Summary Solution: The virtually unlimited array of tools, power and flexibility available from the cloud can solve today’s problem of siloed data management and analytical needs.
Speaking in generalities, most financial institutions are leveraging robust legacy technologies that perform specific business functions in silos. These application systems – and the data they store – are designed for operational purposes that don’t necessarily account for downstream analytics needs. Further compounding their limited nature, the data warehousing implementations themselves are complex, time consuming and expensive.
Operating in parallel to these systems are various customer-facing and departmental analytical functions that own wholly distinct databases and processing systems, developed using arbitrarily unique software, commercial products and hardware. As a result, it’s entirely plausible that an origination’s systems are unable to communicate with foundational accounting or marketing systems, for example. In this environment, cross-functional analytics initiatives become cumbersome in transferring data, mapping to common definitions and synthesizing information through reports. The result is a technology suite that is limiting in terms of functionality, storage and computing capabilities, stifling innovation and further business growth.
But there’s a solution for organizations that are swapping outdated, siloed approaches for enterprise-wide technology: the cloud. Beyond the benefit of streamlining processes, moving to the cloud has the potential to enable traditional companies to take advantage of advanced analytics and data-driven decisioning. Creating a pathway to popular cloud services like Amazon Web Services (AWS) or Microsoft Azure isn’t complicated, and the possibilities at the end of journey are limitless.
Why It’s Effective
Cloud technologies create an innovative environment by providing unlimited storage and computing capabilities. Instead of siloed operational databases, data lakes in the cloud can serve a single data store for all operational, analytical, web, clickstream and call center functions, in addition to almost any novel internal and external data stream. The cloud enables the discovery of new use cases for data that were impractical or impossible in the legacy environment. This more collaborative data approach also supercharges new product (or feature) launches and process changes, condensing the path to implementation down to days instead of weeks.
The Financial Equation
In many ways, the cloud represents the democratization of infrastructure as a service, helping keep IT costs low by utilizing a pay-as-you-go model while providing maximum flexibility. No matter the size of the company, organizations can use the same tech stack and infrastructure as industry innovators like Uber and Netflix. And when paired with data warehouse-as-a-service technologies such as Snowflake, business users are able to interact with the data in the cloud through an easy-to-use SQL web interface.
Employing a simple, data-centric cloud infrastructure makes cross-functional data analysis faster, cheaper and easier. The expandable or elastic nature of this infrastructure and the use of open-source tools has the potential to push data-driven decision-making across your entire organization. And with all teams using the same infrastructure at the same time, they are empowered to coordinate, share data and collaborate, hastening time to market for new products and solutions. (Click here to learn more about how data lakes and data warehouses can support next-level analytics and insights.)
Advancing Your Analytics Practice to the Next Level
Beyond its capacity to solve for distinct problems, the cloud also enables advanced analytics practices by allowing for the collection of data from historically disparate sources, performing exploratory data analysis (EDA), developing machine learning models and visualizing results, all without concern for running out of computing power or storage. And where specialized modelers or quants were historically used to perform exploration of correlations, using cloud-enabled advanced analytics tools, business analysts are empowered to deploy advanced techniques like machine learning to automatically explore correlations and extract insights.
Not without flaw, cloud technologies do bring about concerns surrounding data privacy and security. Data architects should account for these concerns in the design and implementation of the cloud infrastructure. The cloud infrastructure should be open-architected and built with a solid governance plan.
Tools like AWS Identity and Access Management (IAM) rules should be used to help define, enforce and audit user permissions across AWS services, actions and resources. All data must be encrypted, both in-flight and at-rest, with extra security methods applied to nonpublic personal information (NPI), personally identifiable information (PII), and other sensitive data. Data security and privacy cannot be an afterthought. Organizations should also work toward reducing the surface area of the infrastructure and employ the use of virtual private networks to increase the privacy and control of the overall infrastructure.
As the adage goes: Garbage in, garbage out. Implement data quality monitoring to ensure the integrity of data as it flows from origination (potentially operational) systems through to downstream analytical datastores and cloud data lakes. Problems with the accuracy, completeness and timeliness of data causes a majority of the financial reporting issues (in some extreme cases, regulators might ask to restate the financial quarterly/annual results) in typical financial institutions.
Given the risk of faulty metrics, it is imperative that leaders focus on data quality from the onset and maintain that culture as their program matures. Additionally, a mindset of continual learning is key, helping teams bridge gaps to adoption of advanced analytics tools and methods while enabling analysts to stay up to date on new tools and trends as technology evolves.
The cloud isn’t like a light switch that requires users to be all in or all out in order for the technology to work. Standing up a cloud environment can be performed in stages, where organizations can begin to forklift initial data sets in order to allow users to start harnessing – and understanding – its capabilities. Then, as more clean data comes online, that information can be methodically added so that your organization can fully realize the power of the cloud.
The cloud enables organizations to supercharge their data-driven decision-making by leveraging insights from across the organization and using its computing power to compress manual processes that once took months into hours. If your organization is ready to take that jump, our team at Flying Phase is at the ready to guide you through the journey. Click here to reach out to our team to see how your organization can take those first steps.