Currently, Organizations collect vast amounts of data in all forms across all departments and functions. This mass of data tell facts that are relevant for key decision making that leads to successful business operations and smooth organizational administration. Comprehensive Data insights can help businesses understand challenges and devise solutions while also helping them predict worst case scenarios. Due to the complete visibility that it provides, demand for Data Analytics applications is on the rise.
At Gowitek we have developed Data Analytics applications for industries as varied as Retail, Healthcare Energy , Agriculture , Manufacturing and more. We have repeatedly used Golang for developing our Data Analytics Applications.
It is a great fit at every stage of the data analytics process.
The process of data Analytics begins with Data Collection. Any Data Analytics application should be able to collect and store vast amounts of data. Collected data must be free of errors, should be able to take logical, cost and privacy considerations. An ideal Analytics solution should also be able to store incoming data such that further modeling and reporting is possible. It must further enable joining data from multiple sources in a logical manner.
Databases in Golang such as InfluxDB, Minio, CokroachDB are proof that Golang is suitable for data gathering and organization. Go has several APIs for all of the commonly used datastores such as Mongo and Postgres. This kind of resource backup makes it easy for Golang Data Analytics applications such as recommendation engines to collect and organize data..
The following sequential step is to Process data sets to clean up messy raw data. This step helps to move data further into the data modeling process. Usually, this cleaned data is stored as a fresh data set for programmatic pre-processing. Algorithms are then applied to build and validate data models while performing machine learning/ deep learning and statistical analysis through neural network. In Go programming language the gonum organization powers data science computations by providing numerical functionality. Floats, Matrix, Stats, gograph are all great Golang projects related to data analytics, statistics and arithmetic. These help Golang programmers develop arithmetically sound and comprehensive Data Analytics applications.
Good data visualization of results means sound decision making by users. The most challenging part of the data scientist’s job is taking the results of the investigation and presenting them to the public or internal consumers of information in a way that makes sense and can be easily communicated. Again Golang projects such as gophernotes, dashing-go and gonum plotting make it easy to create powerful visualizations. Creating Custom APIs for this purpose and utilizing resources such as D3 contribute to the comprehensiveness of Golang Data Analytics applications.
At Gowitek we have worked on several Data Analytics projects spanning industries such as Agriculture, Manufacturing, Healthcare, Retail and more. Scalable and efficient Data Analytics solutions strongly support business goals and solve core challenges. To know more about how data analytics can boost your business, please drop us a message.