construct such a solution (if one even exists) by hand, if the constraints are “tight” enough (for example, 20 players and 8 rounds).
Small point solutions that solve a small but non-trivial problem like this might be fairly quick to develop and deploy on the web. But it doesn’t take much feature creep before you get a pile of extra “features” (particular requirements for some players, minimising the number of repeated pairings, right through to printing out score cards etc); before you know it (or more precisely, after months or years of hard coding) you’d have a full-blown Golf Tournament Scheduler. Such a web app might sell for much more, but would probably attract many less customers. And what happened to the poor casual golfer or golf tournament organiser on a shoestring budget who just wanted to solve his or her original golf player assignment problem?
In the spirit of acknowledging that the future is impossible to predict, I think Biarri must address more wide-ranging, lightweight “point solutions”, particularly at our fledgling stage. More mini-apps with a wider potential customer base will allow us to gauge which itches need the most scratching; more complex apps, as every seasoned developer knows, seem to always cause issues and problems – in short, sheer complexity – quite out of scale with the larger code line count; not to mention being harder to use and understand for users (more buttons!)
Those who have test-driven our Workbench solution will also know that, to some extent, we’re trying to have our cake and eat it to, by allowing these smaller “point” solutions to exist as workflows (standalone web apps) in their own right, whilst also being “nestable” – that is, able to be composited in a larger, more powerful workflow. Look out for Geocoding as a sub-workflow inside Travel Time Calculation, coming to the Workbench very soon. And who knows if the Biarri Golf Tournament Organiser will ever eventuate!
