Data and connected products and services are two sides of the same coin.

Lesson 1: Start from the problem or need you want to solve, not with the solution, by simply aiming to make your product connected.

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.

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?

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.

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.

Lesson 6: Empower, train and give exciting problems to your IoT star employees so you can keep them during 2018.

The IoT is quite new, so companies pursuing Internet of Things and big-data strategies are finding it challenging to recruit the right talents with a comprehensive understanding of data, telecoms, software, commercials, strategy, etc. For this reason, it is important that a company invest continuously in the training of its employees, especially in the areas of data, business and technology, so they can have a broad understanding of IoT applications and their implications. Otherwise, not only will the company continue hiring workers who lack the modern skills a competitive business environment requires, but it will lose also its best talents. Both results for the company are enough to forecast a future with limited potential for success.

Lesson 7: Continuously apply all of the above six lessons.

The above lessons need to be remembered for a long time, since IoT and data projects are long and evolve gradually. If we apply these tactics in the beginning, but forget these lessons after six or 18 months, it is very easy to end up making some big mistakes or losing good opportunities.