By 2025, researchers estimate that the world will create 463 exabytes of data every day. (To put that in perspective, one exabyte holds over 212 million DVDs.) Yet many companies struggle to make the leap from generating piles of data to realizing its full potential.
There’s a reason for that. The sheer amount of data that’s available demands new ways to store and analyze the data. These five tips will help turn your data into ideas, strategies, and real-life results.
Free your data from legacy databases
Migrating to the cloud is the first step in transforming your data strategy. Commercial, on-premises databases are slow and expensive to maintain. What’s more, they lock you into proprietary systems with licensing terms that are punitive and come with frequent audits.
Open-source databases avoid these downsides. They’re scalable and efficient, enabling you to use data whenever and however you want. However, getting the same performance on open-source databases as you get on commercial-grade databases can be tricky. Many organizations update further by choosing fully managed cloud databases and analytics services like Amazon Aurora, which provides the performance and availability of commercial, on-premises databases at one-tenth of the cost. Other organizations ease into the cloud with managed database services, which take existing on-premises databases to the cloud but shoulder the work of infrastructure management (think maintaining daily operations, upgrading security, and automating routine tasks). That lets you focus on growth and innovation, not managing infrastructure.
Further Reading
How cloud computing will power innovation in 2020
Modernize your data warehouse
Traditional on-premises data warehouses don’t have a lot to offer when it comes to handling the exponential growth of data coming from machine-generated sources like devices, applications, and event data. As a result, companies are moving to scalable, cloud-based data warehouses. These cloud-based systems allow you to store, process, and analyze more data faster and more efficiently, and lower operational burdens. That’s thanks to data warehouses that integrate with data lakes to structure, clean, and transform data on an as-needed basis.
At WB Games, Amazon Redshift data warehousing plays a surprising role in the creative process. Developers know players spend hundreds of hours in the worlds they create, and playtime soars in the weeks after a new release. To keep things exciting for even the most dedicated gamers, WB developers use real-time analytics to understand user behavior. Using these findings, creative teams launch customized narratives and experiences as the game unfolds.
Leveraging Amazon Redshift allows them to easily scale without compromising performance. When WB Games releases a console game, usually at midnight, players who have preordered the game receive it within an hour and start streaming. “We end up with a land rush effect,” says Matt Howell, executive director of analytics at WB Games. “It’s important to be prepared.” This ability to scale up and down as needed, and pay only for what you use, is the kind of flexibility you can only have working on the cloud.
Further Reading
Data warehousing in the cloud
Increase agility by breaking down silos
In the past, high data-storage costs forced businesses to make tough choices. “Customers used to have to decide what data to keep and what to throw away,” says Herain Oberoi, AWS director of product marketing – database, analytics, and blockchain. If a new scenario made old metrics relevant, you were out of luck.
Now, cloud storage accommodates vast quantities of information with data lakes, like those built on Amazon S3, which provide scalable storage in the cloud. These data lakes are centralized repositories that bring data warehousing and advanced analytics (including solutions powered by machine learning) together to help you get more value from your data. This provides optimum scale, flexibility, durability, and availability. But best of all, data lakes make performing analytics on all your data faster and reduce the time it takes to get insights out of that data.
Invista, a global chemical and fiber manufacturing company, upgraded to a data lake environment after discovering their old system’s limitations. “The first time we tried to get data out of one of our plant sites, it took about two months,” Invista Analytics Leader Elizabeth Gonzalez said in a recent interview. “Now, it takes minutes for a data scientist to look at data that a process engineer in Asia-Pacific is seeing at the same time.”
A data lake also helps make information accessible across the organization. This breaks down organizational silos, ensuring that everyone is working from a single version of truth. When teams hoard their own metrics, “each one has an imperfect view of our business,” said Matt Howell, executive director of analytics at WB Games—the publisher behind Mortal Kombat and Lego Star Wars—at the 2019 re:Invent conference. Capturing and sharing data across the organization broadens the picture: “We’re all working from the same, consistent understanding of what our business is—and the same vocabulary.” This shared perspective supports the agility teams need to be prepared for, well, anything.
Further Reading
A smarter way to jump into data lakes
Choose the right tools for the right job
There’s no one-size-fits-all approach to data analytics. However, two constants are giving your team purpose-built tools and modern cloud architectures. “Becoming a data-driven business isn’t just about giving people access to data,” Oberoi says. “It’s about using data for everyday decisions as well as big ones. That means providing the right tools and deciding what strategic and operational metrics to measure.” The future is unpredictable; the infrastructure you use to curate and analyze data today should be able to adapt and scale based on the job you need them for tomorrow.
Tailored analytics solutions improve the quality of each team’s metrics and maximize cost efficiency, too. The e-commerce site Woot reduced operating costs by 90 percent when they migrated from Oracle to AWS.
Further Reading
5 dashboards for the data every business needs to track
Develop a strong data culture
Tools and tech matter, but that’s only part of the equation. They’re only as good as the people who use them, and the skills gap makes it hard for companies to harness their full potential. So, it’s important to overhaul organizational thinking and create a culture where data is valued as an essential asset with endless possibilities. Lead from the top by demonstrating your own enthusiasm and involve senior team members in analytics initiatives. Then, spread the word and accelerate the learnings: Share the positive impacts on your business, be transparent with decision-making, and give everyone the opportunity to pitch in by providing ongoing technical skill development. This movement is about democratizing action, not just access.
Remember, too, that getting real value from data requires a variety of expertise. People with strong problem-solving and storytelling skills can ask good questions, interpret results, and, above all, turn analytics into narratives that inspire decisions.