Mobile, social, M2M, CRM, ERP, IoT. There’s a lot of data out there. Consequently, today’s situation demands smart products and services that can handle this large volume of data. This demand presents a massive opportunity to extract intelligence, gain insight, to make everything smarter – products, services, places, cities, grids.
Several Analytics vendors offer recommendations to enable their clients to increase revenue, reduce churn and improve customer experience. But often times these recommendations are too little and too late.
The solution to this problem lies in the problem itself.
Most Analytics vendors use traditional database systems. These are slow to ingest data, analyze it real-time, and make smart decisions. These database solutions were not designed to scale to this large volume – and this speed.
The nature of analytics models — upselling, cross-selling and providing operators with real-time insight and analysis — requires a fast data approach. By fast data, I mean as-it-happens information where data is processed as it streams in, enabling real-time analysis and decision-making. Many of the opportunities to deliver value rely on real-time, in-the-moment insights, without which opportunities to improve the customer experience would be lost.
For example, a credit card fraud detection company needs to detect the fraud as it is happening. A retail company needs to know how their latest collection is selling. A PR firm needs to know how people are talking about their customer’s brand in real-time. A few minutes late and the fraud happens, sales dip and the negative vibe goes viral. If decisions such as these are not taken in real time, the opportunity to mitigate the damage is lost. One thing remains constant across industries – Fast Data is becoming crucial for modern enterprises, and businesses are now catching onto the real need for such data capabilities.
Stream processing is designed to analyze and act on real-time streaming data, using continuous queries. Adding intelligence to signals and patterns makes the data smart, and produces smart insights that can be used for actions, alerts, and triggers. Essential to stream processing is Streaming Analytics – the ability to continuously calculate and update results about data analytics as new pieces of information flow within the stream. This enables analysis of data in motion. In contrast to the traditional database model where data is first stored and indexed and then subsequently processed by queries, stream processing and analytics takes the inbound data while it is in flight to process, analyze and produce smart insights.
Analytics now requires stream-processed data and results within the context of live processes. And, companies like VoltDB combine the capabilities of an operational database, real-time analytics, and stream processing – all in one easy-to-use platform. It enables applications to use real-time streaming data to enrich user experience, optimize interactions, and create value.
Running billions of transactions per day to rapidly ingest data, deliver real-time analysis and decision-making on a per-event basis in milliseconds is why VoltDB is considered as a S.M.A.R.T. analytics partner to various industries ranging from mobile, financial services, online gaming and healthcare by detecting credit card faults to detecting faulty products in a manufacturing line to sales forecasting to traffic monitoring to detecting network failure, among many others.
The coming years will hail a golden age not for any data, but for fast and smart data.
S – Streaming Data Pipeline
M – In-memory
A – Analytical
R – Real time business insights
T – Transactional Database