Categorisation of Big Data business cases and applications

There is always the question what are the business cases for Big Data applications. People keep on asking me when it makes sense to use Big Data applications. The answer is that there are many business cases for Big Data applications. I’ve tried to categorise Big Data applications based on their major fields. I found 5 fields that are relevant to Big Data applications: Social Media and Big Data, Big Data and Business applications, Smart Applications using Big Data, Big Data in the public sector and Big Data in Healthcare. This categorisation might not be complete at all and I ask you to send me feedback either by posting a comment here or on other media such as Facebook, …

Let’s first start with Big Data in Healthcare applications. The illustration below shows some typical use-cases for Big Data Healthcare applications.
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Big Data is helpful for insurance companies, since they can improve the premium. Insurance companies can analyse data and potential risks and adjust the premium to individuals. However, this is dangerous since it will definitely lead to discussions if this is ok to do or not (and I am not going to answer or discuss this in this blog post). Another huge field is the medical analysis.We already saw the human genome being solved by using large amount of data and distributed algorithms. With Big Data, it is also possible to figure out the problems individuals might get in touch with during their live. Risk management is another sector for Big Data applications. This focuses on regions or governments and not on individuals. This is useful when there are diseases in a specific region.
Now let’s focus on Big Data in public sector applications:

There are already some use-cases available in public sector. A very interesting Big Data case occurred during the 2008 and 2012 US Presidential elections. Nate Silver, a political analyst from the New York Times predicted each state correct (in 2012) on how people will vote. This showed us the importance of Big Data and Analytics in political campaigns. Politicians can optimise their campaign to figure out where they should go in order to win an election. Another use-case is the case of governmental services. There is a high number of data services offered by the US Government providing various datasets. These datasets typically cover various topics such as crime stats and many more. However, this requires Big Data technologies. Something that is well-known and useful for each and everyone is the weather forecast. All of us want to know when it will start to rain, how warm it will get and so on. With a lot of data we can improve this predictions significantly. There is also the possibility to work with environmental analysis and to see how the overall climate changed and how we can expect it to change in the future. Another important use-case for Big Data is about transports. Governments and Administrations can better plan transportation systems based on various datasets. It also gives us the chance to build intelligent traffic systems that react to higher load and starts to redirect traffic to lower utilised roads.
 

Another important Big Data field is Social Media. Large companies such as Facebook, Twitter or Linkedin use NoSQL-Databases and the create enormous amounts of data every day. The different sections where Big Data is important for Social Media is Messaging, Sharing, Likes and Interests and Advertising. Messaging was described by Facebook with their Inbox being done with Cassandra (and later on HBase). Users message each other a lot and this data needs to be persisted somewhere. But it is not only done by Facebook. Twitter and other Social Media platforms use messaging. Another field is sharing. This basically indicates tweets and shares on Facebook. Hash-Tags in Tweets can be considered as “interests”. On Facebook, this would represent likes. Social Media platforms also need to make some revenue at some point, which is basically achieved via advertising. Therefore, it is necessary to scan the data and find out what the user is interested in and how to target the user best.

There are also a lot of traditional fields where Big Data comes into play. We can basically call this “business”. Big Data can be done in financing. By scanning large amounts of financial data, it is possible to adjust financial strategies or improve them. I wrote about “Social Media marketing” earlier in this post. However, there are also a lot of possibilities with traditional marketing and Big Data. By analysing data, marketers can get better insights and possibilities with their buyers. CRM and ERP applications store a lot of data as well and e-commerce systems also require a lot of data. If companies have a lot of data available, they can also introduce a yield management. This would enable companies to better sell their products even on weak shopping days.

Last (but not least) I want to draw your attention to another field: the field of Smart Applications. Big Data is not only relevant to traditional applications but there are a number of applications that make our lives smarter. Whenever we talk about Big Data, velocity is mentioned as one of the “V’s” for Big Data. Real-Time Data and applications fulfil this approach. We get a lot of data from sensors and somehow this data needs to be processed. Another use-case for Big Data is fraud protection and transaction monitoring. A lot of business is handled via the internet nowadays and this needs to be checked if there is any fraud going on. Banks offer e-banking systems and transactions have to be monitored before they get processed. Location Services also deal with a lot of data – I recently had a chat with a researcher on location based services and he told me that each device or individual in a location has a cubic complexity – this leads to enormous data! Smart applications can also be used for disaster analysis or even prevention.
This categorisation is far away from being complete. Please leave your comment below to add your thougths and I would be happy to add them. I will also cite your thought. You can also mail me your thought or discuss it on LinkedIn/Twitter/Facebook. If you want to read more about Big Data, please make sure to subscribe to this blog!