IBM’s Big Bet on Big Weather Data: A New Data Strategy
The move by IBM to acquire the Weather Channel is a great example of how IBM is transforming Big Data to Big Profits. In the above figure, IBM plans to predict hail storms and help car owners and insurers avoid expensive and often avoidable losses. It is a great idea of how Big Data can help insurers manage risk with a new paradigm for intervention and prevention. It also shows that IBM is on a quest to become a data company and sees its future revenue streams tied to monetizing Big Data. In an environment where Google has acquired a host of data-creating firms of note, including Nest and Waze, it should be of little surprise that IBM feels a need to do the same. However, the marriage of IBM and Weather Channel is very interesting and offers a great lens into how data firms are positioning themselves to create new data products and thus new revenue streams. It also shows how IBM and Google are taking very different data strategies.
Buying Firms for Data
In the case of Google, it has a great focus on creating data at the individual user level. Combining Google user data with Nest or Waze data offers a compliment, leading to a completeness of what the individual is doing. For IBM, its business is (and mostly has always been) IBM to big businesses. Individuals can currently get free weather reports from the Weather Channel and many other sources. Giving away weather data will not be part of IBM’s plan to monetize the Weather Channel. Many are left wondering, what can IBM do with all that weather data?
Data Products for Retail
Consider a large retailer like Wal-Mart, Target, Costco, or even Starbucks with hundreds or thousands of outlets. How does Costco or Target determine the impact of a snowstorm in the Midwest on sales or plan for an El Niño weather year in North America? How does management at Target compare same-store sales between a wet spring and a dry spring the year before? Currently, they hope that stock analysts understand and appreciate that people shop less when it rains a lot. But to what level can that impact store level revenue? In a down year for revenue, is it because of weather or eroding market position? Can Starbucks better understand store-level beverage preference with weather data? I think so. Combining Weather Channel data with IBM’s immense data science skills through IBM Watson offers a great opportunity for businesses to learn about and prepare for the impact of weather on their sales. Answers to these previous questions will now be achievable. Firms will pay IBM for such customized solutions, on a regular basis. Correcting for same store sales, given local weather conditions might even allow IBM to create data products used to manage stores each day! It is a great business model for monetizing weather data.
New Weather Forecasts for Supply Chains
For logistics firms, the possibilities are even more powerful. Anticipating adverse weather will help not just airlines, but the likes of UPS, Fedex, the USPS, and train lines. Delays cost money in today’s world of just in time delivery. And the impacts amplify, as you get further down the supply chain. IBM’s use of weather data can well help supply chain operators and logistics firms prepare better, allowing customers down stream to better prepare for weather-related shocks. It will also require a close linking of weather effects on specific travel operations. Again, this combination of business data is right in IBM’s wheelhouse.
Micro-management of Water
Agriculture, especially in California, is under great political and public scrutiny. Using sensors to apply water more precisely and using weather data to monitor for local rainfall will allow big agribusiness to manage water with greater precision. However, that takes data and algorithms to get the decisions on when to irrigate correct. IBM has the data and analytics to make that happen.
Better Power Generation Management
Power production in the US is highly driven by our desires to be comfortable, especially in the summer months with our air conditioners run. Seemingly small improvements in operations at power plants have huge benefits to the power utility’s profit. Optimizing power generation to weather and building more precise weather models would be helpful in this industry.
Clean the Roads, Please!
Municipalities are all under cost pressure. In my resident Illinois, it seems towns regularly can’t get the road salt order right. It seems the towns underestimate the need for salt. Could it be wishful thinking? In the end, the roads don’t get cleared or the towns pay huge fees for extra salt in February. How about taking the decision of salt ordering away from “the experts” and allowing an algorithm to evaluate the impact of continental weather patterns and truck salt usage, and along the way help towns across the north save precious money. Here is another great example of how IBM can leverage big weather data for big benefits.
Leveraging the IBM’s Strengths
Plus, IBM has relationships with many large firms already, so developing new data product offerings around weather data will be easier than it would be for a company like Google or Apple that is focused on the individual user. IBM also has Watson and a deep bench of talent for developing customized analytical solutions.
Expect to see a lot more data products offerings from IBM, all tailored to help businesses make the most of Big Data. Also, expect a lot more acquisitions of firms that have amassed Big Data.
These ideas on the importance of data products and data fusion to create value from Big Data are some of those that I explore in great detail in my recent book, From Big Data to Big Profits: Success with Data and Analytics (Oxford, 2015).
About Russell Walker, Ph.D.
Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the Kellogg School of Management of Northwestern University.
His most recent book, From Big Data to Big Profits: Success with Data and Analytics is published by Oxford University Press (2015), which explores how firms can best monetize Big Data. He is the author of the text Winning with Risk Management (World Scientific Publishing, 2013), which examines the principles and practice of risk management through business case studies.