Big data has been creeping into almost all aspects of business and marketing that it has become a core component of modern strategies and innovative techniques. Many businesses are now using data to gain insights that will help in business decision making and formulation of tailor-made solutions for specific customer pain points.
Unfortunately, data alone can’t provide the insights or benefits businesses seek; without the means or tools to properly manage and transform data, its full potential can’t be fully realized.
In today’s data-driven business landscape, there’s no excuse to not have a solution in place for data analysis and management. The availability of modern systems and methodologies has allowed companies in different industries to harness the power of data to predict and, to a certain degree, control business outcomes.
Customer experience has a key component in keeping customers engaged and coming back for more. Customers want “instant everything” and if a company can’t provide, they will look elsewhere.
For organizations with large amounts of data stored in multiple systems, access to pertinent operational data can be a source of slowdowns and request delays. This is where a modern operational data store (ODS) comes in. By unifying the API layer, it decouples applications from systems of record to ensure always-on services. High user concurrency is no problem with an ODS, with its in-memory speeds and short application response times.
Even if your data is stored in legacy systems, an ODS will help you migrate data to the cloud, effectively being one of the drivers of your organization’s digital transformation.
How to Be a Big Player in the “Experience Economy“
In 1998, the term “experience economy” was coined to refer to a phenomenon that shows consumers spending money on experiences rather than physical products.
Experiences are linked to events, and these are more memorable than any product a customer can buy. Even the value of a product is linked to how that certain product makes a customer feel or what a product signifies. The experience economy shifts the focus from goods or services into what effects these have on the life of the people who buy them.
It’s a similar case when it comes to how consumers experience your brand. Companies , especially startups, must ensure that they provide the best customer experience possible. Aside from looking to veteran companies, startups should also consider how they manage their data to provide the best experiences. Al and machine learning can empower data analytics to quickly sort through large amounts of data and determine what’s useful to the business.
Having an ODS in place additionally helps with quick data access; by putting operational data at the forefront where users can easily access it, it helps the organization focus on the data services that are required to deliver executive operational insights rather than on the minor details of integration and migration.
A number of companies employ an API-first platform, which can pose challenges when a company begins to scale. The main challenge with this platform is unpredictable usage; because client applications are deployed to several end users, there is a tendency to subject API’s to high-load conditions.
This could lead to major performance issues, including round-trip latencies from client application requests to responses from backend systems and scalability problems with backend systems because they are not designed to serve high-concurrency repetitive requests.
Enter the ODS
An ODS can act as an organization’s “digital integration hub,” acting as intermediary for a data warehouse so that frequently accessed operational data resides closer to the user.
It also helps applications by providing a high-throughput, low-latency in-memory database in between backend systems and the API management layer. With this high-performance database, companies have the power to do the following:
- Use the in-memory database as a data cache for requests from API’s that return data to analytical systems. Processing in the database allows for quick turnaround times compared to referring back to backend applications.
- Use cached data for database requests to avoid placing too much load on systems of record and overloading backend applications.
- Reduce residual load on databases and backend applications by handling most of the requests from the database.
- Every request handled through the database using cached data reduces the load on the back-end applications and other systems of record, preventing them from becoming overloaded.
Startups looking to optimize business processes and improve outcomes can do so by maximizing what value can be obtained from modern data architectures like an ODS. By rethinking how they approach data analytics and management, imagination can bring innovation and provide added value to the business.
The ODS is just one way of changing the game when it comes to data management. For organizations to completely harness the power of data, a digital transformation should be considered—and the ODS is the ideal first step toward that milestone.