The explosion big data has created in recent past is phenomenal. The entire business environment has been flooded with data volumes and the diversity of new data sources. Yet, many organizations still struggle with the analytic opportunities big data provides. Reason being they are still focused on assembling insights from structured data by using age-old tools for business intelligence (BI and its capabilities) and Data Warehousing (DW).
Whereas, an entirely new world of insights is waiting for the organizations. The journey towards this world walks through the exponentially growing volumes of unstructured and semi-structured data, especially from new sources such as machines, sensors, logs, and social media.
As the traditional BI/DW tools were not designed for these new data sources and data types, they represent a dotting challenge for today’s dynamic scenario. Although they are not going anywhere, there’s a specified need to complement them with the latest technologies. Operational Intelligence is one such bridging gap for the new sources of big data and the growing business needs.
Operational Intelligence (OI) is a form of real-time dynamic, business analytics that delivers visibility and insight into business operations. It is an upcoming class of analytics that provides visibility into business processes, events, and operations as they are happening. This real-time information can be acted upon in a variety of ways: alerts can be sent, business processes can be triggered and executive decisions can be made and implemented using live dashboards. The practice of OI is enabled by special technologies that can handle machine data, sensor data, event streams, and other forms of streaming data and big data.
1. Real-time data handling: OI stands out in capturing and processing data in seconds or milliseconds from multiple sources and that too from both traditional and new, including streaming data, event streams, and message queues.
2. Advanced analytics: OI links and correlates related events, regardless of their origins or latency, to discover problems or opportunities that merit immediate attention.
3. Analyze big data and machine data: Ingest and analyze multi-terabyte data volumes daily and tens of terabytes to petabytes of historical data, ranging from relational data to human language text, with an emphasis on real-time machine data.
4. Business visibility: OI facilitates complete views of business entities and situations based on both real-time and latent data, presented in terms that not only provides business benefits but also actionability.
5. Continuous insights: OI is capable enough to predict threats and opportunities, and recommend the next possible actions. This enables you to address a threat or opportunity in a way that both maximizes business objectives and rules out the possibilities of risk.
Timely action: Any kind of data or information derived out of it, only makes sense when it is timely. Operational Intelligence enables you to trigger analytics-driven automated processes and workflows in line with the business objectives.
To be most effective, an operational intelligence initiative should answer these questions:
What is the purpose of Analytics? (Doing Analytics & Being Analytical – the difference)
Whenever an analytics project gets started, it requires an understanding of the business challenges that exist and makes its use necessary. Organizations should always be aware of the fact that why they are leveraging analytics and what problems they hope to solve.
Does OI consider timeliness of data?
Majorly the concept of operational intelligence involves ensuring data latency requirements meet the needs of the business. However, developing realistic expectations surrounding latency is important because not all operational analytics require real-time data.
What is the Current Business Intelligence Infrastructure?
It is the current BI infrastructure that determines how analytics can be leveraged. For operational analytics to work, the right infrastructure needs to exist. Therefore, understanding what exists and what is possible, evaluates what can be leveraged by using the current infrastructure and to recognize any gaps that may exist.
What are the Current Benchmarks?
Another way to determine operational analytical needs is to benchmark other deployments within the same industry to identify what competitors are doing, how they are building up their infrastructures and what types of analytics they are using. Understanding what the competition is doing helps to provide a way to develop the most important aspects of analytics, which metrics to identify and what data assets to leverage.
What are the Strategic Analytics Goals?
BI goals of an organization always provide a foundation for Operational Intelligence. It becomes important to completely understand the strategic goals which include those that will provide a competitive advantage, identify trends or sales performance and also operational insights.
It is important to understand that although operational analytics might not apply to every organization, still it can be observed that traditional BI applications alone can no longer maintain data visibility and competitive advantage. Organizations need to leverage their data in a variety of ways, also they need to identify whether or not leveraging real-time data provides competitive edge. And if it does provide a competitive edge, organizations need to adopt the best way to leverage the data.