Organizations don’t have the time or human capital to waste on technologies that don’t work or are difficult to assemble. Creating a future-proof data stack allows organizations to avoid adoption fatigue, build a data-centric culture and make the most of technology investments.
To define the prerequisites of a future-proof data stack, it’s important to understand what a modern data stack needs to do. Every organization will have a different set of goals, but generally, digital transformation initiatives are driven by one or more of the following priorities:
Moving from on-premise storage and computing to hybrid or cloud database management often pays for itself, sunsetting expensive technical infrastructure and allowing organizations to shift valuable IT resources to areas that add value and generate revenue.
Data estate modernization moves data out of silos and creates standard views of customer data, product data and sales and marketing data across an organization. While spreadsheets will always hold a place in the hearts of business analysts, automating data workflows increases efficiency and democratizes access.
Sometimes, a digital transformation initiative begins with the desire to use one tool. Technology development is exciting, with advancements, seemingly every day, to machine learning applications, predictive modeling, and smart workflow automations. Additionally, tools are constantly being developed and tailored for industry-specific and business-line uses. But shopping for advanced data applications without a functional data foundation can be like a person at the hardware store becoming infatuated with the idea of a rainforest shower. If you don’t have the plumbing to support it, you can’t have a personal waterfall in your master bathroom. The appetite to implement a new and exciting technology tool can lead to the discovery that current data architecture isn’t built to support aspirational data thinking – and can be a powerful, but tactical at best, change agent to support digital transformation.
As organizations expand digital footprints, they expand their data ecosystems. New data sources create insights never available before – including customer behavior and demographics, churn indicators, and cross-sell and upsell opportunities. Along with new data, comes pay per use tools that allow organizations of any size to use the same technology as Fortune 500 companies – oftentimes, without the enormous technical debt. The ability to leverage internal and external data for strategic decision-making will be essential for growth and survival for many organizations – and today’s technology tools create a level playing field.
The least exciting, but oftentimes most compelling, reason to invest in digital transformation is to keep data safe and in compliance with federal and state compliance mandates. Data breaches have long-term consequences for an organization’s clients and can permanently damage client relationships. Data security compliance is only going to become more vital in the coming years. For that reason, it is critical to replace patchwork security measures with one overarching, robust security layer that scales with the organization.
Whatever the reason, the decision to begin a digital transformation initiative can be equal parts exciting, overwhelming and nerve-wracking. The sheer volume of technology solutions can cause decision paralysis, compounded by the fact that that each solution will require its own roll-out, integration and adoption processes. For those reasons, and so many more, most organizations will want to ensure components of their data stack are future-ready and future proof. What are the most important prerequisites of a future-ready data stack?
When Passerelle data architects talk about an organization’s ability to scale their data infrastructure, we like to use the phrase “enterprise-minded.” An organization doesn’t need to have a billion dollars in revenue to be enterprise-minded – it needs be focused on growth, and specifically, the ability of data architecture to grow with the organization.
For Camden National Bank, moving data from siloed transaction systems and to Snowflake’s Data Cloud provided a flexible data architecture allowed it to respond to fast moving changes fueled by the COVID-19 pandemic. With business-line users equipped with better data tools throughout the organization, Camden National Bank pivoted prospecting efforts away from declining industries and towards sectors that were experiencing growth.
For most organizations, we recommend a cloud data warehouse or data hub approach for data management over “black-box” solutions that can create multiple versions of the truth and can create roadblocks to collaboration between business lines. While some of these platforms can augment a modern data stack they should never be at the center – the most scalable modern data stack prioritizes governed data and makes it readily available in a central cloud data warehouse.
Data governance should be at the part of any digital transformation project. Data Governance promotes data quality within the entire data life cycle, enabling a mature cloud storage environment where data can be accessed by users throughout an organization and leveraged for data science applications and 3rd party data integration.
Data Governance provides a framework for continual improvement and optimization of a data ecosystem and use within the organization, enabling adoption to changing and emerging organizational needs and compliance with external regulations. Data Governance is not one-size-fits-all, the appropriate Data Governance framework depends on the size of the organization, necessary compliance protocols and the needs of the business users.
Generally, Data Governance requires technology to support data as a team support – data ingestion that takes data out of silos; data integration that masters data to create one master record; data warehousing that makes data secure and accessible; data cataloging that tracks data lineage and changes; a data dictionary to ensure all users are speaking the same language; a method for measurement and audit of data quality; and a visual analytics platform to provide self-service insights to business users, regardless of their technical ability.
How a new technology is rolled out can mean the difference between success and failure. Whenever possible, Passerelle take a use-case based approach to implementing new technologies, so organizations can realize immediate value and build off their success.
For Material Bank, Passerelle started deploying Talend data integration on low-risk internal systems before expanding to external client data systems with Talend API services. Talend API integration enabled 500% growth in fulfillment in 2020-2021, and Material Bank is currently expanding its business to Europe and Japan with the help of Talend Cloud API Services and Passerelle engineering.
Use case-first deployment builds transformation champions within an organization and can help with building momentum. As part of Berkshire Bank’s digital transformation roadmap, business teams from throughout the bank meet to share success stories and opportunities for improvement. This company-wide embrace of use cases creates a data-use culture, furthering the stickiness of Berkshire’s technology investments.
Passerelle created Data Rocket as an accelerated modern data stack that delivers a first use case in 90 days. Built to be scalable and to introduce data governance at every stage of the data life cycle, Data Rocket integrates industry-best technology providers into one framework, removing the headaches that go along with assembling and integrating a cohesive data stack. Would you like to learn more? Download the Data Rocket White Paper here, or request a complimentary consultation.