Why User Experience 2020 Requires Fast Data Access

eWEEK DATA POINTS: In the same way analytics delivers actionable insight to the business, it must now also deliver actionable insight to the customer–directly or indirectly.

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Digital transformation is, in part, driven by the need for enterprises to deliver better customer experiences, meet new-gen expectations and, for some businesses, remain competitive (and survive).

However, shifting from physical to online engagement was yesterday’s challenge. Today’s mandate is to create compelling opportunities and competitive differentiation and to do so by providing customers with actionable insight. Similar to businesses, customers want--or in some cases, demand—the information needed to make real-time, informed decisions.

In the same way analytics delivers actionable insight to the business, it must now also deliver actionable insight to the customer—directly or indirectly.

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For users—whether they are end users of a shopping site, customer support engineers using a ticketing system or business analysts looking for insights to drive the company forward—one thing remains the same: They want greater access to data than ever before. This has big implications for the future of database technology.

By the minute, companies are collecting large amounts of data, and a management system is crucial to retrieving, updating and generally managing all data relevant to a business’s daily operations.

With such large amounts of data being presented, moved and changed every day, there comes a new set of challenges for companies around the globe to ensure that the data is converted into valuable, actionable insight. To keep up, users must be able to interact with data on a higher level of efficiency, flexibility and accessibility.

In this eWEEK Data Points article, Shane Johnson, senior director of product marketing at Redwood City, Calif.-based MariaDB Corp., outlines user expectations for data access and what that means for the future of database technology.

Data Point No. 1: Customer insight

In the first phase of digital transformation, customer experience was defined by simplicity and efficiency; for example, ensuring that customers can find a product and purchase it as fast as possible. The second phase will be defined by opportunity and insight. In this case, informing customers that a product they’ve viewed before will be sold out within hours because of a variety of moving parts—including how many are being added to shopping carts—impacts the frequency with which they are being purchased and current inventory levels.

Data Point No. 2: Data exploration

Software-as-a-service (SaaS) businesses often provide transactional services: advertising, communication, payment processing, customer relationship management and others. However, because their customers are data-driven businesses themselves, basic reporting (e.g., monthly usage statement) is no longer enough. SaaS businesses must now provide their customers with a way to perform ad hoc, interactive analytics on the underlying data. These analytics, for example, can identify spikes and patterns in usage.

Data Point No. 3: Historical data

In practice, current data is often enough for online transaction processing (e.g., purchasing a product online or checking the current balance of your checking account). Current data is typically from the last 30 days or the last year. However, to meet the growing demands of data-driven businesses and opportunistic customers, organizations have to retain historical data for years and use it to either create actionable insight for their customers or, in the case of SaaS businesses, make the data itself available via full, self-service analytics.

Data Point No. 4: Hybrid applications

Until recently, most customer-facing applications and services were built on top of transactional databases. After all, they’re used for online transaction processing. However, as these applications incorporate greater analytical capabilities in order to improve the customer experience and meet rising expectations, they’ll begin creating hybrid workloads with analytical requirements that exceed what traditional transactional databases are capable of. As a result, they’ll turn to hybrid transactional/analytical databases to unlock greater analytical capabilities while at the same time continuing to meet transactional performance expectations.

Data Point No. 5: What does this mean for database technology?

Considering all the expectations listed above, one thing is certain: Modern applications need a different approach when it comes to a database. So, how do you attack these requirements without investing in a big system that has separate ETL and data-transformation processes? To keep up with today’s explosion of data and needs for agile management, the way forward is with a database that unites transactions and analytics. For databases, specifically, look for a relational database that can scale and support both transactional and near real-time analytical workloads.

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