“Sumo Logic helps us [eNett] glean valuable, real-time operational and security insights from that data across our modern application stack so we can focus won delivering the best customer experiences possible.”

James Jones, Infrastructure Manager



  • Singapore


  • 255 employees

eNett makes the most of machine data insights with Sumo Logic for better regulatory compliance and improved customer service

eNett, a financial services organization is on the line to constantly adhere to an evolving collection of compliance regulations, while concurrently striving to reduce its security exposure. This was taking up a significant amount of time for the organization, taking them away from their primary message of providing the best customer experience. At the same time, eNett was looking to empower its front-line customer support staff to speedily resolve client issues without needing to disturb its software developers.

Read on to find out how eNett deployed Sumo Logic’s cloud-native machine data analytics platform to replace an assortment of cumbersome manual procedures along to fulfill their audit and security commitments and streamline client support processes to reduce the number of cases that reach software developers.

  • Challenge


    As is the case with every financial services organization, eNett was obligated to obey a constantly evolving collection of compliance regulations, while concurrently striving to reduce its security exposure. Carrying out these responsibilities consumed a significant amount of time and effort, which siphoned off resources from the company’s primary mission. At the same time, eNett eagerly sought techniques to empower its front-line customer support staff to speedily resolve client issues without needing to disturb its software developers.

  • Solution


    eNett deployed Sumo Logic’s cloud-native machine data analysis platform to replace an assortment of cumbersome manual procedures along with a legacy system

  • Results


    With the new platform in place, eNett is in a much better position to fulfill its audit and security commitments. In addition, its client support processes are now far more efficient; with a major reduction in the number of cases that reach software developers.

Historically, travel agencies have had a recurring headache reconciling the payments that they made on behalf of their clients to the providers (e.g. hotels and airlines) that supplied the actual travel services. These charges were typically grouped onto a single credit card that was owned by the travel agency, which was in turn responsible for associating the correct portion of a large, batched payment to the appropriate customer. Unsurprisingly, settling these accounts was a time-consuming, error-prone chore.

To help rectify this age-old dilemma, eNett’s payment solution offers Virtual Account Numbers (VANs) that work like virtual credit cards. This enables travel agencies to make fast, easy, and safe payments to travel providers. eNett’s clients - located worldwide - can use an API connection to create a VAN, which is a unique 16-digit Mastercard number that is utilized only one time. Each VAN is instantiated with the precise amount of funds necessary to complete an individual customer’s transaction, while also furnishing a significant amount of meaningful accompanying metadata. VANs have proven to be very popular: each hour, the company creates thousands of these secure, low-risk payment vehicles.

Keeping pace with the demands of such a dynamic business led eNett to implement an agile software development strategy: it updates its software every two weeks, and releases patches on an as-needed basis. With such as aggressive delivery schedule, the company needed to remove all impediments to its software developers’ time. Additionally, as a FinTech organization, eNett is subject to stringent regulatory codes in all the markets in which it operates. To make matters more challenging, these regulations are in continual flux, which forces eNett to perpetually evaluate and optimize its compliance procedures. Auditing system logs is one small part of the overall picture, yet that task alone meant manually poring over enormous volumes of machine data – from multiple, diverse sources – scanning for login failures, access control issues, and other anomalies. eNett’s IT management rotated this responsibility among a team of nine administrators. Fulfilling this operational burden consumed somewhere between 45 minutes and 2 hours each day, yet still routinely overlooked problems.

eNett was able to make do under these circumstances for about 18 months, but when confronted with yet another change to compliance guidelines, management realized that the time had come for the company to jettison its cumbersome manual practices. eNett now sought a specialized, unified solution to aggregate its entire log collection into a single repository and then apply the power of automated analytics to extract meaning from this machine data.

The company identified three primary use cases for this new application:

1. Eliminate the unpleasant task of manually screening machine data. This tedious, time-wasting undertaking was a drain on staff productivity.

2. Eradicate the need for software developers to log into production systems to review logs. With machine data spread among so many sources, resolving issues forced developers to carry out problem tracking and forensic review on live servers. This introduced an unacceptable security risk.

3. Make front-line customer support staff much more efficient. eNett’s existing defect tracking regimen utilized multiple layers of increasingly skilled experts. Unfortunately, this meant that up to 48 hours could elapse by the time a given issue reached the person(s) who could resolve it.

eNett initially considered rolling out a self-maintained, on-premise application, but it soon became apparent that this approach would incur extensive costs, introduce additional and unwanted operational overhead, and did not fully align with the company’s technology stack.

The company conducted a six-week evaluation, and selected Sumo Logic based on each of these factors:

• The strength of its cloud offering

• The power of its query language

• The productivity improvements that would cascade throughout the organization

• The impressive cost savings and return on investment it would deliver

In fact, eNett would have chosen Sumo Logic merely on the benefits from the regulatory compliance use case alone.

The rollout proceeded quickly; the VAN transaction volume created each day by eNett’s customers resulted in more than seven GB of daily machine data that was ingested into Sumo Logic. To reduce the potential for operational disruption, eNett ran the new Sumo Logic solution in parallel with its legacy procedures for approximately six months. During that time, the Sumo Logic user community expanded from two to 60 people, chiefly composed of software developers and customer support.

eNett has employed Sumo Logic’s query language and graphical tools to generate an extensive series of dashboards and reports, meaning that it’s no longer necessary for the administrative team to manually scrutinize machine data for issues. Because all logs are aggregated into a single destination, software developers can carry out their research on a single source of truth without needing to interact with production systems: this has diminished traffic and boosted security. Finally, the client care process itself is demonstrably more efficient: level two customer support teams are now equipped with the tools they need to speedily diagnose deficiencies. This has gone a long way towards lightening the load on eNett’s busy software developers.

Machine data will have a major role to play as eNett continues to enhance its product capabilities through ongoing improvements to the application and infrastructure architectures. Because of its ability to trace transactions through the entire stack, Sumo Logic will make it easier for eNett to quickly identify the precise location of an issue. The company also plans to broaden its use of Sumo Logic’s anomaly detection and advanced alerting features. The result will be even less need for users to proactively interact with machine data; instead, they will automatically be notified of any uncovered flaws.