The world today is connected aggressively.
Networks, devices, systems, processes, and humans- everything is bound by data.
And as the quote goes, “Data is a precious thing and will last longer than the systems themselves”, today’s hyper-connected world is fueled thoroughly with data and has an impact on every single action and decision that humans take across the globe.
No longer confined to any particular tech or industry, data has helped revamp global operations across diverse industries and has significantly transformed the ease of living and day-to-day living.
But how is this new and connected world of tomorrow powered by data? What implications will it have on the way people live, and businesses operate? Will there be unforeseen challenges that can convulse the data-driven world, or are we ready to take on the hurdles?
Data collection has been one of the attributes of modern civilizations and has been existent for a long time, but never has been as important as it is today. What started with simple data markers that were not interconnected, Big Data evolved as an all-knowing, all-encompassing umbrella that holds enormous amounts of data that is precious for several industries and sectors.
With Industry 4.0, the importance of data has been aggrandized and data is essentially the driver of the key components – from risk assessment, supply chain, maintenance to sales.
From Big Data to AI-powered tools, from cloud manufacturing to smart factories, data is flown through and collected via several diverse elements of the manufacturing industry where digital transformation is deeply penetrated. Big Data brings in operational transparency, helps in strategic decision making, helps in offering bespoke solutions tailor-made to suit the customer requirement based on previous purchase preferences, and significantly helps in improving the overall product quality. Naturally, this further leads to better after-sales service, operational efficiency with detailed tracking of employee and machine productivity, close monitoring of logistics, and predictive maintenance and support.
The automotive sector has undergone a huge revamp in recent years, primarily to suffice the industry’s growing needs and balance the fossil energy deficit. The automotive industry is largely moving towards electric cars or autonomous/self-driven cars that are most certainly the future of automotive. Modern cars are heavily connected to other interconnected systems, such as home automation and IoT devices powered by data. The large amounts of data collected from IoT based sensors in cars are important to enhance the driving experience, offer the latest traffic and weather stats, streamline insurance in case of incidence, provide predictive maintenance to avoid on-road failure, and reduce accidents and improve the safety standards of the car.
The Healthcare industry is immensely data-driven. Data in healthcare has been incremental in maintaining a 360-degree view of patient data, offering predictive modeling and analysis, extending preventive healthcare, and using the data to treat large subsets of the population in a particular geography. Connecting patient data with employee data can also help organizations in the healthcare sector achieve their business goals, align necessary care with optimized resource planning, and further improve the patient experience.
The banking sector, although highly organized across the globe, has been hesitant when it comes to a complete digital overhaul. While the financial sector has been welcoming to the change caused by digital transformation, the conventional banking sector has taken slow and steady steps into implementing data-powered tools while ironically, they have a vast amount of precious customer data that is documented to perfection. Banks that are making wide use of this data can create a more immersive customer experience powered by Artificial Intelligence and Machine learning.
Cognitive banking involves the use of robotic and automotive AI to execute, process, and implement data to improve the overall digital banking experience. From customer communication, selling and upselling of financial products, to building a loyal customer base, data can do wonders to the banking sector, when tapped into with precision.
Reducing energy consumption, optimization of non-fuel resources for energy generation, refining energy efficiency, and offering consistent energy with minimum power outages are the key benefits of Big Data, IoT-powered sensors, and other data-driven technologies used in the energy sector.
The growing demand for smart cities further highlights the importance of data in the energy sector. India alone has deployed more than 1500,000 smart meters. The rate of adaptation of smart metering and smart grid systems is also increasing across China – two countries with the largest populations in the world. Big Data Analytics has been immensely powerful in smart metering, which helps forecast the energy consumption to manage the demand against the supply and reduce energy wastage.
The world of tomorrow is truly expected to be connected with data breathing life into it. However, this interconnected world of data and the companies that drive this data-powered innovation are met with several challenges, which, if not mitigated, can cause more harm than good. Let’s discuss…
We all agree that data is gold, but when collected in copious amounts, there’s no denying that there are tons (virtual) of junk, unusable, and uninterpretable data. Dirty data or data with substandard quality, which can be inaccurate, incorrect, duplicate, inconsistent, incomplete, or data that has violated rules and compliance, can create complexities for the systems that are data-dependent. Maintaining data quality based on user requirements or organizational framework is a real challenge and can only be mitigated with data profiling, data annotation, and analysis to continuously make the data readable and usable. A data science platform can be critical in data cleaning, quality investigation, and maintenance and is crucial for the data to work the wonders it can.
Inconsistency in data collection can cause challenges related to data integration when companies depend on more than one source for data collection. Raw data needs to be normalized before it can be accessed by data-powered systems and be meaningful enough to drive any insights. Data Science and integration platforms and ETL tools play a key role in automating and creating transformations, building extensible frameworks, and scheduling query performance optimization to integrate and transform the data of the interconnected world.
The power of data is indispensable and goes downhill in no time if not maintained under a tight security profile. Data security is an immensely crucial factor that companies who invest, rely on, and use data have to consider upon. Data security and data privacy are under constant threats from predators waiting to prey upon critical data. Data science tools that help create a controlled environment for the data flowing in and out of the data analytics tools, open-source technologies, and other elements used in a data-driven company can help maintain security and compliance.
The data science market is estimated to reach USD 101.37 billion by the end of 2021!
In the hyper-connected world of tomorrow powered by data, data science platforms are the way to move forward. Spanning across Data ideation, integration, and exploration, and Model Development and Deployment, data science platforms encompass all the necessary tools for managing the entire data lifecycle. Ushering in ease and simplicity for data scientists and Citizen Data Scientists, data science platforms are the forward-looking solutions that companies must invest in if they haven’t already!