Exponential data growth has placed immense pressure on systems that have to keep that data moving at high speeds from machines to warehouses or data lakes and into analytics platforms. Data management and storage are only a subset of today’s data-driven requirements. Deriving analytical value is the real goal. That value gets hard to show, without systems that can analyze streams of information in real time.
To facilitate real-time analytics on streaming data into their ecosystems, enterprises have been exploring some architectures and technologies. Rather than relying on the traditional approach of custom coding and integrations that apply in only limited situations, the use of general purpose visualization tools simplifies the extraction of value from streaming data. The use cases are many from predictive maintenance, financial services risk reporting, operations optimization, cybersecurity, and more.
What qualifies as predictive analytics?
In historical data analysis, one exports a set of historical data for batch analysis. In streaming analytics, one analyzes and visualizes data in real time. In other words, with machine learning, you can learn from historical data through statistical analysis. When you pair predictive analytics with computational power, you can surface insights reliably and quickly. This is the essence of artificial intelligence. The insights from machine intelligence and predictive analytics will impact how you sell, market and provide support to your users.
Can the predictive model be updated in real-time?
Model building is an iterative process and involves rigorous experimentation. It isn't practical to update the model on every new observation arriving in real-time. Firstly, the retraining of the model involves feeding the base data set including the new observation data point which means you'll have to rebuild the model. Unless the model is a simple rule-based one, you can't “incrementally update the model” with every new observation. Most of us deal with more complex statistical or machine learning techniques. Secondly, there is no tangible benefit in feeding a large volume of data along with the new observation every time to rebuild the model. The model doesn’t do all that much more with just an addition of a single data point. It only makes sense to rebuild the model after aggregating a considerable volume of data to experience a tangible difference in the model.
Then what is real-time predictive analytics?
It is when a predictive model built based off a set of aggregated data is deployed to carry out a run-time prediction on a continuous stream of event data to enable real-time decision making. There are two aspects involved in getting to this point. The predictive model built via a stand-alone tool has to be exported in a consumable format. Also, a streaming operational analytics platform will have to consume the model and translate it into the required predictive function and to compute the predicted outcome, feed the processed streaming event data. This deployment of a complex predictive model, from its parent machine learning environment to an operational analytics environment, is a possible path to achieving a constant run-time prediction in real-time, on streaming event data.
Real-time data pipelines are often architected as follows:
- Through a distributed messaging system meant to capture and publish feeds, application data is ingested.
- A transformation tier is called to enrich data, distill information, and deliver the right formats.
- Data is stored in a real-time, operational data warehouse for easy application development, persistence, and analytics.
- From here, to power real-time dashboards, data can be queried with SQL.
- It is crucial to build an infrastructure that can carry out fast data analysis that allows for real-time dashboards, predictive analytics, and machine learning, as new applications generate increased volume and data complexity.
Cloud-based predictive analytics
Cloud-based predictive analytics also solves the issue of disparate data sets by integrating processing capabilities and systems in the cloud. This way instead of spending months integrating data systems and silos into an inflexible traditional Business Intelligence platform, the cloud enables you to connect and analyze real-time and legacy data without any cost or headache. This makes it much easier to segment customers at the micro-level so you can understand customer needs and behaviors, track customer journeys, and develop more targeted marketing messages. However, an entirely cloud-based BI stance comes with its disadvantages. For one thing, the cloud-based BI is still evolving, so it's bound to lack the range and functionality that the traditional BI solutions provide, although complex and costly.
Start with open source software and integrated infrastructure based on an open, customizable and flexible architecture. Since the open source and the cloud are intrinsically elastic, you can smoothly boost your BI efforts with the incorporation of production-ready, pre-packaged, open source software components that meet your evolving needs at a low cost. Take into account the below strategies as you build a predictive analytics stack:
- Consider embracing the cloud. It maybe time to use the cloud for the collation of your disparate data sources.
- Build an open architecture. Don't go monolith, instead craft a solution based on components that work in sync through standard protocols. This will ensure you keep pace with innovations and re-build parts of your stack as new technologies become available.
- Siloed decision-making is no less problematic than siloed data. Converge the priorities of the C-suite. The CTO should focus on the results from any investment in predictive analysis not through serialized planning, but rather through agile joint planning sessions.
- Embrace open source. For faster turnaround and feature delivery, leverage agile development processes and open source technology and prioritize a DevOps culture and related technologies.
A predictive analytics solution based on open source and cloud, while future-proofing your investment, makes intelligent data more accessible.
A data scientist needs an aggregated mass of data that will form the historical basis on which the predictive model could be built. The model building exercise is a deep subject by itself. The main point to keep in mind is that model building for predictive performance requires considerable historical data, involves rigorous experimentation and is a time-consuming process. Meaning, a predictive model, in its true sense, can't be built in “real-time.” None of this sound easy, but the competitive advantage enabled by predictive analysis alone should prove to be a sufficient catalyst. If you’re struggling with more questions, we might have the answers you look for.