by Hannah Barrett, on May 20, 2025
...and then it came down to partnership. Everybody at Arkatechture that I get on the phone with, it's really comfortable. I never feel like I'm being sold something that's not real." - Jamie Clisham, SVP Analytics & Product at Machias Savings Bank
What to Look for in a Data Analytics Partner
The need for advanced data analytics in banks and credit unions is stronger than ever, helping institutions improve personalization, decision-making, efficiency, risk management, and overall competitiveness.
With a wide range of solutions to choose from, how can financial institutions identify the right partner?
When Jamie Clisham became Vice President of Data and Analytics at Machias Savings Bank in 2023, she faced a pressing challenge: replacing an outdated data platform. Instead of simply upgrading, she saw an opportunity to enhance the bank’s analytics capabilities.
Drawing from her experience, here are key considerations for selecting a data analytics partner—along with insights from the vendor perspective.
1. Ensure Seamless Data Integration
For Machias Savings Bank, a top priority was integrating data from 12 critical systems, including loan origination, online banking, CRM, and general ledger platforms. A provider’s ability to unify multiple data sources was essential to creating a complete, real-time view of operations.
With knowledge workers now using an average of 11 different systems—nearly twice as many as in 2019, according to Gartner—financial institutions need platforms that can efficiently process structured and semi-structured data formats like XML and JSON, without disrupting existing operations.
2. Choose a Platform That Empowers Your Data Team
Machias wanted a solution that would allow its data team to build custom analytics tools without heavy reliance on IT. They chose a platform with collaborative development capabilities, enabling them to design tailored branch scorecards and a householding model.
Providing employees with self-service access to data has fueled innovation and efficiency. Advanced users can write SQL queries, while others use an intuitive interface to analyze data quickly. Customizable enterprise analytics ensure financial institutions can adapt the platform to meet their specific business goals.
3. Plan for Scalability and Minimize Maintenance Burdens
Machias opted to move from an on-premises data warehouse to a cloud-based system to improve scalability and reduce maintenance demands. The transition led to cost savings and eliminated the need for internal teams to handle unexpected system failures.
This shift also allowed staff roles to evolve: report writers became analysts, and system administrators took on the role of data stewards, as the platform provider assumed responsibility for most administrative tasks.
4. Align Data Strategy With Business Objectives
Selecting a data platform isn’t just about technology—it should support key business goals through a well-planned data strategy. For Machias, that meant improving efficiency, tracking initiatives, and gaining deeper insights into their most profitable customers and products. The platform also played a crucial role in their broader legacy modernization efforts, ensuring a smooth transition with well-organized, clean data.
By implementing the platform ahead of a core system conversion, the bank reduced risks associated with overlapping technology changes and unfamiliar reporting structures.
5. Take a Thoughtful Approach to AI and Emerging Tech
As generative AI and other innovations reshape financial services, Machias considered a provider’s expertise in emerging technologies, security, and data privacy when making its selection. The bank sought a partner proficient in modern data solutions like Snowflake and Tableau, ensuring they could adapt to future advancements responsibly.
Avoiding outdated, rigid systems was also a key consideration. Many financial institutions find themselves stuck with legacy platforms that are expensive to maintain or migrate. Choosing a flexible, future-proof solution is critical.
6. Look for a True Partnership, Not Just a Vendor
Beyond technical capabilities, Machias prioritized working with a provider that aligned with its culture and long-term vision. A strong partnership ensures continued support and guidance throughout their data journey.
By focusing on integration, customization, scalability, alignment with strategic goals, emerging technologies, and strong partnerships, Machias Savings Bank turned what could have been a simple system upgrade into a transformative analytics strategy.
It wasn't just the technology, it was all about the partnership as well. I work with the team almost daily- so it's really important to me that it's truly a partnership." -Jamie Clisham
5 Key Questions to Ask When Evaluating Data Analytics Providers
- Can the platform integrate smoothly with our existing systems?
- How much flexibility and customization does it offer?
- Will it scale with our needs and remain easy to maintain?
- How does the provider handle data privacy and security?
- Will they be a long-term partner, providing ongoing support?
Hear the full story from Jamie Clisham and Ben Jordan in the free on-demand webinar!
