Customer experience and trust rely on building IT systems that scale

Maintaining strong customer experience and trust in IT systems calls for scaling capabilities that can maximise the value of insights

Solving for scale is the key to ensuring IT delivers on its responsibility to create trust-enhancing customer experience.

Adopting hybrid or public clouds remains IT’s go-to means of achieving scale. While this transformation has improved price performance and advanced tech-driven business capabilities, including faster and deeper data insights, it hasn’t always engendered greater customer trust.

That’s due in no small part to widely publicised hacks that have exposed personally identifiable information (PII), as well as to increased customer dependence upon mobile commerce, inconsistent inventory systems, and unreliable online ordering. By some measures, performing at scale is all about improving transactions per second.

According to a 2022 PwC study, “87 per cent of business executives think consumers have a high level of trust in their business. But only 30 per cent of consumers say they do.” One of the biggest threats to trust is a data breach, according to the study. Significantly, 91 per cent of consumers say that if businesses gain their trust, they will likely buy products or services from them. 

Building customer trust in IT systems requires more than scaling capacity in storage and processing. It also requires generating timely insights from structured and unstructured data to improve uptime and help prevent trust-destroying incidents.

IT scale and trust depend on analytics and insights

In many companies, the problems that undermine customer trust begin with a lack of real-time visibility and access to the flow of operational data. Operational teams need to assess what’s happening across the business, but they can’t when there’s a lack of shared telemetry or data access. Data security and performance issues become exacerbated when companies attempt to scale their services to reach a more significant number of customers.

“When scaling up a cloud-based system for hundreds of millions of users, I have to make sure one user doesn’t impact the performance of another user,” explains Abhishek Das, founder of Acante, a data security company. He believes that security teams need visibility into every service and must examine the telemetry to identify where problems are happening and how to resolve them.

The lack of operational insight is a chronic problem for security operations teams (SecOps), which rely upon real-time, unfettered access to operational data to assess threats and prevent or at least investigate intrusions. And, the reverse is also true: IT operations teams are under pressure to be more security-conscious and understand the implications of new code or technologies from a security perspective. Das believes a security operations centre (SOC) team needs to apply machine learning and an observability tool to correlate and take the proper action.

How do you increase your operational resilience? Learn how to make use of your data in real time.

Seeing value in real-time insights

The playbook for deepening customer trust starts with mining operational and environmental telemetry for real-time performance insights and cybersecurity analysis. “You must look at structured and unstructured data sources, retrieve data quicker, and correlate them together,” said Das.

Here’s how three companies approached and solved customer trust challenges by improving observability and scaling operational insights.

  • Optimising customer experience and preventing downtime are crucial objectives of WePay, an online payment service provider owned by JPMorgan & Chase. The company found that entering all available logging and telemetry data in a search-powered solution could reduce the time to identify the customer impact during incidents by 90 per cent. WePay’s security team also taps the streaming data to protect the business against external threats and satisfy all compliance regulations.
  • The quest for greater insights led Albert Heijn Technology (AH Tech), a European supermarket brand with more than 1,000 stores, to try observability for harvesting a vast amount of data from its distributed IT infrastructure and 13,000 points of sale. The company has seen a 40 per cent decrease in end-user IT incidents and raised store sales by 10 per cent while reducing product shortages.
  • Increasing speed, monitoring, and scalability are priorities for Auchan, which previously relied on on-prem infrastructure to manage data but faced problems given how quickly its operations were growing. By using a search-powered solution to enable its teams to observe data in a clean dashboard and choose which information to capture and analyse, Auchan is able to process — and actually observe — the roughly five million data flows that move through its database daily.

Increasingly, companies are turning to search-powered solutions to tame and analyse their data — from cybersecurity to applications to customer interactions and transactions. 

Learn how to analyse your data in real time to improve your customers’ experiences.

This article was written as part of a content campaign with Elastic.


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