The proliferation of analytics databases throughout the enterprise has required an increasing level of effort to develop data management practices.
Skilled data scientists and analysts are a hot commodity in the race to obtain an edge through better analytics and an understanding of how your business was, is and will be.
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 database administrators.
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. The 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.
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