Data Science Industry – Year 2022 (Predictions)

Introduction

Data Science Industry Predictions Year 2022

If the COVID-19 pandemic in 2020 demonstrated one thing, it’s how data is more critical than ever for businesses. To get the most out of this data, shops will need to increase their investment in data science.

Last year was a tumultuous time with many lessons to be learned. Digital transformation has been stimulated by the COVID-19 crisis, usually involving digitalisation of processes, modernisation of business models, access to data for all workers in an organisation, and upskilling them all.

The COVID-19 pandemic has proven the need for everyone to be data-fluent and examine where that data is coming from. It has also proven the need for everyone to be data-fluent, informed citizens, as data can either inform us on or misinform us about the state

As data science becomes mature, more and more companies are trying to increase their digital resilience.

In order to be a data-driven organisation, it is important for an organisation’s employees to be able to ask questions when they have them.

A data science process uses information from analogue or digital sources to draw insights. With this method, teams can solve problems like self-driving cars, protein folding, and algorithmic trading programs.

The applications of data science are widespread. It’s about creating data literate organisations and societies where everyone is equipped with the skills they need to be informed, citizens, and employees. Over the coming years, we will see better tools across the spectrum of data fluency. 

Let’s explore the data science industry-wise trends, events, business scope and career predictions for 2022.

  1. Retail Industry 

Retail companies are gathering data at an unprecedented rate through several sources, including website clickstreams, online customer reviews & feedback, loyalty cards, and point-of-sale transactions.

Such massive data require advanced analytics to help them interpret it correctly and get an insight into their customer behaviour which will help them improve sales and customer experiences.

In recent years, we have seen many e-commerce companies and brick-and-mortar stores use big data analytics to improve the shopping experience, which helps increase revenue from higher sales, improve customer retention and conversion rates. With these insights, they can optimise pricing strategies and targeting of customers.

How Data Science helps in the Retail Industry

The retail industry is evolving with the advent of technology. E-commerce sites like Amazon, Flipkart have changed the way products are bought and sold. With online shopping, customers don’t need to browse through different stores to buy their products. They can go on the website of their choice and make the purchase. Customers favour brands with the best online presence, product reviews, and ratings.

With increased competition and customer demands, retailers need to take every opportunity they can get to remain profitable and relevant in the market today. Many of them are now turning to data science for help in competing with e-commerce sites and keeping their business competitive.

Latest Data Science trends and events in the Retail Industry

Data Science and the retail industry is a topic that is very new but fast-evolving. Retailers are challenged with how to leverage their customer data and transform it into actionable insights to remain relevant in a competitive market.

Personalised Marketing: With the help of data science, retailers can predict customer preferences based on their shopping history and offer products accordingly. It will drive more sales and is an excellent way to remain competitive in a retail market that has become very dynamic, with online shopping also increasing rapidly.

Predicting Customer Behaviour: By looking at the past buying behaviour of customers, retailers can determine what works and what doesn’t. It will enable them to streamline their product portfolio as well as marketing strategies for the best results.

Accurate Forecasts: Almost half of a retailer’s revenue comes from forecasting monthly sales. Data science tools can help by providing more accurate forecasts, which will help in better budgeting, planning, and inventory management.

Inventory Management: Data science tools can help retailers determine the best time to restock their product inventories and predict future demand for particular products. It will ensure that they never run out of stock and yet at the same time don’t carry too much excess inventory just in case demand does not meet their expectations.

How Data Science will impact the Retail Industry in the future (Predictions)

Retailers worldwide are using Data Science tools to increase sales, improve customer experience, and remain competitive in the market.

Data Analytics in Retail is expected to grow at a CAGR of 17% and reach $8766.1 million by 2027. International analysts predict that this growth will be driven by the increasing use of technology in the retail industry. (Source)

With Data Science’s help, retailers can create custom-made portfolios of products based on what their customers have bought in the past and predict future demand for particular products. It will enable them to make better decisions and react quickly to changes in the market.

Data science also enables retailers to create personalised marketing strategies to increase sales by providing customers with more relevant offers on products that they might like based on past purchases. It will help them drive more revenue from each customer and improve their overall earnings.

Inventory management is another area where data science can have a considerable impact. Retailers can use data science to determine the best time to restock their product inventories as well as predict future demand for particular products, thus helping them avoid stockouts and excess inventory at the same time.

All these machine learning tools are available on platforms like BigML that offer an easy way for retailers to use the power of Data Science.

Data Science is becoming more critical for retailers to remain competitive in a market that is very dynamic with ever-increasing competition from online shopping sites.

Future business scope and career for Data Scientists in the Retail Industry

Data science and predictive analytics tools will have a massive impact on the retail industry in the future. Retailers will use data science to predict future demand for particular products and personalise their marketing strategies by providing customers with more relevant offers depending on what they have bought in the past. It will help them drive more revenue from each customer and improve their overall earnings.

Retailers will be able to manage their inventories by using data science to determine the best time to restock products and predict future demand for particular products.

More companies will adopt these machine learning tools and invest in data science to remain competitive and relevant.

Retail companies are investing heavily in Data Science to remain competitive and relevant. 

More companies are adopting these tools to remain competitive in a market that is very dynamic with ever-increasing competition from online shopping sites. It will drive the demand for data scientists, making it an excellent career choice for anyone who wants to work in this dynamic industry.

  1. Healthcare/ Medicine Industry

The healthcare industry is rapidly changing. We are in an era where medical devices, data, and information sharing networks will impact every segment of our health care system. From advanced clinical systems to novel research tools and from convenient point-of-care devices to cloud-based data storage solutions, these technologies have the potential to transform the delivery of care and improve health outcomes.

The healthcare sector will gain from data analytics. With many people suffering from chronic diseases, there is a growing need for personalised healthcare options depending on their health conditions and demand.

How Data Science helps in the Healthcare Industry

The healthcare industry is leveraging data science and analytics to improve patient outcomes and reduce costs. Data scientists are helping develop evidence-based strategies for various applications, from identifying the root cause of an outbreak through tracing transmission paths; to analysing electronic medical records (EMRs) and other clinical information; to building predictive models for drug discovery and clinical trials.

Big data analytics have been helping healthcare companies to track the patient’s daily activities like diet and exercise, which allows them to understand their health better. Real-time healthcare analytics is helping medical professionals to make faster decisions and gain a deeper insight into patient’s behaviour, lifestyle, and daily patterns.

Data Science has a significant role in the Healthcare industry through Machine Learning Algorithms, Data Mining, Spam Filtering, and Social Media Data Mining.

The healthcare industry has benefited from the IoT revolution that gave us the connected devices we have today. Along with traditional medical equipment, these devices generate vast amounts of data and are being leveraged to improve health outcomes for millions worldwide.

Latest Data Science trends and events in the Healthcare Industry

Data Science is affecting the ways that medical research is being conducted and how it’s disseminated. Researchers are using data science to uncover new insights into cancer, cardiovascular disease, heart failure, diabetes, and many more conditions. 

Online healthcare platform Practo has built a robust Data Science Platform that utilises data from both within the company and external sources to provide actionable insights to its users.

The healthcare industry is taking advantage of big data analytics and predictive analytics techniques such as:

  • Machine learning
  • Deep learning
  • Text mining
  • The internet of things
  • Mobile analytics
  • Data analysis
  • Image processing to develop intelligent healthcare applications
  • Artificial Intelligence (AI): For reducing medical errors, improving diagnosis rates, expediting lab results interpretation and improving drug discovery, and more.

The healthcare industry is undergoing rapid transformation with advances in information technology.

How Data Science will impact the Healthcare Industry in the future (Predictions)

The use of data-mining techniques for medical image analysis such as predictive analytics, deep learning, and unsupervised machine learning is rising in healthcare applications.

Data science will play a pivotal role in addressing new challenges such as the exponential increase of data volume generated by wearable technologies like fitness trackers or smartwatches connected to smartphones, more people seeking medical care using telemedicine, and telestroke, and digital health care services.

Data science will help provide better treatment options tailored to different physician and patient groups with the potential to reduce overall healthcare costs.

Kai-fu Lee predicted in his book “AI Superpowers” that by 2035 the U.S., China, and India would have more than one million doctors, nurses, and other healthcare professionals who would be using data science techniques to diagnose patients.

Healthcare organisations are also planning to implement more artificial intelligence systems into their radiology departments to reduce human errors that happen with scale-out workloads. Today, there’s no shortage of opportunities for data scientists in this sector.

Future business scope and career for Data Scientists in the Healthcare Industry

Recent job postings for data scientists show openings across industries, including healthcare, pharmaceuticals, and more. 

The big global data in the healthcare market is expected to reach $34.27 billion by 2022, at an estimated CAGR of 22.07%. Furthermore, the market for big data in healthcare is expected to grow by $42.3 billion between 2016 and 2024. (Source)

There will be a need to improve the accuracy of diagnosis systems to reduce risk and avoid adverse events. Research is being done on applying AI algorithms such as machine learning and deep learning to medical imaging applications like radiology, pathology, and dermatology.

Healthcare organisations also face challenges such as identifying patients at risk of early death or developing a disease, which can be addressed by accurately predicting these specific health events. 

While we have seen many advances in healthcare technology recently, there is still a long way to go to lower costs, increase efficiency and improve the quality of healthcare in the country.

Data scientists have work cut out as the pace of technology development quickens and adds more points of data to analyse from multiple sources. One thing is clear – our health and its cost will be determined by data science findings.

  1. Banking and Finance Industry

Financial institutions are also harnessing big data to improve their services and increase revenue. They can analyse big data such as social media posts, sentiments favouring or against a particular financial product and provide advice for a client’s investment strategy backed by real-time insights into the market trends.

They’re also looking into providing a better client experience with data science-driven insights and using these technologies to target new services and cross-sell existing products.

Machine learning is expected to have more significant usage in the finance industry, thus empowering data scientists with tools that facilitate the automatic discovery of predictive models rather than building from scratch, even for those who don’t have a deep background in statistics or computer science.

How Data Science helps in Banking and Finance Industry

Data science can be applied in many ways to the financial industry, such as fraud detection, improved investment strategies, and cyber security.

Most of the big banks are looking for new data scientists with different backgrounds that will help them in their efforts to improve compliance processes or customer service. 

Data scientists familiar with Python Programming language and Digitalization, Data Visualization, and Data Engineering skill sets will find the most job opportunities in this sector.

Latest Data Science trends and events in Banking and Finance Industry

The finance industry has been using big data technologies in recent years.

For example, some of the world’s biggest banks such as HSBC, American Express, JPMorgan Chase & Co., Barclays Bank PLC, Bank of America Corp., Citigroup Inc., Wells Fargo, and more have been using Apache Spark for aggregation and processing large amounts of data daily.

How Data Science will impact the Banking and Finance Industry in the future (Predictions)

In this sector, big data analytics can help to improve operational efficiency through lower costs, speed up transaction time during crisis situations by using real-time analytics on customer activity with social media monitoring.

Data science in the finance sector will help to reduce operational costs, increase customer loyalty and improve transparency.

In addition, more financial institutions are looking to invest in big data technologies and hire data scientists who will allow them to build customised solutions that fit their own unique needs and work collaboratively with other IT professionals on their teams.

Future business scope and career for Data Scientists in the Banking and Finance Industry

As data science becomes more prevalent in this sector, professionals who have the required skills and experience are expected to have excellent job prospects.

According to a survey from IDC, the largest investment made by 28% of banks and other financial institutions in the upcoming years will be big data and analytics.

In 2018, the big data and analytics solutions market was estimated to be around $189.1 billion. This figure is predicted to double in 2022. Data analytics revenue has been growing at an average of 13.2% annually since 2018 and is expected to hit $274.3 billion by 2022. (Source)

In addition, most of the new jobs available for data scientists in this industry will not require a PhD or Master’s degree, as most of these positions will go to candidates who have strong programming skills and experience with traditional statistical techniques such as regression analysis maximum likelihood estimation.

The demand for Data Scientists in the financial and banking sector will continue to rise in the upcoming years. There’s also a strong need for statisticians who can apply data science techniques to large amounts of data with Python or R programming skills.

  1. Construction Industry

The construction industry has always been vital for the world economy. What this sector lacks in profitability, it makes up by being a colossal opportunity creator for countries and companies worldwide.

It isn’t easy to imagine how our civilization would look without buildings, roads, and bridges. Consequently, the construction industry accounts for over 11% of global GDP and is one of the largest employers worldwide. (Source)

The demand for data scientists in this sector has been rising since 2018, mainly because more companies are using predictive analytics to reduce operational costs and increase customer satisfaction.

In addition, big data can enhance operational efficiencies and increase productivity in the construction industry.

How Data Science helps in the Construction Industry

Data scientists are mainly used to monitor and analyse a company’s or country’s construction projects. They also help determine which infrastructure projects need to be constructed based on their geographical location, demand for new buildings, and availability of resources.

In addition, big data is increasingly being used by the private sector to reduce inefficiencies in many aspects of the construction industry.

Data science allows companies to monitor and analyse all the activities of their workers, including those that take place outside office hours, and help to prevent accidents from happening on site.

In addition, data scientists are vital for predictive maintenance, helping companies identify infrastructure in need of repairs before catastrophes happen, and they can save the industry millions of dollars.

Data science can also help construction companies to reduce expenses and increase process efficiency by using geospatial analytics tools. In addition, data science is also used in construction projects through mobile apps, artificial intelligence for factory productivity optimisation, and other applications. (Source)

Latest Data Science trends and events in Construction Industry

Data scientists will be in high demand for creating and enhancing models that can predict which projects are likely to fail in the upcoming years.

Data science is used in the construction industry for risk management and compliance with local regulations on safety issues. The latest data science trends have shown a growing interest in analytics tools such as machine learning to monitor Jobsite activities and increase safety.

The construction sector is also using big data to improve the accuracy of market forecasts, identify new opportunities, enhance design quality, improve logistics processes, reduce costs across the supply chain, manage claims and risk management issues, enhance corporate reporting, etc.

How Data Science will impact the Construction Industry in the future (Predictions)

Although data science has already brought many benefits to the construction industry, it will have a much more significant impact in the upcoming years.

Artificial intelligence and machine learning will be used in multiple areas of this sector, including automating some of the activities carried out by data scientists.

For example, AI chatbots are expected to take over tasks such as helping construction companies maximise their resources and improve quality control across all stages of a project.

In addition, data science will allow for optimising land development in the future by collecting information on climatic conditions, soil properties, parks, and schools within a given neighbourhood.

Data science will also be used to help construction companies reduce the impact of human error in large projects that take years to complete by using data collected from smaller, less complicated projects as a benchmark for success.

Big Data Analytics in the Indian Construction Industry

Analytics tools such as machine learning are widely used in India across various sectors, including the construction industry. This country is committed to making data-driven decisions and has recently announced an initiative called ‘Digital India’ that aims to help Indian citizens get better access to internet services.

As a result, the construction sector is slowly adopting big data analytics tools and applying predictive maintenance methods to its operations. Companies are also using analytics tools to maximise their resources to attract investors and maintain a competitive edge in the market.

In 2016, Microsoft partnered with Larsen & Toubro Limited (L&T) – one of the biggest construction companies in India – creating a marketplace that connects startups working on innovative technologies such as machine learning and IoT with one of the largest construction companies operating in India- this is just one example of how this sector will embrace data science and integrate it into its operations in the near future.

The construction industry is using various analytics and data strategies to improve its capability and performance. It’s no surprise that the big data trend in this industry will continue to rise due to how valuable information can be obtained through analysing large amounts of data. 

In addition, companies are starting to adopt a data-driven strategy, and many of them are looking for employees with the appropriate skills to provide them with the tools they need for success.

Future business scope and career for Data Scientists in the Construction Industry

With more businesses using big data analytics technologies in this sector, professionals who have experience with math, statistics, computer programming, and machine learning will find themselves very much in demand.

Big Data Analytics has the potential to revolutionise operations and provide several benefits to the construction industry, such as:

  • Reduced cycle time through predictive analytics.
  • Improved utilisation and profit margins.
  • Improved capital planning and scheduling capabilities.

The demand for data scientists and predictive analytics in the construction industry will continue to grow as more companies invest heavily in big data technologies.

  1. Transportation Industry

Data Science and AI have become essential factors in the transportation industry. The world is progressing towards driverless cars and a more efficient way to transport goods. 

Self-driving truck company Starsky Robotic conducted a live test drive with its complete autonomous driving system and will be testing it in its lab later this year.

This is an excellent example of how big data and machine learning are transforming the transportation industry. Big Data Analytics can be used in the Transportation sector to make better decisions about shipment optimisation, trip planning, etc., which helps companies save time and money.

How Data Science helps in the Transportation Industry

There are many ways in which Data Science can help the transportation industry. For example, it can optimise freight-based planning using data from past shipments and present and future traffic predictions.

It’s also used for developing a more efficient scheduling system that integrates supply chains with similar needs. Big Data Analytics can help companies make better decisions about when to deliver shipments and how much to charge clients.

Data science technologies are improving the shipment process in several ways:

– Using data to optimise a weekly or monthly shipment plan can help save thousands of dollars per year.

– The ability to monitor shipments for any delays or issues throughout their journey can give you peace of mind knowing your goods.

Latest Data Science trends and events in Transportation Industry

Data science in the transportation industry has become an integral part of how companies operate. There are many conferences and events aimed at bringing together professionals from this sector to discuss machine learning and analytics applications for the transportation industry.

Smart transportation uses IT and AI to manage and coordinate transportation systems efficiently. The application of smart transportation networks gives travellers an effective way to coordinate traffic.

In addition, the use of smart transportation also ensures safety for the drivers. The market is experiencing growth due to its increasing demand across all geographic locations.

The application of data science in the transportation industry is not limited to on-demand services or vehicle connectivity. It also includes fleet management, dynamic route planning, vehicle tracking and monitoring, and much more.

How Data Science will impact the Transportation Industry in the future (Predictions)

In the next five years, we will see more advanced technologies with the potential to transform how companies monitor and operate their fleet. – The development of self-driving commercial vehicles is expected to go mainstream, with car manufacturers planning on launching an autonomous vehicle by 2023. (Source)

The demand for AI talent is increasing to help identify insights from large datasets collected by transportation companies, which contributes to the overall growth of big data, as there is a shortage in the supply of advanced skills required for complex analysis.

Another major area of growth is the application of big data in electric vehicle fleet management. With more people shifting towards using electric vehicles, there is a growing need to understand the impact of this shift on various aspects such as supply chains, logistics, etc.

The future for big data in the transportation industry is dependent on the full exploitation of AI technology. It includes collaborative robotics, machine learning, image recognition, etc. With rapid advances in this field across all industries, the transportation industry is expected to adopt AI-based technologies on a larger scale.

The future for big data in the transportation industry is dependent on the full exploitation of AI technology. It includes collaborative robotics, machine learning, image recognition, etc. With rapid advances in this field across all industries, the transportation industry is expected to adopt AI-based technologies on a larger scale.

Future business scope and career for Data Scientists in the Transportation Industry

Frequent traffic on roads and railways can lead to a rise in road traffic accidents, leading towards fatalities. People are moving towards electric vehicles, but these cannot be done without proper planning by transportation companies. 

With the development of autonomous vehicles, there will be a growing demand for high-end tech professionals who have expertise in machine learning, big data, and artificial intelligence.

Since more companies are moving towards machines rather than humans, they will need employees who know how to work with big data technologies. It’s no surprise that professionals working within the transportation sector or other related industries will see a growing demand for their skills.

As the transportation industry is expected to witness a significant surge in the use of autonomous vehicles, companies need to prepare by developing a plan to embrace disruptive technologies. The growing need for data professionals will lead to promising career prospects within this industry.

Conclusion

In conclusion, here is what we could look at in the field of Data Science in 2022

  1. Rise of Artificial Intelligence (AI) and Machine Learning(ML) based Data Science applications:

We believe there will be a rise in data science/ machine learning models as the back-end engines for AI and chatbots. This will lead to increased use case discussions/implementation within enterprises and different verticals. For example, we see machine learning for targeted marketing/advertising, fraud detection, anti-money laundering (AML), credit scoring, etc.

  1. Automation – Automating the Data Scientist’s Life:

We believe that tools will automate the data validation and feature engineering process and simplify it based on users’ needs.

  1. Data Science platforms/frameworks – Will be adopted widely within enterprises:

There will be more demand for data science platforms/frameworks like H2O, Apache Spark, AzureML, Google Prediction API, etc., by enterprises to embrace the predictive analytics culture across their organisation. These platforms/frameworks will automate data science/ machine learning-based analysis within enterprises.

  1. Machine Learning (ML) and Deep Learning (DL) – Major focus areas:

We believe that there will be increased use cases in these two areas across different verticals, especially in Natural Language Processing (NLP), image recognition, image processing, etc.

  1. Spark and Frameworks – R will be the preferred choice:

Spark is gaining a lot of momentum in enterprises, especially in data streaming and machine learning. As Hadoop and its ecosystem mature and with YARN, we will see more Spark-based applications/ pipelines being used within enterprises. However, do note that the open-source community for Spark will grow exponentially over the next few years, and there are already a lot of great tools being contributed by the open-source community. This will benefit data scientists tremendously as they won’t have to re-write their code/applications or convert them into Spark pipelines. In addition, the R language will also maintain its status as the preferred language for data scientists. With the recent add-on in Spark 2.0 (Structured Streaming), we will see a rapid increase in the use of Spark within enterprises with Structured Streaming APIs that allow analysing streaming data and structuring it into a table to join with static data sets.

  1. Data Scientists will be critical in Data Engineering teams:

With the rise of AI/ML-based applications within enterprises, we see data science skills becoming a must for building data engineering teams. In addition to traditional ETLs and data pipelines, real-time streaming pipelines are increasingly based on Apache Kafka, Flink, Spark Streaming. This will call for increased data science/ machine learning skills within the data engineering team to build or incorporate data science models into these pipelines.

  1. Data visualisation – Will become more robust and reach the masses:

We believe that tools/frameworks like D3.js (JavaScript library for visualising data in web browsers) will have broader penetration across enterprises. There will be increased use cases where enterprises can benefit from these visualisations to convey information about their business/products, market research, etc.

  1. Improvement in ease of doing Data Science – The focus is on simplicity:

Data scientists are spending too much time figuring out how to set up their environment, do the bare-bone ETLs of getting data into a structured format, and then work on it. We believe that there will be an increased focus on simplifying the process with simplified interfaces/APIs in open-source tools like Python, R, Spark 2.0 Structured Streaming APIs, etc.

  1. Open-source tooling – A huge focus area:

For widespread adoption, the open-source community will play a huge role in simplifying data science tools and platforms with simplified APIs, libraries, modules, etc.

  1. Big and small enterprises use Artificial Intelligence (AI): 

Enterprises use AI to solve specific use cases across different verticals. Enterprises want to build solutions that address these specific business problems. And, they are looking at simple ways to get started with AI within their enterprise.

  1. Traditional analytics tools/platforms will continue to evolve rapidly:

There has been a focus on building innovative analytics tools/platforms for enterprises. Some of these tools are focused on solving specific problems, while others try to solve many different problems/ use cases across enterprises. The key themes that we see evolving in 2019 within these traditional analytics tools are the following:

  1. Simplification – There will be an increased focus on simplifying the process of building AI/IoT-based solutions by providing simplified APIs, interfaces, modules for data scientists to build these solutions from scratch. The OpenShift team recently released a cool new tool called PipelineAI that facilitates the creation of custom machine learning pipelines and deploys it as a web service.
  2. Automation – There is an increased need for automated data science pipelines across enterprises to simplify deploying data science models significantly. This requires close collaboration between Data Engineers, Data Scientists, and Data Operations teams.
  3. Edge analytics – Enterprises are looking at building/deploying AI/IoT-powered applications to process data collected closer to where it is generated.
  4. Training and deploying Machine learning models: Enterprises want tools that can help them train and deploy their machine learning models within their existing enterprise stack with minimal customisations and configuration changes.

These are a few trends to look out for in Data Science in 2022. As always, we believe that the best way to predict the future is by inventing it.

We would like to know what you think lies ahead for data science in 2022

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