Riding the Next Big Wave in Big Data
Aug 18 2017
But many are drowning under the oppressive weight of legacy data management
platforms and antiquated analytics approaches according to a recent study by IDC on 502 enterprises
around the globe.
Using traditional ETL methods requires an average of five to seven days to migrate
data from the warehouse to analytical databases, and enterprises are acutely aware of the negative
impact of untimely data on their ability to make the most of business opportunities.
Transformational models of the last decade have stressed strategic decision making
based on predictive insight gained from historical and landscape data. Despite this emphasis across
enterprises on big data analytics projects, Gartner research discovered that only one in ten
succeeds based on lack of in house skills.
Problems arise in areas such as data management and consolidation, data quality, and issues
running modern analytics on technology developed in the 1970s.
Organizations are mired in data collection without the ability to get on top of
ever growing data sets, watching business opportunity pass by and crash on the shore. Controlling
the strategic direction of the enterprise has focused on hindsight, reaction, and corrective
measures rather than real time agile responses to a live environment.
Day to day business is driven by line and field staff making tactical decisions in
real time to increase efficiency and open up new opportunities, and requires the ability to run
queries on data retrieved from local servers, outside IoT sources, and social network feeds.
Building scalable systems to effectively deal with this infinite amount of structured and
unstructured information requires an intensive investment in top system engineers and data
scientists and analysts.
Datatron simplifies this investment through a scalable and customizable platform that allows
enterprises to run queries on landscape and real time data for optimum insight to inform your
business as it is happening.
Our platform lies on top of your existing environment, accessing your historical
data through a single interface, while simultaneously streaming and cleansing live data
automatically. This allows data analysts and business users to focus on analysis and not data
movement, accessed at speeds unattainable through traditional ETL methods.
Speed your innovation and increase your agility through the ability to predict
incoming waves based on your historical
Filed under Datatron Platform | Tagged: Data Cleansing Predictive Analytics ML