One of the characteristics of Internet of Things (IoT) products is that they generate a lot of data; even many people think that generating, collecting, and using these data is the real focus of IoT products. But this is not the case: what the Internet of Things IoT should provide should be the information and insights generated after a planned and in-depth analysis.
This point is the insight that the author got after many failures, and I will share it with readers here.
What is your data usage strategy?
For the average user, the IoT product of the Internet of Things is not much different from other products. If it is useful, otherwise it is useless, the difference lies only in the value from the use.
The reason for this is that the biggest challenge manufacturers face when designing IoT products is to develop a “data usage strategy†for the product, which is a set of “how to extract value from the collected dataâ€.
A good data application strategy is more than just data collection and management; its starting point is to "defining the ultimate goal that this product wants to achieve" and then creating a corresponding IoT Technology Stack. Understand the data content that you want to collect, store, analyze, and transfer at each level.
IoT Technology Level of IoT: Device Hardware - Device Software - Communication - Cloud Platform - Cloud Application
The above step is also the main role of the Data Decision Area in the Internet of Things IoT Decision Framework.
IoT Decision Architecture: Providing information, not just data
IoT Decision Architecture: Providing information, not just data collection, the better, right?
wrong.
Here, let me explain why it is important to have a clear data application strategy.
In the early years, the author once developed an IoT rapid application program for a semiconductor manufacturer; the customer asked the company I worked for to design an automated process to characterize the new hardware chip (CharacterizaTIon).
The seemingly professional "qualitative" word actually means inputting data to the chip in various ways and recording the output to ensure that the actual performance of the chip is as close as possible to the mathematical model of the design engineer.
It is almost impossible to do all the input settings by hand; but if you can use the computer to input and store the output data to the cloud, you can save a lot of time and improve the quality of the product itself.
Because of this, customers hired us to design such a system.
When the system was designed and installed, the customer was very happy; since then, they were able to perform a complete input test that was not possible in the past. All in all, the case was very successful.
A few months later, I received a call from a customer. He said, "We are drowning in data, what should we do?"
Because we have developed a system that includes many high-speed sensors and actuators, we generate several gigabytes of data per second (on, every second).
That is to say, once the system is started, it will produce data that can be analyzed in a few weeks. Perhaps the new system helped them solve the problem of insufficient data collection, but created new (and possibly more serious) problems: the resulting data was too late to manage and analyze, let alone aggregated into meaningful information.
Must have a data application strategy first
In retrospect, in addition to helping customers develop new systems, we should also spend more time understanding what customers want, rather than just focusing on meeting their apparent needs.
Please don't misunderstand what I mean. From the perspective of the case itself, the system was very successful; we handed over the finished product within the customer's budget and time limit, and the customer was very happy to accept it. But as far as the results are concerned, we have created bigger problems for our customers.
Spend more time understanding what customers really want, rather than just meeting their apparent needs.
This is not a single case. After talking to colleagues in charge of products around the world, I found that the same thing has been happening; there are too many companies to focus on "eliminating symptoms" without further understanding what customers really want.
In this case, the problem is that we place too much emphasis on providing "data" rather than "information."
Fortunately, the customer still trusts us very much, and let us continue the second phase of the plan to solve the problem of “drown by dataâ€; this time we also have a deeper understanding of the customer’s entire company (not just the use of the unit). ) the demand.
So we quickly discovered that the customer does not have the ability to use a large amount of data, and there is no data analysis expert in the team; even more troublesome is that the team members do not have enough background to successfully take over the system.
In the next few months, I worked with the client to develop a data application strategy and a data management system to solve the above problems. The method included reducing data production and adding a private cloud with data analysis and visualization capabilities. The server is managed so that it is centralized (even if the data comes from other departments).
Since then, the situation has been much better; and this has become an important case that I cannot forget in the future.
Both machines and "networking" can produce extremely large amounts of data, and they don't slow down because of fatigue. Therefore, if a clear application and processing strategy is not developed to allow this data to be converted into effective information, IoT is meaningless and will only be a source of junk data.
The importance of industrial knowledge
There is an old joke that says this:
A young man drove a sports car, passed a shepherd and his flock, and stopped to ask: "If I guessed that there are a few sheep in total, can I take one?"
The shepherd promised, so the young man took out the computer and began to count the number of sheep with the latest cutting-edge technology. After the calculation, he said: "Your sheep has a total of 280." Then grabbed one.
After listening to this number, the shepherd replied: "Young people, if I guess about your profession, can you bring this sheep back?"
The young man agreed; the shepherd said, "I guess you are a management consultant."
The young man was shocked: "How do you know!" The shepherd said: "First, the fees you charge are very expensive. Second, what you tell me is what I already know. Third, you don't understand what I raise. What, because you are taking my dog."
This fable also applies to the role of product manager. Many product managers don't understand the nature of the customer's business before developing the product, which leads us to solve "the problem that doesn't need to be solved" or just create a lot of worthless data. A system that performs well in technology can also cause unpredictable problems.
In the same way, the system we developed for our customers also made the mistake of “not understanding the nature of the customer's business firstâ€. Although we know how to develop IoT IoT systems for customers in specific industries, the customer's industry was really unfamiliar at the time. Therefore, although the system performs well in technology, it has produced problems that were not anticipated in the past.
Although we are not not taking the time to understand the customer's industry and the difficulties they have encountered, we have not gone deeper to observe the challenges facing this industry. In other words, the system we developed is valuable in some respects, but it does not completely solve the customer's problems.
So what does this “Shepherd and Consultant†tell us?
The biggest focus is "understand the customer's industry"; as a consultant or product manager, researching the customer's industry is a must-do homework, rather than forcing the promotion of the "professional". In other words, you must develop your own knowledge of a particular industry, the so-called "domain knowledge."
If you can become an expert in the customer's business field and understand the difficulties they face, you can ask more precise questions, make your product positioning more correct, and provide customers with more valuable services.
Focus: Provide "information" with insights
Many of today's IoT products in IoT focus on generating a lot of data, rather than providing useful information, which leads customers to be unable to convert revenue from the functionality of the product or to pay extra cost to obtain information, thus disappointing the product.
As a product manager, understanding the customer's industry and the challenges they are most often facing should be taken for granted; to achieve this basic requirement, we can create effective data utilization strategies and meet the real needs of our customers.
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