Data Engineering & Insights – How Improved Data Utilization Will Make Organizations 400% More Profitable by 2025

  • Headquartered In: Australia and United Kingdom
  • Company Size: 500+ Employees OR $500M+ in Annual Revenue
  • Industries: Financial Services, Banking, Insurance, Healthcare, Life Sciences, Retail, E-Commerce, Energy and Utilities, Telecommunications & Communications, Media and Entertainment, Manufacturing, Travel and Hospitality, Logistics, Airlines 
  • Titles: Information Technology and Engineering exclude Cybersecurity
  • Job Level: Director+

Today, organizations are inundated with a huge amount of data, and the volume is growing by the minute. Around 90% of the data generated in the world was in the last 2 years alone and every day, 2.5 quintillion bytes of data are produced by humans. But today’s organizations lack in-house teams and technological expertise that are focused on drawing this data into their systems and generating the right insights.

The perceived cost of migration, complexity in drawing data from multiple sources, presence of data in multiple formats, dependence on deep-rooted legacy systems, the sheer volume of data, and lack of systems and processes make it difficult for organizations to achieve the right insights, thereby limiting their true potential.  

To gain maximum value from their data assets, organizations need to build data pipelines to help transform and transfer the data into a format that is ready for use by data scientists. Growing data volumes offer enormous potential for organizations to generate business value from captured data, offer exceptional customer service, and help stay ahead of the curve in today’s competitive global market. 

Organizations need to establish a robust data foundation and use data mining to generate valuable insights. The objective is to address major obstacles that restrict firms from scaling and transforming themselves into data-savvy rivals. Key focus areas should be to ensure maximum fluidity of your data from origin to destination with quality data insights and visualization.

Top reasons our community cites for adopting or considering this solution:

  • Operational dependence on deep-rooted legacy systems.
  • Lack of in-house skills and technological expertise.
  • Lack of implementation processes.
  • Sheer volume of data and data chaos.
  • Poor data quality such as duplicate data, inaccurate data, ambiguous data, and inconsistent data.
  • Generating the right insights from data (Descriptive, Predictive).
  • Lack of proper data visualization tools and expertise.

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