The insideBIGDATA Guide to Streaming Analytics is a useful new resource directed toward enterprise thought leaders who wish to gain strategic insights into this exciting new area of technology. Many enterprises find themselves at a key inflection point in the big data timeline with respect to streaming analytics technology. There is a huge opportunity for direct financial and market growth for enterprises by leveraging streaming analytics. Streaming analytics deployments are being engaged by companies in a broad variety of different use cases. The vendor and technology landscape is complex and numerous open source options are mushrooming. It’s important to choose a platform that will supply a proven and pre-integrated, performance-tuned stack, ease of use, enterprise-class reliability and flexibility to protect the enterprise from rapid technology changes. Maybe the most important reason to evaluate this technology now is that a company’s competitors are very likely implementing enterprise-wide real-time streaming analytics right now and may soon gain significant advantages in customer perception & market-share. The complete insideBIGDATA Guide to Streaming Analytics is available for download from the insideBIGDATA White Paper Library.
We are experiencing a significant upward trajectory in terms of uptake of streaming capability across industries like financial services, retail, healthcare, telecommunications, oil & gas, ad tech and many more. Streaming analytics helps enterprises by visualizing the business in real-time, cutting preventable losses, detecting urgent situations and automating immediate actions. In recognition of these benefits, many important verticals are going through a committed proof-of-concept process.
The centralized architecture teams of an increasing number of enterprises are evaluating different technologies for streaming. As a result, it’s a good time for business decision making in terms of allocating budget. Creating business use cases and detailing business value is going to be required as part of the due diligence process.
The business value for streaming analytics can be outlined as follows:
- Routine business operations (real time systems)
–– Manufacturing control systems
–– IT systems and network monitoring
–– Field assets monitoring and alerting, e.g. trucks, oil rigs, vending machines, radio towers
–– Financial transactions processing, e.g. authentications, validations, fraud
- Cutting preventable losses
–– Loss of lives and assets
–– Manufacturing defects
–– Major security breaches in retail
–– Stock exchange meltdown
–– Brokerage – fraudulent or risky trades
–– Medical/clinical – complex analytics in ICU
–– Disaster warning systems
–– Preventative maintenance – machine, plants
–– Customer churn
–– Brand reputation on social media
- Finding and monetizing missed opportunities – increased revenue, cost savings
–– Listening and learning from customers (social)
–– Context sensitive inventory, products, ads
–– Recommend, up-sell, cross-sell
–– Network optimization for cost, quality of service
–– Dynamic capacity management
–– Dynamic re-routing of traffic, cargo
–– Insurance adjudication, drone image analysis
- Creating new opportunities – new business models, products, services, revenue
–– Tractors are becoming soil sensors
–– Telecom giants selling data and insights
In order to derive business value from streaming analytics, organizations need to think a little bit differently about how they analyze data. Traditional analytical tools are optimized for request and response from static data. With streaming analytics, the data is pouring in continuously and you don’t know what’s in that data. Application developers need to stop thinking about request and response and start thinking about detecting interesting events as they come in. Streaming analytics offers organizations an opportunity to ingest and glean instant insights from real-time data coming in via transactions, cloud applications, web interactions, mobile devices, and machine sensors.
The emerging Internet-of-Things (IoT) will also fuel demand for streaming analytics capabilities in the near term. Sensor data from thousands of Internet connected devices can give companies valuable insights on the health of a network, system or application.
The best way to look for use cases in support of business value is to consider the most challenging business processes and walk through them at each stage to identify situations where additional data might help. Ask yourself if there are data sources available that would provide information in real-time to make the process more efficient. On the customer side, walk through the customer journey and see if additional data can improve or detect something in real-time.
One common characteristic of streaming data value is that it decreases non-linearly over time. The key is to ingest data, compute actionable insights and react to them in real-time. The longer you wait, the less business value.