In the last few years, we have witnessed huge growth in the field of data analysis. Nearly 93% of the start-ups that have been launched in India in the last 6 years are directly involved in data analysis or plan to become data analyst companies in the next 2-3 years. A quick research on the latest data analysis trends would reveal that these data analytics companies are heavily investing in agile Big Data Intelligence, Data Science, and Cloud computing projects.
From optimizing data analytics software to optimizing hardware used in the enterprise, a data analyst has to take care of every minute aspect of a big data project. If you are planning to start a data analytics project in India this year, this blog would help you gain a bird’ eye view of the existing trends and what data science models you can use to expand your career.
Agile Data Labelling
Data labeling is a very important component of data management and processing techniques. These work in supervised machine learning architecture where the software is expected to return both accuracies as well as quality. That’s where the agile concept is introduced in data labeling.
The concept of agile frameworks has been around in the industry for some time now. However, applying agility to modern data management streams highlights the severe complexities that big data engineers often face when dealing with the raw data. If you critically review Machine learning projects, you would appreciate the importance of agile data labeling tools that makes any AI ML software development project more efficient and productive.
So, we briefly covered agile. Let’s go to the next big thing in data analytics management. It’s called auto labeling.
A majority of data analytics companies in India and around the world spend more than 80 percent of their time on building AI ML algorithms that specifically work toward improving the fine nuances of data processing – data preparation, cleansing, and labeling. Auto Labelling helps save time and cost associated with data processing by removing manual steps.
Auto labeling is a powerful medium built on advanced machine learning models. These ML models are created to generate synthetic labels using unstructured raw data sources, therefore allowing analysts to streamline their entire data processing methods in a single unified dashboard.
90% of the projects taken up by data analytics companies fail at the data integration stage. Close to 40 percent of these companies prefer to outsource these efforts to AutoML software to save time and gain accuracy of view and results in the long run. Experts say that’s not the best way to do it as ML models are still dependent on the efficacy of data you provide to manage data integration! It’s like attempting to solve a problem with zero visibility and knowledge of the premises.
So, how to tackle this?
Simply get the steps involved in Extract Transform and Loading in one single window!
Use data integration practices that allow different disparate systems in the data analytics workflow to sync together and merge different types of data within ETL / ELT cycles.
There are many data integration tools that are currently available for data analysts.
How to do ETL?
90 percent of the ETL projects are currently developed on the Python platform.
Why Python? It’s not only available in open source but also branded as the best programming language for cloud computing, data processing, and machine learning modeling. Not many programming languages can boast of supporting so many processes in one go. However, that’s not the end of the story. Python is popular in ETL because of the benefits it provides to data analysts when it comes to granting access to high-quality, clutter-free Big Databases for training various techniques, such as NLP, Computer vision, and neural networking.
ETL workflows work with multiple file formats, such as PDF, FLV, SQL server, etc, and are usually processed through the data integration tools to pour data into diverse channels. Top data analytics companies that solve complex problems in the fields of marketing and sales, e-commerce, and geo-location analytics use ETL data integration tools to plug gaps in the conventional data processing batches.