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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.
By Dimitrios Spiliopoulos

Lesson 3: Think big, start small, fail quickly (learn) and scale fast.

We need miniature versions of our grand idea so we can validate its parts, and then iterate and tweak constantly. We see quite often that big corporates think big, but then plan and prepare for years until they launch the first product in scale. This approach can have several risks since the technology and trends change quicker and the competition (especially from startups) is moving faster. Disruption is coming from many sides and it is quick. Besides, if we don't start small, we will not be able to receive the valuable feedback of the market, adjust our products and decide which we will finally scale. The "start small" tactic helps also in engaging with internal stakeholders and keeps their interest and commitment high.

Analogy with data analysis: In the beginning, you need to test samples of your data quickly, without affecting the whole datasets. You need to fail and learn quickly, try again, leverage old lessons and finally find the answer to the question you are looking for.

Lesson 4: Break the silos of the company's departments and data.

When we plan to start designing and later executing an IoT project, the senior leadership team needs to ensure (or at least try its best to ensure) the collaboration, support and involvement of stakeholders from different departments (IT, IoT, finance, operations, logistics, customer support, marketing and so forth), in order to use the expertise of different fields and guarantee the continuous commitment of all departments. Otherwise, we may see the example of a single team being the project leader without any other department to care about it, as though it were not their job or responsibility. This attitude can be catastrophic for any IoT project, especially for the big ones. Moreover, try to keep regularly engaged in the process with all relevant stakeholders, with updates, workshops, small deliverables and more.

Analogy with data analysis: We need the same approach regarding the data. Each department ideally needs to have the data in the same format and provide it easily and in a secure way to the analysts, so they can integrate it and play with it. Integrating data from different departments and data sources could create insights of which the company had no idea. In some cases, it can be even a game changer.

Lesson 5: Explain the data with storytelling.

Merely collecting data from sensors or internal systems and later integrating all these datasets is not enough. The data needs to be analyzed and then presented in a simple way, in the right context and in an attractive format. The best way to achieve this is by using the effective method of storytelling, combined with proper visualization.

To be clear, I am not referring to the dashboards and capabilities of the IoT analytics platform. Rather, I am focused on the importance of how humans (analysts, platform users and so on) read and describe the results of the analyzed data. Using the storytelling method, most of the employees, decision makers and customers will be able to understand and use the results. Using results in the right way is the most important goal, but unfortunately it does not always happen (employees may become lost in many Excel files or insights without context).

Analogy with data analysis: The big-data team (or teams with similar names) needs to be able to transform the data to information, and the information to actionable insights for the rest of the business (or customers). Insight is more than just summary—it is those hidden patterns in the data that cannot be easily seen, but a good data analyst can find them and communicate them effectively with the right storytelling and visualization.

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