Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

Fortunately, advances in analytic technology have made the ability to see reliably into the future a reality. Today, the most common usage of business intelligence is for the production of descriptive analytics. . Descriptive Analytics: Valuable but limited insights into historical behavior. The vast majority of financial services companies use the data within their applications for what is called “ Descriptive Analytics.”

An Interview with a Data Scientist


For simple reporting projects, I might spend 8 hours getting the right data and then just a couple of hours producing the needed visualizations. We plot visualizations and look at the data to understand how occurrences of different categories of data columns.

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Five Steps for Building a Successful BI Strategy


And every business – regardless of the industry, product, or service – should have a data analytics tool driving their business. Our go-to approach for analytics that feeds well into a BI strategy is the Evolution of Analytics chart (below).

Using IBM Watson to Answer Two Important Questions about your Customers

Business Over Broadway

IBM Watson Studio , an end-to-end analytics solution to help you gain insights from your data, was designed for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale.

Themes and Conferences per Pacoid, Episode 10

Domino Data Lab

And by “scale” I’m referring to what is arguably the largest, most successful data analytics operation in the cloud of any public firm that isn’t a cloud provider. Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. Introduction.

15 Analytics Capabilities to Consider When Choosing Your Next Big Data Solution

The Kini Group

You need analytics to make sense of everything. And analytics can never be a one-off project. Successful and practical analytics always answer a few paramount questions: What happened? There’s more to analytics than this, though. Explore Predictive Analytics.