As the value of data visibility as a differentiator becomes more evident, there remains the challenge of connecting to vastly different apps and data sources, wherever they are.
Almost all types of work involve some form of exchanging data. And when those exchanges are automated, they’re often handled as hybrid integrations between applications, data sources, and systems. These integrations are designed to serve a purpose; many are part of larger business processes running in the background delivering efficient workflows.
And organizations are learning that extracting and analyzing the data can give them a competitive edge. According to Gartner, “By 2022, enterprises using a cohesive strategy incorporating data hubs, lakes and warehouses will support 30% more use cases than their competitors.”
What use cases are we talking about? It could be early warning of anomalies for operations teams. Or dynamic pricing schemes for sales. Or intelligent contact centers that provide a more responsive customer experience.
One thing these use cases have in common is timeliness.
Timely or time-consuming?
Unfortunately, traditional data warehouse solutions involve time-consuming batch processing. And when you want to capture new data, it can be a technical effort that takes IT specialists days or weeks. This makes reacting to data insights a slow-motion process. Any analytics you build on this data are historical.
Other challenges include the variety of packaged and legacy apps as well as built-in-house apps that have proprietary interfaces. Connecting to each of these is a separate effort, but these systems of record can contain essential customer, product, sales and financial data. And as businesses move to the cloud, SaaS apps are also part of the data landscape. What’s more, all this data is stored in different formats. Extracting it in its native form requires a data mapping effort to reconcile different representations of the same concept, when all you really want is a single view of your customer or product.
And the answer is… webMethods DataHub
But here’s the funny thing – if you’re a webMethods Integration Server customer, you’ve already solved the challenge of connecting to vastly different apps and data sources, wherever they are. webMethods connectors make it easy to integrate to hundreds of on-premises and cloud apps and even mainframes with EntireX and ApplinX. Data around revenue streams, business insights, real-time operations information and customer activity is already flowing through your hybrid integrations.
What if you could take advantage of that stable, low-risk platform already processing your integrations and APIs and extract the pieces of data you’re interested in analyzing? Today there are more modern methods to access that information through a combination of APIs, integration, in-memory, and data lake technologies. That’s why we’re introducing the webMethods DataHub.
The webMethods DataHub is an integrated toolset for accessing, transforming, and analyzing data passing through the webMethods Integration Server. You don’t need to make changes to any existing APIs or services; just configure the requested fields on a business-analyst-friendly UI and they immediately start getting collected in a fast in-memory store.
Then point the webMethods DataHub at the data lake you’d like to use – common data lakes are all supported. Your selected items are offloaded and put into a data lake on your timeframe. Once the data is in a data lake, business analysts and data scientists can use their choice of BI tools to access a sophisticated SQL interface, provided by Dremio. Without the complicated coding, data processing, and batching of ETL systems, this entire process happens fast – as fast as 5 minutes. And that opens up new possibilities for what you can do.
A new strategy
webMethods DataHub is new strategy for getting operational insights faster and with a lower total cost of ownership (TCO) than traditional ETL. With webMethods already connected to your systems of record, you’re most of the way there. webMethods hybrid integrations automatically store data in a common format that can be easily mapped to a common model if desired, and you can add just about any other data source with prebuilt connectors. And with the speed at which you’ll get your selected data, just imagine what you could do:
- Insurance companies can tailor offerings to their customers by analyzing customer data and rapidly updating products
- Retailers can gain better insights – by leveraging and combining the data from different systems – and use them for competitive ads
- Even energy companies can see benefits by monitoring energy consumption to react more quickly to surprises
Collecting high quality data that you can trust as it’s happening to deliver better customer service and grow your business? That’s how you turn your integrations into insights!
And click below to learn more about webMethods 10.11