Matt: What are some trends that CIOs and CTOs should be thinking about as we enter 2018?
Bill: Data monetization. Organizations need to understand how effective they are at leveraging data and advanced analytics (deep learning, machine learning, artificial intelligence) to power the organization’s business models. Organizations have spent the past couple of years gathering all sorts of detailed internal and publicly-available data. Now’s the time for these organizations to start monetizing that data.
Matt: What are some current and new risks corporations should be aware of when entering 2018?
Bill: I think the biggest risk organizations face is the risk of doing nothing. Too many organizations are waiting for their favorite enterprise software vendor to solve the big data and advanced analytics problem for them. But the smart organizations – those organizations that are leading the digital transformation process – are those organizations who understand that you "buy for parity, but build for business differentiation."
Matt: What were some most common mistakes you've seen with data strategies of large companies in 2016 and 2017?
Bill: Too much focus on gathering data and not enough focus on developing a strategy for monetizing the data. Organizations do not spend time and effort on identifying, validating, vetting and prioritizing the business and operational use cases. Organizations need to improve their ability to leverage advanced analytics to uncover new sources of customer, product, service, operational and market insights, and how those new sources of insights can be used to optimize the organizations key business and operational use cases.
Matt: What was your biggest personal success in 2017?
Bill: Completion of our research paper with the University of San Francisco on “How to determine the economic value of data”. The research paper has lead to all sorts of interesting conversations with business executives. Here’s a link to that research paper.
Matt: I've heard financial and transportation companies are beginning to invest more in BI, can you speak to the types of industries that find your services the most valuable?
Bill: I can’t speak for Business Intelligence (BI) as BI has been around for decades now. Everyone likely already has a BI solution that reports on what happens. Today the real interest – across all industries – is to leverage Advanced Analytics (deep learning, machine learning, reinforcement learning, artificial intelligence) to predict what’s likely to happen, prescribe corrective actions, and prevent costly or dangerous outcomes. For companies with a strong BI biases, I ask them what’s more important to the business: reporting or predicting/preventing? The answer is usually self-evident.
Matt: What has been the most difficult challenge you've had in 2017, and how did you deal with it?
Bill: Ankle surgery. LOL! Seriously. I’ve been unable to travel for almost 8 weeks now, and the best part of my job is working with customers to help them pull together a business strategy that exploits the economic value of data and advanced analytics. All of my best perspectives come from conversations with customers.
Matt: What does it take to have a sustainable data strategy in terms of technology, processes, and people? Where do you think most companies go wrong?
Bill: Not all data is of equal value, and value is really determined in how the organization is “using” the data to make better business and operational decisions. Organizations must start by understanding and validating their top priority business and operational decisions or use cases. If I know my top use cases, then I can figure out what data and advanced analytics I’m going to need to support/optimize those use cases. Advanced Analytics support the “business monetization verbs" such as improve, increase, reduce, mitigate, prevent, eliminate and optimize.
Matt: Thanks Bill!
Here's another video of his I highly recommend. Bill goes into some of these topics in even more detail, especially the importance of knowing the value of your data.
His methodology, ties the value of data to specific use-cases. He used an example with Chipotle, showing how comparing 3 or 5 data sets (event marketing, new product effectiveness, cross-selling, etc). If Chipotle wanted to same store sales by 7.1%, that's worth $190M, and that's a basis for valuing the data.
In order to value where to invest analyzing your data, tie it to use cases. Then you'll know what data to protect, enhance, or to simply put in cold storage for later and ignore for now.
To see more from Bill, check out his blogs here.
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Originally posted at: www.mattholmes.io