“We unlocked hidden insights in data we already had in hand…and, we learned about a different way to approach work and problem-solving generally” – Waylon Butler, Director of Data and Analytics, AIPP
AIP Publishing knew they had a wealth of data at their fingertips that could be mined for innovation. For example, they had a lot of data on their published authors, but it was rarely interrogated beyond each individual article process. They also use a number of internal systems to track operational tasks, including JIRA and Freshdesk. With data held in various systems, they had a hunch they were missing patterns and opportunities. But they didn’t know how to get started and unlock the insights they needed. So, they engaged 67 Bricks for a short technology consultancy engagement, to see where their efforts were best focused.
We ran two different experiments to explore customer data and information on internal processes, Experiment one was around author relationships. AIPP wanted to trial using graph databases to explore hidden connections between their customers. We uploaded their data into a sandboxed graph database called Neo4J, and from there uncovered relationships they had not previously seen.
This unlocked insights such as;
- Authors who frequently collaborated with each other
- ‘Power collaborators’ which could be automatically identified by PageRank
- Authors who haven’t collaborated but are interested in the same field, who could perhaps review or collaborate on a Review paper or book.
- Clusters of popular topics which could inform special issues or new journal launches
The possibilities these sorts of insights could provide for editorial, sales or marketing teams were very exciting and offered a new way of thinking about AIPP’s customers.
The second experiment was centred around operational data – namely ticketing data from the systems used to track operational tasks. AIPP uses several of these systems, including JIRA and Freshdesk, which aren’t easily interrogated in a holistic way. To get started, we enriched their JIRA data using existing information, such as time to resolution, time in QA, and so on. From here we could make inferences about processes which could be improved, and despite gaps in the available metadata, such as the type of request, we were able to identify clusters of similar tickets. This revealed which processes the team were spending the most time on and should therefore be tackled first. AIPP also discovered that their use of these ticketing systems was not as robust as they thought – they identified system improvements that would improve data capture, putting them in a better position in the future to mine for insights.
Waylon Butler, Director of Data and Analytics at AIPP, told us that ‘the most useful learning from this experiment work with 67 Bricks was the meta-lesson about how much can be learned in a short period of time. We unlocked hidden insights in data we already had in hand. We discovered improvements in systems and workflows that seemed pretty robust. We learned about the utility of new data mining and analysis techniques. We learned about the power and ease of a variety of technical tools. And, we learned about a different way to approach work and problem-solving generally.’ For anyone considering something similar, he counsels that ‘one does not need a big project that is extensively planned – just get the data, some expertise, and some curiosity together, ask questions, then repeat on a short cadence’.