Written by Michiel van Staden Nov. 11, 2021
Even just the word data already has so many different meanings, to different people.
A google search of the term surfaces some of the nuances nicely. Firstly there is mention of "facts and statistics", which refers to data that has already been cleaned and summarised in some way towards insights. The list of similar words includes "details, particulars, specifics & features", which gives it more of a personal information flavour, the likes of which are mentioned in and thus increasingly discussed as a result of new privacy regulations. At the end, a slant is also given in the direction of more raw data, "the quantities, characters, or symbols on which operations are performed by a computer".
How you come to see data is largely dependent on the form in which you are mostly exposed to it. In an organisation, this means that your specific role and associated contact with data, would largely determine how you come to view it and what it means to you.
A data engineer, somebody in tech or even some data scientists, might very well generally encounter data in a relatively raw form. The data might be coming straight out of a production system, to be piped onward and upward. In these settings, key considerations are around the volume of data generated and the formatting of this information towards some form of conformance, as it is stored. From direct users of the data stored in whatever shape, there will be demands around ease of access for their analysis and reporting needs. In these type of 'back-end' divisions, there is always the risk of being removed from the business relevance of this data.
Closer to 'business', in say an analytics or reporting role, you could be lucky enough to only be exposed to nice clean data, arranged into well structured rows and columns. In these fortunate circumstances, it is easy to be blissfully unaware of the amount of work that goes into engineering the pipes, to deliver the data in structured form. Some leverage this luxury to move closer to business, whilst something like developing slick dashboards and visualizations can also become your data be all and end all. For those less privileged, access challenges could be your daily grind, consuming your data focus around just getting reports out.
As an end user, executing on targeted campaigns, data could mean a list of contact numbers to you. In management or finance, what you come to experience directly as data, is mostly just a latest sales number or a line item on a financial statement. At this end of the funnel, in 'business' roles, you are mostly not privy at all, to data in raw form, and you are unlikely to get entangled in the logistics of data access, processing and compilation into reports or more specific spreadsheets. Your daily function revolves around getting relevant marketing material out, meeting targets, attending meetings or putting together presentations, where reports or other high level numbers might feed into. In your world, data is much more closely tied to a specific business outcome.
In being exposed to data in a specific form or setting, it is natural to come to see that as the one and only incarnation.
For those closer to its raw form, data can lose a lot of its practical use relevance, embodying only considerations around how to process the shapes and volumes, whilst users only working with the very refined versions of data, often do not have insight into what it takes to get to that point.
When these different parties thus engage with each other, it is easy to think about data in your own context, just assuming the other person will understand it in the same way.
From this point of view, techies can get caught up in selling data solutions purely based on their technological elegance, without mention of the business relevance, whilst end users can be very demanding of what they need from data, missing an understanding of what that would entail.
When engaging around data by only looking at it from your own perspective, and not fully considering where the other person is coming from, the chances are good that you might very well not get what you were expecting.
In the absence of getting the other person to buy into the relevance of what you're proposing, or input into the practicalities of making your ask a reality, your idea will probably not go anywhere.
By not confirming mutual understanding and appreciation for the details of the piece of work and potential, something could end up being delivered that does not end up meeting the requirements or adding any value.
If you are in any way unsure about what the other person is referring to, my experience has taught me to ask, always ask.
In the absence of making sure you are 100% aligned, there is so much potential for doing a whole bunch of unnecessary work, or just not delivering at all on what was expected.
Taking more time to listen to each other, and understand the parts of the data value chain that we might not have that much experience with, can go a long way towards creating more connected data informed organisations, that can increasingly connect the dots, spot relevant opportunities and then develop solutions that deliver.
A good starting point is finding a coach that can provide you with a safe space, where you can unpack some of your thinking, hear it played back to you and be challenged around your assumptions.
Mentorship can offer you some guidance in navigating these kinds of considerations, give you some pointers on alternative perspectives to consider, and suggest ways and means of approaching your stakeholders differently.
Ultimately it comes down to developing your people skills towards increasingly collaborating more closely with a range of different people around making data work.
What interesting interpretations of the word data have you come across?
71% of Fortune 500 companies can't be wrong – mentorship is crucial to career growth. Our free 'state of mentorship' shows you the facts, stats and studies on this career superpower.
Including 10% discount on your next session!