What are the biggest problems IoT data scientists face?
IoT is creating innumerable opportunities for businesses through the intelligent use of data curated by data scientists. In 2019, both LinkedIn and Glassdoor ranked data scientist as the most promising job due to the exponential popularity and relevance of big data, data mining and IoT. Although the job might indeed be attractive, the role is undergoing rapid change. This sensor-driven influx of data poses new challenges to the professionals tasked with converting the information into actionable insights.
According to studies, it is estimated that by 2020, 1.7 MB of data will be generated per person every second of the day. Over 26 billion IoT devices generate this data. IoT data scientists are hired by companies and cities to distill this data into an incisive course of action.
As once-static data evolves to become more dynamic and processing takes place in real-time, IoT data scientists are faced with new hurdles that impact every level of the organization. To better understand how to transform IoT data into a successful strategy, data scientists must address the real-world challenges faced in this arena. Here are the top four challenges IoT data scientists currently face.
Managing expectations
An effective data science department depends on the breadth and depth of the manager’s experience. The depth of experience comes from working with alternative situations and helming projects from the ground up. The breadth of a manager’s experience requires a background with the entire landscape of technology, programs and variable outcomes.
For IoT data scientists, the limited lifespan of the technology coupled with the hyper-advancements in both hardware and software reduces the capability to have adequate depth and breadth. This sharp curve leads IoT data scientists to enter projects with unclear parameters, vague goals and an overabundance of data. The result is rogue data with unnecessary hours spent on data cleansing.
New technology is exciting, especially to a boardroom looking to maximize ROI and streamline processes wherever possible. However, IoT is still in its infancy, and the number of devices, wearables, appliances and vehicles equipped with sensors and IP addresses is expanding at a rate beyond compare. To battle this discrepancy, businesses must invest in the architecture of systems and processes to support their IoT data scientists.
Language barriers
Like all scientists, IoT data scientists operate with a unique language. However, unlike most scientists, data scientists’ work is not confined to a lab. The results of their insights have implications affecting all realms of the businesses they work within: production, operations, distribution, customer service and R&D. Terms like actuator, chirps, mesh network and Z-wave are all imperative to an IoT data scientist, but likely sound like jargon to stakeholders. At the same time, developer languages like C, Python and Java are defining the possibilities and limitations of IoT software. Between the hardware and the software, IoT data scientists are faced with communicating highly complex realities to business partners.
In order to successfully communicate actionable strategies within the business, a degree of industry translation is required. Highly analytical data scientists might not have the business acumen to express and demonstrate the importance of their research, but companies determined to operate on the bleeding edge must strive to keep all parties IoT-literate as a way to mitigate misunderstandings through the language barrier.
Building the dream team
While companies are eager to mine their data and begin improving business, hiring an IoT data scientist is not a one-step solution to the problem. An effective IoT data scientist requires sufficient support and direction in order to operate efficiently. Additionally, data scientists’ resumes are notoriously filled with a litany of tech skills, but hiring managers are increasingly growing aware of the importance of soft skills such as problem-solving, communication and teamwork.
To curb this dilemma, companies must develop a framework of responsibilities for their IoT data scientists and simultaneously prepare hiring managers to screen for soft skills as well as IoT technical expertise. A whiz analyst won’t do any good working with a faulty system, and neither employee will succeed if they can’t execute teamwork and effective communication.
Perhaps most importantly, hiring managers must form a team with complementary technical skills, drawing on elements of engineering, analysis, infrastructure and quality control.
Data overload
IoT data scientists have access to abundant data such as speed, temperature, location, rate and proximity, which is gathered from an enormous number of interconnected sources in real-time. While the opportunities are endless, one of the hazards is data overload.
Data overload impacts data scientists, and it can also become burdensome to the network and raise questions about data storage. To overcome data overload, invest in proper data management architecture that performs pre-processing by applying cleaning algorithms. The system should leverage machine learning tools to create iterative improvement of the data, which produces more valuable insights as time passes.
Additionally, consider developing system-level solutions that automate the distribution of information to a necessary destination. If a sensor alerts a low level of fuel, rather than sending that information to an IoT data scientist, program that information to notify the relevant employee responsible for the machine.
The IoT data scientist
Sensors are capable of gathering more information than ever before and distributing the data immediately. As a result, IoT is becoming the testing field for the world’s most finely-tuned technological advancements, and companies — large and small — are reaping the benefits. IoT data scientists are gatekeepers to the future of business. However, a successful operation requires business leaders to grasp the struggles of the emerging field firmly. If the goal is practical insights, start with the knowledge that IoT data scientists need extra support, flexible resources and, more than anything else, room to grow.
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