3 key aspects for building effective BI solutions
The industry shift toward real-time analysis of “bigger, faster and wider” data flows dictates new directions for the development of data warehousing and Business Intelligence (BI) solutions for every business.
To satisfy the requirements of today’s enterprises, we need to understand the following:
- What data is being collected?
- How clear is the data?
- Where is it stored?
- In what form is it stored?
- How can it be reached?
A critical imperative for any business practice is the ability to process volumes of stored data from a variety of different sources, such as CRM, CMS, file systems , etc., and turn them into well-structured integrated Business Intelligence and analytics for meaningful reports, planning, and forecasting.
Properly leveraging such a massive amount of data requires a single, well-organized system that can help identify new business opportunities, and support related business decisions on both strategic and operational horizons, e.g., sales and distribution, customer management, etc.
Based on experience, the CoreValue data team has identified key aspects of building a comprehensive data and Business intelligence solution for enterprises that includes extended infrastructure for streaming data processing for improved BI, and a Cloud-based data warehouse for improved performance and scalability of complex data volumes that saves on capital and operating expenses.
The Cloud and big data is compelling from both financial and educational perspectives. The Cloud allows enterprises to focus on important business aspects like innovation and value-based insights, instead of infrastructure maintenance and the overhead of servers, deployment, equipment and IT staff management.
Cloud service is also moving swiftly toward resolution of particularly challenging data cyber-security concerns. By applying the latest security and encryption solutions, which meet industry-specific compliance constraints and requirements for a Cloud-based data warehouse, the global industries now have a powerful instrument for data protection.
Real-time data provides on-demand access to all types of business-critical information, e.g., historical data, market data, user input, etc. Such an approach provides instant tactical support and allows the companies to react immediately to various events as they occur, e.g., customer application, end-user reminder, push notification, etc.
Real-time data processing is also vital for customers, who prefer to pay premiums, and see their status on-line and on-demand. Add this to skillfully applied BI analytics, which helps to manage and provide services more efficiently, and customer satisfaction becomes another benefit.
Data Science Modeling Infrastructure – Sandbox
Data science modeling infrastructure (Sandbox) empowers data experts to build models for later production implementation. It makes data available for live data monitoring, continuous query processing, automated alerting and reaction, and machine learning.
Data scientists can address complex analytical projects by pulling data from all layers with Sandbox. It also facilitates implementation of Data Science models for a larger number of projects/applications, and therefore delivers Business Intelligence and analytics programs faster.
For example, insurance data professionals engage machine learning algorithms to manage expenses and risk analytics, monitor fraud, and aid other general insurance issues. The critically important risk assessment and event probability computed with machine learning methods predefine underwriting and claims decisions.
Imagine being able to leverage customer management, risks analytics, workflow and other important daily operations based on real-time data access. Then imagine being able to do that without the overhead of equipment, maintenance and staff management. Big data and Business Intelligence analytics holds a great promise for every industry in terms of transformation it into a more efficient, cost effective and customer-oriented enterprise.
Ready to think about Business Intelligence for your business?
Chief Technical Officer, CoreValue