Powering IoT with Data Science
Did you know? IoT-enabled devices are set to generate a revenue of over $300 Bn by 2020. Experts consider this to be the mere tip of the iceberg.
With enormous potential in this technology, the Internet of Things will soon power all data science efforts. As things get more and more connected over the Internet mesh, we will witness newer opportunities that simplify the lives of people, make processes more efficient, and open avenues for businesses.
Amidst all these developments, the true winners would be businesses who can make sense out of this IoT-powered data and leverage it for real business decisions. Otherwise, all the resources spent in IoT and data science will go down the drain.
Good data management will facilitate better data science. And, IoT is increasingly getting higher traction for being the generative force behind all of this critical data.
Achieving business value with IoT + Data Analytics
The paper Data Science and Machine Learning in the Internet of Things and Predictive Maintenance, by the Data Science Group at SAP, clarifies that data science faces many new struggles in the domain of IoT while the traditional challenges with data management, storage, processing, and analytics have not yet gone away.
How can organizations then achieve and maximize the business value from putting IoT and data science together?
Why and How Data science for IoT is different
In 2018, IoT devices outnumbered the global population for the first time. IOT Analytics reports that 7 Bn devices were connected to the internet in 2018. Forecasts suggest that the number would rise to 10 Bn by 2020. The Extreme Data Economy is promoting governments, businesses, and organizations to analyze and react to IoT data simultaneously.
Here’s what’s involved in transformation with IoT and data science:
- Working with hardware – This facet of IoT data analytics can get underestimated rather quickly. IoT consists of a range of devices and a multitude of radio technologies. It is a rapidly developing ecosystem and would need attention to a wide variety of IoT devices for all industries, including Retail, Healthcare, Smart Homes, Energy, Transportation, Manufacturing, and Wearables.
- Edge processing – Traditional data science often deals with data residing on the cloud. However, edge data processing is what IoT needs. With edge computing, data storage is brought closer to where it is needed. This improves the speed of outcomes and the efficiency with which decisions are made.
- Deep learning – Deep learning algorithms play a critical role in IoT analytics. Deep learning can help mitigate risks such as capturing all data scenarios for deviations in analytics, monitoring sensor data continuously to bring positive outcomes, and more.
- Real-time processing – IoT involves data that is both Big and Fast. Hence, real-time apps can be the real leverage for IoT and data science. Using methodologies such as real-time tagging, aggregation, and temporal correlation, data can be processed in (near) real-time for maximized results.
Data science for IoT applications and data looks a lot different than the science applied to traditional data.
Opportunities for enterprises with IoT and Data Science
Forbes maintains that robust analytics will likely lead to 3x more success with IoT initiatives.
The current IoT environment, coupled with data science capabilities, can lead businesses to exploit various opportunities such as:
- Real-time Decision making – Businesses can leverage decision making with an opportunity to extract valuable information about their users to predict future trends. Big Data can make information transparent and visible throughout the organization, thereby boosting organizational performance. IoT tools use techniques such as predictive modeling, clustering, and classification to open various data mining avenues, that further facilitate decision-making.
- Improved analytics and efficiency – Thanks to big data technologies and cloud-based mining tools, the demands of data storage and processing capabilities are falling off. Companies can harness the power of real-time analytics at a substantially lower cost. Using a mesh of devices to extract real-time information and process data can be beneficial for improving the overall efficiency of an organization.
- Value-added applications – Using IoT and data science, organizations can implement value-added applications with key technologies such as deep learning, machine learning, and artificial intelligence. The resulting applications can facilitate data-driven business transformation. An in-depth eye-to-detail was not possible before IoT which increases the value of such applications.
The current trends have shaped the Internet of Things as a forerunner in generating data. This is exactly the reason why it makes sense to combine the power of IoT and data science.
While IoT facilitates data collection at various touch points within and outside of an organization, data science powers the effective use of this data to steer operations, improve productivity, maximize profit, and offer exceptional customer experiences.
Harnessing the power of the vast amounts of data brought to us by IoT can be the single most significant data leverage we know of.