Sun. Mar 21st, 2021

Salesforce is making it a lot easier to connect a company’s data stores with its cloud-based analytics function by including wizard-like connectors in the platform.

If your business is growing, and you know you need to update your IT system to match that growth, chances are you’re considering adding an analytics function to help you make better business decisions.

For enterprises with legacy systems that have performed well for a long time but may be leaking oil as they get more mileage on them, new IT systems available are already infused with cloud connections, automation, built-in analytics and other features that weren’t commonplace just a few years ago.

In the case of customer relationship management, for example, Salesforce has made its mark in the sales and marketing world with its worldwide cloud-based subscription service. With the advent of its new Einstein analytics engine, even more capability is now available through the platform.

“For most business people, AI has been too complex and out of reach,” John Ball, general manager of Einstein, told eWEEKa year ago when Einstein was launched. “You have to collect and integrate a lot of data, convert it to a specific machine format and hire scarce data scientists to work on it and have an infrastructure that’s secure and scalable.

“Even if you have all that, the last mile where a lot of AI projects get tripped up is you have to be able to surface the insights in the context of your business applications—that’s just too hard for the vast majority of companies out there,” Ball said.

Making It Easier to Connect Data Stores

However, starting Sept. 14, Salesforce is making it a lot easier to connect a company’s data stores with its cloud-based analytics function. Getting started is often the most dreaded part of upgrading an IT system, and Salesforce has taken a lot of the drama out of this by offering new, wizard-like connectors into the CRM platform.

“Every CRM user is on the hunt for a more complete picture of their customers, as well as their business,” Ketan Karkhanis, General Manager of Salesforce Analytics, wrote in a blogpost. “This requires analyzing customer data to fill in blank spots on the canvas and surface the insights that will drive business growth.”

Gathering data that goes beyond a CRM often means collecting numerous documents and spreadsheets from different transaction systems, data warehouses and geographies. It can mean attempting to traverse data silos and always feeling like the reports on hand are out of date, Karkhanis said.

So Salesforce Analytics has launched new updates for connecting data inside and outside of Salesforce. New functions in the platform include:

  • Augmenting CRM data with external data sources: Salesforce users can now connect to even more web-based data services with new connectors: Google BigQuery and, as part of our strategic partnership with Amazon, Amazon Redshift. Connectors give you an easy way to connect to external data with Salesforce CRM data in order to explore it within Salesforce Analytics. With these connectors, business leaders can ensure that all business data, including compensation, orders, accounting, shipping or inventory data, are connected to your CRM data for a more complete picture of your business.

“For example, wouldn’t it be helpful to understand exactly how customers are using your products?” Karkhanis said. “Many products collect usage and customer feedback data. Using customer engagement data can help build great products and better serve customers. If the customer is storing this data in Google BigQuery, the company can now bring this data into Salesforce Analytics and analyze it in lenses and dashboards alongside CRM data for additional insights.”

  • Intelligently prepare and clean data: With smarter data prep, users can now get Salesforce Einstein AI-powered suggestions on how to prepare, clean and combine data from diverse sources. Users see a preview of what their data should look like, and with one click, can act on automatically generated suggestions. This could mean changing different date formats across geographies to be more consistent, converting dollars to euros for a consistent currency or rounding up so that every input has the same amount of decimal points.

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