When asked to solve a data analytic problem, the description of your
task can be quite ambiguous at first. It is up to you as the data
specialist to translate the task into a concrete problem, figure out how
to solve it and present the solution back to all your stakeholders. These
are the key steps to be carried out
• Frame the problem: Who is your client? What exactly is the
client asking you to solve? How can you translate their
ambiguous request into a concrete, well-defined problem?
• Collect the raw data needed to solve the problem: Is this data
already available? If so, what parts of the data are useful? If not,
what more data do you need? What kind of resources (time,
money, infrastructure) would it take to collect this data in a
usable form?
• Process the data (data wrangling): Real, raw data is rarely usable
out of the box. There are errors in data collection, corrupt
records, missing values, and many other challenges you will have
to manage. You will first need to clean the data to convert it to a
form that you can further analyze.
• Explore the data: Once you have cleaned the data, you must
understand the information contained within at a high level.
What kinds of obvious trends or correlations do you see in the
data? What are the high-level characteristics, and are any of
them more significant than others?
• Perform in-depth analysis (machine learning, statistical models,
algorithms): This step is usually the meat of your project, where
you apply all the cutting-edge machinery of data analysis to
unearth high-value insights and predictions.
• Communicate the results of the analysis: All the analysis and
technical results that you come up with are of little value unless
you can explain to your stakeholders what they mean in a way
that’s comprehensible and compelling. Data storytelling is a
critical and underrated skill that you will build and use here.
