Sollers Blog

The data stream models, with its ability to cater to numerous types of data, have been providing empirical evidence of streaming algorithms based on the performance of synthetic and real data streams. It is crucial to analyze such data provided its ability to process the data in a single pass, or a small number of passes with minimal usage of memory.  Clustering is a key data mining task. This is the problem of partitioning a set of observations into clusters such that the intra-cluster observations are similar and the inter-cluster observations are dissimilar.

Data Visualization (DV), in simple terms, is the presentation of a data ornate with graphics, which looks aesthetic, when large amounts of data and complex statistics for the easy comprehension. DV is no doubt witnessing exponential growth. No matter whether you are an expert or a novice, a gamut of books are available to hone your skills. If you wonder how you need to know more about DV, all you need to do is to enter the Amazon book section, where you will find in excess of 15,000 items. Let us now see five must-read books on Data Visualization.

FDA defines Data Integrity as data, which is complete, consistent and accurate. It further defines data integrity as data, which is -

Machine learning is a field of Data Analysis in which model building is automated. Algorithms learn from the data analyzed previously and improve the algorithm based on the findings. It is an application of artificial intelligence where computers (machines) learn from the analyzed data and adapt automatically. It is not a new technique, but it is attaining fresh momentum as big data and data analytics gain prominence.

Data analytics is the use of qualitative and quantitative techniques and processes to increase productivity for leveraging business gains for enterprises. It scrutinizes the collected data to analyses and identifies patterns to create unique solutions and software to address business challenges.

Data Mining is a science of unearthing hidden patterns and relationships within your data to help you take better business decisions. It can help you in spotting sale trends, developing marketing campaigns or predicting customer churn. It has several applications in today’s business, which is required to deal with a large amount of data. Data mining requires a Data Warehouse as a source of data. Principles of data mining have been around for a while, but they have gained prominence with the advent of Big Data and Data Analytics.

Some of the leading business trends across the world have had a great influence on the transition that has occurred in the market for predictive analytics professionals (PAPs) in the recent times. A phenomenal gesture has been observed which will lead to shaping the business landscapes over the coming years after the inclusion of the variety of major shifts such as the widespread adoption of data-driven initiatives, blending of data science and predictive analytics and the arrival of several professionals entering the analytics market.

The concept of Data Journalism extends beyond the level of vague understanding of a common man by segregating the two words such as "Data" and "Journalism". So many years ago, journalists used to work on a spreadsheet that is filled with loads of data with numbers. But those days had their bit of time and it was all over. Now everything is dealt in digital medium and numbers hold the prominence to describe all types of data. For instance, if the career history of an individual has about more than or equal to 300,000 confidential documents, it could be described with just two numbers. Whatever the kind of data may be, such as photos, videos, audios, political votes, diseases, and corruption, there are only two numbers, zeros and ones used to describe the entities.

Data Collection and its importance -  Invariably data remains to be the biggest asset of all the organizations. One cannot deny the fact that every company is desperately looking for various sources and integrated systems aiming for a secure data collection with maximum efficiency. A survey conducted by a leading IT portal in the US reveals that decisions made with the confidence on reliable data lead to the success rate that is 79% higher than those decisions taken only with pure intuition. The implementation of centralized data collection process is the only requirement for an organization to make data-driven decisions. Upon the application of such data systems, an organization would be able to retain its position strongly with the influence of its competitive advantage and sustainability.

eSource is starting to gain traction with the increasing implementation of novel technologies in clinical trials faces cultural barriers at biopharmaceutical enterprises. Operations demonstrating the generation of data have been streamlined to a larger extent with the impact produced on clinical operational productivity. The utilization of eSource on workload reduction at study sites will have to uncover several breakthrough trends including eSource solutions providers, clinical Ink and applied clinical trials raw data from relevant site impact survey for analysis.