SYE, a framework designed by ITS engineers to enhance the business user experience with capabilities of dynamic data rules without worrying about underlying technologies. ITS engineering team is comprised of industry big data experts and professionals who have implemented enormous hadoop solutions using Hortonworks and Cloudera distributions. ITS Datamatics Engineering Team focuses on enabling companies to derive insights and revenue from their data by giving them the capacity to aggregate structured and unstructured data, to handle high- velocity streaming data, and to process these large datasets at scale. Our services has assisted our customers cut “time to insight” by up to 500x, and our assistance in pipeline design and productivity improvements have created millions in value for our customers, and their customers as well.
ITS accelerate and empower business to face the 7 key challenges of Big Data
- How to integrate and effectively “Blend” Big Data with their existing data sources and streams?
- How to effectively manage the sheer volume of Big Data?
- How to manage the technology complexity of managing and analyzing Big Data?
- How to translate and derive insight from Big Data?
- How to extract maximum value from Big Data initiatives?
- How to explore and create value from Big Data using cutting edge visualization techniques?
- How to preempt current and future state analytics requirements to enable companies to change and adapt to evolving needs?
ITS Engineering Team apply their expertise to above by solving customer problems in a diverse number of Big Data verticals, including Media and Entertainment, Life Sciences (bioinformatics & medical image analysis), Financial Services, and Oil & Gas.
Organizations have been doing look-back analysis for years, but generating trends based on historical data is now a very common technique. New software tools on the market today enable organizations to engage in predictive and deep analytics. These tools allow organizations to look forward, helping them to become more competitive and to answer questions such as:
- What do we know and not know about our customers?
- What can we do with this information?
- How can we innovate and transform using this information?
- The combination of trend analysis and predictive analytics enabled by Big Data has the potential to be transformative.
Customer Centricity: Organizations with global footprints can apply Big Data to develop a single view of the customer, which can promote delivery of an enhanced customer experience and, in turn, improve branding and increase revenues.
Customer risk analysis: Retail lenders and other financial institutions can also apply Big Data to analyze behavior profiles, spending habits, and cultural segmentation—thereby gaining a 720-degree view of customer risk that will enhance the lender’s risk management capabilities.
Customer retention: Using Big Data, financial institutions can analyze their internal customer logs and social media activity to generate indications of customer dissatisfaction, allowing time to act.
New products and services: Social media analytics generated from Big Data can be leveraged in various stages of new products and services—from conceptualization to launch. Organizations can use social media to ascertain pre-launch sentiments and expectations to effectively define marketing strategies.
Algorithmic trading and analytics: Organizations can leverage Big Data to store large volumes of historical market data to feed trading and predictive models and forecasts. Institutions can also use Big Data to perform analytics on complex securities using reference, market, and transaction data from different sources.
Organizational intelligence: Organizations can use Big Data to measure organizational intelligence using employee collaboration analytics. In addition, a Big Data-based culture of innovation empowers workers to learn more, create more, and do more.
Risk management: Increased regulatory focus requires Organizations to manage enterprise risk across risk dimensions. Big Data can enable market events across geographies to be captured in real-time via unstructured data sources such as news, research, graphs, audio, visuals, and social media.
SYE Datamatics framework encompasses of five distinct phases to achieve big data solution.