Automated data management and analytics: the intelligent disruption

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Artificial intelligence is revolutionizing the way we do business. Let's look at the case of disruption in automated data management and analytics.
Artificial intelligence and machine learning are taking analytics and data management to a level of analysis and prediction that was previously unthinkable. Not only are they being used to automate business processes, recommend and guide user behavior, and offer new products and services that attract and delight customers, but they are revolutionizing the way business is done. Let's see how approaching data from a smart disruption strategy impacts it.






84 % of C-suite executives believe they need to leverage AI to twitter database their growth goals. However, 76% say they are struggling to scale up its use.

Source: Accenture


What to expect from automated data management?
In a data-first world, the only way to scale operations to meet today’s business needs is through intelligent automation. In the past, process automation was primarily based on business rules encoded in deterministic business logic and middleware.

However , the challenges we face today are much more complex and dynamic in nature and therefore require artificial intelligence algorithms applied to massive amounts of metadata. In this way, AI applied to data allows for better results, automating the data flow and guiding the behavior of knowledge workers with intelligent recommendations.

So, instead of needing to hire a large team of data science specialists to scale all the work that needs to be done, you can simply invest in a data management platform built from the ground up with unified enterprise metadata powered by AI and machine learning.







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How does the intelligent data catalog work?
Once relevant data is found that can be trusted, the most important part of the decision-making work is already done. An intelligent data catalogue actively contributes to achieving this goal, as it can:



Discover.

Classify.

Provide powerful search capabilities.

Recommend data that is suitable for use in an analysis project.



Automated data management and analytics scan and collect metadata from enterprise systems, including a variety of database types, applications, and tools. They then automatically generate a graph of metadata and relationships, so that end users and developers can query the metadata for other applications or integrations.

The best tools provide highly detailed lineage down to the attribute and column level and even support scripting languages ​​like BTEQ and PLSQL, so analysts can explore data provenance and verify its reliability.

Using supervised and unsupervised machine learning, some augmented analytics solutions can even identify and classify entities within unstructured data, such as text that often appears in Microsoft Office applications and PDF documents.