<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=95044&amp;fmt=gif">
Back to blog

Building a Path to Predictive Analytics with Your Data and Big Squid.

Tuesday, November 27, 2018

What will your business intelligence investment look like in 2018?  Does it incorporate machine learning and predictive analytics? Does it drive new insights and compel better decisions?  Will you be able to build upon your view of the business today; and paint a picture of what lies ahead?  Can it direct decisions at the front lines of the business?  All interesting questions to ponder; and if you don’t know the answers, you’re in good company.  The "buzzword bingo" floating around in today's business landscape means most of us have heard of “machine learning”, “artificial intelligence”, “predictive analytics”; but few have actually integrated any of them into the daily operation of our businesses.

For true trailblazers in the Business Analytics world, such concepts will be ubiquitous to Business Intelligence in the coming years. These solutions will be used pervasively across their organizations, in each department and each role within it.  For others, this notion seems unattainable, let alone, approachable.

From our standpoint, Big Squid believes that the greatest barrier to bringing powerful solutions like predictive analytics to the broader business community, is the scarcity of resources with the knowlege and ability to build and deploy such capabilities. Equipped with an understanding of sophisticated mathematical approaches and armed with the necessary programming chops, data scientists are difficult to find, afford, and scale.  Fortunately, we offer a platform in which the valuable time and resources of the Data Scientist doesn't need to be taken up with the implementation of this sort of solution.

Big Squid is solving this problem by bringing predictive analytics to the business decision maker.

What does that mean?

Big Squid's SaaS platform for business leaders, leverages the data within your existing business intelligence platform in order to make predictions about the future of your business. With the recent boom in Business Intelligence (BI) investments for midmarket to enterprise level companies, the question has become what to do with ALL that data?

The crux is:  How do you operationalize the data now that you have it? How do you yield insights upon which you can make better business decisions?

This is where Big Squid’s Predictive Analytics Platform becomes an invaluable extension of your BI tool by applying machine learning to gain insight ino the propbable future state of the metrics and key business drivers that matter most to your organization. 

Filling the Predictive Analytics Gap


BI platforms are incredibly valuable and offer deep understanding of the Descriptive and Diagnostic phases of your Business Intelligence value chain. They are essential to gain insight into what is in real-time within all aspects of your business. Today, many mid-market to enterprise level companies struggle with removing the complexity of data science and make informed data-driven decisions about the future. To offset the imbalance of Data Scientists in the marketplace, businesses are finding the need to use innovative solutions (like Big Squid), that leverage the existing employee base. Such solutions can turn data specialists into Citizen Data Scientists, empowering them to bring new, forward-looking insights to the business from their data in their exising BI platform.

The traditional workflow below should look familiar.  The problem with this flow is that a data scientist is required to spend a majority of their time collecting and preparing the data in order to begin the valuable analysis and engagement phases.  This is where the business can make decisions and take action.


If you are already using a BI platfrom, this is great news:  because you’ve already prepared your data for visualization in, you’re ready for predictive analytics.

Collecting, preparing, cleaning, and ultimately staging data for visualization is by far the lionshare of the work required for building a predictive model.  And since that lift is already in place, extending our analysis to incorporate sophisticated forecasting is absolutely within reach.  

Our approach to predictive analytics vastly simplifies the model exploration and deployment process without sacrificing rigor.  Our predictive analytics platform has significant advantages over other expert tools (SAS, R, Python, etc) in that the platform gives data analysts the ability today to become data scientists.  They can apply their understanding of data structure and business problems, to provide better insights of future trends to key business decision makers. This saves time and money.


While this is just the tip of the iceberg, now you can engage other individuals at your business and start the conversations around how to gain better insight into the future of your business all within your existing BI investment.

Where do we see customers exploring these capabilities? Lots of areas. Here are example solutions we’ve built by vertical:



We want to help you become a data science-Driven Business. Want to learn more? Sign up here to learn how to harness the power of Machine Learning with ease.

Learn More

Nick Magnuson

Big Squid - Your Predictive Analytics Sherpa

Nick Magnuson

Written by Nick Magnuson

Recent Posts

Comment Below

Subscribe to Email Updates