Big data: Value Chain

This is an extension to my earlier article [4] on big data. In this short article I will try to summarize the authors [1] discussion on the business implications of big data. On a broad scale there are 3 types of big data companies that have cropped up and are differentiated by the value they offer.

First set is the data oriented companies or the Data holders:

These are the companies that have the data with/without doing much on it. Some companies’ main focus is to collect the data as much as possible. They are collecting humongous amount of data. Internet companies are collecting data about everything that happens at their site. From the content you viewed to the places you moved your mouse.

-Facebook collects data about people, their relationships, their likes, dislikes, photos, their locations, and posts.

-Amazon tracks the books that you browsed but did not buy.

-Companies like VISA and MasterCard collect all the credit card transactions for the different banks. This data is then licensed to other companies to make some sense out of it.

 But not all the companies are performing analytics by themselves. They might outsource this activity or license their data to others for them to make use of the data. Data acts as a raw material for the business.Twitter also does the data collection in the form of the tweets, but it generally let other companies to perform the analytics on this data, while Facebook tends to perform analytics on their own.

Second set is the Companies/Individuals providing the skills to Analyze data:

These are the consultancies, technology vendors and analytical providers who have the special expertise to perform the analytics. They generally don’t have the data of their own but clients provide them the data to work on it as a project. They may also have not thought about the usages of the data on their own. For e.g. Teradata performs analytics on WalMart retail data and make predictions.

Third set is the companies/individuals having the big data mindset to draw wisdom from data. They have the attitude to think about unique ideas of using data in ways that will unleash the potential value. These are mostly the startups like FareCast, PriceStats, SkyScanner. These companies might not even be asked by someone to look into the data. They may have done this all by their own using the open data (FlyOnTime) or by licensing the data (FareCast)

It is this mindset that makes the data to be used in ways other than what it was intended for. 

-The mobile geo locations are being used to display location specific ads to the user.

-The geo location is also being used in understanding the traffic congestions. The number of mobile users in the locality who are moving may well mean people driving vehicle. If the coordinates of mobile phones from a particular location is not changing within certain time it could mean traffic congestion. Google use this method to show the real time traffic updates on their Map service

-The traffic data can also be used to predict the local economies, retail sales. In case the traffic over a period of few days is lean in the business district of the city that means people are either not going to the office or they have suddenly become green.

-If the number of cars near the retail store is decreasing, that might be an indicator for lower sales and might be a tip for stock investors. More cars correlate to better sales. But this is a correlation that somebody has to work for to understand.  

-The income levels of the people in few African localities has been predicted based on the amount people are spending on charging their prepaid mobile phones

 In todays world we have the data in abundance, the big data technologies are also affordable and easily available, but the scarce part is the knowledge to extract wisdom from the data. The uses of big data are limited just by our imaginations [2]. In many areas we might see a demise of an expert whose decisions are mainly based on year-long experience, whereas newly emerging data analysts who often come from fields outside of the area analyzed will take over [3].

References

1. Big Data: A Revolution That Will Transform How We Live, Work and Think. Viktor Mayer-Schnberger and Kenneth Cukier, John Murray Publishers, UK,2013
2. https://www.youtube.com/watch?v=bYS_4CWu3y8
3. Thomas Dreier, Book Review: Victor Mayer-Schönberger/Kenneth Cukier, Big Data, 5 (2014) JIPITEC 60, para1
4. http://rohitagarwal24.blogspot.in/2014/10/big-data-pragmatic-overview.html