Other than the resurrection of various Artificial Intelligence technologies, Big Data transformation has bought a meaningful development in the past few years. Today, the Big Data industry is worth $189 Billion in the market. Study shows that there was an increase of $20 Billion in 2018 and keeping this in mind we can predict $247 Billion by 2022.
The Walt Disney Company Chairman once said:
“Technology is lifting of creativity and transforming the possibilities for entertainment and leisure.”
Disney has a significant customer base which ultimately results in massive data. With using Big Data Analytics, Disney could extract the magic again. This problem could only be resolved with big data analytics tools. We plot this example as you could understand how Large industries could manage to handle the data flow and gain insights to improve business using the latest technologies.
The year 2020 has already started, and once again, we can put our caps on and predict the Top analytics trends for 2020.
What is Big Data?
Big Data is extensive data, structured or unstructured, which helps businesses to establish patterns in human behaviour and interactions. The companies would leverage the data to enable better decision making and what customer’s look for.
In the year 2001, Doug Laney, former VP of Gartner’s Chief Data Officer introduced 3Vs: Volume, Variety and Velocity. With accelerating challenges year after year, additional features were defined:
4V’s of Big Data
- Volume refers to the Scale of Data
- Variety refers to different forms of Data
- Velocity refers to the analysis of streaming data
- Veracity refers to the trustworthiness of data
Big Data Analytics Trends and Solutions
The year 2020 is another year of great innovation and evolution for Big Data solutions companies. Read on to get some thoughts on Big Data trends and predictions.
Augmented Analysis is the future of data and analytics
Augmented Analysis is an emerging trend that is heavily used by banks. The data is processed and is automated by using Machine Learning and Natural Language Processing (NLP). To get precise results in a simple and accessible format, we use Augmented Analysis. Here the data is processed through a streamlined automation process from various sources like cloud data, internal data, external portals and different other locations.
This way, the analyst will combine all data, process and check for redundancies with preparing them for analysis. The data is stored in the form of the cluster and used for quick real-time analysis with sophisticated tools. Later the information is automated to identify the pattern and trends.
The Cloud is a new Data Lake
Cloud-based technologies are overgrowing. It uses a process that moves the data integration and preparation from an on-premises solution to the cloud. If you want to be with a prevalent trend, then use the hybrid deployments. Be an early cloud adapter and switch your industries online. Move all data entirely and utilise cloud storage for dynamic workload with using the multi-cloud methodology.
Digital Transformation is on Top-Level of Data Strategies
Without data, there cannot be Digital Transformation. New technologies are developed to help businesses. Companies embrace business operations from manual to online. Today Digital transformation has become a priority for most of the activities, and the world is very soon growing digital. According to the IBM research, 1 out of 3 leaders use Digital transformation to help companies get accurate data.
Artificial Intelligence and Machine Learning will continue to Evolve
These two AL and ML trends are accelerating in data-driven organisations. One can make use of ML and AL algorithms within data pipelines and show more traditional BI and data integration platforms. The year 2020 will bring an automation framework that allows data scientists to create close to the production-ready outline.
Data-As-A-Service is one of underplayed user
Data-As-A-Service access data online from shared spaces. It is useful for a large organisation where employees need to share vast data between departments. It works similarly as downloading a volume of data like music or movies from the internet. Data-as-a-service is an architecture that uses a central hub in an organisation and promotes self-services with improving the productivity of the organisation. So, keeping data at one location can help multiple users to access data with ease.
Evolution of Healthcare Services
Researchers believe that medical data is vast. As the information is used to store the patient disease, cure and other preventive measures. Previously, there was no proper tool that connects all medical records centrally. But with IoT devices, this could be possible to manage hospital equipment. Many researchers have discovered the usage of IoT devices that track and monitor patient’s conditions. Few of the scientists have created a robot to attend patients and perform operations.
R & D in various Industries
After looking at the results, Big Data analytics is now used for multiple R & D operations to manage the organisation’s insights, customer preference, and create better products that customers wish for. The R & D department uses Big Data analytics to carry out simple social media management, manufacturing automation, improving product quality, better customer support, and to automate sales pipeline. One can make use of robust Big Data Analytics services to enhance the business reach and increase business ROI.
How do Companies deal with Big Data?
Companies mainly prefer three operations (which was recently used by Rolls-Royce to adopt Big Data-driven approach)
- After Sales Support
How do Companies deal with their Big Data?
Big Data Analytics use two operations (which was recently used by amazon to server their customer in a better way)
- Create a personalised recommendation system
- To improve Customer Service operations.
On a Final Note
It’s clear that new technology like Big Data is transforming based on how companies are operating. Digitalisation is everywhere, and the latest technology has become viral with implementing data into operations. Ultimately Big Data is used to enable smart decisions and help companies use a robust analytic environment, which includes predictive analytics, descriptive analytics and prescriptive analytics.