"Twenty-five or 30 years ago, the thing that was differentiating companies was how well managed they are. But if you think about the companies that have just kicked butt in the past decade, they are deep-domain, technology-led, innovative companies—Google, Amazon, Apple. So I think this notion of, if the only common thread you have as an industrial company is the fact that you think you're well managed, you can still be a pretty good company, but you're not going to be a dominant company, a competitive company over time."
Jeff Immelt, GE CEO
Despite struggling with the pivots to mobile, cloud and search, Microsoft shares provided a decent return over the last ten years:
Certainly better than Blackberry, who lost their way:
But of course the main games in town were being played out at Apple and Google:
Jeff Immelt’s quote above was in the context of the investment they are making in software and in particular in the Predix data analytics platform. GE is targeting $15bn of revenue from the data analytics / software business by 2020 – or about 14% of 2014 industrial revenues. That may not sound much as a percentage of overall revenues but that is $15bn of high margin business that would have accrued somewhere else without a proactive step into the world of data analytics.
GE have realized that at major turning points in economic opportunity, the returns do not accrue evenly and they had no choice other than to be in the game.
Will your company be a Blackberry, a Microsoft or a Google of the data world in ten years’ time?
As we’ve reached out to players in the data world in our marketing of dmi.io we’ve developed a ringside view of the changes taking place. There’s a new breed of data businesses popping up, and they are challenging longstanding assumptions around how data is gathered, distributed and consumed. Innovative new ways to collect data (devices and sensors, crowdsourcing), cloud based storage, and interactive, demand-driven API consumption are catering to the needs of a new set of customers (people and machines) who want to pay for only what they need and seek multiple sources of data to test in their algorithms.
Let’s look at a couple of examples in two different sectors – Real Estate and Energy. Compstak employs a crowdsourced model to gather commercial real estate information for investors, brokers, asset managers and appraisers. Participants can contribute comps in any format they like, and Compstak applies data science techniques to add value to that raw data and create meaningful insight into the comps that matter to a researcher or potential buyer. If you’re a data provider in the comps market, how are you responding to this?
In the Energy Sector, AutoGrid is applying big data innovation to take advantage of the terabytes of data being collected from smart meters, to provide energy companies with improved tools for forecasting power use and distribution strategies, amongst other use cases. It takes data-driven decision making for the Energy industry to the next level with real time information and advanced analytics.
Focusing on the next set of customer problems you would like to tackle, and understanding the way in which new emerging capabilities could play a part is a good starting point for assessing the threats and opportunities in your current data offerings. How would you customers or your internal processes benefit if your data was:
- closer to real-time?
- embedded into your customer’s workflows?
- priced on a usage rather than subscription basis?
- available in the cloud?
- Inter-operable with other third party data?
- enhanced with new collection mechanisms (sensors, crowdsourcing etc)?
- supplemented with predictive analytics?
This is a challenging environment to think about, and ensure that your company is not a footnote of history in ten years. The good news is there is time to do so now, and abundant opportunity for the brave! That’s one of the emerging opportunities we identified as we developed the dmi.io concept. dmi.io provides a marketplace and platform where you can start to experiment and innovate at low cost – for example run some experiments on pricing, bundling or new data analytics. In addition we’re using our marketing dollars to help you find new buyers of your data.
I’d be happy to spend more time sharing some of our insights in the emerging world of data, and how dmi.io can help you. Just drop me an email and we’ll set up some time.