Ok, lets pick right up from where we left off in our last post and dig into buckets three and four from our business value use cases for big data.
Our third big bucket of value from big data projects was focused on driving real business value by more effectively predicting outcomes. Again, the key to this bucket, like the others, is that today, companies have a much different and more effective way of bringing disparate data sources together and extracting some kind of signal from all of the noise. I mentioned GE in my previous post as a good example of driving value in this space. They have committed billions of dollars over the last few years to become a software company because they see the business opportunity in front of them with being able to predict when machines will fail. Much of their marketing has gone towards talking about airplane maintenance or optimizing monsterous windmill farms in the middle of the ocean. I think the common marketing pitch they give these days is that by better diagnosing problems with machines up front and eliminating downtime, there are trillions of dollars to be realized across the industrial machine space. Yes, you read that right, Trillions.
But, predicting outcomes doesn't need to be focused on such a large class of assets or within a set industry to be valuable. Using data to better predict outcomes really cuts across all industries and across all lines of business within an enterprise. IT organizations are using predictive analytics to determine how best to optimize their hardware and software to save costs in their data centers. Security teams are using predictive analytics to help find Advanced Persistent Threats within a network and cut off hackers before they even get started stealing data. Sales organizations are using predictions across diverse data sources to more effectively target prospects that have a higher likelihood to buy. Marketers have been using predictions for years to make more personalized offers to customers when they are checking out online, think "People who bought this item also bought.....". As we move into the future, marketers are getting ever more creative with big data and using predictions to make even more personalized offers across multiple platforms. And Customer Service leaders are using predictive analytics to more effectively match call center agents with customers that are calling in and have a particular type of personality, class of the problem or recent their activity.
Our third big bucket of value from big data projects was focused on driving real business value by more effectively predicting outcomes. Again, the key to this bucket, like the others, is that today, companies have a much different and more effective way of bringing disparate data sources together and extracting some kind of signal from all of the noise. I mentioned GE in my previous post as a good example of driving value in this space. They have committed billions of dollars over the last few years to become a software company because they see the business opportunity in front of them with being able to predict when machines will fail. Much of their marketing has gone towards talking about airplane maintenance or optimizing monsterous windmill farms in the middle of the ocean. I think the common marketing pitch they give these days is that by better diagnosing problems with machines up front and eliminating downtime, there are trillions of dollars to be realized across the industrial machine space. Yes, you read that right, Trillions.
But, predicting outcomes doesn't need to be focused on such a large class of assets or within a set industry to be valuable. Using data to better predict outcomes really cuts across all industries and across all lines of business within an enterprise. IT organizations are using predictive analytics to determine how best to optimize their hardware and software to save costs in their data centers. Security teams are using predictive analytics to help find Advanced Persistent Threats within a network and cut off hackers before they even get started stealing data. Sales organizations are using predictions across diverse data sources to more effectively target prospects that have a higher likelihood to buy. Marketers have been using predictions for years to make more personalized offers to customers when they are checking out online, think "People who bought this item also bought.....". As we move into the future, marketers are getting ever more creative with big data and using predictions to make even more personalized offers across multiple platforms. And Customer Service leaders are using predictive analytics to more effectively match call center agents with customers that are calling in and have a particular type of personality, class of the problem or recent their activity.
Finally, big data has real value in a category I explained last time as "plumbing". While no where near as eye catching or interesting as these other kinds of use cases that drive business value, updating the "plumbing" can be of tremendous value to many organizations. In fact, a solid place to start a big data program can be a use case as mundane as just moving away from a traditional Data Warehouse approach to storing/operating on data. These traditional approaches can be incredibly expensive and can be incredibly frustrating to use/maintain for generating reports across the business. The big data alternatives seem to be able to leverage alot of the same tools that are user facing, but put in place a much more innovative back end infrastructure that allows companies to significantly reduce their costs for the data warehouse technology and help to speed up processing, or creating reports, by orders of magnitude.
I know that was a lot to ingest and consume all at once across these two posts. But I think it is important for business people to understand that all of the hype you may hear about Big Data or Hadoop etc... has real legs and real value behind it. The dirty little secret, well not so much a secret anymore, is that the real challenge with big data programs is less about determining outcomes to focus on and more about the complexity of the technology itself.
But we will get to that more in depth in one of the upcoming posts.
But we will get to that more in depth in one of the upcoming posts.
As always, please feel free to comment and share your experience with big data programs and their associate value for your company.
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