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Seven Lessons for IoT and Data Teams to Remember in 2018
Data and connected products and services are two sides of the same coin.
Mar 07, 2018—
We often hear comments like "Let's make our existing product connected, and later we will find the way to make money!" or "We need more and more data so we can solve business problems." Is this approach enough? Starting the thinking process from connecting the products and collecting huge amounts of data?
Last week, I visited the Customers Insights and Analytics Exchange conference, where many data and insights experts (heads of departments) presented their experience within their organizations, the challenges they face and the lessons they learned. I was impressed to see how many similarities there are with the challenges that IoT adopters face. For this reason, I was inspired to write about some lessons for both data and Internet of Things (IoT) teams, which are like the two sides of the same coin (data and connected products/services).
Lesson 1: Start from the problem or need you want to solve, not with the solution, by simply aiming to make your product connected.
Many companies start developing connected products without conducting the necessary analysis of their customers, their business models and the potential challenges. The driver for their decision sometimes is just what the competition is doing or what is "cool" so they can create some buzz around it with good public relations. Apparently, these are not good enough reasons. If you don't ask the whys, there are many chances to end up wasting money, time and effort without seeing the impact in your revenues and profits.
Analogy with data analysis: The same is true for data analysts, who may start analyzing huge amounts of data without knowing first what they are looking for. What is the big question you are trying to answer? The advice here is, again, to start from the problem you want to solve, not from the data.
Lesson 2: Prioritize the implementation of IoT projects based on the impact on your bottom line in both the short and long terms.
Everyone has good ideas, and the technology has progressed so much that companies these days have the chance to make their ideas real. However, considering how rapidly trends, competition, technology and customers change, it is critical to prioritize the most impactful problem you want to solve, and to then decide which IoT project you should implement. Choosing, for example, the most economical, easier or coolest project is not often the best idea. Define first your criteria (long-term impact, competencies, integration with existing solutions, etc.) and then prioritize.
Analogy with data analysis: It is the same for data analysts. Which question is worthy to solve first? Where should you focus your time and energy?
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