How has technology helped push data science forward?


Without technological advances, imagining a more efficient and fast-paced world would be difficult. Advances in internet connectivity, data analysis, and software development have led to incredible business and public services developments. In addition, data science has emerged as a major focus for many firms, with dedicated scientists working in-house to help develop plans and forecasts for marketing, product design, and customer outreach.

It will come as no surprise to learn that data science has come a long way in the past few decades. The hardware and software we use in the modern era allow us to manage complex information on a nearly unprecedented scale. It is even possible to study an online master’s in data science, preparing engineers for fruitful careers in number-crunching and statistical analysis for many years to come.

How is technology helping to shape data science for the better, and what might we expect in the coming years?

Artificial intelligence is the way

It is nearly impossible to talk about emerging tech in the New 20s without mentioning AI and machine learning in at least some shape or form. AI is quickly becoming established technology, however, with millions of people already using some form of rudimentary AI via smartphones or home assistants to help make their lives that little bit easier. In business and industry, it is clear that AI is paving the way for simpler delegation and automation. 

AI is used to help data scientists derive complex information from automated schedules and points. For example, we may even use AI to derive data from physical processes through the use of smart sensors. This means that machinery usage data, for example, can be easily converted into numbers for planning and data analysis. 

AI is ultimately helping to make data science more efficient while continuing to broaden its scope. Human effort can only stretch so far, and while there will always be a need for manual data science to some degree, it stands to reason that AI can help to erase much of the risk of human error along the way.

AR and VR open up new dimensions

While virtual reality (VR) and augmented reality (AR) are frequently seen as entertainment or marketing tools, the same technologies are helping data scientists to measure and map human and machine interactivity. This technology is increasingly being used in line with AI and machine learning to help us better understand consumer demands.

Given that one of the major facets of data science is the ability to dig deeper to understand user intent and thus predict behavior, it is reasonable to expect both AR and VR to support the practice for years to come. AR, in particular, has proven useful in the marketing and product design stages in recent years, with the technology helping business owners and product developers gain insight into their prototypes at the app level (without fully developing products).

Cloud computing broadens its scope

In the early days of computer science and data management, few observers expected data to explode in the way it has in the past decade. Data is always growing, and the best way to manage big data without an end point in sight is to make storage “unlimited”. Although it is not necessarily possible to make it unlimited in the truest sense of the word, it is clear that data scientists worldwide have come to rely on cloud computing, scaling up and up to accommodate more and more information.

Data scientists simply cannot work to finite storage solutions. Cloud computing, based off-site and seemingly infinite in scalability, can allow huge amounts of data to be processed and stored without excessive expenditure on hardware or in-house resources. Cloud computing has quickly become the default solution for many data scientists, with power consumption and technical wrangling likely to be kept to a minimum.

Cloud computing will likely maintain its popularity with business and private users in the decades to come, and in terms of data science, there’s no turning back.

IoT rises up

IoT, or the Internet of Things, refers to the technological phenomenon of smart devices communicating. Much in the same vein as AI and machine learning helping data scientists to retrieve and manage complex data without human input, IoT devices can autonomously monitor and correlate themselves. With technology such as sensors, data scientists can enable machines to effectively capture data for them without the risk of human error.

The use of IoT in data science can also help analysts build clearer, more reliable plans and reports, along with those that can scale up at an unimaginable size. IoT devices are helping data scientists to capture more information more frequently and at a level of precision that was previously unseen. As big data continues to scale up to a near infinite size, cross-machine communication is likely to persist for as long as businesses demand responsive growth.

Will technology remove the need for human data scientists?

It is highly unlikely that the need for human data scientists will ever dissipate. While technology is currently helping us to capture, manage and analyze data at an incredible pace and scale, there will always be a requirement for contextual analysis. Crucially, although technology is at its most advanced at the moment, there is still some room for improvement.

Businesses employ data scientists not only for their technological prowess, but also for their inherent ability to forecast. IT predictions alone prove highly complex, and forecasting doesn’t get any simpler regardless of the industry in which you work. By coupling the analytical prowess of a data science graduate with the latest in data management technology, business owners have the best tools at hand to confidently create new plans and opportunities.

Learning to become a data scientist is potentially a very lucrative move. You’ll be relied on to manage and process data and help business owners corner new markets. If you have a real thirst for numbers and for making big changes with the correlations they display, now is the time to consider entering this field.

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