Business intelligence and big data in the new era

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Discover how business intelligence and big data have evolved into data engineering and take note of the new critical business capabilities
Business intelligence and big data provide business users with clean, quality data that they can trust and generate more insights that lead to effective actions. Today, the industry is moving toward data management environments that deliver insights from artificial intelligence and machine learning, while leveraging the cloud for agility. And while the amount of data in these environments remains large, the business intelligence technologies that managed big data are no longer powerful enough to support this evolutionary step.


How business intelligence and big data have evolved into data engineering
The most difficult challenge for artificial intelligence and advanced analytics is not the technology, it is actually managing data at scale , which has far exceeded the technologies that traditionally managed it.

Hadoop is one of the key technologies that enabled overseas chinese in uk data to manage large volumes of diverse information and different types of data. Computing, storage, and big data management were closely linked to drive the success of data and analytics in data lakes and data warehouses.

But cloud adoption and the advent of serverless technologies have ushered in the era of big data engineering , effectively decoupling storage and compute, and enabling faster processing of multi-latency, auto-scaling, petabyte-scale data.

These technologies are the ones chosen by leading providers, such as Microsoft Azure or Amazon Web Services (AWS), a decision that has driven the evolution of business intelligence and big data to a new stage, where data engineering takes center stage.







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What questions does data engineering answer?
Business lines (sales, finance, marketing or supply chain, among others) need to answer key questions such as:

How can data help me predict what will happen?
How can the information be used to understand what has happened?
How can teams collaborate better and prepare data more easily?
Data scientists, on the other hand, spend 80% of their time preparing data, rather than building models, which is why they ask themselves:

How to find the right data for modeling ?
How to ensure the availability of this data in a machine learning environment?
How can I ensure that the data can be trusted for modeling?
Can data preparation be simplified so that more time can be spent modeling?
How to deploy and run machine learning models in production?
Similarly, data analysts don't have the right data to gain business insights that drive action, and they want to know:

How to find the right data to increase business knowledge?
How to ensure the availability of that data in the data lake?
How to ensure that data can be trusted?
Can data preparation be simplified so that more time can be devoted to analysis?
How could collaboration be easily achieved with peers and IT for ongoing changes?
All the answers to these questions depend on data engineers . It is these professionals who help data scientists and data analysts find the right data, make it available in their environment, ensure that the data is trustworthy and sensitive data is masked, and ensure that they spend less time on data preparation.