The Pareto Principle refers to the concept that 80% of consequences are generated from 20% of causes, meaning the remainder is less impactful. Those working in the data industry may have a different version of the 80-20 principle. A data scientist generally invests approximately 80% of their time cleaning up data instead of working on actual analysis and delivering key insights. While many data scientists spend over 20% of their time working on data analysis, they will inevitably spend numerous hours organising information. This process can include removing duplicate data and ensuring all information is formatted appropriately. 

Studies suggest that an average of 45% of the total time spent is on this workflow. Another report by CrowdFlower puts the estimate even higher. Preparing data is vital, but inappropriate information will generate inaccurate data if not handled correctly. The main question asked is ensuring a data scientist’s time is allocated to necessary tasks rather than procedures that should be reduced. Over half of data collected by businesses is often not used, suggesting that time invested in data collection could be improved. The challenges highlighted here suggest companies are still exploring how to utilise information in this new data generation. 

We are still in the early days of data transformation. The success of technology leaders who place data at their core is influencing others to follow a similar path. Data hold considerable value, and businesses are aware of this, as proven by the rise of data-focused AI experts in organisations. Companies need to implement the correct measures, and one important area is focusing on people as much as we are on the actual technology. 

Data can enhance the operations of any function within a business. While emerging technology could provide endless opportunities in the future, the priority today for each business is utilising the data available and ensuring the relevant people have access to this information to make vital decisions. This person doesn’t have to be a data scientist. It could be an engineer looking to explore potential errors in a manufacturing process. All of these people require the data in front of them to continue to generate vital insights. All people can utilise data, especially if a business invests in them and ensure all employees have basic data and analytical skills. In this process, accessibility is the key ingredient. 

Implementing data and analytics enhances the bottom line for any business as long as it includes a clear plan with appropriate measures. The initial step should focus on making data more accessible and simple to use. Creating an all-inclusive data culture is just as critical for a business as the data infrastructure.