The ongoing pursuit of data solutions occupies mindshare of consumers, vendors and service providers alike as they invest considerable amount of time, costs and efforts. The past attempts to concur data have resulted in solutions that combined databases, applications and tools with limited success. We are still struggling with a few unresolved, persistent legacy challenges such as - 

Data everywhere

Today, every enterprise has huge data…

This article was written by Bernard Marr. 

Almost everyone can agree that big data has taken the business world by storm, but what’s next?  Will data continue to grow?  What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? fast data? — appears on the horizon.   

Let’s look at some…

Apart from…

How does a company break into the huge potential marketplace that is the Industrial Internet? Spending on the Industrial Internet—the convergence of industrial machines, data, and the Internet (one aspect of the Internet of Things)—was $20 billion in 2012. Analysts are forecasting that number will reach $514 billion by 2020, creating nearly $1.3 trillion in value. MIT Sloan Management Review recently published a case study of one company that…

Great article by Mike Ferguson. Articles about the big data, AI, data science or IoT ecosystems are always popular. Many have been posted here (see screenshot below):

Sometimes, the keyword…

Data Science Central shared its predictions for 2016. More predictions can be found here. In this article, we share Scott Mongeau's predictions. The full version of this (long) article can be found…

You cannot argue that the marketing landscape has changed drastically over the years - gone are the days of throwing different strategies on the wall to see which one would stick. Instead, companies today are equipped with enough information about their customers and tactics thanks to the big data boom of recent years. In…

With the growth in the market penetration of smart phones, the number of mobile phone users is likely to increase. The popularity of social media is also expected to increase and 14 more applications will see growth in the mobile data traffic market.

Mobile traffic data was estimated at 3.9 trillion megabytes in 2011 and it is expected to reach 39.9 trillion megabytes in 2016, whereas mobile data revenue was estimated at USD 326.1 billion in 2011 and is expected to reach USD 628.1…

This article was written by Sunil Ray. Sunil is a Business Analytics and Intelligence professional with deep experience in the Indian Insurance industry. 

Introduction

GraphLab came as an unexpected breakthrough on my learning plan. After all, ‘ Good Things Happen When You Expect Them Least To Happen’. It all started with the end of Black Friday Data Hack. Out of 1200 participants, we got our winners and their…

Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with a specific model and data set) are expected to be accurate. 

Confidence Interval. Confidence intervals are used to assess how reliable a statistical…

Data center infrastructure management (DCIM) solutions are the most effective way to reduce data center operating costs. DCIM solutions provide significant value to IT organizations that are concerned about the energy consumption and improvement in operational efficiency in their data centers. DCIM is a combination of IT and facilities management that helps enable faultless monitoring of all systems in IT and facility infrastructure.

DCIM Emerges as a Strategic Tool for…

Credit analytics is a way to arrive at creditworthiness of a business or an organization i.e. the ability of the Business or organization to meet its financial obligations. In technical terms, the crux of credit risk analysis lies in identifying the appropriate level of default risk associated with investment in a particular entity.…

There are many ways to choose features with given data, and it is always a challenge to pick up the ones with which a particular algorithm will work better. Here I will consider data from monitoring performance of physical exercises with wearable accelerometers, for example, wrist bands.

The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har.

In this project, researchers used data from…

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.

 …

As worldwide establishments continue to look for ways to make big data actionable, solutions such as speech analytics are well placed to help customers and balance the need of revealing new revenue opportunities, reducing costs and optimizing the customer experience.…

Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud and target prospective buyers. 

Predictive analytics is business intelligence technology that produces a predictive…

For a few years now Big Data has drawn the attention of many firms and CIOs. The rewards are major –new business insights, new customer segments, speed (faster time-to-answer, faster time-to-decision, and faster speed-to-market) to driving operational cost efficiencies and even corporate strategies. 

In the past few months I have been connecting with senior managers who want to start their Big Data efforts this year. You will need to take the necessary time to focus on some upfront…

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