Why Customer Intelligence Needs Data Virtualization

0
85

Brands are turning to data virtualization to provide optimal CX and facilitate their transformational journey in this ever-changing digital world.

Consumer electronics company Logitech faced an issue when they built their cloud-based data analytics platform — some data remained on-premises. As a result, the company started looking for a solution that seamlessly integrates all its on-premises and cloud components. To that end, the data virtualization platform from Denodo helped establish a hybrid data fabric to integrate on-premises and cloud sources in real-time. The solution accelerated Logitech’s data science and analytics efforts giving it a 40 to 70% performance improvement with the same volume of data at 30% less cost.

“Through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture,” as outlined by Gartner’s 2018 Market Guide for Data Virtualization. New technologies that promise to unlock the power of data are gaining traction. As previous data paradigms make way for newer ones, data virtualization is one of these technologies that is gaining traction. 

How does it matter

A larger focus on improving customer experience (CX) is the key to remaining ahead of competitors. To provide optimal CX, organizations must gather key insights about customer behaviors by gathering and analyzing extensive customer data from internal and external sources, a process termed Customer Intelligence (CI), to help gain insights into the demands, motivations, and actions of customers. 

But, the picture is incomplete without a pragmatic data integration in place. Why? When responding quickly to data requests, having a large toolbox with the proper combination of data integration capabilities can be highly beneficial. Today, many data integration tools exist, but data virtualization is becoming the favorite. 

Here are some of its benefits: 

  • Data Visualization technology abstracts underlying data sources so that data consumers can use them without having to worry about the specifics of how the data is kept, whether it is structured or unstructured, and so on. 
  • Data sources remain unchanged since data virtualization technology employs metadata and integration logic to virtually combine data so that it may be viewed as a single huge repository. 
  • There is no need for complicated ETL or ELT logic to get all of the data into a data lake or a data warehouse.

CI and data virtualization go hand in hand

As digital transformation accelerates, companies have understood that they need to understand consumers clearly to keep pace. They need to analyze various data types and generate a comprehensive collection of integrated customer data and insights that can be shared across marketing, sales, and customer service divisions to solve issues beforehand. To that end, data virtualization works and offers multiple benefits in terms of customer intelligence:

A 360-degree view: Data virtualization allows new consumer insights created in various underlying analytical data stores to be presented as if they were all in one database. Thus, it provides a complete perspective of each customer, including all their interactions, relationships, and opinions, which can be combined in a logical data warehouse to provide a complete, integrated customer view of all insights used across the company.

Augments performance across departments: The benefit of employing data virtualization to build a single integrated view of customer data and insights in a logical data warehouse is that it allows machine learning models to leverage a considerably larger set of consumer data as a source. As a result, more precise insights into marketing, sales, and customer service can be obtained.

For example, retailers need to place the seasonal product assortments in each store and in the correct categories on the web, which are personalized for each viewer. However, these attempts are hampered by the fact that product information is frequently fragmented. This is where data virtualization allows for a uniform, real-time picture of items, inventory availability, color and size possibilities, and other pertinent characteristics across all data sources, regardless of how fragmented they may be.

Take towards multi-channel integration: A customer, whenever contacts a company via text, call, over mail, or in-person, expects to have the same information across channels. Unfortunately, this isn’t always the case because each channel communicates with a different department inside the organization, and they may or may not receive the same information at the same time. Data virtualization can provide each channel recipient with a consolidated view of the customer; it helps customers feel as if dealing with the brand is a smooth experience, regardless of the channel they choose.

Helps achieve targeted marketing: Retailers are attempting to provide customers with more customized marketing communications that represent their appropriate segments and each customer’s individual history. Data virtualization aids this process by providing complete, integrated, dynamic views of each customer in motion that are drawn from various sources without duplication.

Conclusion

Data needs are increasing at the same rate as the amount of data companies store. The burden of managing many types of data has outgrown the capacity of traditional data integration solutions such as Extract Transform Load (ETL) systems or data warehouse software. Managing data effectively and exploiting it when needed in a competitive business environment is critical. Thanks to data virtualization, businesses can easily access and use production-quality data, which in turn helps them to be more flexible.

If you liked reading this, you might like our other stories
Taking the Self-Service Route
BORIS, a Logistical Nightmare?