Business markets and competition are moving much more quickly these days and predicting, planning and forecasting is more important than ever. It is also important to ensure that every team member is a real asset to the organization and can contribute their knowledge and skill with full Insight into the effects and outcome of activities and processes and the ability to correct the course and make recommendations using clear, concise information. Advanced analytics is the…

What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals.

With the right tools, today’s average business user can become a Citizen Data Scientist, using data integrated from various sources to learn, test theories and make decisions. AutoML comes into play as…

As the need for advanced analytics increases in organizations, enterprises large and small struggle to find and sustain the professional resources they need to meet their requirements for data, analysis and strategic direction.

In some businesses, Data Scientists, professional analysts and IT staff are often buried under requests for analysis and data and, as a result, these teams are unable to focus on strategic issues and on crucial questions that require 100%…

As an Applied Data Scientist at Civis, I implemented the latest data science research to solve real-world problems. We recently worked with a global tool manufacturing company to reduce churn among their most loyal customers. A newly proposed tool, called SHAP (SHapley Additive ex…

Developed at MIT’s Sloan School of Management in 1950s system dynamics is a methodological approach to model the behavior of complex systems, where change in one component leads to change in others (like the dominos effect with feedback loops added). This approach is widely applied in industries such as healthcare, disease research, public transportation, business management and revenue forecasting. The most famous application of system dynamics probably is in…

1 Introduction

Credit risk or credit default indicates the probability of non-repayment of bank financial services that have been given to the customers. Credit risk has always been an extensively studied area in bank lending decisions. Credit risk plays a crucial role for banks and financial institutions, especially for commercial banks and it is always difficult to interpret and manage. Due to the advancements in technology, banks have managed to reduce the costs, in order to…

Contents

1. Introduction

2. The Four Pillars of Success        in Analytics

3. Analytics…

To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. To accurately predict and plan, every enterprise must select a business intelligence solution that will support their efforts and provide business…

As we all know CRISP DM stands for Cross Industry Standard Process for Data Mining is a process model that outlines the most common approach to tackle data driven problems. Per the poll conducted by KDNuggets in 2014 this was and “is” one of the most popular and widest used methodology. This method of gleaning insights out of the data is very dear to the industry experts and data miners.

As the title suggest I will align some of the most useful R packages with this most popular and…

Ever wonder how you can create meaningful insights with your data? Tired of being asked what's your big data strategy, are you doing predictive analytics, or how you are making use of the latest technologies? There is a way for you to get what you need with Data Science. The key to new knowledge is to make sure you use Data Science in your organization.…

If you haven’t started using artificial intelligence in your business, you’re falling behind on the curve. Many business owners today are leveraging AI, whether they are aware of it or not. This is done through everyday business software suites that integrate machine learning and automation to carry out such functions as email communications, voice recognition and response and predictive analysis.

The extent to which businesses employ AI solutions needs to be increased if they are to…

At Grakn Labs we love technology. Here is our December 15th edition by Filipe Pinto Teixeira, where he looked back at Predictive Analytics.

This article is about how to use a standards-based approach to deploy analytics on the edge of Industrial Internet of Things (IIoT) networks.

IIoT is here today. Wherever you look in aerospace, defense, energy production and/or transport, healthcare, manufacturing, mining, materials or smart cities the proliferation of…

Watch out folks - there is a new breed of health analytics firms sprouting like weeds - mining "big data" about you and making broad and bold predictions about your state of health to make workplace decisions.

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An insightful person once said, “Prediction is like driving your car forward by looking only at the rearview mirror!”. If the road is dead-straight, you are good . . . UNLESS there is a stalled vehicle ahead in the middle of the road.

We should consider short-term and long-term prediction separately. Long-term prediction is nearly a lost cause. In the 80’s and 90’s, chaos and complexity theorists showed us that things can spin out of control even when we have perfect…

The data flood that you are witnessing every minute is trying to tell you hidden secrets about your business growth, are you listening? In the previous years, we may have taken a glass half empty perspective for the analytics sector but this definitely is about to change now.  Big data analytics  is indeed one of the fastest growing markets and is expected to mature in 2016 and…

“Predictive analytics” is a commonly used term today. Wikipedia describes it as ‘encompassing a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical…

"Move out of the way – I am ready to model.” That is the typical sentiment of a Data Science team when given a business…