P2P

55 I L T A N E T . O R G T here's lots of talk these days about the problem of data silos. Yet, the origin of silos isn't inherently a bad thing. Practices and departments rely on their individual stores of data and, as stewards of that data, maintain it to achieve their individual goals. The challenge emerges when there is a need to share and combine data to serve complex needs across the firm. Silo roadblocks can make it nearly impossible to closely collaborate across organizations, feed clean and complete data to emerging artificial intelligence applications, and get solid insights from predictive analysis. One solution is to strike a balance between the data and reporting requirements of departments and practices with firmwide requirements for data governance, security, and analytics. We have found that the solution to data management is to begin with a clear set of business objectives that allow us to build an initial data platform - and to continue building on business case-focused objectives. This contrasts to two approaches to data management which have already led to high-profile failures in our industry: the "boil the ocean" approach to master data management, and the "unicorns and rainbows" approach of promising enticing but unachievable levels of competitive AI analytics out of the gate. We have found the emergence of firm intelligence platforms (these are evolving out of the experience management space) provide an opportunity to establish data management best practices in conjunction with attainable results. A platform like Foundation, for instance, allows us to both integrate with existing data sources and to contextualize data based on matter lifecycle workflow inputs. In other words we (1) define the problems we are trying to solve, (2) leverage existing, previously siloed data, and (3) contextualize the data with previously unavailable matter information. Add a powerful reporting engine, and you have the ability to show concrete business analysis via an achievable data management initiative. This is not a hypothetical scenario. At Fireman & Company we have developed a six-month data platform implementation model that supports projects like experience management, enterprise search, and practice- focused intranets. This approach to leveraging firm intelligence has already succeeded at several of our Am Law 100 clients. So - in performing this work, what have we learned? Data Enrichment is Transformative Bringing data together in a data lake provides us with the ability to designate "trusted sources" and also to identify the contextual gaps that stand in the way of rich data analysis. While mapping data is the first critical step, we often find that additional context is needed to improve the usability and searchability of the data. Matter characteristics that cannot be captured during new business intake or time recording are critical to locating a matter and assessing its relevance for later use. Capturing and deriving details about third party relationships, client availability details, and the like significantly improves the value of combined data to the firm. A firm intelligence platform provides the infrastructure and utilities to take the pain out of rendering important client, matter, and people data actionable. Automated tools and processes provide the means to passively collect data from systems like time and billing, HR, CRM, and external data (Author's caveat: Yes, many of these systems may require quality upgrades and adoption reboots!); map that data together; algorithmically derive new meaningful details; and setup on-demand surveys to proactively and judiciously collect essential details not discoverable elsewhere.

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