3 Risks of Data Analytics and How to Prevent It

February 17, 20202:24 pm3171 views
3 Risks of Data Analytics and How to Prevent It
3 Risks of Data Analytics and How to Prevent It

Data analytics refers to a set of data that provide operational insights into business. It helps many executives, especially HR department, to make data-driven decisions that can improve hiring decision, increase ROI, create better work environments, and maximise employee productivity. According to Jeff Furst, data analytics is not only useful in terms of decision making. It also helps create better transparency within the workforce. Additionally, data analytics allows HR to forecast the future of business or recruitment by combining historical data and current trends. 

Achieving the advantages of data analytics, however, can be challenging because given to the present state of technology, there are risks associated with data analytics. Here are the three most common risks of data analytics and how to prevent them. 

1. Poor data quality 

Data-driven decision making is here to stay, said Vegard Flovik, a physicist and data scientist, we might often claim that the best solution to improving decision is by adding more data. While adding more data can “magically” improve performance, this is not the case, added Flovik. The problem is when you add more and more data, there is a risk of adding data that is misinformation. As consequences of this asymmetrical data, the information output might not reflect on the changes made in another system, leaving it outdated. 

To prevent this, it is advisable to use a centralised system. This system can input automatically with mandatory or drop-down fields, leaving room for human error.  The system can also ensure that a change in one area is instantly reflected across the board, minimising – better yet, eliminating – the chance of adding inaccurate data. 

See also: Security Knowledge: How Hackers Steal Your Employee Data

2. Data overwhelmed 

Data overwhelmed is driven from a result of the amount of data collected in big data and analytics. In an organisation, businesses might receive information about many things and interaction that takes place on a daily basis, leaving analysts with thousands of data set. This large set of data will require the analyst to work harder which increase burnout and stress. As consequences, poor data and human error might increase.

To prevent this, there is a need for a data system that automatically collects and organises information. Thus, analysts can place their attention more on sorting out relevant data, reducing time and work needed to get relevant data. 

3. Inaccessible data

Managers and decision-makers need access to all organisation’s data at any given moment to make sure they get the right result to drive business success. However, moving to a centralised system can be challenging for decision-makers and managers. According to iDashBoards review, a centralised system often results in delayed data when information has to be passed throughout management of a core analytics team, resulting in a slow decision-making process.

To prevent this, there should be an effective database and authorised employees to securely view or edit data from anywhere. Organisation should make changes that enable high-speed decision making by making sure that data inserted are relevant and maintain good communication between stakeholders. 

Read also: Employee Login-Logout Procedures Could Harm Company’s Data Security

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