Limitations of traditional analysis
Lack of alignment within teams
There is a lack
of coordination across multiple teams or divisions within an organisation. A
select group of executives may be granted access to data analysis produced by a
selected set of team members. However, the insights generated by these teams
are of minimal value and have little impact on organisational measurements.
This might be due to teams working in "silos," each with its own
processes and separated from other departments. The analytics team should focus
on giving right answers to the business's inquiries, and data analytics team
results should be successfully conveyed to the relevant employees in order to
inspire the suitable course of action and behaviour that will benefit the
organisation.
Lack of commitment and patience
Analytics
solutions are not difficult to implement; yet, they are costly, and the ROI is
slow. It may take some time to establish standards and processes in order to
begin collecting data, especially if earlier data is unavailable. Analytics
models, by definition, improve in accuracy over time and need a commitment to
implementing the solution. Because business users may not perceive immediate
advantages, they may lose interest, resulting in a lack of confidence and the
failure of the models. When a corporation decides to use data analytics
approaches, a feedback loop and procedure must be in place to identify what is
working and what is not, and corrective measures must be made to repair what is
failing. Without this closed loop method, senior management may determine that
analytics isn't working or isn't particularly useful, and the entire endeavour
may be abandoned.
Low quality of data
There may be
times when appropriate data is not accessible or is lacking to do proper
analyses. Rubbish in, garbage out, as they say. If the data quality is poor,
the decisions made with this data will be bad as well. As a result, efforts
must be made to improve data quality before it can be used successfully within
businesses. One of the most major disadvantages of data analytics is the
scarcity of high-quality data. Companies may already have a lot of data, but
the question is whether they have the right data. A top-down strategy is
required, in which the business issues that must be addressed are identified
first, followed by the data required to answer these questions. In other cases,
data gathered for historical purposes may be insufficient to answer the
problems we have now. Even when we have the necessary settings to acquire data,
the quality of the data collection may be poor at times.
Privacy concerns
When personal
information, such as purchases, online transactions, and memberships, is made
available to corporations whose services customers utilise, their privacy may
be infringed. Some companies may elect to share their datasets with other
companies for mutual benefit. Certain information acquired might be used
against an individual, country, or society. Organizations must exercise caution
while collecting data from clients and ensure the data's security and
confidentiality. To secure sensitive data, only the data required for the
analysis should be acquired, and sensitive data should be anonymized. Customers
may lose trust in organisations as a result of data breaches, which might harm
the company.
Complexity and bias
Some of the
analytics technologies developed by organisations resemble a black box concept.
What is within the black box is unknown, as is the logic used by the system to
learn from data and build a model. Consider a neural network model that learns
from diverse scenarios to identify who should be authorised and who should be
refused for a loan. Although utilising these tools is straightforward, no one
in the business knows why choices are made the way they are. If companies are
not diligent and utilise a low-quality data set to train the model, there may
be hidden biases in these systems' results that are not readily evident, and
organisations may be breaking the law by discriminating against race, gender,
sex, age, and so on.
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