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| Business Intelligence Series |
Probably, in some organizations there are still recounted stories about a hero who knew so much about the business and was technically proficient that he/she was able to provide data-driven answers to most business questions. Unfortunately, the times of solo representations are for long gone - the world moves too fast, there are too many questions looking for an answer, many of them requiring a solution before the problem was actually defined, a whole infrastructure is needed to be able to harness the potential of technologies and data, the volume of knowledge required grows exponentially, etc.
One of the approaches of handling the knowledge gap between the initial and required knowledge in solving problems based on data is to build all the required knowledge in one person, either on the business or the technical side. More common is to hire a data analyst and build the knowledge in the respective resource, and the approach has great chances to work until the volume of work exceeds a person's limits. The data analyst is forced to request to have the workload prioritized, which might work in certain occasions, while in others one needs to compromise on quality and/or do overtime, and all the issues deriving from this.
There are also situations in which the complexity of the problem exceeds a person's ability to handle it, and that's not necessarily a matter of intelligence but of knowhow. Some organizations respond with complexity to complexity, while others are more creative and break the complexity in manageable pieces. In both cases, more resources are needed to cover the knowledge and resource gap. Hiring more data analysts can get the work done though it's not a recipe for success. The more diverse the team, the higher the chances to succeed, though again it's a matter of creativity and of covering the knowledge gaps. Sometimes, it's more productive to use the resources already available in organization, though this can involve other challenges.
Even if much of the knowledge gets documented, as soon the data analyst leaves the organization a void is created until a similar resource is able to fill it. Organizations can better cope with these challenges if they disseminate the knowledge between data professionals respectively within the business. The more resources are involved the higher the level of retention and higher the chances of reusing the knowledge. However, the more people are involved, the higher the costs, especially the one associated with the waste of effort.
Organizations can compromise by choosing 1-2 resources from each department to be involved in knowledge dissemination, ideally people with data and technology affinity. They shall become data citizens, people who use data, data processing and visualization for building solutions that enable their job. Data citizens are expected to act as showmen in their knowledge domain and do their magic whenever such requirements arise.
Having a whole team of data citizens opens new opportunities for organizations, though such resources will need beside domain knowledge and data literacy also technical knowledge. Unfortunately, many people will reach their limitations in this area. Besides the learning effort, understanding what good architecture, design and techniques means is unfortunately not for everybody, and here's where the concept of citizen data analyst or citizen scientist breaks, and this independently of the tools used.
A data citizen's effort works best in data discovery, exploration and visualization scenarios where the rapid creation of prototypes reduces the time from idea to solution. However, the results are personal solutions that need to be validated by a technical person, pieces of the solutions maybe redesigned and moved until enterprise solutions result.
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