data science trends

Data Science Trends You Need to Know [2022]

There is a constant addition to the large pool of data that is already present in the world. By 2025, 463 exabytes of data will be created every day. This massive explosion in data is undoubtedly going to create an unending demand for data scientists and analysts in the coming years.

The surge in data and data science advancements are conducive combinations for a growing business. Companies can utilise these powerful advances to deliver their products seamlessly and gain an edge over their competitors.

Future data scientists and analysts are in for a treat as there will be large vacancies for these posts in the following years. This highlights the need for prospective data scientists and defines why data science is a must-have skill set in the oncoming future

Here are 8 data science trends to keep an eye on:

1. Big Data Analytics Automation

Every enterprise wants to know its future, and the union of Big Data Analytics with Automation is a giant leap towards this goal. Automation and Big Data Analytics are both splendid technologies that enterprises employ to increase their productivity. 

According to Alteryx, “Analytics Process Automation (APA) will help in unlocking predictive and prospective insights that will lead to wins and faster returns on investments”.

Businesses can use APA technology to upscale their self-service modules and reduce operational costs. The ease of searching for categorical data is also a considerable plus-point of process automation. Such tools make data accessibility a cinch.  

According to a Snowflake survey conducted in 2018, more than 48% of executives termed Data Analytics as an essential part of their work culture. This makes Big Data Analytics Automation technology a must-have skill for budding data scientists.

2. In-Memory Computing

Enterprises need more and more data storage daily to store the ever-growing amounts of information collected every day. In-Memory Computing technology aims to make data storage convenient and cost-effective for enterprises.

Data is conventionally stored in a centralised database which is essentially a server containing thousands of data-storage devices. The In-Memory Computing technology revamps this by keeping all this data on the RAM present in different computing devices in the chain.

RAMs are exponentially faster than conventional storage devices like Hard-Disks or SSDs. This translates into immediate data accesses and assists in implementation of real-time data scaling. This technology goes a long way in solving bottleneck issues within conventional storage methods.

According to GridGain, the bandwidth offered by in-memory computing is 100 times faster than the most expensive storage in the market. 

According to Mordor Intelligence, “The In-memory Computing Market was valued at USD 15.96 billion in 2020. It is expected to reach USD 61.66 billion by 2026 and grow at a CAGR of  25.37% over the forecast period (2021-2026)”. 

This will create a plethora of employment opportunities for future data scientists who are well versed with this technology.

3. Data-as-a-Service

Data-as-a-Service offers users access to their files digitally through the internet. The pandemic has led to a massive surge in the data-as-a-service industry. MarketsandMarkets estimates Big-Data-as-a-Service (BDaaS) market size is estimated to grow from USD 11.3 billion in 2019 to USD 42.7 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 30.5% during the forecast period.

The DaaS technology lets subscribers stream their files on-demand directly from the cloud server, making it a great help for SMEs. Small and medium-scale industries cannot afford to build solutions for analysing their data entirely.

SMEs can perform every action that their mammothic counterparts perform, but in a cost-effective manner using DaaS technology. The entire concept of DaaS requires zero investments and makes data-sharing convenient, making it a perfect option for enterprises. 

The growing popularity of DaaS technology will need experts to handle technology with smart skills and wits. If you are a data scientist, you should consider learning more about the implementations of DaaS and the ways in which it can benefit data usage standards.

4. Graph Technology and Analytics

Enterprises believe gaining high-quality, accurate data is sufficient for getting better results. However, this could not be further away from the truth. Structured and precise information is not enough in most cases unless this data is converted into valuable, usable insights.

Graph technology and analytics make it surprisingly easy to visualise data and find meaningful patterns within it. This helps an enterprise develop products that cater to the end user’s needs, but in a productive way.

It is believed that in the next 3-4 years, graph technology is going to see rapid growth in its applications. By 2024, more than 30% organisations will use graph analytics to analyse their data. It will also create scope for future data scientists, and understanding graph technology will surely help them in their careers.

5. IoT-Analytics Integration

With the implementation of IoT in smaller devices, more and more devices are connected to the internet every day. According to Statista, more than 75 billion IoT devices will be connected to the internet in 2025.

Many IoT devices give precise information about the user base that was never accessible a few years ago. The information provided by IoT sensors is reliable and eventually helps in creating precise analytic solutions.

The IoT sensors on these devices transmit a massive amount of data that needs correct handling. The accurate analysis of this data can provide a competitive edge against other enterprises.

IoT analytics provide better insights and return on investments to a business. This makes expert data scientists highly valuable in the industry; future data scientists can make an intelligent move towards this field of analytics.

6. Real-Time Intelligence

Real-time intelligence helps in automating the decision-making process by using past and

current information in conjunction with each other. Real-time data analysis is way more critical for enterprises as in many cases user behaviour can rapidly change. Using a survey to gather data about user behaviour can take time, and this collected data may become obsolete before it’s put to use.

Real-time intelligence technology comes into play here; it gives current information about different parameters within an enterprise. The best part of such technology is that it can help an enterprise make decisions according to the insights derived from analysed data. 

It is believed that more than 50% of new business systems will use real-time intelligence by 2022. Improving technology and making it fully automated will have challenges that many data scientists will face in the near future.  

7. Data Governance

GDPR and CCPA compliance policies were implemented to bring order in their data collection practices. These laws regulate the reckless misuse of consumer data with the provision to

reject salt of confidential customer data to third-parties. Businesses are also required to maintain a copy of their customers’ personal data.

According to PwC, “52% of tech, media, and telecom respondents rank data privacy among the top 3 policies impacting their business.” These laws witnessed ground-breaking success in protecting consumers’ data.

Many businesses are still puzzled about compliance laws and have not managed to

comply with them completely. Budding data scientists can update themselves with the latest information as the regulations on data collection will constantly increase in the future.

8. Super-Sized Data Science in the Cloud

Storage demands and processing capabilities are witnessing a surge due to growing data

collection by enterprises. Building a localised data storage warehouse might have been feasible a few years ago when the demands were limited to some gigabytes of data. In the present situation, the scenario has changed tremendously. Today enterprises need tons of storage and processing capabilities that require a hefty investment to begin with. 

Cloud solutions are a great way to handle data assets using affordable solutions from tech giants like Google, Microsoft, and Amazon. These cloud services offer multiple perks over the conventional localised data storage solutions like high scalability and zero downtime. Cloud services are exceptionally affordable and ensure data security is of the highest order. 

Cloud data storage and computing solutions are going to see massive booms with more small

and medium sized enterprises coming into the picture. 

Bookmark These Data Science Trends

Kloudio, as an organization, caters towards providing cost-effective solutions for organizations of all sizes. Each data platform is capable of supporting growing teams and meeting the demands of enterprises. 

By offering smart solutions, organizations can enjoy the comforts of using self-service data reporting tools, without having prior knowledge or existing technical skill sets. The idea is to wean away from technical skill sets, without compromising on data insights. 

To see what Kloudio can do for your organization, create a free account today. 

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