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6 Lessons for Data and IoT in 2018

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6 Lessons for Data and IoT in 2018

With organizations starting to embrace IoT and data-based initiatives at a large scale, let's see how you can position yourself for success.

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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 at 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 their lessons learned. I was impressed to see how many similarities there are with the challenges that IoT adopters face. For this reason, I got inspired to write about some lessons for both data and IoT teams — which are like the two sides of the same coin (data and connected products/services).

Lesson 1: Start With a Problem

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

Many companies start developing connected products without doing the necessary analysis of their customers, their business models, and potential challenges. The driver for their decision is sometimes just what the competition is doing or what is ‘cool’ so they can create some buzz around it with good PR. 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 revenue and profits.

Analogy With Data Analysis

It's the same case 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? So the advice here is, again, start from the problem you want to solve, not from the data.

Lesson 2: Prioritization Strategies

Prioritize the implementation of IoT projects based on the impact on your bottom line in both the short and long term.

Everyone has good ideas, and technology has progressed so much that companies today have a chance to make their ideas real. However, considering how rapidly the trends, competition, technology, and customers change, it is critical to prioritize the most impactful problem you want to solve and then decide which IoT project you should implement. Choosing, for example, the most economical, easier, or cool project is often not the best idea. Define your criteria (long-term impact, competencies, integration with existing solutions, etc.) and then prioritize.

Analogy With Data Analysis

Again, it is the same case for data analysts. Which question is worth solving first? Where do you focus your time and energy?

Lesson 3: Failing and Scaling

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

We need miniature versions of our grand idea so we can validate its parts, then iterate and tweak constantly. Quite often, we see big corporations think big, but then plan and prepare for years until they launch the first product at scale. This approach can carry several risks, since technology and trends change quicker and 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 what we will finally scale. The 'Start Small' tactic also helps 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 whole datasets, fail and learn quickly, try again, leverage the old lessons, and finally find the answer to the question you are looking for.

Lesson 4: Breaking Down Silos

Break down 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) collaboration, support, and involvement of stakeholders from different departments (IT, IoT, finance, operations, logistics, customer support, marketing, etc.) in order to use the expertise of different fields and ensure the continuous commitment of all departments.

Otherwise, we may see the example of just one team being the project leader without any other department caring about it, like it's not their job or responsibility. That is an attitude that can be catastrophic for any IoT project, especially for the big ones. Besides, try to regularly engage all relevant stakeholders in the process with updates, workshops, small deliverables, etc.

Analogy With Data Analysis

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

Lesson 5: Tell a Story

Explain data with storytelling.

Just collecting data from sensors or internal systems and later integrating all those 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 dashboards and capabilities of IoT analytics platforms, but on the importance of how humans (analysts, platform users, etc.) read and describe the results of the analyzed data. Using the storytelling method, most employees, decision makers, and customers will be able to understand results — and use them. The use of results in the right way is the most important goal, but unfortunately, it does not always happen (employees are lost in many Excel files or are providing insights without context).

Analogy With Data Analysis

The Big Data team (or teams with similar names) needs to be able to transform data into information and information into actionable insights for the rest of the business (or customers). Insight is more than just a summary, it is the hidden pattern in 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: Keep Good Employees

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

IoT is quite new, so companies pursuing the Internet of Things and Big Data strategies are finding it challenging to recruit the right talent with a comprehensive understanding of data, telecom, software, commercials, strategy, etc. So, for this reason, it is important for the company to 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 on with people who do not have the modern skills that a competitive business environment requires, but it will lose its best talent. Both results are enough to forecast a future with limited potential for success.

Besides, empowerment for training and career development are important to keep your workforce motivated and inspired. In addition, considering the lack of IoT talent, it is good for the HR team to try to match business needs with the interests and career goals of its ambitious employees. Otherwise, again, it will be difficult to keep the best talent around for a long time.

Analogy With Data Analysis

Analytics expertise is in particularly short supply, and it’s expected to become more scarce as the field expands. For many companies in IoT, the key hire is often the data scientist or data architect, so continuous empowerment, training, and involvement with exciting/difficult problems could help increase your data employee retention rate.

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