Once your analysts have a clear business question to answer, do they start new analysis each time, potentially reinventing the wheel?
After creating or leading data and analytics teams for many years, I began to notice this pattern of behavior. What we seemed to lack was a consistent knowledge management solution or corporate memory that could easily spot what should be remembered.
Funnily enough, as I became convinced of the need for holistic customer insight, I found a partial answer among researchers.
Avoiding reinvention is such an important issue for analytics and insight teams that I’ll use this post to share my own experience.
The lack of secondary research approach for analytics
Researchers do a somewhat better job than insight teams because of their understanding of the need for secondary research. Experienced research analysts/managers will be familiar with considering the potential for desk research, or searching through past research, to answer the question posed. Perhaps because of the more obvious cost of commissioning new primary research (often via paying an agency), researchers make more effort to first consider if they already have access to information to answer this new question.
But, even here, there does not appear to be any ideal or market-leading knowledge management solution. Most of the teams I have worked with use an in-house development in Excel, interactive PowerPoint slides with hyperlinks to file structures or intranet-based research libraries. Whichever end-user computing or groupware solution is used, it more or less equates to an easier to navigate/search library of all past research. Normally, a user can search by keywords or tags, as well as through a prescribed structure of research for specific products/channels/segments etc.
See also: Why Customer Experience Is Key
Some research teams use this very effectively and also recall those visualizations/graphics/VoxPops that worked well at conveying key insights about customers. It is worth investing in these area as it can save a significant amount of research budget to remember and reuse what has been learned already.
However, while also leading data or analytics teams (increasingly within one insight department), it became obvious that such an approach did not exist for analytics. At best, analysts used code libraries or templates to make coding quicker/standardized and to present results with a consistent professional look. Methodologies certainly existed for analysis at a high-level or for specific technical tasks like building predictive models, but there was no consistent approach to recording what had been learned from past analysis.
I’ve seen similar problems at a number of my clients. Why is this? Perhaps a combination of less visible additional costs (as analysts are employed already) and the tendency of many analysts to prefer to crack on with the technical work together conspire to undermine any practice of secondary analytics.
The many potential benefits of customer insight knowledge management
Once you focus on this problem, it becomes obvious that there are many potential benefits to improving your practice in this area.
Many analytics or BI leaders will be able to tell you their own horror stories of trying to implement self-serve analytics. These war stories are normally a combination of the classic problems/delays with data and IT projects, plus an unwillingness from business stakeholders to actually interrogate the new system themselves. All too often, after the initial enthusiasm for shiny new technology, business leaders prefer to ask an analyst than produce the report they need themselves.
So, one potential advantage of a well-managed and easily navigable secondary analytics store is a chance for business users to easily find past answers to the same question or better understand the context.
But the items stored in such an ideal knowledge management solution can be wider than just final outputs (often in the form of PowerPoint presentations or single dashboards).
I have seen teams benefit from developing solutions to store and share across the team:
- Stakeholder maps and contact details
- Project histories and documentation
- Past code (from SQL scripts to R/Python packages or code snippets)
- Metadata (we’ve shared more about the importance of that previously; here I mean what’s been learned about data items during an analysis)
- Past data visualisations or graphics that have proved effective (sometimes converted into templates
- Past results and recommendations for additional analysis or future tracking
- Interim data, to be used to revisit or test hypotheses (suitably anonymized)
- Output presentations (both short, executive style and long full documentation versions)
- Recommendations for future action (to track acting on insights, as recommended previously)
- Key insights, summarized into a few short sentences, to accumulate key insights for a specific segment, channel or product
- Bespoke Excel or PowerPoint front-ends with hyperlinks to simple folder structures
- Evernote app, with use of tags and notebooks
- SharePoint/OneNote and other intranet-based solutions for saving Office documents
- Databases/data lakes capable of storing unstructured or structured data in a range of file formats
- Google search algorithms used to perform natural language searches on databases or folders
