At some point, you have to cost-justify your analytics project. “We know we need this, but we have to make the case to the accountants.” Sound familiar? You’re not alone.
You are making a decision to make better decisions with data, and those decisions need to make financial sense, otherwise you shouldn’t make the decision. While you cannot know your full ROI until you implement, making data-based decisions is better than whatever you are doing now.
If you are a TL;DR kind of person, here you go: It is less expensive than you think and it is measurably more valuable than you imagined. So if you just Googled “IoT analytics ROI Analysis” for your project, this post is for you.
Let’s start with some stats. 70% of IT decision-makers consider their organization’s ability to exploit value from big data as critical to their future success. That’s what a recent Cap Gemini report found. Also, 64% of respondents said that “big data is changing traditional business boundaries and enabling non-traditional providers to move into their industry.”
Because of this, 27% of those companies face competition from adjacent industries and 53% expect to face increased competition from start-ups enabled by data. 56% of companies plan to increase their investment in data over the next three years. Lastly, 65% of companies agree that if they do not embrace big data, they risk becoming irrelevant. 77% said decision makers increasingly require data in real time.
A recent Gartner report found that by 2017, more than 20% of customer-facing analytic deployments will provide product tracking information leveraging the IoT. By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.
A 2011 IDC report sponsored by IBM concluded that a typical ROI for predictive analytics projects was about 250%, while nonpredictive analytics had an ROI of 89%. In 2012, Nucleus Research reported that across 60 analytics-related ROI case studies, every dollar invested in predictive analytics, business intelligence and performance management products resulted in a gain of $10.66; an ROI exceeding 1,000%.
Your mileage may vary, but simply put, we are living and working in a data-driven world and having better data and a better plan to use it gives you an edge over your competition. In the IoT space, because products are connected by definition, data needs to be a cornerstone of company strategy if you want to win. The following are some tips to consider when making the business case for IoT analytics.
Start with a simple build vs. buy.
If you make connected products and you realize that there is tremendous value in the data those products generate, consider what it would cost to build out the infrastructure internally. Data scientists are in demand and they are expensive.
The McKinsey Global Institute predicts that by 2018 the United States alone could face a shortage of between 140,000 to 190,000 people with deep analytical skills, as well as a shortage of 1.5 million managers and analysts who know how to use the analysis of big data to make effective decisions. Even a small team could easily represent $2mm or more just in annual salaries, then there is the time to build a custom platform with the required high-availability cloud-based real-time properties, and you’re looking at another couple million dollars at least, additional development resources and at least a year and more like two years of time.
Compare whatever hard costs you come up with to a SaaS-based platform that is easy to deploy and infinitely scalable and you’ll likely find that buying your IoT analytics platform from a reputable vendor is more cost-effective by an order of magnitude.
Next look at cost reductions.
What are you doing that costs a lot of money that isn’t your core business? let’s say you make refrigerators. So why is your customer service expense so high? When a customer service rep has to sit on the phone and say, “Yes, ma’am, I understand. Can you please describe the sound your refrigerator is making?” Those calls are going to be long and expensive.
Consider where in the delivery of your company’s value you can use analytics to reduce the cost of non-core functions. In this example, have the refrigerator talk directly to your IoT analytics platform on a constant basis so that when something out of the ordinary happens, you can instantly and algorithmically respond to the problem without spending precious and expensive time and resources.
Generally, anywhere information is exchanged presents an opportunity for computers to do it faster. If the information is product-related, a robust IoT analytics platform can do it faster and better.
Then look at revenue opportunities.
There seems to be a general understanding that having more data and being able to mine it for insights yields a certain “soft” benefit. That may be true if you consider product usage data informing you how to make a better product in the future a “soft” benefit.
Semantics aside, there are certainly more concrete examples of revenue opportunities, and these are generally the ones that weren’t there before. For example, if you make a product and those products tell you when they break (it happens) and you are the first to know, even before your consumer, then you can sell that information in the form of a repair request to X number of qualified contractors and whoever calls first gets the repair contract.
You just identified a service revenue bucket for your company that wasn’t there before, and the cost to generate that revenue stream is simply knowing what your own products are doing. You smartypants, you.
The bottom line:
A good product is the foundation of a successful IoT product company, but an IoT analytics platform is the cornerstone of your competitive advantage.