In legacy IT landscapes, information is frequently siloed horizontally across business units or vertically by function. Critical data may reside in legacy IT systems, a data warehouse, or systems that are outside the corporate data center. This mix of data may be in structured, semi-structured, or unstructured formats.
For analytics purposes, operational data is typically replicated, reformatted, merged, and aggregated multiple times before initial analysis can take place. Depending on the data in question, this process might require hours, days, or weeks. This all adds up to a delay in drawing insights from the data.
By the time the data gets analyzed, its relevance has decreased and its costs have increased. Any actions taken on the insights gained though analytics will tend to be reactive rather than proactive. This puts the business in a state where it is continually looking back at what happened in the past rather than looking forward to predict what is likely to happen in the future.
This high latency from event to insight is an unsustainable state of affairs. For businesses to compete effectively in todayâ€™s fast-moving, digitally driven marketplaces, they need to reduce the latency of the environment to just hours, minutes, or even seconds, and they need to focus more on predicting what lies ahead rather than reacting to what happened days, weeks, or months ago.
Hereâ€™s a case in point. A fast-growing company called FarmLogs is using real-time analytics to help growers leverage data collected by sensors in the field to increase crop production. With the ability to see current soil conditions, precipitation levels, and other field measurements, along with analysis of that data, farmers can adjust resources on any given day or moment â€“ Get the full story at Data Science Technology Seeds Smarter Farming.
While this new imperative to shift to predictive analytics is a challenge for CIOs and IT departments, it is also an opportunity. It presents an ideal time for companies to get started on the path of transformation to new, data-driven business models.
And this is where we return to the software-defined infrastructure (SDI) story. SDI enables the movement from reactive to predictive analytics by providing a flexible and adaptive environment that allows you to deploy infrastructure on demand, and then deploy analytics on top of the software-defined foundation. This IT agility positions your organization to take advantage of new data as it comes inâ€”which is often your most valuable data. It gives you access to the resources you need, when you need them, and where you need them.
SDI is an ideal complement to technologies like Hadoop, which allows you to combine diverse datasets and run queries and reports in real time, and in-memory analytics, which moves data closer to the processors. Capabilities like these accelerate time to insight with big data, and help you make predictive analytics a reality.
But, of course, the world wonâ€™t stop there. As we move from reactive to predictive analytics, we need to keep our eyes on the road ahead, and the follow-on destination: prescriptive analytics. I will take up that topic in a future post.
In the meantime, for a closer look at some of the exciting ways people are using data analytics to accelerate time to insight, visit the Intel New Center of Possibility site.