Investing in a powerful data management solution is a crucial component of long-term marketing success, especially given how difficult it is to engage customers in the modern multichannel environment. Because of this marketplace reality, it’s vital that organizations of all sizes deploy a modern data architecture that can adapt and grow to meet changing business needs as well as drive improved customer engagement over the long term. Given the plethora of options, it’s incredibly easy to choose a data management solution that doesn’t possess long-term viability.
To help companies avoid this pitfall, I recently outlined five critical components of a modern data architecture:
These five components, taken together, will ensure that your data management solution can evolve along with changing business needs and adapt to shifting customer behaviors. It’s incumbent on all organizations to find the right solution to address the problem, which is why we designed Redpoint Data Management™ expressly with the intent of making it adaptable for a customer engagement landscape that’s perpetually in flux.
Flexibility at Scale
In my previous post, I wrote that a modern data architecture needs to adapt to the growing volume, variety, and velocity of customer data without an increased failover rate. This is a consistent problem with data management technologies that leverage in-memory processing. As the number of records increases and memory requirements grow over time, failover rates also increase. Higher failover rates mean data processing takes too long, which lengthens data preparation time and shrinks the time spent on data analysis. This presents a problem for the viability of data-driven decision making, especially in the current fast-paced business world.
Redpoint Data Management avoids this problem by conducting 100% of its processing directly in Hadoop through a native YARN application that “evaporates” from the cluster when processing is completed. This results in what we call a “zero footprint install,” which eliminates any significant memory requirements. Because of our processing method, it’s not surprising that MCG Global Services recently found that Redpoint Data Management conducts data-processing tasks 550% faster than Apache Spark and 1,900% faster than MapReduce. This is a substantial difference in processing speeds, and places our solution in a solid position over our competitors to handle the changes in data volume and type over time.
Support for Parallel and Distributed Processing
Parallel and distributed processing reduces time to insight dramatically through allowing organizations to process data queries much more quickly than linear methods. This becomes even more vital as data increases in volume and variety, which makes reducing time to insight a key priority.
Redpoint Data Management was designed with time to insight in mind, and as such conducts its parallel and distributed processing directly in Hadoop via YARN. The solution’s fast processing speeds even occur in database environments that don’t run on Hadoop, which is still a significant portion of the companies leveraging data in their business decisions. This means that companies don’t have to upgrade to Hadoop in order to enjoy fast data processing capabilities.
Democratized Data Access
IT has traditionally held the keys, so to speak, for business data. This made sense when there was less data generated in fewer channels, and the business cycle moved slower, but nowadays is a recipe for poor long-term results.
In Redpoint Data Management, we created a graphical user interface (GUI) that allows DBAs and data analysts to perform their data quality and data integration tasks without specialized Hadoop coding experience. This WYSIWYG interface can allow business users to disintermediate IT and gain insight faster than ever before.
Easy to Use Without Specialized Training
In my previous post, I wrote that a modern data architecture should allow you to query the data and derive insight without having to learn a coding language or take a lengthy training course on the solution’s functionality. Marketers are increasingly performing their own data prep and data analysis, and they often don’t possess the specialized knowledge of DBAs and can’t hire one because of the expense. This makes it important for the solution to be usable by the line of business, no matter their knowledge base.
Redpoint Data Management is designed to be used for advanced data analysis without specialized coding experience. We’ve built it with the business user, and the time-crunched data scientist, in mind. As a result, our no-code approach eliminates the skills gap inherent in the delayed adoption of Hadoop for data processing. Redpoint Data Management allows people without coding experience to benefit from leveraging Hadoop for powerful business analytics, driving insight with existing resources and incurring a lower total cost of ownership.
Ability to Handle All Data Types
As the volume, variety, and velocity of data increases over time, it’ll become even more vital that data management solutions can handle all different kinds of data. A modern data architecture should ensure that data is processed effectively, regardless of its source.
It’s for this reason that Redpoint Data Management has a wide range of data quality and integration capabilities, including ETL / ELT, cleansing, matching, de-duping, parsing, and master key creation. This wide range of functions ensures that Redpoint Data Management users can integrate high-quality data no matter the source or format and leverage it in their analytics efforts.
A modern data architecture is vital for future organizational success, largely because the volume, velocity, and variety of data is only set to increase over the next few years. Redpoint Data Management was designed for this future, and I think it’s one of the most adaptable solutions on the market today. Regardless of whether you agree or not, the fact remains that your enterprise data management solution must be flexible, scalable, and able to handle the tidal wave of data heading your way.