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Trends Worth Paying Attention to in the Data Analytics Profession in 2021

Trends Worth Paying Attention to in the Data Analytics Profession in 2021

madhu mitha 508 13-Jul-2021

It is time to prognosticate trends and make technology predictions again. The year 2021 is about three trends. Three trends that data analytics as professionals must look closely at are data exchanges, open artificial intelligence, and optimize large data storehouse layers. The data maturation and the maturation of machine learning and artificial intelligence landscapes are what link these three.

These technologies are fairly new evolutions. However, it does not seem so as there is a lot of conversation around these topics. All three technologies are going in a similar direction, from concept to practice in an effective and scalable way. These technologies offer value to the companies.

The big prediction for 2021 is that these technologies will conform to the promise to deliver.

1. AIs writing skills and open AI

Open AI released GPT3 last year. It is a research organization that developed this aspect of artificial intelligence that creates a text which mimics the text created by humans. GPT3 can write codes for software, write blog posts and work like a chatbot to answer questions. It is difficult to tell apart if what is created is written by a robot or a human. GPT3 can write a blog post of a thousand words, all you need to do as a user is allot a topic.

Who can benefit from GPT3? Everyone who wants content. It is capable of writing codes and designing websites. It can write articles and develop content. However, it can't replace humans yet. Although, it offers value to an organization when it is short of staff. It is not too far when this technology will be present everywhere. We will be unaware of it.

2. Optimized Large Data Storehouse Layers

The cloud is the place where data is stored in huge amounts besides hard drives. This is usually where organizations hold important information for using it in the future. The challenge organizations face with these storage systems is not being able to find the data when they need it. The larger the data gets, the larger the storage vets, and it feels becoming harder to find data when it is needed.

Technologies like Delta lake, Iceberg, and Hudi have emerged recently. These optimize the storage of these large data making it simpler to find data. As a result, the search had become precise. Organizations are adopting this approach as it makes data retrieval more efficient. These are available for an average person. You don't have to be a data scientist to know about the underlying operations.

3. Data Exchange

An organization has valuable data that can benefit another organization. This is where data exchange comes into the picture. Data exchange needs an effective platform that extends security, transparency, quality, and integration to another level.

Data exchange is interconnected to the other two points mentioned above. It is an essential component of a data strategy.

We expect to witness an unbent growth pattern where all three technologies intersect at the right time.

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