One of the biggest questions surrounding ‘analytics’ and ‘big data’ is how do you actually use it to add value? Big data is still such a foggy term that it attracts many myths ranging from ‘big data is not useful to anyone’ to ‘big data will help us solve all our problems’ – like with most things, the truth can be found somewhere in the middle.
Any classification of how big data can be effectively used to add real benefits is valuable. It will help to lift some of the fog surrounding big data and allow us to have more structured thoughts and conversations about the topic. I have recently come across IBM’s top five ‘high value’ use cases for big data.
It is worth looking at these to see whether they make a sensible starting point, so here they are with my comments:
Find, visualize, understand all big data to improve decision-making. Big data exploration addresses the challenge that every large organization faces: information is stored in many different systems and silos and people need access to that data to do their day-to-day work and make important decisions.
I would like to add: This is a ‘one-size-fits-all’ category that could include anything. The key point is that companies can delve into existing data repositories and transactions using big data techniques. This would also enable them to bring together data from different systems such as financial transactions, operational quality data, HR data, supplier information, etc. that is stored in different places or organizational silos. It enables companies to create a more complete picture and gain new insights from looking at all the available data. One example is corporate email and newsletter provider Constant Contact – they are sending out over 35 billion emails for their clients per year. Applying big data techniques is giving them valuable insights into the performance of these emails e.g. when to send them, how often, what subject lines work best, etc. This helps Constant Contact to optimize performance and to provide feedback to their clients.
Extend existing customer views by incorporating additional internal and external information sources. Gain a full understanding of customers—what makes them tick, why they buy, how they prefer to shop, why they switch, what they’ll buy next, and what factors lead them to recommend a company to others.
I would like to add: Here companies use big data analytics to understand and better engage with customers. Examples would include telecom companies that use the data from phone records as well as social media behavior to create better pictures of customers.;
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