Data Management in the Age of Data Intensity
To succeed in the digital service economy – and in the age of data-intensive applications – you need to harness new data to deliver engaging customer experiences in real time.
Data has become an organization’s most important asset. But many businesses today face challenges when it comes to staying up to date with technological advancements that can keep them agile and competitive. This is especially true in the case of data management.
Rather than adopting a single general-purpose database capable of handling hybrid data processing and data-intensive applications, enterprises often rely on extensive suites of data management tools. Data ingestion, data catalog, governance and security, and transformation are then all separate. Some companies try to put several tools together, but it’s not easy.
When you stick to these traditional data platforms, it hampers your ability to handle data-intensive applications. This slows down your business progress, keeping you stuck in the past.
Here’s how to unlock yourself and move your data infrastructure – and your business – forward.
What is data intensity and why is it important for your customers and your business?
Applications involving a high level of complexity and a large amount of data, and requiring concurrency, low latency, and fast data ingestion, fall into the data-intensive category.
The app that lets customers track UPS trucks in their neighborhood is data-intensive. The same goes for Uber’s ride-sharing app. When you look at what’s going on in powering these data-intensive apps, you may be surprised at just how much it can entail.
Let’s look under the hood of the Uber app. Once you use the app to summon a ride, Uber analyzes which vehicles are near you and which ones are best placed to reach you first. The app then uses geospatial traffic and weather data to assess how long your journey will take and how that compares to the typical length of that trip. Tapping into Uber’s pricing engine, the app then sets a price for your ride. Uber then uses the app to display the car, driver, estimated time of arrival, current vehicle location, and prices for you. Most importantly, Uber does all of the above in seconds to give you a great experience.
See also: A good data analytics program relies on good DataOps
Why are traditional data management platforms and data silos a problem?
You can also leverage your data to deliver exceptional customer experiences. And, like most businesses today, you probably already have a wealth of data to do so. But if you have a dozen or more data platforms, siled data is likely stopping or slowing down those efforts.
When your data is spread across multiple clouds and systems, it can lead to latency, performance, and quality issues. And bringing together data from different silos and getting those data sets to speak the same language is a time-consuming and budget-intensive undertaking.
Your existing data platforms may also prevent you from handling hybrid data processing, which, like Ventana Research explains“enable[s] analyze data in an operational data platform without affecting the performance of operational applications or requiring data extraction to an external analytical data platform. The company adds that: “Hybrid data processing functionality is becoming increasingly attractive to aid in the development of intelligent applications infused with personalization and recommendations based on artificial intelligence.” Apps like this are clearly important because they can be key business differentiators and enable you to revolutionize an industry.
However, if you’re struggling with siled systems, data, and legacy technology that can’t quickly ingest large volumes of complex data so you can act in the moment, you may believe it’s impossible for your business benefit from data synergies. that you and your customers might otherwise enjoy. You can opt out of delivering some promising data-intensive applications and let your database limitations guide your architectural decisions.
But most of your apps probably are data-intensive. If they aren’t, they should be. If you don’t deliver data-intensive applications, you can’t succeed in the digital service economy.
How do you respond to data intensity, deliver exceptional experiences and disrupt industries?
To succeed in the digital service economy – and in the age of data-intensive applications – you need to harness new data to deliver engaging customer experiences in real time. You can only scale quickly and become agile if you can scale your applications effortlessly. And you want to bring analytics to your applications to reduce your costs and complexity, and so you and your customers can benefit from fast, interactive analytics.
You can achieve all of the above and more with a data infrastructure that unifies transactions and analytics. Such an infrastructure can allow you to process all forms of data, load large volumes of that data quickly, perform application analytics without noticeable lag even when the system is very busy, and scale without having to struggling with expensive processes and architecture.
We have long championed and encouraged this modern database approach of bringing transactional and analytical workloads together in a single database. And now many other vendors in the database space, including Snowflake and MongoDBembark.
Using a large number of legacy data infrastructure platforms is clearly a thing of the past. It’s time to get out of the weeds and seize the opportunities now available to you.
The era of data-intensive applications has arrived. Are you ready? Are your data management practices ready?