How Spenmo Successfully Built A Self-service Culture With Holistics

Based in Singapore, Spenmo is an all-in-one software consolidating corporate cards, multi-currency dashboards, automated bill pay, and employee expense claims.

Michael Han - Head of Data at Spenmo, oversees a team of eight members - including one data engineer, one analytics engineer, one data product manager, and four data analysts who successfully enabled self-service for around 200 business users.

But in the past, it was a different story.

When Michael joined Spenmo, the business was experiencing exponential growth, and the volume of internal data requests came with it. It was clear that the data infrastructure would have had to scale with the business, or the data team risked getting bogged down with supporting simple requests instead of pursuing more impactful data work.

Michael, who had previously experienced the burden of a bulky analytics setup with a lot of legacies in larger tech companies, knew that his organization needed a self-service analytics setup. As a long-time follower of the Holistics newsletter on Business Intelligence, Michael decided to adopt Holistics.

The rest is history - the partnership between Spenmo and Holistics is now two years strong and thriving.

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In a recent talk at dbt Singapore Meetup, Michael and Jing Yu - Senior Analytics Engineer at Spenmo - shared their self-service strategy and lessons learned setting up self-service culture at Spenmo. All photos in this case study are credited to Michael and Jing Yu's slide deck.

Michael defined low-impact work as maintenance, repetitive work that is devoid of any meaningful progress toward achieving business objectives - and he described them as brutally painful.

Because of this challenge, Michael was a meticulous architect when it came to building Spenmo's data stack. He knew that the tools they chose would play a crucial role in the productivity of the data team.

As companies expand and their analytical needs belly up, data leaders often resort to hiring more data analysts to cope with the increasing demand. However, every data request has to be routed through these analysts, leading to frustrating request queues, and the bulk of the analyst’s job is turned into English-to-SQL translation.

Having previously worked in large tech companies with cumbersome legacy systems, Michael foresaw the damaging effects of this “data monkey” work - which often lead to demotivation and mounting frustration among data analysts.

As the business user base at Spenmo expanded to 200, and the number of available data analysts remained at 8, the need for a self-service solution became urgent.

The data team at Spenmo recognized that various business units had different data requirements and needs, necessitating a tailored approach for each team.

• For example, they understood that the strategy team needed ad-hoc analysis while the operation team only required monitoring dashboards. To cater to the operational team's needs, Spenmo data analysts developed evergreen dashboards that required no further changes.
• For the strategy team, instead of creating a plethora of ad-hoc reports, the analysts guided them on how to ask the right questions, what type of data request to make, and how to get the correct answers using Holistics.

Spenmo's data team puts their users first and constantly asks themselves: "how can I help them succeed?”.

The answer to this question is the variety of thoughtfully designed datasets that can cater to different analytical needs and maximize the number of use cases to be served by each data table.

At Spenmo, the data team implemented a meticulous process to promote self-service among business users.

Data analysts work closely with the teams and set clear expectations, being transparent about what they can deliver. For instance, when approached with a data request, the data team informs the business user that the turnaround time could take up to 8 weeks. However, they offer to guide and support the user in answering their own data questions with Holistics - which can be accomplished with just a one-hour training session.

They went the extra mile to create an extensive documentation system for data tables they loaded into Holistics with details specific to every dimension and fact table - which helps to ensure that:

• Business users are aware of which data is available.
• Business users understand which datasets to use for exploration.
• Business users understand what data to expect in every column.
• Business users understand the source table which was used for populating the data (important for validation).

As Spenmo’s data team pursued the vision of setting up a maintainable, extensible, and reusable data stack, they sought a BI tool possessing these qualities. After a two-week trial, Holistics emerged as the clear choice. Holistics's product philosophies are aligned with Spenmo's vision, complemented nicely by the balance between value for money and rich functionalities.

Spenmo’s favorite features include:

With Holistics' Modeling Layer, Spenmo analysts love how they can easily manage data logic centrally, define the data model once, and reuse any part of it across the system. This eliminated the need for analysts to repeatedly write SQL queries, saving time and reducing errors - which fits in nicely with Michael’s philosophy of designing a maintainable analytics stack and allow analysts at Spenmo to spend less time on maintenance ad-hoc work, and more time driving business forwards with data.

Spenmo's analysts also rave about the ability to include metric calculation as part of datasets - which allows users to quickly drag and drop from these metric fields instead of creating the calculation themselves.

As analysts at Spenmo are eager to adopt software engineering best practices in BI workflow, they found Holistics’s AMQL is a nice touch - as it's tailored for better reusability, composability, development productivity than currently possible with existing tools:

AMQL (Analytics Modeling & Querying Language) is an integrated set of 2 analytics-as-code languages designed by Holistics to enable data analysts to define analytics logic in code:

• AML: a declarative language used to describe data semantic model and analytics objects such as database tables, their relationship, and visualization.
• AQL: a query language that leverages data semantic model defined in AML to query SQL databases in a higher abstraction manner, especially composing and reusing metric-based queries

Using Holistics’ AMQL, analysts can define analytics logic and govern it with Git version control, allowing for code review and promoting reusability. AMQL allows analysts to easily write code to define metrics, create reports, and build dashboards with a powerful, intuitive syntax.

Holistics empowers business users at Spenmo to efficiently explore data and build reports on their own, eliminating the need to wait for the data team. This is facilitated by a robust documentation system and the data analysts' close guidance, allowing non-technical users to quickly grasp the know-how necessary to answer their own data queries with confidence.

The number speaks for itself.

With a well-designed self-service strategy, supported by a BI tool designed for data analysts’ productivity and self-service data exploration, Spenmo has seen significant improvements in the organization's self-service culture.

• 25% Dashboard Platform Stickiness: % Daily Active User/Monthly Active User. This is equivalent to 7.5 days of usage per active user.
• 99 Daily Active Users: Spenmo’s 8-person data team effectively serves 40% of the company’s analytics demand, while still having time to pursue high-impact, high-leverage data initiatives.