April 26, 2022

The Seven C's

Attending the London Book Fair recently and having my first face to face meetings in over 2 years a good number of the conversations revolved around best practices in data management and reporting to increase trust in and value from data.

Seeing this as an extension from the well known 5Vs of big data (Volume, Value, Variety, Velocity and Veracity) I managed to distil some of the key topics around this into ‘Seven Cs’ (to try and make it more memorable) with the aim to summarise some of the thought processes that aid reporting, analytics and the actions we take.

  • Compliance – Can we use this data? With latest privacy and GDPR requirements the first question of all may well be whether we can use some data or not. And if we think we can, do we need to consider what data governance should be in place in terms of privacy, permissions, security and regulatory requirements for the purposes for which we are looking to use the data.

  • Confidence – What confidence do we have in the data we are looking to use? Variations in the data, its sources and skews in the completeness of the data can all lead any reporting and insights astray, resulting in wrong decisions. For the metrics we use, we must also consider the currency they carry. This will help determine the trust we can put in our data and raise the value.

  • Consolidation – Often we find that we are dealing with more than one source of the same data as well as multiple, disparate sources of varied data. If they all could have an impact on the questions we are asking it’s important to consider how we can consolidate and join this data together. Typically this requires a good understanding of where this data comes from as well as what relationship each source has with others.

  • Consistency – How reliable, persistent and standardised is our data? We need to be sure we can support comparability with minimised error or skew. Consistency in data and our methodologies ensures we can be confident in the value we have in our continued reporting. We need to be able to replicate our reporting for accurate comparisons and decisions.

  • Clarity – The structure of the question you ask of any data is vital. Clarity in the parameters, metrics, filters, rules and methods and how they relate are essential to any findings or interpretations. Building a playbook of core, standardised reports with clear objectives and actionable outcomes is a great start to establishing a data driven strategy that provides results.

  • Context – Data analytics can be a bit of a rabbit hole, with each answer opening a range of further questions with data driving data for data’s sake. Conversely the tweaking or adjustment of reports may be used to justify predetermined answers or decisions. Taking a step back to view and understand the context of any data or report allows us to add perspective to our decisions. When a good report has this perspective and it’s context is understood, template this report and segment it for analysis.

  • Causation – Finally, the 7th C on my list is to consider causation in any report or analyses we make. To go beyond correlation and ‘this shows me what I was expecting/looking for’ to remember to ask the question ‘why?’ By remembering to look at the reasons behind any report or insight, we can more confidently and better act on what our data is telling us.

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