Fast Data Demands Faster Analytics: Three Tips for Accelerating Analytics

By Brynne Henn - June 18, 2020

“Analysts are tasked with being the forerunners of the business — having to know if and when things are changing, and how they are changing. And with the speed and volume of data today, knowing where to look to find answers becomes increasingly difficult.” That’s how Beritt Hoffmann framed the problem in our recent webinar, “The Future of Analytics: Faster data demands faster analytics.”

In the webinar, David Menninger from Ventana Research and Berit Hoffmann from Sisu discussed how teams can accelerate analysis when faced with fast data and more questions than ever before.

Take a look at some of their tips for accelerating analytics below, or watch the whole conversation on-demand.

To inform more decisions with fast data, you need faster answers  

Weekly and monthly data updates are no longer sufficient to stay competitive. Today, organizations are frequently processing data on an hourly basis. By 2022, David explained, more than half of organizations will rely on streaming fast data to inform operational decisions.

In his research for “Changing Data Patterns Are Impacting Analytics,” David found that to meet these operational targets, organizations need to adopt cloud-native platforms and automate the rote processes of analysis with machine learning and AI.

A key takeaway: the objective of moving faster is not just to improve operations, but to do things differently. David explained, “What we’re really talking about is giving you the opportunity to react in the moment and influence outcomes.” 

To make faster decisions, prioritize what matters most

When businesses create billions of valuable data points every day, it’s impossible to check every possible hypothesis to understand what’s changing in your data. This is where proactive analytics and automation can help.

Both David and Berit agreed analysts should look to automate rote, manual tasks, freeing up time to focus on problems that are more valuable to the organization. 

Rather than starting with the manual, hypothesis-driven approach analysts are used to, they can begin with what they know: the metrics that matter and the factors that affect it.

Automated root-cause analysis tools like Sisu test billions of hypotheses in seconds and surface the populations that have the highest impact on their metric. This is the best and most direct way to prioritize analysts’ valuable attention. 

When using typical descriptive analytics tools, analysts have the arduous task of manually weeding through the data to answer more complex questions from the business. As Berit explained,

“BI tools can help answer the ‘what.’ But the immediate next question is, ‘why did that metric change?’ This is where Sisu is laser-focused: answering those tough “why” questions.”


And it doesn’t stop with just one question. Automating the rote work of analysis means that as conditions change by the minute, Sisu will continually evaluate new data as it arrives and rerun the analysis. When key metrics change, Sisu can alert analysts to any particularly actionable new populations.

To make an impact as an analyst, shift how you think about your role

Finally, by finding ways to answer these operational “why” questions faster, you can emerge as a more strategic partner to the business. When your time is no longer occupied by manual slicing and dicing of their data, you can focus on more valuable, more proactive testing and experimentation.

“These shifts will enable analysts to spend more time learning and experimenting more intentionally, proactively running comparisons and A/B tests with the facts derived from tools like Sisu,” Berit projected. 

Listen to the full webinar, and hear more from David and Berit on how analysts can respond to the changing patterns in analytics and get the facts from their data in real time.

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