Back in 2001, when I started working in the enterprise marketing software business, customer relationship management or CRM was seen as the cure all from a sales and marketing perspective. "If only we could more quickly send direct mailers offering a buy one, get one video rental, we could corner the market" one executive told me. CRM deployments at that time were costly and resource intensive. My how times have changed. But one thing hasn't changed – there remains three critical components to consider when standing up a solid customer intelligence software solution – data, insight and action. Data meaning a centralized data repository – containing first and third-party data.

Last week, I introduced the notion that businesses can gain deeper customer insights if they connect their disparate data silos. Businesses can deliver a superior customer experience because they simply know more things about their customers and can use that information to tailor interactions. Using the 2x2 graphical approach to understanding data size (i.e., number of customers and number of variables), we can see how the value of your integrated business data is greater than the sum of its parts. Figure 1 illustrates these two components of size by examining four different scenarios of how businesses use their data. In the lower right quadrant, it is business as usual; when departments keep their data siloed, each department only knows a few things about the customers.

In this Big Data world, a major goal for businesses is to maximize the value of all their customer data. Most customer data, however, are housed in separate data silos. While each data silo contains important pieces of information about your customers, if you don't connect those pieces across those different data silos, you're only seeing parts of the entire customer puzzle. The integration of these disparate customer data silos helps your analytics team to identify the interrelationships among the different pieces of customer information, including their purchasing behavior, values, interests, attitudes about your brand, interactions with your brand and more. Integrating information/facts about your customers allows you to gain an understanding about how all the variables work together (i.e., are related to each other), driving deeper customer insight about why customers churn, recommend you and buy more from you.

Managing Data in Motion, Data Integration Best Practice Techniques and Technologies is a really well written work that surveyed a broad range of practices and technologies used in Data Integration. The book avoided overburdening jargon so it should be quite accessible to anyone who is interested in learning about Data Integration's past, present and future. The author has done a valiant job pulling together a wealth of knowledge into an easily consumable form. In addition to her own words, I also greatly appreciated the sidebars from experts in their own domains. While the Table of Contents is somewhat intimidating in its list of topics, it was really a very easy read.

We propose an Information Integration and Informatics (III) framework for healthcare applications that leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. Healthcare information integration and informatics presents a potential for building advanced healthcare applications given the massive scale of data which is collected by EHR systems. Traditional EHR systems are based on different EHR standards, different languages and different technology generations. EHRs are mainly designed to store individual-level data on patient-provider interactions. EHRs capture and store information on patient health and provider actions.

In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robustify our models to deal with the presence of sparse noise and low-rank interference signals. Specifically, we demonstrate that incorporating the noise or interference signal as a low-rank component in our models is essential in a multi-sensor classification problem when multiple co-located sources/sensors simultaneously record the same physical event. We further extend our frameworks to kernelized models which rely on sparsely representing a test sample in terms of all the training samples in a feature space induced by a kernel function. A fast and efficient algorithm based on alternative direction method is proposed where its convergence to an optimal solution is guaranteed. Extensive experiments are conducted on several real multi-sensor data sets and results are compared with the conventional classifiers to verify the effectiveness of the proposed methods.

In my previous blog, I showed you how to integrate Stanford CoreNLP with Talend using a simple example. In this post I'll show you how to modify that code in order to make the most of Talend's strengths as a data integration tool. Below is a Talend job I have built to read some tweets from a database (see this blog article for information on how to retrieve tweets with Talend), run the text through the CoreNLP sentiment analysis code, and then write tweets back to the database with the addition of the sentiment. In this particular example, the text to be analysed are tweets coming from a database. However, the same job will work with any string input.

Dynamic data integration incorporating data quality and master data management (MDM) assures consistency and reliability for upstream analytics and information sharing. A pragmatic approach that treats all of a company's data as big data will facilitate integration efforts. In Part I, we stressed the importance of data quality. In this post, we focus on MDM and the connection between the two as part of data governance strategy. As financial institutions seek to become data-driven enterprises, data governance should be regarded as a strategic centerpiece to that mission.

In using them to do manual data preparation, you're missing a huge opportunity to extract the most value from your intellectual assets. By automating and accelerating much of this raw data crunching and ETL work, you enable non-data scientists to do data preparation rapidly and simply--and ask their own questions and find their own answers. What's more, in this new Big Data Discovery environment, answers come in minutes, not months. Data scientists are able to focus on Spark-driven advanced analytics that yield game-changing answers. In this next DSC webinar, you will learn: How to automate your data integration process to set up your organization to be truly data-driven How to manage your data as a self-service feature at the speed of thought How to effectively unearth big insights that effectively impact the bottom line in the most efficient cycles.

The 2016 IEEE GRSS Data Fusion Contest, organized by the IADF TC, was opened on January 3, 2016. The submission deadline was April 29, 2016. Participants submitted open topic manuscripts using the VHR and video-from-space data released for the competition. Evaluation and ranking were conducted by the Award Committee. The winners are reported below along with the abstracts of the submitted papers.