Blackstone is one of the world’s largest alternative asset managers with $991B in assets under management. It serves institutional and individual investors through its businesses in private equity, real estate, credit, hedge fund solutions, infrastructure, life sciences, and others to deliver lasting value.
Given its scale, Blackstone has various strategies which can all benefit from data and analytics at cloud scale. Finance team members have the financial knowledge but require additional technical expertise to operate their traditional BI tools like Tableau and Power BI, or for that matter, the more powerful Python tools.
The learning curve with other traditional BI tools is much higher compared to Sigma.
—Han Zhang, Senior Vice President at Blackstone
These tools are also somewhat limiting when it comes to ad hoc analysis. “Though the traditional tools are great with full scale dashboards or visualizations, they take away the raw data from the end user,” says Han Zhang, Senior Vice President at Blackstone. “The data isn't readily available in front of the user—it's stuck in either ‘view details’ or ‘other charts’ inside Tableau, Power BI, etc. For us, the need of the hour was ad hoc analysis over full scale dashboards.”
The company also uses Excel widely. Finance team members are well versed with Microsoft Excel but using Excel is difficult due to the scale and complexity at which Blackstone operates.
Han and his team started looking for an analytics platform with the ease and flexibility of Excel, the compute power and speed of a cloud data warehouse, the ability to collaborate and work in real-time, and the security and scale of Blackstone.
The limitations posed by Excel, Power BI, and Tableau, and the need for speed and scale led Blackstone to Sigma, a tool purpose-built for the cloud data warehouse and an analytics platform trusted by more than 500 data driven companies. Sigma’s simple and intuitive Excel-like interface instantly drew Han to the platform since the team’s primary requirement was ad hoc analysis—aggregate analysis involving Min, Max, Average, or Sum, and more sophisticated portfolio analysis involving Index or Vlookups on huge data sets that ran into billion row records.
If you know how to use Excel, you can pick up Sigma very quickly.
—Han Zhang, Senior Vice President at Blackstone
With Sigma, they’re able to achieve data democratization by unlocking their existing pool of “Excel experts.” They no longer need dedicated technical experts and Tableau developers, and their Finance teams can self-serve and perform most of the ad hoc analysis themselves.
“I see Sigma as a happy medium between Excel, Tableau, and some of our traditional BI tools,” explains Han.
Currently, there are more than 700 active Sigma users analyzing million to billion row datasets, globally. In addition to solving for simple analysis at scale, the most recent feature from Sigma—Input Tables—has opened up a world of new possibilities for Blackstone. Input Tables provides teams across the company with the power to directly add data to Snowflake, their cloud data warehouse, and take control of their data and analyses. Han tells us how they’re able to leverage it for ad hoc analysis without having to write custom logic back into the database. This saves them both time and effort as they no longer need to go back to a data engineer or an administrator. Blackstone has also embedded Input Tables in their internal applications.
As one of the first alternative asset managers on Snowflake, and a heavy user of Sigma, Blackstone has been a true data pioneer that uses advanced analytics capabilities to power their business through portfolio analysis, root cause analysis, and scenario modeling.
I think the biggest value driver for Sigma is that you're not using specialized Python developers to analyze billion row records anymore. You're just adding an Excel user.
—Han Zhang, Senior Vice President at Blackstone
Having jumped onto the cloud database wagon pretty early, Han and his team found out that none of their existing tools were able to leverage the full capability of Snowflake as an elastic computing database where you can grow and shrink your environment dynamically. However, this changed once Blackstone found Sigma, an analytics platform purpose-built for Snowflake. “Sigma is a more governed and performant alternative to using an Excel connector to Snowflake,” says Han.
While most traditional BI tools present data to you, Sigma’s Input Tables take data analysis to another level by presenting the users with a unique opportunity to augment that data by directly writing back additional data or their commentary to Snowflake.
Blackstone uses Sigma for a variety of use cases including ad hoc analysis, root cause analysis, mapping, forecasting, planning, and even embedding. They’re able to slice and dice data based on different geographical rollups, change parameters and original assumptions, and enrich the data for future analysis.
Han and his team have gone a step further and embedded Input Tables in their data stack and internal applications. They use it as a way to collect data as “data input screens were unnecessarily cumbersome to create in various applications.” Now, they’re able to combine it with warehouse views which gives them the ability to take those data inputs and then directly write it into the next downstream application.
The alternate asset management leader can log considerable savings across the board using Sigma. Compared to other tools, like Tableau, the learning curve is drastically cut short with Sigma. What would have taken a beginner Tableau developer weeks of training to come up to speed with Tableau, only takes hours with Sigma!
For more experienced BI developers, learning how to implement new calculations used to take hours of googling. With Sigma in-app support, picking up new techniques takes minutes with direct access to Sigma engineers.
Plus, the team did not have to hire additional analysts with a Tableau skillset to fully meet the demands of their Finance teams. Also, the move from the traditional BI tools to Sigma brought down the analysis time from days to hours, resulting in significant time savings.
Sigma on Snowflake creates a single governed environment for regulated financial data, making compliance with its internal policies a lot easier. Blackstone, as Han explains, now has a single source of truth to more efficiently govern data and control who has access to that data.
All in all, with a secure and intuitive spreadsheet-like interface laced with new functionalities such as Input Tables, Blackstone is now able to unify data and business teams, conduct root cause analysis, and perform scenario modeling at speed and scale. Employees can run aggregate analysis on insurance loan portfolios running into billions of rows, FDIC bank suite datasets dating back to the 1930s, and leverage Blackstone’s cloud infrastructure to the fullest.
