Journey into IoT
Once upon a time, technology drove business processes. This was necessary and good, because technology was limited and in order to take advantage of all that was new, businesses needed to adapt to the available technology. And so they did, gaining reports and metrics and trends.
Today, reports and metrics and trends are no longer our destination, but rather waypoints on our journey toward a better treasure: business value. IoT doesn’t have to be limited to providing the same sort of details we’ve always had. Instead, we can gain insight and predictability. More than this, we can provoke action, automating processes and accelerating solutions. IoT can now become a platform of insight, connecting assets and their data to people and processes. The result? Business outcomes that yield value and organizations that are no longer siloed. It’s as if you’ve kissed the frog: what were once separate, competing departments and systems have transformed into a handsome, healthy, big-picture thinking ecosystem.
To get from silos to an ecosystem, it’s going to take more than a kiss. It’s going to be a journey. Before setting forth, you need to check your mindset. Stop thinking about the information you can gather from your assets as your destination. This is an “output” mindset. Instead, develop an “outcome” mindset. Start with business goals, and then pursue the technology, processes, and people who can contribute to achieving those goals. In this mindset, data graduates into insight and insight stirs cross-departmental action.
IoT can provide insight, graduating into predictive and prescriptive action
Once you’ve adopted the outcome mindset, you’ll need to develop the map by selecting and prioritizing use cases. Key players from all relevant departments should consider both the value and degree of difficulty of the array of possible use cases, and then agree on the first steps and overall route. Including people from multiple disciplines and departments will help break down silos and build up the ecosystem mentality.
As your organization plans, conversations will begin to address barriers to success. Some of these barriers will be perceived, others will be real. Data and data science are at the center of strong IoT solutions, so while there are many barriers, we’ll look at several examples dealing with data.
A common perceived barrier is “We don’t have embedded technology in our assets.” This is a perceived, but not real barrier because you don’t need to start with embedded technology, you need to start with data. Data is available from other sources: engineering specs, maintenance records, and warranty information are just a few examples of existing sources that you can put into play before moving toward embedded technology. Also, you may be able to add external sensors for additional data like temperature, humidity, and vibration.
Often, a real barrier to implementation is data itself. If it doesn’t exist, that’s a barrier. If it exists, but it is unusable, that’s a barrier. You may need to spend time considering your data management strategy, including data cleanliness, as one of your initial steps.
A second common, and real, barrier is too much data. Data costs time and money to process. Only process the data relevant to your use case. A good data scientist can help you find the small, essential data within the mountains of potentially useless data. This can make the difference between a high cost low value solution and a low cost, high value solution.
One final note on barriers: you may be tempted to view this as a project. It’s easy to fall back into old habits, investing long periods of time and lots of money to roll out something grand, with a delayed ROI. A well-thought out IoT strategy should be executed in short sprints, taking proof of concept to production in very short periods of time. This allows you to take appropriate and wise risks, quickly introducing new capabilities, all the while reevaluating the path by asking new and better questions. This will encourage reinvestment in additional IoT sprints.
Once upon a time, technology drove business, but today it is necessary and good to embrace business goals driving technology. IoT can provide insight, graduating into predictive and prescriptive action. Godspeed on your quest.
Summary of actionable steps for your journey:
1. Don’t begin by considering what you could know (output) and instead determine what actions would further business goals (outcomes). Prioritize goals by considering the value they’ll bring to the company and their level of complexity. Use short sprints and a fail fast approach to obtain short ROI.
2. Don’t wait to start until all of the embedded technology is in place. Instead begin by discovering the treasures in the data you already have. Consider adding external sensors as well as additional outside data sources (third party weather data, for example).
3. Gain insight from data. Identify what kind of data will help you achieve your identified first step outcomes, making sure you’re strategically managing data and cleaning up data. Automation may play an important role here.
4. Use an IoT platform to move all siloed information to an information ecosystem, in which all departments are able to see the consistent data insights. Use case studies and testbeds with vested groups will help generate both ideas and buy-in.
5. Determine actions that need to be taken, and find the solutions leading to your stated goals. Consider technology, processes, and people.
6. Be sure to have people evaluating the data insight to discover if there are new or better questions that need to be asked.