We’ve been talking a lot about machine learning recently. But not simply because it’s a hot topic these days (though it is!). But because it’s an approach that’s adding real value at both the consumer and enterprise level. How? By making the applications we use at home and at work smarter and thus most useful.
We’ve explored a lot of the ways machine learning is being used today. We’ve also just announced our approach with our solution combining prescriptive analytics with machine learning to improve decision making in the facilities world. What’s particularly interesting is seeing how research firms like Blue Hill Research see machine learning powering the next wave of digital transformation and enterprise data.
Advancing Enterprise Data Deployments to Machine Learning
Its premise is that companies go through three phases, what they call Commodity Storage, Self-service Everything (where most companies are) and Machine-learning Ubiquity. This is all highlighted in the recent report, Beyond Self-Service: How Machine Learning Drives Enterprise Data’s Third Wave, where it highlights “how innovative enterprises are using machine-learning enabled technology…to accelerate data flow, shorten communication spans, empower line-of-business stakeholders and deliver greater bottom line value…”
The Commodity Storage phase was based on the availability of low cost storage allowing for more data and scalable environments. These big data and data warehouse deployments supported access to data but often were hindered by antiquated and centralized workflows. These processes ended up “cumbersome, slow, reactive, costly, prone to error and tediously linear.”
Such problems led next to the Self-service Everything phase where data (was supposed to) become more immediately available, “offering faster time to insight and the promise of faster/better decision making.” These new data tools and technologies certainly bring benefits to many companies as those closer to the business have greater access to relevant and applicable data. As we’ve seen across our customer base, this can bring newfound visibility across operations.
source: Blue Hill Research
The challenge at this stage is getting tied to existing data sources and constraints with consuming burgeoning volumes of data from internet enabled devices (e.g. the Internet of Things). How can enterprises build on their existing data and move from seeing what happened to impacting what they should do and make happen?
Machine Learning Powered by Data and Analytics
This is where Blue Hill sees the next step with Machine-learning Ubiquity: “where automation, artificial intelligence (AI) and machine-learning workflows combine to move data closer to where it offers the most immediate value: the point where it can be acted upon.”
The Engine for Better Decision Making
As Blue Hill details, the underlying Decision Engine model “takes customer data, prepares it and makes customized recommendations based on historical behavior and learning algorithms…the recommendation engine is already charting where proposal amounts appear relative to benchmarked bell curves, and it also proves quick access to relevant historical transaction data”
This approach that combines prescriptive analytics with machine learning technology puts new powers in the hands of decision makers by using their existing data to let them make smarter decisions. And a powerful element is that the model gets smarter over time, so recommendations continue to get better.
What machine learning does, as we say as well, is stop the reliance on guesstimates and gut decisions and instead rely on “data-informed action.” Blue Hill believes enterprises need to “move data as close as possible to the point of action.” And that’s what Decision Engine does, by delivering data-informed accept/reject decisions directly to those responsible for making such decisions.
Cool Tech? Yes, But Driving Enterprise Change
As the report highlights, “ServiceChannel’s experience with GoodData illustrates how an enterprise can use machine-learning to speed time to insight, reduce risks associated with manual processes and ensure workflow dynamism…to get data closer to its customer decision-makers, applied machine learning algorithms in a recommendation engine, and created a new data-workflow model to automate its customer processes.
Blue Hill concludes, “But ServiceChannel’s success isn’t just about cool technology in practice. The technology enables new customer engagement. But it’s vision and commitment that drove enterprise change.”
We’re excited about being the only facilities management software provider to bring machine learning to the sector, in an impactful and powerful way. This is just the first step in how we see this technology advance facilities managers to truly being a 21st century profession.