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Augmented Analytics for Companies Big and Small

Analytics2Go offers an automated Machine Learning platform for enterprise level companies and SMBs (Small and Medium-sized Businesses).  Our automated platform leverages augmented analytics to uncover hidden, valuable business insights from your data that go beyond simple graphs and reports.

All About Us

We all have a shared passion for analytics, and we do not like doing book reports or reporting about great data science insights that people cannot figure how to use effectively. We want companies to benefit from the analytic insights we discover. However, for many companies, it can be difficult to go from insight to results. The practice of data science is generally pretty far removed from the work of most company decision makers, who focus more on the what, when and how of their businesses. Data scientists are mostly concerned with the why. 

Why does performance fluctuate? What drives these fluctuations? Are the fluctuations driven by similar factors across the entire scope of the business, or are they the same across all customer segments?  Usually, the answer is no. Data scientists focus on identifying and understanding the behavior of significant business drivers. Their goal is to find a set of business drivers that can be used to predict outcomes consistently as operating conditions change. While they seek guidance from the insights of company executives and opinion leaders, their focus is on what the data can tell them and finding hidden insights that may be useful to the company, including, for example: 

  • Price elasticity for different products or customer segments
  • Predictors of manufacturing quality
  • Revenue and profitability drivers

Without any intention to minimize the importance or significance of the work of data scientists, it is inevitably focused on data-driven insights that are commonly referred to as performance drivers.  How does the typical manager responsible for an important recurring decision, such as S&OP decisions, consider insights about price elasticity, manufacturing quality, or profitability, when he or she is planning orders or production volumes for the next planning period. In fact, they cannot. This is a major problem and a huge challenge for how analytics is practiced today. 

We formed Analytics2Go to bridge the gap between insights and decisions in order to ensure that analytic insights are made available to all of the decision makers responsible for an individual company’s important recurring decisions. Our strategy for bridging that gap is to design and deploy best-practice micro-apps, which are narrowly scoped to support individual decisions in real time. 

We deliver data science value to individual decision makers by providing optimized decision recommendations to to them at the time the decision is being made. The recommended decisions or actions reflect industry best practice and data science insights. 

Our micro-apps are narrowly scoped to make them simpler to deploy and easier to adopt. Some analytic projects are beginning to look like Enterprise ERP implementation initiatives; that is not Analytics2Go. We are here to make it less complex and more affordable to make consistent incremental progress using analytics in your organization without hiring your own data scientists and without accepting a Big Bang effect in the future. 

We hope you will take the time to get to know us. We are different; we are better. We simplify the data science process and make it affordable. We scale it for normal human beings who are entrusted with making important decisions. We design analytics for the company decision makers, not data scientists. 

The Micro-Computer Revolution

 

The combined effect of micro-computers, smart phones and tablets has prompted a huge increase in the production of data.

The Internet Revolution

 

The development and wide-spread use of the Internet and social media has triggered an immense increase in the volume of shared data.

The Micro-App Revolution

Bite-size apps are being used to optimize decision-making in real time, which has led to a massive increase in data consumption.