Liberate your Data scientist from Data Overload
Aug 18 2017
The proliferation of analytics databases throughout the enterprise has required
increasing level of effort to manage them.
In a study of data management practices, IDC found that 60% of reviewed organizations had more
than five analytical databases, and over 30% had more than ten, each managed by one to two
It is a case where more is not necessarily better, and enterprises are looking for
options to simplify and gain greater agility. Data merged from diverse sources requires excessive
turnaround, while relevance diminishes as time ticks away. On average, it takes five to seven days
for data to reach an analytical database. By this time, data is already historical, and
opportunities for on point decision making are long gone.
Real time decision making based on live data streaming requires the ability to
integrate analytical queries and information from current transactions. Difficulty arises because
transactional databases are not able to perform rapid analytical queries, and analytical databases
are too slow at transactional processing to meet requirements.
Constraints in technology require ever more from your data scientist, who must not
only be the best software engineer, but the best data analyst to manage, cleanse, and build models
from ever increasing data stores. Traditional analytics databases do not have the performance or
speed to process large data sets, and data scientists typically can only take a sampling of
historical data for analytics purposes.
The inflexibility inherent in legacy systems requires an army of data scientists and software
engineers to scale input or integrate new data sources, and most data scientists spend 80% of
their time in data management and cleansing, rather than building predictive models.
A scalable, flexible, and fast platform integrating massive quantities of
historical and real time data is in order, and Datatron fulfills that call. Automatic data
aggregation and cleansing capabilities provide relevant information when it is relevant to
proactively follow on leads and business opportunities.
Our software implementation specialists assist your data analysts to build
predictive models on our easy-to-use platform, specific to your business needs. And these models
increase in accuracy over time through deep learning capabilities, unveiling new insight
previously unattainable through traditional analytical models.
Through Datatron’s automated capabilities, data scientists can shift their
attention from the burden of data management and cleansing to high value business analysis, and our
accessible user interface and query capabilities enable business users across the enterprise to gain
immediate access to actionable information. Datatron’s self-service data platform democratizes the
data analysis experience, providing timely recommendations to your front-line, pivoting your
business for greater vision and competitive advantage.
Filed under Datatron Platform | Tagged: Data Cleansing Predictive Analytics ML Platform