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).
Lessons for IoT Teams and Analogies in Data Analysis Teams
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?
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.
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.
In addition, empowerment for training and career development are important to keep the workforce motivated and inspired. Considering the lack of IoT talents, it is a good idea for a human-resources team to try to match business needs with the interests and career goals of its ambitious employees. Otherwise, it will be difficult to keep the best talents in an organization for a long time.
Analogy with data analysis: Analytics expertise is in particularly short supply, and it’s expected to become scarcer as the field expands. For many companies in the IoT sector, the key hire is often the data scientist or data architect. Therefore, continuous empowerment, training and involvement with exciting but difficult problems could help to increase the data employee retention rate.
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.
Dimitrios Spiliopoulos is passionate about the IoT and new technologies, and has earned an MBA degree from the top-tier IE Business School in Madrid. Currently working as a contractor for the IoT team of O2 (Telefonica UK) in London, he previously worked for the global IoT team of Vodafone. Dimitrios is a mentor at IoT Startupbootcamp in London, IoTUK and Reload Greece. He helps IoT teams to develop new propositions and partnerships, as well as companies to start their IoT journey and create great value for their customers through new services, business models and connected products. Feel free to connect with him on LinkedIn.