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Use the Data Insights Iceberg to Manage Stakeholder Expectations

One of the main challenges in data science projects is managing stakeholder expectations. Often those in the business will have little idea of the complexity and timescales of seemingly simple tasks.

Sourcing Data

Consider sourcing data. In some organisations, with a non-collaborative culture, something as simple as getting a file of data from IT can take weeks. Add on time to check the data, spend time with someone to explain it, handle revisions and ultimately automate the feed and it easily stretches into months.

In one organisation it took over a year to set up a CFT for a simple file transfer from a provider company.

Explaining these challenges to the business sponsors isn't always straightforward.

In data project implementation phases, when the question of timescales arises, saying it could take 12 months+ is likely to be a project killer. Therefore it's standard to estimate timescales based on reasonable timeframes. However, if after 3 months of a 3 month project, little progress is made due to data sourcing difficulties it also reflects badly on the data science team.

Data Quality

Poor data quality can be another consumer of considerable amounts of time. Checking and tidying data makes up a significant percentage of data projects. However these tasks have no visible output. Again, it's not an easy conversation with business sponsors to say little visible progress is made due to bad data quality.

Proactively manage stakeholder expectations

Business sponsors often expect to see visible results hence it's important, especially in the early phases of projects, to appropriately manage expectations. In projects that are starting from ground zero, where significant time is likely to be spent on data sourcing, analysing and cleansing, it's important stakeholders are aware of the project steps / tasks. Usually many early-stage tasks require completion before any visible output is produced.

To manage these stakeholder expectations I use the data insights project iceberg. The iceberg reflects a data project, showing significant effort occurring below the waterline. Regularly sharing this with stakeholders helps them to better understand that significant work and progress can be made without any visible output to show to the business.

If they are aware of this from project initiation it is simpler to manage stakeholder expectations while there is nothing tangible to see from the project.

Andrew Watson is a Tableau and Alteryx Director at TAR Solutions.

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