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Data Science: What to Expect in 2019

Introduction

2019 looks to be the year of using smarter technology in a smarter way. Three key trends — artificial intelligence systems becoming a serious component in enterprise tools, custom hardware breaking out for special use-cases, and a rethink on data science and its utility — will all combine into a common theme.

In recent years, we’ve seen all manner of jaw-dropping technology, but the emphasis has been very much on what these gadgets and systems can do and how they do it, with much less attention paid to why.

In this blog, we will explore different areas in data science and figure out our expectations in 2019 in them. Areas include machine learning, AR/VR systems, edge computing etc. Let us go through them one by one

Machine Learning/Deep Learning

Businesses are using machine learning to improve all sorts of outcomes, from optimizing operational workflows and increasing customer satisfaction to discovering to a new competitive differentiator. But, now all the hype around AI is settling. Machine learning is not a cool term anymore. Furthermore, organisations are looking for more ways of identifying more options in the form of agent modelling. Apart from this, more adoption of these algorithms looks very feasible now. Adoption will be seen in new and old industries

Healthcare companies are already big users of AI, and this trend will continue. According to Accenture, the AI healthcare market might hit $6.6 billion by 2021, and clinical health AI applications can create $150 billion in annual savings for the U.S. healthcare economy by 2026.

In retail, global spending on AI will grow to $7.3 billion a year by 2022, up from $2 billion in 2018, according to Juniper Research. This is because companies will invest heavily in AI tools that will help them differentiate and improve the services they offer customers.

In cybersecurity, the adoption of AI brings a boom in startups that are able to raised$3.65 billion in equity funding in the last five years. Cyber AI can help security experts sort through millions of incidents to identify aberrations, risks, and signals of future threats.

And there is even an opportunity brewing in industries facing labour shortages, such as transportation. At the end of 2017, there was a shortage of 51,000 truck drivers (up from a shortage of 36,000 the previous year). And the ATA reports that the trucking industry will need to hire 900,000 more drivers in the next 10 years to keep up with demand. AI-driven autonomous vehicles could help relieve the need for more drivers in the future.

Programming Language

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle suggests that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83%). Additionally, 3 out of 4 data professionals recommended that aspiring data scientists learn Python first.

Survey results show that 3 out of 4 data professionals would recommend Python as the programming language aspiring data scientists to learn first. The remaining programming languages are recommended at a significantly lower rate (R recommended by 12% of respondents; SQL by 5% of respondents. Anyhow, Python will also boom more in 2019. But, R community too have come up with a lot of recent advancements. With new packages and improvements, R is expected to come closer to python in terms of usage.

Blockchain and Big Data

In recent years, the blockchain is at the heart of computer technologies. It is a cryptographically secure distributed database technology for storing and transmitting information. The main advantage of the blockchain is that it is decentralized. In fact, no one controls the data entering or their integrity. However, these checks run through various computers on the network. These different machines hold the same information. In fact, faulty data on one computer cannot enter the chain because it will not match the equivalent data held by the other machines. To put it simply, as long as the network exists, the information remains in the same state.

Big Data analytics will be essential for tracking transactions and enabling businesses that use the Blockchain to make better decisions. That’s why new Data Intelligence services are emerging to help financial institutions and governments and other businesses discover who they interact with within the Blockchain and discover hidden patterns.

Augmented-Reality/Virtual Reality

The broader the canvas of visualization is, the better the understanding is. That’s exactly what happens when one visualizes big data through the Augmented Reality (AR) and Virtual Reality (VR). A combination of AR and VR could open a world of possibilities to better utilize the data at hand. VR and AR can practically improve the way we perceive data and could actually be the solution to make use of the large unused data.

By presenting the data in the form of 3D, the user will be able to decipher the major takeaways from the data better and faster with easier understanding. Many recent types of research show that the VR and AR has a high sensory impact which promotes faster learning and understanding.

This immersive way of representation of the data enables the analysts to handle the big data more efficiently. It makes the analysis and interpretation more of an experience and realisation that the traditional analysis. Instead of the user seeing numbers and figures, the person will be able to see beyond it and into the facts, happenings and reasons which could revolutionize the businesses.

Edge Computing

Computing infrastructure is an ever-changing landscape of technol­ogy advancements. Current changes affect the way companies deploy smart manufacturing systems to make the most of advancements.

The rise of edge computing capabilities coupled with tradi­tional industrial control system (ICS) architectures provides increasing levels of flexibility. In addition, time-synchronized applications and analytics augment the need for larger Big Data operations in the cloud. This is regardless of cloud premise.

Edge is still in early stage adoption. But, one thing is clear that edge devices are subject to large-scale investments from cloud suppliers to offload bandwidth. Also, there are latency issues due to an explosion of the IoT data in both industrial and commercial applications.

Edge soon will likely increase in adoption where users have questions about the cloud’s specific use case. Cloud-level interfaces and apps will migrate to the edge. Industrial application hosting and analytics will become common at the edge. This will happen using virtual servers and simplified operational technology-friendly hardware and software.

The Rise of Semi-Automated Tools for Data Science

There has been a rise of self-service BI tools such as Tableau, Qlik Sense, Power BI, and Domo. Furthermore, now managers can obtain current business information in graphical form on demand. Although, IT may need to set up a certain amount of setup at the outset. Also, when adding a data source, most of the data cleaning work and analysis can be done by analysts. The analyses can update automatically from the latest data any time they are opened.

Managers can then interact with the analyses graphically to identify issues that need to be addressed. In a BI-generated dashboard or “story” about sales numbers, that might mean drilling down to find underperforming stores, salespeople, and products, or discovering trends in year-over-year same-store comparisons. These discoveries might in turn guide decisions about future stocking levels, product sales and promotions. Also, they may determine the building of additional stores in under-served areas.

Upgrade in Job Roles

In recent times, there have been a lot of advancements in the data science industry. With these advancements, different businesses are in better shape to extract much more value out of their data. With an increase in expectation, there is a shift in the roles of both data scientists and business analysts now. The data scientists should move from statistical focus phase to more of a research phase. But the business analysts are now filling in the gap left by data scientists and are taking their roles up.

We can see it as an upgrade in both the job roles. Business analysts now hold the business angle firm but are also handling the statistical and technical part of the things too. Business analysts are now more into predictive analytics. They are at a stage now where they can use off-the-shelf algorithms for predictions in their business domains. BA’s are not only for reporting and business mindset but now are more into the prescriptive analytics too. They are handling the role of model building, data warehousing and statistical analysing.

Summary

How this question is answered will be fascinating to watch. It could be that the data science field has to completely overhaul what it can offer, overcoming seeming off-limit barriers. Alternatively, it could be that businesses discover their expectations can’t be met and have to adjust to this reality in a productive manner rather than get bogged down in frustration.

In conclusion, 2019 promises to be a year where smart systems make further inroads into our personal and professional lives. More importantly, I expect our professional lives to get more sophisticated with a variety of agents and systems helping us get more of out of our time in the office!

Follow this link, if you are looking to learn more about data science online!

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