Let's try a little experiment.
Think about what might happen if you show this line chart to three different
people. You'd very likely get three different interpretations, even if they
understood that line charts are used to show change over time.
One person might assume the X access represents a few days, while another
might guess it is showing a span of many years.
Maybe one person would assume the five colored lines along the Y access
represent the sales of different products.
Another may suppose they represent the purchase patterns of different customer
types. The point is a line chart with the title, a legend for the X axis and
the Y axis, and each of the data values it contains is a much more effective
data visualization. When you clearly indicate the meaning of each item by
giving them context, suddenly this line chart can be easily understood by
others. Context helps eliminate the risk of misinterpretation, this saves
your stakeholders time, and ensures they have accurate information to make
data-driven business decisions.
As you likely know, in data analytics, context turns raw data into meaningful
information. When you contextualize, you put something into perspective.
This involves considering its origin, and other relevant background
information, the motivation behind it, the larger setting in which it exists,
such as a particular time period, and what it might have an impact on.
Contextualization gives something greater meaning to help people
understand it more completely.
This also supports fairness, and reduces the chance of bias when your
users seek to gain useful insights from the data you're presenting, which brings
me to context in a BI setting.
In BI, there's another aspect of context that professionals care about a
lot, and that's contextualizing the tools we create for our users.
One key practice that promotes context is to put the data being shared in a
central location.
Typically, this would be a well-designed dashboard.
Then the second step is ensuring there's a common method for everyone to
interact with that dashboard.
It's important for stakeholders to be able to easily understand access, and
use the dashboards you create.
This way people don't have to go elsewhere, or switch context in order to
find the information they need, and that empowers all users to be much more
effective in their work. For example, let's say a company's finance team needs
a dashboard to analyze costs across the entire company, so you design a
dashboard that shares key insights about each department's particular
spending, but then what if it turns out that the Operation Department's costs
are revealed to be unusually high?
The finance team would want to be able to take a deep dive into that
department's spending in order to figure out the root causes of the cost
increase, so it would be important to iterate on your dashboard, so it
includes supporting information about each department as well.
Another part of building an effective solution is prioritizing the
cross-functional relationships that exist within your organization.
It's necessary to consider how the BI work you're doing aligns with overall
business objectives, and how it will be used by your colleagues.
For instance, if your new BI tool will monitor five different metrics, and be
used by 10 different stakeholders, it's important to consider how each user will
access, and interpret the data.
Basically, to make an effective dashboard, it's necessary to first
understand how each particular stakeholder will actually use it, and by
taking the time to think this through, you ensure that you create one robust
dashboard rather than many less effective ones.
Also, because you've created a single, accessible, shared dashboard, this
allows for some great collaboration among users.
For instance, that finance team member may be dismayed by a seemingly small 5%
year-over-year growth number, but the salesperson can put that number into
context by pointing out that 5% is actually a good result, and higher than
expected. Given that the market segment as a whole was experiencing a 10%
decline, the salesperson can provide that specific market context, whereas
the financial analyst would likely only be aware of broad industry trends.
A single dashboard output can bring about countless insightful
conversations. Expressing results contextually helps you confirm that
you're using the right data for the stakeholders.
You'll also know that it's in the correct format.
It can be effectively used and shared, and the results make sense.
This boosts people's understanding, and as a result, the ultimate business
benefits.