Brian goes on to recommend this deal and also cross-sell a mortgage protection product to Jason, who shortly walks out of the bank happy with the prompt service he received and being able to get his mortgage and insurance sorted within 30 minutes.
Brian is excited because with the real-time recommendations suggested by the system, not only was he able to further engage the client but also cross sell an additional insurance product.
Welcome to the future of banking. Welcome to the power of Real-Time Business Intelligence.
Times have changed and so have today’s businesses that run the global marketplace. Today’s customers are well-informed and demand services on their terms.
To serve today’s customer requires the ability to make instantaneous decisions on products and services, taking into account a full spectrum of not just their relationship with the business, but also other exigencies of the market and ecosystem the business is operating in.
Traditional database solutions have been limited in catering to these needs in that they have been historically used to make long term, strategic decisions due to the rigid data latency issues involved. This had left a gap in the market for a “run time” solution that could help businesses make on the spot, tactical decisions with their data – this gap is now being filled by the state-of-the-art OLAP systems that power most Real-Time Business Intelligence platforms.
An OLAP system is different from traditional OLTP systems in that it is geared for fetch data quickly (using efficient SELECT queries) whereas OLTP systems are aimed at processing transactions quickly (i.e. INSERT/UPDATE/DELETE queries). And because of this very difference, it is difficult for most data management systems to cater to both.
When designing systems for the modern business, specific stress must be Laid on the quest for “fresher data”. Some of the key techniques used to generate this level of Real-Time Business Intelligence involve –
* In-memory analytics: Allows for analytical computations and big data to be processed in-memory and distributed across a set of nodes.
* In-database analytics: Allows data integration and analytic functions inside databases so you won’t have to move or convert data repeatedly.
* Grid computing: Allows for processing of jobs in a shared, managed pool of IT resources.
Big data provides a fantastic opportunity to understand customers in ways that can genuinely transform the business.
High-performance analytics can help organizations reach customers at precisely the right time and place, and hit them with the right message, so organizations can keep and grow their profitable customer base.
As a Leading Revenue Management and Business Assurance solution, SunTec’s Xelerate platform comes with built-in real-time analytics. Many of today’s Fortune 500 companies are using the Xelerate platform to make informed business decisions and offer pointed solutions to their customers. Using the built-in realtime business intelligence, Xelerate can deliver real-time insights into customers and product offerings they are Likely to choose, aside from giving their business leaders a real-time view of profitability and revenue figures.
While there are a number of upsides to implementing Real-Time Business Intelligence systems, there are challenges too. The inflexibility of legacy systems to transition data, the need to manage unstructured data that often requires significant manual effort and the fact that there might be security and compliance requirements add to the challenges.