The data warehouse is the solution that companies have been using since the 1980s, to store their structured data in one repository. That data was used for business intelligence, reporting and basic data analysis.
With the explosive growth of data and the need to turn that data into business value, the shortcomings of the data warehouse came to light. Data warehouses were ok for structured data. They required a lot of work, though, to maintain a consistent, standardised schema for that data to be readable and usable. For that resources with specialized skills were needed, which created additional costs and overhead.
Data lakes were created to address the challenges of the data warehouse, intended to be a more agile, flexible data architecture. They collect and store massive amounts of unstructured, structured and even streaming data for a number of use cases such as AI, advanced analytics and machine learning.
But despite everyone’s hope they did not turn out to be the solution. Instead of making more data available and accessible to business users and data consumers, they were more focused on aggregating data and centralizing its control.
Finding, using and creating value from the data residing in your data lake still remains a challenge.
The extended usage of the data lake architecture, created new technologies and tools for data preparation, data integration, data sharing and data cataloguing, to help organizations unlock more value out of their data.
The key to unlocking the full value of your data is by employing technology that embeds a value-oriented strategy based on the following principles:
With ConnectIQ, you can deliver repeatable, scalable data-driven outcomes from your data lake and data warehouse without sacrificing the financial investment and resources you’ve already put in.
Data lakes were created to address the challenges of the data warehouse, intended to be a more agile, flexible data architecture. They collect and store massive amounts of unstructured, structured and even streaming data for a number of use cases such as AI, advanced analytics and machine learning.
But despite everyone’s hope they did not turn out to be the solution. Instead of making more data available and accessible to business users and data consumers, they were more focused on aggregating data and centralizing its control.
Finding, using and creating value from the data residing in your data lake still remains a challenge.
The extended usage of the data lake architecture, created new technologies and tools for data preparation, data integration, data sharing and data cataloguing, to help organizations unlock more value out of their data.
A new approach to getting value from data
Perhaps it’s time to consider a new approach.The key to unlocking the full value of your data is by employing technology that embeds a value-oriented strategy based on the following principles:
- Adopt self- service: Allow fast access to every data consumer in your company to the data they need, eliminating data waits and dependency on DBAs.
- Automate processes and data pipelines: Maximize efficiency with built-in automation to create, publish and deliver data pipelines with the most recent data.
- Ensure governance and control: Ensure data governance, compliance with regulations and data security, even when you democratise data access.
- Facilitate collaboration: Cultivate collaboration between data domain experts (who know the data best) and business stakeholders (who know use cases best) to deliver value and business outcomes.
With ConnectIQ, you can deliver repeatable, scalable data-driven outcomes from your data lake and data warehouse without sacrificing the financial investment and resources you’ve already put in.