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Augmented Analytics: What It Is And How To Assess A Potential Platform Posted on : Oct 13 - 2021

Today's business environment is dominated by a single topic: data. With each enterprise sitting on a potential goldmine of data from user behavior, sales, marketing campaigns and systems operations, company leaders are under pressure to derive as much value as possible from their first-party resources.

Many companies, however, have taken an under-informed and ill-leveraged approach to data and analytics, putting rudimentary systems in place without understanding what they do or how to maximize their performance.

In the past, effective data analytics required expensive, highly specialized talent to sift through mountains of raw data. However, a growing collection of augmented analytics tools have more recently evolved, making data-driven insights accessible to any business, regardless of whether they have the budget to field a large, experienced data analysis team.

What exactly is augmented analytics, and how does it help to democratize data-backed decision-making?

Augmented analytics, according to Gartner, "is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning and AI model development, management and deployment."

As more companies begin to turn to augmented analytics to support their operations, what should they look for when assessing a new tool or platform? Here are five questions to ask before investing in augmented analytics technology.

Does it support your data sources and models?

Augmented analytics platforms are not one-size-fits-all. They vary widely in terms of the types of data they can ingest, as well as their ability to connect to different storage platforms. While the vast majority of platforms can ingest structured data, many platforms will struggle to process and categorize unstructured data. Before committing to a platform, your company must understand its own data assets and whether or not they will be compatible with augmented analytics.

Depending on the maturity of your existing data programs, you will also want to check whether an augmented analytics platform supports your data models and needs. More complicated deployments would involve knowledge graphs and multiple fact tables; high-performance augmented analytics tools will be able to integrate these models easily, while less developed tools will lack these capabilities. View More