The lecture videos used in the week as well as the texts highlight the importance of big data but provide at the very basic level that clean data equals quality data. In that regard, it is important to know that any unclean data will negatively affect the decisions made in a business enterprise.
Share your interpretation of data cleansingI could define data cleansing as a procedure or process that assists in the detection and hence correction of any inaccuracies in tables, data sets, record sets, and database information (Azeroual, Saake, & Abuosba, 2019). That way, it becomes identify the most irrelevant parts that could bring about errors hence necessitate immediate corrections, modifications, deletions, and even replacements. That is the simple process in which data cleansing can be explained for it eliminates any coarse and dirty data.
- Describe 3 decisions a business may make as a result of data mining
A business such as one that is engages in online transactions could make crucial decisions on different areas such as predicting customer behaviors, customers purchasing trends, and fashion patterns (Dai, Wong, Wang, Zheng, & Vasilakos, 2019). Secondly, such a business could use data mining to acquire information from competitors and thus adjust accordingly while the third decision could be made with regard to knowledge acquisition. Many organizations employ automated data mining tools to analyze large data sets while also facilitating the extraction of trends in data.
- negative impact of data that has not been cleansed
Uncleansed data will interfere with predictive analytics, knowledge-acquisition decisions, and gathering information pertaining to competitors. Dirty and unclean data is one characterized by many mistakes, incomplete values, and errors (Azeroual et al., 2019). They make their use produce unreliable results hence making organizations end up with poor decisions. Business costs associated with uncleansed data is huge for it negatively affects financial flows, efficiency is curtailed, credibility challenged, and productivity lost owing to the costly investments that have to be made to counter the negative effects of uncleansed data.