Data is the energy source of the digital economy but making it valuable is difficult. Professional services firms need strategies that improve their ability to provide insight, but the landscape has changed.
This article was first published in Briefing Magazine, November 2021
The traditional data warehousing approach has been the foundation when embarking on a data project, but there are alternative approaches that deserve consideration. Cloud technologies and storage cost savings are driving new thinking. As firms adopt a lower physical footprint approach to data storage and software, Wilson Allen’s innovation efforts will focus on methods that do not always follow the familiar trodden path of building and maintaining physical warehouses.
SQL warehouses, both on-prem and cloud-based, provide a structured home for data that are leveraged from source systems and stored in alignment with reporting needs. They are not as dense as the underlying transactional engines that feed them. ETL (extract, transform, load) or ELT (extract, load, transform) processes are designed and timed to avoid contention around source transactional engines. Once built, warehouses connect to visualization tools that can read a SQL source or populate SSAS models that enable self-service for power users.
Today, modern analytics strategies can reduce the footprint of your data store. Powerful visualizations tools like Microsoft Power BI facilitate data modeling and deliver a rich visualization library while enabling zero or low footprint solutions. Assuming the size of your data sources and contention criteria are suitable, Power BI houses a model and stores source data to eliminate the need for a data warehouse. Using composite data models and flows, firms can model and connect data from any number of different data sources. A connected data estate can be housed entirely within Power BI, allowing the rapid creation of powerful data mashups.
The rise in alternative cloud-based storage is driving demand to move away from storing, connecting, and surfacing structured, relational data. Now, it’s possible to combine PMS, CRM, ERM, NPS, risk/intake data with DMS documents, images, and web content to build a comprehensive data estate for an enterprise. The result delivers insights heretofore not possible. In addition, through Microsoft Azure’s AI and machine learning capabilities, it’s possible to train models that can find previously unobtainable correlations and go beyond reactive diagnostics to achieve truly predictive and best-in-class analytics.
In the short to medium term, zero to low footprint approaches combined with on-premise and cloud-based SQL warehouses will prevail in the legal industry. This is born out of necessity as well as technology limitations. But the desire to improve the speed-to-value proposition will drive firms to seek out and adopt alternative ways to reach their goals, much as it has in other industries.
At Wilson Allen, we strive to understand a firm’s data journey and to help achieve a balance between existing investments and new technologies to drive better information. We act as both an advisory partner and as hands-on resources, to ensure that a firm’s vision results in a fruitful outcome.
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