8 Reasons to Choose Analytics2Go
by Mike Romeri, on Feb 15, 2018 12:56:43 PM
1. We Believe All Organization Must Use Analytics to Improve Their Decision-Making
Decisions made with the benefit of effective analytic insights produce better outcomes. Complex decisions made without analytics are made intuitively; the decision-makers do not have the ability to collect or analyze all the relevant data that pertains to their decisions in real time.
Our own experience and that of top analytic experts indicates that analytic decision-making outperforms ad-hoc (i.e., intuitive) decision-making by 10% or more, initially, and the performance advantage grows over time.
Competitors using analytics effectively will create a sustainable advantage over companies working without analytics. As company leaders recognize the risks they face by not implementing analytics, they will invest the time and money needed. Adoption is inevitable, because the alternative is business failure.
2. Our Goal Is to Deliver Advanced Analytic Benefits to Companies Effectively and Efficiently
Most companies already have descriptive analytics. Many companies employ business intelligence to some degree. Few companies currently employ predictive or prescriptive analytics.
Predictive analytics rely on machine learning techniques that enable more accurate prediction of the outcome of decisions or actions (e.g., a change in price). Prescriptive analytics are the decision recommendations, which arise from comparing the expected outcomes. We think all companies should have access to these powerful techniques.
Our mission is to democratize advanced analytics. Join the ranks of Amazon and other enterprise companies, and reap the benefits of data-informed, smart business insights and decisions.
3. Soon, Making Business Decisions without Analytics Will Seem as Unusual as Shopping without Amazon
If you work for a large company, imagine receiving machine-learning-enabled decision recommendations for each key decision within every planning and execution business process or workflow. This is now possible. Analytics2Go can help you enhance your use of data to make better decisions incrementally. Start with optimizing the most important decisions in your most critical business processes to accelerate the achievement of analytic results.
If you are a small company, we offer a more standard approach to optimizing the key decisions companies in your vertical need to make. Here is an example that we have created for quick service restaurants (QSRs). These companies need to keep service delays to a minimum to avoid lost sales. Our 30-minute Demand Prediction App recommends specific kitchen production quantities that optimize for the risk of lost sales and scrapped product.
The app helps companies improve daily revenues by 2% to 6%. In the future, as we cover more verticals and optimize more decisions, we hope to be able to answer every inquiry with the phrase: “Sure, we have an app for that."
4. Augmented Analytics Greatly Simplify the Consumption and Use of Real-Time Analytics
Two important decades-long trends have converged to create augmented analytics. I have already mentioned the analytic maturity mode in section 2 above. The other trend is process improvement, which comes in many forms: 6 Sigma, business process management (BPM), and workflow automation, among others.
We offer augmented analytics. Gartner defines the main augmented analytics design requirements as follows, to:
- Collect data from multiple sources
- Clean data so that it is ready for analysis
- Enable and conduct analysis
- Generate useful insights
- Provide optimal recommendations in context at the time of need
If you use workflow automation or other IT-enabled business process software, this means that analytic-optimized recommendations—and possibly supporting data explaining the recommendation—will be delivered to your computer screen at precisely the time you need it for decision-making. Just imagine that. It will soon become your standard experience, if your work for an analytic competitor.
5. Leading Companies Have Developed Reliable Best-Practice Techniques for Applying Advanced Analytics
I have heard or read way too many poor explanations of the terms machine learning and advanced analytics. Most of the explanations focus too much on what data scientists do, not what analytics can do to improve decisions. Why is that? The most relevant subject is how and why analytics produce such significant results for the companies who use them. I have the utmost respect and admiration for the skills, intelligence and work ethic of all the data scientists I know. I only wish to highlight the need to understand the value of analytics, not disrespect the important work of data scientists, which benefits companies and society at large. So, how does it happen?
Most companies who use machine learning effectively are able to predict the outcomes of decisions or actions with an accuracy rate (i.e., correct within a narrow margin of error) of more than 80%. Most companies who do not use analytics have a hard time predicting the actual result of planning and pricing decisions. Most companies can only predict the outcomes of key recurring decisions with an accuracy of about 60%. This higher level of accuracy provides two advantages; the first is the obvious advantage of eliminating surprises, most of the time at least.
The second advantage of better predictions is that they make it worthwhile to consider decision alternatives more comprehensively, systematically and granularly. Consider the seemingly simple decision to update online pricing for some consumer product category of, say, two dozen SKUs. Machine learning techniques routinely consider millions of slightly different alternatives to identify the best alternatives—often resulting in a revenue difference of 15% or more.
Below is a simple graphic that every executive needs to understand and remember. Predictive analytics allow decision alternatives to be forecasted with confidence. The predictions can be compared directly based upon the outcomes they achieve with respect to revenue or profitability or working capital, etc. And most important, the experience of the data science profession over the past 10 years indicates that analytic-enabled decisions routinely achieve a 10% better outcome than decisions that do not use analytics, the so-called ad-hoc decisions.
The Analytics2Go Platform Enables You to
- Use analytics without hiring expensive data scientists
- Avoid external data sourcing challenges and complexity
- Outsource responsibility for data model maintenance and performance
- Deliver optimal decision recommendations when they are needed
6. Most Companies Outside the Fortune 1000 Struggle to Implement Advanced Analytic Techniques
Most analytic projects fail to meet expectations for three main reasons:
- Lack of data science skills
- Data is not available
- Analytic insights not available when needed
Corrective actions often make matters worse:
- Narrower scope does not improve performance
- Data sourcing and curation are inadequate
- Too little piloting
- Models not maintained
A2Go delivers Augmented Analytics-as-a-Service:
- We provide data science resources, IT platforms and data automation
- The platform recommends decisions at the point of need
7. We Have Standard Techniques for Overcoming Analytic Data Sourcing and Consumption Problems
As a part of our strategy to reduce complexity and improve the productivity of data science projects, we have created a series of tools and techniques, which can be used confidently to plan and execute effective analytic projects. A good example is the standard analytic data model for every company and every micro-industry that we evaluate. Below is a view of a representative data model for the electronic equipment rental business:
Analytic models like this one are used in their final form are used to:
- Identify all internal and external data requirements
- Create an analytic roadmap
- Reduce analytic design and implementation risks
- Align effectively with in-flight initiatives
We believe that techniques like this one (and many others), as they are continually improved, will make the delivery of analytics affordable and feasible for all companies.
8. Our Analytic Platforms Automate Data Handling and In-Context Decision Recommendations
Below is a process flow diagram that shows how we have created a public analytic forum where the world’s best data scientists will build affordable apps for millions of companies. By standardizing this process and supervising it, we will make it feasible for billions of dollars of value to be created by our customers, as they apply analytics to their business.
Let us hear from you. Feel free to ask for further information. We are here to help you achieve your analytic goals.
 Alfred Spector, CTO of Two Sigma, 2016 Academic Presentation