Skip to main content

Limitations of Traditional Data Analysis

 Some limitations of traditional data analysis are:

Lack of data sources meaning that it relies solely on very particularly organised data such as ones through surveys, interviews, questionnaires or really any other form of data collection. Because of this it’s limited to give a complete picture of what the business world it truly like. 

Limited scope means that it’s most often limited in information and provides insight to a specific part of the business world. For example, a survey about customer experience at a business may say that the employee was rude or unkind but it doesn’t give an insight about how the customer may have behaved to receive this kind of treatment. 

Another part is that it’s time consuming to complete because if this it means the business can’t make decisions fast or efficiently. An example would be that if a survey or questionnaire took a long time to be taken and additionally to be sorted and analysed which can take weeks if not months to be finished. 

Some other examples are that it isn’t always accurate and can be very prone to many mistakes and things that aren’t true for example a questionnaire may not reflect everyone’s thoughts and feelings and only a very select few. 

A final examples would be that it can be very expensive and require particular resources to complete which makes it particular different for small businesses for example to complete and make difficult choices. 


https://fastercapital.com/topics/the-limitations-of-traditional-data-analysis-methods.html



Comments

Popular posts from this blog

Types of Visualisation

Big data visualisation refers to the techniques and tools used to present the large and complex data into such a way that it’s easy to read and understand. Some examples include: Heat maps which is used to show the amount of data points or activities across different regions and categories.   Network diagrams helps to visualise relationships and interactions in data for example social connections/data flows etc.  Geospatial maps show the mass amount of geographical data with traditional data sets to provide spread apart analysis.  Stream graphs show trends and patterns across loads of different categories and regions.   Same for the parallel coordinates which also show patterns but also correlations across numerous different variable.  Chord diagrams help to identify clusters, patterns, trends etc to help with making intense decisions and also helps with analysis etc.  https://www.geeksforgeeks.org/what-is-big-data-visualization/#what-is-big-data-visualizat...

Data Mining Methods

Data mining is the process of using statistical analysis and machine learning to reveal hidden patterns or odd things in large datasets because of this you can help make important decisions and predict what’s going to happen. it’s where you take data such as structured data, an image, video, text etc and train it, deploy and serve it then you can get actionable insights and application events.  The techniques are classification which is used to organise data into different classes or categories so it trains a model on labelled data and uses it to predict the class. Regression is used to predict numbers or continuous values based on relationships so it finds the function or model that best fits the data to make accurate predictions.  Clustering is used to group similar data uncover patterns or structures in the data without any classes or labels.  There is also association rule, anomaly detention, time series analysis, neural networks, decision trees, ensemble methods and ...

Types of Problem suited to Big Data Analysis

 One of the greatest challenges is storage with insane amounts of data generated everyday. Also because Unstructured data can’t be stored in traditional databases.  Processing big data is also a problem which refers to reading, analysing etc or useful information from raw information because of this the changing from all this data to finding all the useful parts is very challenging.  Security is another problem because non-encrypted info is more likely to be stolen or damaged which makes it such a large concern for organisations.   https://www.simplilearn.com/challenges-of-big-data-article#:~:text=Storage,be%20stored%20in%20traditional%20databases.