Publish Date: 24-Feb-2020
2019 was a big year for not only data science, artificial intelligence (AI), and big data, but also all the technologies related to it. Organizations from around the world, across a broad range of industries, have been undergoing a major shift and transition what people are referring to as a digital transformation. That implies, companies are taking everything right from business processes such as hiring, pricing, strategy, and marketing, to operations, and using digital technologies to make them multiple times better.
Data science has become a significant part of all those transformations. With this breakthrough technology, companies are no longer required to take important decisions on the basis of hunches, assumptions, best-guesses, or online surveys and testimonials. Rather, they are required to analyze huge volumes of real data to settle on to a particular decision based on real, data-driven facts. That’s what is included in data science—deriving value through large amounts of data.
An online survey by Google Search Trends suggests that this major pattern of integrating data by the companies into their core businesses to add more meaning and significant value has grown considerably, with an increase in interest by more than four times in the last 5 years. Data is providing businesses a strong edge over their competitors. With more information and better data scientists to derive meaning through it, organizations can accumulate data about the market that their rivals probably might not even know existed. Basically, everything is essentially revolving around data.
In the highly competitive and rapidly-evolving world around us, staying ahead of the competition requires constant development, in terms of innovation and technological advancements. Companies are no longer inclined towards patents, rather particularly interested in catching new trends and adapting to Agile methodologies. Businesses can’t last long if they depend on the rock-solid methods of old. In the event that another trend like data science, big data, or AI comes along, it needs to be anticipated in advance and adapted quickly.
Mentioned below are the 4 important trends to watch out for data science in the year 2020. These are the trend that has garnered a major interest the previous year and will continue to evolve in 2020.
Indeed, even in the modern digital era, data science still requires an individual to complete a task manually. Storing data, cleaning, visualizing and exploring data, and ultimately, modeling data to get some genuine results. That manual work is simply asking for automation and, in this way, has been the ascent of automated data science and machine learning (ML). Almost every step of the data science pipeline has been or is getting automated.
Auto-data scrubbing or data cleaning has been vigorously explored in the course of recent years. Big data cleaning frequently takes up most of a data scientist's time. Startups and enterprises alike, for example, IBM, offer automation and tooling for data scrubbing.
Feature engineering, on the other hand, is a major part of data science that has undergone noteworthy disruption. Featuretools provides a solution for automatic feature engineering. In addition, modern techniques based on deep learning, for example, convolutional and recurrent neural networks, become familiar with their own features without the requirement for manual feature design. Deep learning is often considered a subset of machine learning that uses artificial neural networks (ANN) designed to mimic the way individuals think and learn.
Perhaps the most important automation is happening in the ML space. Companies such as Data Robot and H2O have built their reputation in the industry by introducing ML platforms, providing data scientists a strong hold on data management and model building.
AutoML, a method for building high-quality, custom machine learning models, has gained widespread popularity in 2019 as these automated platforms outperform the state-of-the-art. Google, specifically, is investing vigorously in Cloud AutoML.
At large, organizations are investing heavily in building and purchasing tools and services for automated data science. Anything to make the process less expensive and simpler. Simultaneously, this automation additionally takes into account SMEs that can use these tools and services to leverage data science without having much expertise in the cutting-edge field.
Privacy and security are interdependent. All the organizations across the world want to move ahead of the competition and innovate products, but not at the cost of losing the trust and loyalty of their customers because of privacy and security concerns. Hence, they are compelled to make it a priority, at least to a bare minimum of not causing any sensitive data leaks.
Data privacy and security have covered the headlines numerous times over the last few years as the concerns are magnified by major public hacks. Last year, in November, an unprotected server on Google cloud was exposed, comprising the personal information of about 1.2 billion individuals–that is, 4 terabytes of personal information consisting of names, phone numbers, unique email addresses, and LinkedIn and Facebook profile information. This data breach was considered as one of the largest data exposures of all time, where the FBI came into the picture for investigation.
Who placed the data over there? Who is answerable for the security of that vast amount of data? It was found on a Google Cloud server, which truly anybody could have made.
After reading headlines, individuals are becoming more and more careful of whom they are giving their personal information (including email addresses, passwords, credit and debit card details, and social security numbers, among others) out to.
An organization that can ensure the privacy and security of its client's data will find that they have a far simpler time persuading clients to give them more information (by proceeding to use their products and services). It likewise guarantees that, should their government pass any laws requiring security guidelines for client data, they are already vigilant.
In general, data science is fueled by data; however, the majority of it remains anonymous. If this sheer volume of data falls in the hands of an offender, it can cause major data leaks and breaches and upset the privacy and livelihood of individuals. Data isn’t just limited to raw numbers; it describes real individuals and real things.
As we see data science advance and grow, we'll likewise see the transition of the privacy and security laws surrounding data. That includes processes, protocols, and various techniques for forming and maintaining the privacy, security, and integrity of information. It won't be a shock if cybersecurity turns into the new popular buzzword of 2020.
Throughout the years that data science has emerged from a specialty to its own as a field, the data available for analysis has likewise multiplied in size. Companies are gathering and storing more data than ever.
The huge amount of data that is handled by a typical Fortune 500 company is multiple times greater than the data handled by a personal computer. The base configuration of the latest computer or a laptop includes 64GB of RAM with an 8 core CPU and 4TB of storage. The number is enough in case of small personal projects but is not even considered close to being enough for the level of data handled by a global company such as a bank or a hospital that has data and records covering millions of customers and patients.
This is how cloud computing comes into the scenario. Cloud computing is a model that allows any individual to access practically limitless processing power, regardless of their location. Companies such as Amazon offers cloud services at large, such as Amazon Web Services (AWS) that offers servers with 96 vCPUs based on custom Intel processors and up to 768 GB of RAM. These servers are established in an autoscaling group where hundreds of them can be launched or stopped without any delay, which means the availability of on-demand computing power.
Companies are now providing much more than cloud computing services, such as full-fledged platforms for data analytics. For example, Google Cloud offers a highly-scalable, cost-effective platform called BigQuery, which is a data warehouse designed specifically to turn big data into well-informed business decisions. BigQuery offers data scientists an all-in-one platform to store and analyze petabytes of data. This platform can also be linked to other GCP services for data science.
Everything from data to processing power is evolving and growing. As data science evolves, we might eventually see data science tasks being performed absolutely on the cloud because of the sheer volume of the data.
Overall, data science is growing. As it is becoming more advanced, it's establishing itself into every industry, both technical and non-technical, and every substantial business, whether small, medium or large. As the field grows and advances over the long haul, it wouldn't come as an astonishment to see it democratized at a large scale, making a lot of tools accessible in our software toolbox.
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