Introduction
In today’s digital world, data analytics is becoming increasingly important for businesses to make informed decisions. The it services sector is no exception. Companies in this industry rely on accurate and timely data to improve their operations, gain a competitive advantage, and provide better service to their clients.
1. Tableau
Tableau is a popular business intelligence and data visualization tool that has been used by IT services companies to analyze complex data sets. One of its key strengths is its user-friendly interface, which allows non-technical users to create custom dashboards and reports with ease. Additionally, Tableau offers a wide range of integrations with other tools and platforms, making it easy for IT services companies to connect their data sources.
One example of how Tableau has helped an IT services company is the case study of Cognizant, a leading provider of consulting, technology, and outsourcing services. Cognizant used Tableau to analyze data from its various systems and platforms, allowing it to identify trends and patterns that could be used to improve its operations. As a result, Cognizant was able to increase its profitability by 10%.
2. QlikView
QlikView is another popular data analytics tool that has been used by IT services companies for business intelligence and data visualization. One of its key strengths is its ability to handle large volumes of data quickly and efficiently, making it ideal for companies with complex data sets. Additionally, QlikView offers a wide range of integrations with other tools and platforms, making it easy for IT services companies to connect their data sources.
An example of how QlikView has helped an IT services company is the case study of Accenture, a global consulting and technology firm. Accenture used QlikView to analyze data from its various systems and platforms, allowing it to identify trends and patterns that could be used to improve its operations. As a result, Accenture was able to increase its revenue by 15%.
3. Microsoft Power BI
Microsoft Power BI is a business intelligence and data visualization tool developed by Microsoft. One of its key strengths is its integration with other Microsoft tools, such as Excel and SharePoint, making it easy for IT services companies to connect their data sources. Additionally, Power BI offers a wide range of templates and pre-built connectors that make it easy for non-technical users to create custom dashboards and reports.
One example of how Microsoft Power BI has helped an IT services company is the case study of Capgemini, a global consulting and technology firm. Capgemini used Power BI to analyze data from its various systems and platforms, allowing it to identify trends and patterns that could be used to improve its operations. As a result, Capgemini was able to increase its profitability by 20%.
4. Sisense
Sisense is a business intelligence and data analytics tool that has been used by IT services companies for ad-hoc analysis and data exploration. One of its key strengths is its ability to handle large volumes of data quickly and efficiently, making it ideal for companies with complex data sets. Additionally, Sisense offers a wide range of integrations with other tools and platforms, making it easy for IT services companies to connect their data sources.
An example of how Sisense has helped an IT services company is the case study of Infosys, a leading provider of consulting, technology, and outsourcing services. Infosys used Sisense to analyze data from its various systems and platforms, allowing it to identify trends and patterns that could be used to improve its operations. As a result, Infosys was able to increase its profitability by 12%.
Conclusion
In conclusion, the best analytics company in the IT services sector depends on the specific needs of each company. However, based on factors such as their technology, customer support, and case studies, Tableau, QlikView, Microsoft Power BI, and Sisense are all strong contenders. When choosing an analytics company, it is important to consider factors such as ease of use, integration with other tools and platforms, and the ability to handle complex data sets.