Enterprises need to reevaluate their approach to data architecture to ensure that their analytics efforts deliver actionable insights to power business decisions.
If there was ever any doubt about the need for data and artificial intelligence (AI) – if their value was ever in question – few would know by the developments of the past few years. With the fear of failure looming, organizations are investing billions of dollars in technology solutions that promise to deliver actionable insights to provide an edge. A survey by NewVantage Partners shows that most organizations (99 percent) have already taken the plunge, and nearly 92 percent say the pace of investment is accelerating.
More than 64 zettabytes – approximately 64 trillion gigabytes of data – were created in 2020, but according to a report by IDC, very little (less than two percent) was actually saved by businesses. The rest of the data was merely created or replicated in the process of consumption or briefly cached and eventually overwritten with newer data. And of the data that is collected and stored by businesses, less than 1/3 (32 percent) was put to work in a meaningful manner.
It’s a hard reality that most data remain either unused or ultimately go to waste. Enterprises need to reevaluate their approach to data architecture to ensure that intelligence is at the heart of every business decision. They should start by formulating a focused question and acquiring all relevant data. It is important to know that the information is trusted and in the right form for quick analysis to ensure that any and all goals can be met. By using exploratory analytics to find patterns, trends, and relationships that may exist within the data, business leaders can begin to uncover additional opportunities they may have otherwise missed.
In short, organizations need to evolve from being institutions with rigid and inflexible processes for how data is shared, analyzed, and consumed. Those outdated processes were developed long before the amount of data generated became so vast. By switching to a data analytics framework that continuously draws on information from inside and outside of the enterprise – what we call Active Intelligence – organizations can make the most of their information and ultimately make better decisions.
Ensure data is analytics-ready
The first step toward analytics-ready data involves profiling and cataloging. Raw data will need to be moved, regardless of its source, to a destination repository such as a data catalog. This is an ongoing process that allows for the continuous flow of information to reflect real-time changes. By doing this, enterprises can speed up the discovery and availability of data to the cloud of their choice by automating data streaming, refinement, cataloging, and publishing.
With data freed from its previous constraints and vetted, the data pipeline can be filled with analytics-ready information that can be found and consumed more easily. This has become a critical part of delivering analytics for Big Data, as the data pipeline empowers enterprises to compete more effectively. Users will be able to trust in both the source of and the data itself, just as Innovative Aftermarket Systems (IAS), a provider of automotive warranties, trusts its own cataloging procedures. The company uses its data catalog as a secure, governed repository from which users can organize and access analytics-ready data. This provides the tools IAS employees need to share and access a consolidated view of their data, fostering a new level of insight to inform future strategic decisions.
Transform data into actionable insights
Once data is in an analytics-ready format, organizations must then turn that information into actionable insights that can lead to smarter, more informed decision-making. Whether dealing with day-to-day business challenges, the aftermath of the pandemic, or other global events, organizations must be prepared to take swift action. Their data must then be as agile as the rest of their business – easily found in one place for users to search and understand the data, irrespective of where it is stored. This should include all data regardless of the format, quality, or curation.
However, enterprises won’t be able to use that data at all if they don’t understand how to read and work with the information. That’s why it is imperative that organizations not only encourage data literacy but also equip their non-technical users with the right tools to easily view and analyze data for faster results and immediate action. By integrating data into existing workflows and processes, Active Intelligence can be achieved as data use becomes a seamless part of everyone’s job.
Real-time data = faster, more accurate decisions
These steps are essential for businesses looking to embed analytics into automated workflows and deliver sophisticated, context-aware alerts in real-time. In other words, they are necessary components of driving informed action.
Real-time information was especially important to Direct Relief, a non-profit organization that provides medical resources in emergencies. The organization used data to help deliver 2,400 tons of medical supplies to Wuhan, China, 55 US states and territories, and more than 100 countries during the pandemic. This initiative could not have been completed without a data analysis application that could be updated in real-time to continuously track the pandemic’s changing dynamics. From age and the growth rate of new cases to co-morbidities and fluctuations in COVID-19 testing, real-time insights led to a faster and more accurate response.
Realizing data’s full potential
Timing is everything; having data alone is not enough if businesses are not prepared to use the information they’ve obtained. But in an era when analytics promises to boost the bottom line, it is vital that organizations take their data seriously. They must ensure that their data remains agile by going through an intelligent analytic pipeline, evolving data from a raw collection of information to actionable insights that can inform meaningful decisions. The end result creates a framework with an architecture and a set of tools that go beyond traditional data movement, automation, and transformation. With it, enterprises can fully realize the potential of their data.
