Data Management in the Era of Data Intensity
To succeed in the digital services economy – and the era of data-intensive applications – you need to leverage fresh data to deliver engaging real-time customer experiences.
Data has become an organization’s most important asset. But many enterprises today are challenged when it comes to staying current with the technological advancements that can keep them agile and competitive. This is particularly true in the case of data management.
Rather than adopting a single, general-purpose database that can handle hybrid data processing and data-intensive applications, companies often rely on large suites of data management tools. Data ingestion, data catalog, governance and security, and transformation are then all separate. Some companies try to stitch together multiple tools, but it isn’t easy.
When you stick with such traditional data platforms, it impedes your ability to address data-intensive applications. This slows your business progress, keeping you stuck in the past.
Here is how to get unstuck and move your data infrastructure – and your business – forward.
What is data intensity, and why does it matter to your customers and your business?
Applications involving a high level of complexity and a significant amount of data, and requiring concurrency, low latency, and rapid data ingestion, fall into the data-intensive category.
The application that enables customers to track UPS trucks in their neighborhoods is data intensive. So is Uber’s ride-sharing application. When you examine what goes into powering these data-intensive applications, you may be surprised at everything that can entail.
Let’s look under the hood of the Uber application. After you use the application to summon a ride, Uber analyzes what vehicles are near you and which ones are best positioned to get to you first. The application then employs geospatial data on traffic and weather to assess the length of your ride and how that compares to the typical length of that trip. Tapping into Uber’s pricing engine, the application then sets a price for your ride. Uber then uses the application to display the car, driver, estimated arrival time, present vehicle location, and pricing for you. The kicker is that Uber does all of the above within seconds to provide you with a great experience.
Why are traditional data management platforms and data silos problematic?
You can leverage your data to deliver exceptional customer experiences, too. And, like most companies today, you probably already have a wealth of data available to do that. But if you have a dozen or more data platforms, siloed data is probably stopping or slowing such efforts.
When your data is spread across multiple clouds and systems, it can introduce latency, performance, and quality problems.
