ACCA PM Syllabus A. Management Information Systems and Data Analytics - Benefits and Challenges - Notes ^{5 / 5}

Analytical findings from big data offer significant advantages for decision-making and performance management.

Here are some examples:

1. Marketing:

   Insights gained from analysing browsing histories and purchases help understand customer preferences, leading to personalised recommendations like "Customers who viewed this item also bought..."

2. Customer service: 

   Feedback from various sources, including social media, can be consolidated to improve customer support and address concerns.

3. Competitive strength:

   Leveraging information allows businesses to identify and respond to changing customer preferences faster than competitors, gaining a competitive edge.

4. Customer loyalty: 

   Data collected through loyalty programs enables personalised offers, such as discount vouchers, tailored to individual customers.

5. Operational efficiency:

   Accurate sales volume forecasting aids inventory management, minimising waste, particularly for perishable goods.

6. Performance measurement: 

   Accessing a variety of insights helps evaluate organisational performance, avoiding overemphasis on readily available information.

However, there are risks associated with big data use:

1. Cost:

   Establishing the necessary hardware and analytical software can be expensive, although costs are decreasing over time.

2. Time and resource requirements: 

   Identifying and analysing relevant data for organizational purposes can be time-consuming and resource-intensive.

3. Regulation: 

   Concerns about data collection have led to laws governing its usage, storage, and protection, with potential reputational and legal consequences for non-compliance.

4. Data loss and theft: 

   Aside from regulatory issues, companies face legal liabilities if data is stolen and individuals are adversely affected.

5. Incorrect data (veracity): 

   Inaccurate or outdated data can lead to erroneous conclusions and unreliable insights.

6. Overfitting:

   There is a risk of finding patterns in the data that do not exist or cannot be applied to other data or used for future predictions.