Have you ever wondered what it takes to build a world class data science team in less than 6 months? In this article, we will highlight the objectives and scope of developing, operating, and maintaining a great data science team for enterprise applications using business analytics, data analytics, and in advanced cases, AI and machine learning solutions.
Let’s explore the scenario.
#1 Understanding data science
Data science is ruling the technology space, enabling businesses to produce highly sophisticated products and solutions that solve complex yet common problems. Thousands of professionals apply in such organizations through advertised jobs online or popular networking channels such as Monster, LinkedIn, Glassdoor, and so on, hoping they would be able to be part of an ongoing data science project. The recent trends show that a majority of companies prefer to hire data analysts and scientists from the top data science course in Gurgaon and Delhi NCR and the exact reason for this phenomenon is the pace at which analysts from these courses speed up the product development process.
#2 What is enterprise software development?
Enterprise software or enterprise application software/ EAS remains one of the biggest hiring destinations for IT analysts and software developers certified by data science courses in Gurgaon. The basic purpose of these EAS suites is to automate one or more business processes, including ERP, Content Management, IT networking and services, CRM, and Marketing analytics.
#3 How do Data Science and Software Development work together?
In recent times, we have witnessed a growing synergy between software development, product management, and data science. With new technologies entering the market and breaking the ceiling in the industry with their rapid adoption, businesses are integrating their data science initiatives with product development using EAS concepts.
Let’s try to understand how this synergy spins a face paced career graph for data scientists and in return, ensures greater efficiencies for the businesses that hire these highly trained professionals.
New technologies are built on foundations of traditional software models
Enterprise software development is a core foundation of any modern day IT and DevOps company. These activities involve the sporadic development of SaaS solutions that can enable companies to modernize their IT resources with superior API and connector integration, Cloud computing analysis, and consolidated IT integrations powered by organized workflow automation tools. A great example of an enterprise software solution would be your Google suite that includes Google Analytics, Gmail, Google Sheets, and so on. Microsoft Office, IBM, and Oracle also offer a huge range of enterprise software suites.
Scenario 1: Enterprise applications for IoT, 5G, and Big Data
IoT sensors are generating a huge volume and variety of data across devices, networks, and access points. These require a sophisticated chain of Big Data management systems that could piece together all the important information in a unified manner. Data analysts speed up the process of data collection and analysis, enabling businesses with a clear analysis of how existing IoT connectivity and sensors devices.
Scenario 2: SaaS based Blockchain / Blockchain as a Service (BCaaS)
Decentralized finance or DeFi is getting wide scale popularity in various industries, including in healthcare, media and entertainment, and in banking and insurance. What started as a movement in the open source (OS) environment has steadily garnered the attention of world class DevOps teams who now create enterprise Blockchain software as a Service to enterprise customers. Big blockchain and crypto companies like Binance, Coinbase, Crypto.com, BharatPe, and Bitcoin are changing the world with their global crypto solutions. If any data science professional is interested in the Blockchain market, gaining hands on knowledge in enterprise software development is a must!
Scenario 3: Automation and Personalization
With data science training, you can master Data analysis in EAS.
Finally, let’s come to the automation and customer experience or personalization segment of data science. These are considered the most important components of any business decision. From a branding perspective, business leaders are spending 50x more on their digital transformation processes, and a bulk of this activity depends on how fast the organization adapts to automation and meets the customer requirements with superior Customer experience.
Without data science, it is impossible to think of having a future in either automation or in personalization development. All IT leaders acknowledge the critical role AI and machine learning play in sustaining these digital transformation goals, and yet the starting point in this journey always point to stability in Enterprise Software application development and maintenance.