Steftalks Webinar: Use Data and AI to Identify Emerging Risk - Stefanini

Steftalks Webinar: Use Data And AI To Identify Emerging Risk

In this webinar, learn how AI’s recent advancements have enabled sentiment analysis, key phrase extraction & trending to deliver meaningful insights from data. 

During these challenging times of economic uncertainty, companies are embracing new technologies and approaches to better serve their customers at an accelerated pace.  Mining unstructured data sources (e.g., news, press releases, SEC filings/proxy statements, earnings call transcripts, and more) for leading indicators of risk is a powerful way of staying connected with your customers. 

Keep In Touch with Consumers

Big data and AI have a reciprocal relationship: The latter depends heavily on the former for success, while also helping organizations unlock the potential in their data stores in ways that were previously cumbersome or impossible.

There are several ways that big data and AI are deriving meaningful insights from data:

1.       AI is creating new methods for analyzing data

Once data has been collected, companies often don’t know what to do with it. AI and machine learning are both bringing to life new methods for analysis. What used to be statistical models now has converged with computer science and has become AI and ML.

2.       Data analytics is becoming less labor intensive

People still play an important role in data management and analytics, but processes that might have taken days or weeks (or longer) are picking up speed thanks to AI.

3.       Humans still play a factor

Businesses need to combine the power of human intuition with machine intelligence to augment these technologies – called augmented intelligence. More specifically, an AI system needs to learn from data, as well as from humans, in order to be able to fulfill its function.

4.       AI/ML can be used to alleviate common data issues

The value of your data is overwhelmingly linked to its quality, which means low or no value. Fortunately, machine learning data can be cleansed using machine learning. ML algorithms can detect outlier values and missing values, find duplicate records that describe the same entity with slightly different terminology, normalize data to a common terminology, and more.

5.       Analytics become more predictive and prescriptive

Today, AI is moving big data decisions to points further down the timeline, in more accurate ways, by using predictive analytics. Traditionally, big data decisions were based on past and present data points, generally resulting in linear ROI. With AI, this has grown to epic and exponential proportions. Prescriptive analytics, leveraging AI, has the potential to provide company-wide, forward-looking strategic insights helping to advance the business.

The Webinar

In this webinar, learn how recent advancements in AI have enabled sentiment analysis, key phrase extraction and trending to deliver meaningful insights from data.  You will also learn how Accenture, Bitvore, and Stefanini teamed together to build a Salesforce Einstein Analytics app to enable end-users to rapidly visualize key indicators from within Salesforce.  With this app, account managers can start from an overview of a market segment, industry, or geographic area and drill down to a company level to identify and track new opportunities and risks directly in Salesforce. Featuring Eric Edgerton, Stefanini Director Salesforce Practice, Shoven Shrivastava, Senior Salesforce Einstein Analytics Consultant at Stefanini, and Mirella Reznic, Head of Product and Innovation at Bitvore, this will be a highly engaging talk you won’t want to miss!

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