Modern Analytics

What is Modern Analytics? Modern analytics is a field of study that combines existing knowledge about analysis and data interpretation...

What is Modern Analytics?

Modern analytics is a field of study that combines existing knowledge about analysis and data interpretation with modern technology and techniques to make it more agile and accessible. Modern analytics is characterized by the shift in focus from data specialists and scientists and toward individual users.

This allows more stakeholders in an organization to create their own analyses of existing data and to find solutions for their existing questions more quickly and effectively. At its core, modern analytics combines two key aspects—the self-service analytics model of data querying and a deep connection to fields such as AI and machine learning to automate much of the collection process.

Augmented Analytics

The first of these two concepts separates modern analytics from traditional analytics. In the new model, users can access databases themselves via business intelligence and data analytics software. This expedites the discovery process and gives users the freedom to perform the studies they need specifically, as well as making ad-hoc analysis much simpler.

The second tenet, a reliance on AI, machine learning, and other automation tools, is vital in providing the power to make modern analytics run. Using these tools allows analytics software to separate the storage, scrubbing, initial discovery, and data prepping and provide ready-to-use sets and models to users.



How Can I Use Modern Analytics?

There are myriad ways you can deploy modern analytics in a business or organizational setting that can enhance your operations. One of the most common ways to use modern analytics is in managing workflows and improving business processes. Data can help find key areas where bottlenecks or inefficiencies occur and can help different departments improve by offering actionable insights on prominent issues.

On the financial side, in organizations where different users must perform unique analyses on the same data, relying on a few data specialists can cause significant delays in research and even lead to losses. On the other hand, modern analytics lets users make queries and create their own predictive models whenever they need them.

Finally, modern analytics are incredibly useful in reducing the need for manual scrubbing and data preparation. With its heavy use of machine learning algorithms and AI, modern analytics tools can scour through massive data loads and find relevant information, initial patterns and trends, and clean data before users ever come into contact with it.

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Augmented analytics