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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