Feature
San Francisco-based Birst today unveiled what it's calling its biggest software release to date, Birst 6.0.
The latest version of the business intelligence (BI) platform advances builds on its networked approach to BI and analytics, and also introduces machine learning and artificial intelligence capabilities which the company plans to expand on in the coming months.
Today's release focuses on what the company calls Connected Data Prep, a largely automated multi-step process which aims to simplify how business users gain analytic insight.
"What we have developed is a way for the average business user to take advantage of the collective knowledge of BI insights embedded within the organization," Pedro Arellano, Birst VP of product strategy, told CMSWire.
Birst Connected Data Prep
Connected Data Prep is a multi-layered offering which builds on data prep tools from Birst's earlier product iterations in this area, while adding new drag-and-drop tools and a visual interface for standard (i.e., non-IT) business workers.
Data prep is the process of standardizing and cleansing data so it can best work in a BI environment. Most providers offer some element of this in their applications. Connected Data Prep's technology then automates the next step, creating the analytics objects necessary to support the task or BI query at hand, for example, what percentage of leads converted into sales on Black Friday?
Networked Analytics That Cuts Out the Middle Man
The value add is what comes next.
Birst added a new tool set on top of the data prep process that lets a user combine cleansed and standardized data with other analytical insights in the organization. The user can find these insights in a network that's similar to an app store, where users can see what a particular analysis does and select it for their own use, provided the governance and permission tools allow for that.
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Arellano used the example of a company that uses Marketo for its marketing leads and Salesforce for its CRM operations to explain.
"There is a lot of value in being able to analyze lead and opportunity data in combination," he said. A typical example would be understanding the percentage of leads that convert into sales opportunities and the details around those conversions. That cannot be calculated in a vacuum — the two systems need to come together to gather all of the relevant data.
However, because the data in the systems are structured differently, this is harder than it should be. A user could go to an extract, transform, load (ETL) architect and ask for a data extract or the company could invest in a systems integration, Arellano said. Birst, by contrast, lets the user define what data they want from both systems, and automatically creates the analytical objects that they need.
"It is the perfect tool for someone that doesn’t speak in SQL,” he said. "That person can now say to the system, 'OK, I am looking for opportunities in such-and-such market.' The system delivers that and then it is on to the next request, which might be past conversion rates for that market," Arellano continued.
Layering in Machine Learning
Arellano said Birst's machine learning capabilities will make it that much easier to tweak this process of automated and connected BI as version 6 unfolds over the next year. The capabilities are embedded throughout Connected Data Prep — they can be found in the intelligent defaults for application connectors, usage-pattern discovery of data sources (the user won’t always know what analysis or data is already available in the network), smart profiling and cross-data source sampling.
But as the product evolves, the goal remains the same, Arellano said: better BI for business users. "Our argument is there is a large population of business people still underserved by analytics today."
