For quite some time now, experts in the field of data analysis have been
having arguments and debates about approaches to data analysis.
Some believe that data-driven analysis is the fundamental way, while
their opponents believe that the best way is the data-driven analysis. So
which method is better?
Let's start by taking apart the data-driven method. Its advantages lie in
the fact that all processes, from data collection and processing to
analysis, are automated. This means that this method is less prone to
error than an analyst-driven analysis. In addition, this approach is very
suitable for start-up businesses: it can help to understand your target
audience and refine the product and increase sales by analyzing
customer feedback about the quality of the product itself, the missing
features and their behavior before purchase (how they learned about the
company, why they chose our product). However, this approach has
disadvantages. First, it is a huge expense to buy various services and
tools for analyzing customer actions. Internet activity such as page
views, time on the website, clicks and transitions can be tracked with the
help of the classical services, but as the company grows, their
functionality may start to be insufficient. Secondly, data analysis requires
competent specialists who can not only set up an analytics system, but
also get other departments involved in the process. This can also include
the cost of staff training. Third, it is the resource cost of cleaning up the
data. For correct results, the data must be clean and contain no contain
erroneous, outdated or irrelevant information. The cleaning process itself
is quite time-consuming, so it will have to devote a lot of time and
specialists.
Let's move to the analyst-driven method. Its advantage is that it gives
great results when the amount of data under study is so limited that only
a professional with extensive experience can isolate new trends and
patterns from it. However, its disadvantages are quite significant. First, if
the data is too big, it will be difficult for any person, even the most
experienced professional, to understand the results. Second, this
approach uses supervised learning techniques, where other data from a
human analyst is used to customize the analysis. This type of analysis
requires a more detailed and complex data collection process and more
in-depth data processing. Third, this method is more error-prone.
In conclusion, I would like to say that both approaches have pluses and
minuses. However, in view of the fact that in today's world, companies
receive a huge amount of information every day, which should be
collected in a certain format, structured, analyze, and then draw some
conclusions. They have limited time for these operations, so data-driven
analysis will show itself better in these conditions.