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Data-Driven Decision-Making: How a Hybrid Cloud Strategy Can Help

This blog post was written in partnership with DataStax. To read a free report about creating a hybrid cloud strategy, head here.

Today, most agencies are dealing with a large volume and variety of data across departmental organizations. There are a myriad of systems across government programs record, manage and report information critical to supporting the agency mission.

This data is living. It’s the lifeblood of the programs, providing critical insight for leaders working to optimize delivery of critical agency services. Additionally, for many organizations like the Department of State, globally dispersed missions require distributed systems supporting users around the world.

Many of these key legacy systems are not designed to handle the ever-increasing volume, variety and velocity of data generated through agency’s ongoing digital transformations. This data management challenge is exacerbated as not every system can be easily migrated to the cloud due to both technical and policy constraints. And while modernizing by moving entire system architectures to the cloud is desired, it might not be feasible in the short term.

However, agencies still want to leverage cloud capabilities as much as possible to reap the time-savings and cost benefits offered by the cloud. This is where a hybrid cloud data management solution comes in to address these challenges while providing the architectural flexibility to modernize systems effectively.

In order to get data to the point where it can be used for effective decision-making at agencies, Brian Shealey, Enterprise Sales Manager in the Federal/DoD space for DataStax, a leader in hybrid cloud database transformation, said that in any enterprise IT organization, architecting an effective approach to integrating operational data is the hardest part “to get right.” An enterprise data layer — or “data fabric” must be architected in a way that is flexible, scalable and secure while enabling multi-model analytics to drive machine learning to speed critical agency insights. It must be able to run seamlessly, at scale across any infrastructure model required – on-premises, cloud, hybrid, or multi-cloud.

“In order to effectively modernize decision support, you have to have a great data analytics strategy to build better insights driven across the data that’s going to live and be consumed both on-prem and across cloud platforms,” Shealey said.

Shealey pointed to the commercial space as an example of a way that this is happening. Retailers, financial services, and healthcare organizations have been employing enterprise data layers or a data fabric for years to drive real-time business insights and power Customer 360 systems.

A data fabric is an integrated operational data environment that allows data from various systems across the enterprise to be persisted, analyzed and fused to drive new business insights; while making it easily accessible via API’s. Typically, the data fabric is made up of various components allowing the data to move in and out easily via automation mechanisms. The newly generated insights allow businesses to drive customer experience and brand differentiation equating to real business impact.

For example: A customer purchases a specific brand of baby formula at XYZ Retail and upon checkout automatically gets a coupon sent to them for diapers with a focused expiration date in their email inbox. This drives a positive experience for XYZ Retail with the customer and also increases wallet share by ensuring the customer is likely to return for the diapers. Ultimately, this approach has been critical in ensuring these brick-and-mortar retail businesses are not killed off by web-based retail disruptors.

In that retail example, the customer-tailored marketing is driven by real-time analytics against transactional data to automate the action of sending the coupon. That transactional data — likely from the point of sale system, is integrated with other data within their data fabric on allowing XYZ Retail to send a coupon for another consumable item (diapers) where the expiration date on the coupon was auto-generated based on historical insights from consumer demographics and buying behaviors, maximizing the likelihood of getting the customer back for their next purchase — as opposed to just buying from a competitor online.

This is all driven by the fusion of data within the data fabric in real-time. Without the ability to run the real-time analytics against data generated by and stored in various systems, this type of action would be nearly impossible.

While this example is one that modern consumers experience daily, the pattern of leveraging real-time analytics at scale to improve business operations – is nearly identical need most agencies want to address now.

“Agencies have data siloed across various systems, and they want to drive better insights,” Shealey said. “The way to do that is to have an effective real-time distributed data and analytics strategy, so you can drive those mission insights in real-time across a hybrid cloud model.”

An effective data management strategy that creates this so-needed “data fabric” will help programs across agencies access data, analyze it leveraging machine learning to improve agency operations through better-informed, and faster decision-making.

DataStax offers the industry-leading multi-model data analytics platform optimized for hybrid and multi-cloud architectures — built on Apache Cassandra ™.

“It allows you to solve problems that were previously very difficult to address,” Shealey explained. “When we say multi-model, it’s multi-model data and multi-modal analytics.”

This means that DataStax addresses a variety of analytical needs, whether real-time, batch, or graph analytics. If, for example, you’re looking to gain better insight into improvement opportunities in the DoD world in terms of readiness, you really want to understand relationships between data. This data could take different forms across systems, and be in complex formats, and on a fairly large scale.

Your agency is probably already cognizant of the fact that efficiently accessing and understanding the vast amounts of data from different government sources could improve decision-making. Access to data is key to allow for any decisions made around it. If your agency wants to optimize access and security around its data, it should look to a data strategy. That strategy has to occur on a hybrid cloud-enabled, multi-modal data and analytics platform like the one DataStax offers that can provide the performance and scale needed for your agency to glean real-time mission insights.

To learn more, go to https://www.datastax.com/industry/federal.

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