Perspective

Software Outsourcing Codea operates at the intersection of energy, excellence, and invention.

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CSR Initiatives

We set ambitious goals and make strategic investments to drive improvements...

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Diversity

Codea is committed to creating a diverse and inclusive workforce. There´s growing...

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Innovation

The Spark Innovation Ecosystem ‘Applies Next´ beyond what is developed in house...

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Cloud Consulting

We impeccably manage your cloud enablement from choosing relevant cloud services to the successful execution of cloud transformation.

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DevOps

DevOps

Artificial Intelligence

Our AI services & solutions help gain high-quality and high-accuracy AI capabilities.

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Data Science Consulting Service

Data Science Consulting

Robotic Process Automation

Robotic Process Automation

Staff Augmentation

Codea has already helped 200+ clients augment their teams via hiring dedicated developers. Keep reading to find out what staff augmentation means, what are its benefits, and how to get the most out of augmenting your team with software developers.

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Our Industries

Discover how Codea´s breakthrough technologies are transforming industries with smarter ways to do business, new growth opportunities and strategies to compete and win.

Freight & Logistics

Codea FR8 offers an accurate freight and warehouse management system with built-in redundancies.

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Retail Solutions

ERP for retail helps retailers manage their businesses.

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Hospitality Solutions

Hospitality Solutions comes with integrated reservations systems.

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Fleet Management

Our Software helps companies to overcome the industrial challenge.

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A FEW CLIENTS WE HAVE HAD THE PRIVILEGE OF WORKING WITH






Why Data Science?
8 Ways a Data Scientist Can Add Value to Business

1. Empowering Management and Officers to Make Better Decisions

A data scientist can add value to a business by empowering the management to make an informed decision on the business process and manpower management. Using various analytics and tracking tools, a data scientist can track, measure and record workforce performances and guide the management how to enhance workforce performances and bridge the gaps between various departments to generate more revenue and make more profit. Their data-driven analysis and performance metrics can automate an organization's success by helping management achieving their goals and targets rigorously.

2. Directing Actions Based on Trends—which in Turn Help to Define Goals

A data scientist collects data-driven from different analytics and data driving tools to examine, analyze and explore data of an organization and recommend the necessary actions and steps to be taken to improve the perforce of it and achieve its business goals perfectly. He (data scientist) can measure the journey of customers between the time of discovering a business and purchasing products through an actionable insight-driven from data analysis and other records derived from intelligent analytics, call to action buttons, and touchpoints. From the data-driven analysis, he can direct the organization on how to increase customer engagement and enhance growth hacking that subsequently improves profitability and revenue generation.

3. Challenging the Staff to Adopt Best Practices and Focus on Issues That Matter

The responsibility of a data scientist is to make stuff familiar with the latest technologies and analytics adopted by an organization so that they become well-versed with the latest system and prepare them accordingly. They get to know how to use these and get an insight into their executed works and performances on a daily or monthly basis. Thus, they can understand the product capabilities, present and upcoming challenges and the right ways to achieve these accurately.

4. Identifying Opportunities

Using the current analytic system, a data scientist can interact with the existing processes of an organization and drive the latest data to do a SWOT (strength, weakness, opportunity and threat) analysis and identify the opportunities. It subsequently helps him develop additional methods and analytical algorithms to bring opportunities in the organization’s favour and guide the management on how to reap the rich harvest of the best opportunities.

5. Decision Making with Quantifiable and Data-driven Evidence

Data scientists specialize in data-driven analysis and informed decision making. To do this job accurately, they collect, gather and analyze data from various channels, analytics, call-to-action-buttons, and touchpoints. After that, they prepare intelligent business models and effective paradigms, utilizing existing data. Thus, they can decide a variety of potential action plans to ensure the best business outcomes.

6. Testing These Decisions

Certain decision making and implementing decided strategies help an organization win a battle. Subsequently, testing the plan and viewing and reviewing the results after half of the time can help the organization to analyze how the decisions have affected the organization and driven it to achieve its goal. Here the role of a data scientist becomes very important. He can analyze the data and measure how the key metrics worked on the organization’s performance and what significant changes required to achieve its success.

7. Identifying and Refining of Target Audiences

Most of the companies collect data from Google Analytics and analyze these well to count the number of clicks and the clicks from which counties. From there, they can identify demographics and understand the users and customers and their journey and behaviour to a web page. Unless the data are used well to identify demographics, it fails to identify and refine the interested groups and ensure whether it has perfectly hit the target audience.

This is why the importance of data science is to take existing data and analyze and combine it with other data points to generate insights into an organization and learn more about its customers and targeted audience with their behaviour.

The role of a data scientist is to identify the key groups or target audience with precision through a thorough analysis of disparate sources of data. With this deep insight and in-depth knowledge, organizations can tailor its services and products to meet the expectation of customers and targeted groups. This helps the organization to increase profit margins.

8. Recruiting the Right Talent for the Organization

Going through resumes and shortlisting these all day is routine work in a recruiter’s life. However, with the advent of big data analysis and data mining, the concept has changed. Now, data scientists can go through social media, corporate databases, job search websites, and other touchpoints to bring adequate data about the candidates and recommend the right fits for the organization according to its needs.

By examining large pre-existing databases, data scientists can collect and generate new information and forward these for in-house processing. They process relevant resumes and applications and conduct data-driven aptitude tests and games to make the recruitment process expeditious and accurate.

Conclusion

Data science can add value to any business interested in using their data for making an informed decision. From preparing statistics and recording performance metrics to getting insights across workflows and hiring new candidates, data science can make everything easier, faster and more valuable for an organization. Data science helps senior staff make better-informed decisions.