Big Transactional Data

BIG DATA!

Big data is a wide term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from raw data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making. And better decisions can mean greater operational efficiency, cost reduction and reduced risk.

Analysis of data sets can find new correlations, to "spot business trends, prevent diseases, combat crime and so on.  Scientists, business executives, practitioners of media and advertising and governments alike regularly meet difficulties with large data sets in areas including Internet search, finance and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,  connectomics, complex physics simulations, and biological and environmental research.

The challenge for large enterprises is determining who should own big data initiatives that straddle the entire organization.

Working with big data is necessarily uncommon; most analysis is of "PC size" data, on a desktop PC or notebook that can handle the available data set.

Relational database management systems and desktop statistics and visualization packages often have difficulty handling big data. The work instead requires "massively parallel software running on tens, hundreds, or even thousands of servers".  What is considered "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make Big Data a moving target. Thus, what is considered "big" one year becomes ordinary later. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.

Datasprint wanted to simplify and breakdown the barrier of entering the BIG data analysis market.  How?  Simply by offering a platform that can easily connect to your data securely either via a direct API connection or by a manual upload process all in under 3 weeks. In the simplest terms we take your transnational data, crunch it through the super cloud platform and display actionable trends that your teams at all levels can understand via a visualisation dashboard and really intuitive menu and story board system.  

Our platform works on a two folded approach, firstly your teams can review data in proven visualisation displays and secondly your team can make critical business decisions based upon the trends discovered and take critical action.  Upon the FREE trial Datasprint offers its customers, users on the platform are able to engage and interrogate the last three years of data up to the current LIVE working week to identify critical actionable trends. 

So what is Datasprint?

DataSprint is a software as a service tool specialising in retention across ecommerce and the online gambling and gaming verticals. Our gaming operator clients regularly approach us to help them with their most important customers, their VIP’s.

Their ‘issues’ around their management of VIP’s range from the promotional: ‘are my VIP promotions driving real value?’ through to the strategic: ‘How do I define a VIP?’ to the tactical:’ am I spotting VIP’s when they visit my site?’  

If you want to learn more on how Datasprint can help redefine retention for your customers please get in contact. 

Simon Elias is a director of DataSprint a Software as a Service (SAAS) tool focused on improving Profits, Customer Conversion and Customer Retention for their clients.

www.datasprint.uk

info@datasprint.uk