Data science has become a critical tool for finance, retail, and government policies. In 2017 data is surging ahead as the key business driver in all sectors. The power of data science lies in the ability to take data and transform it into actionable insights. It can help in decision making. Besides, data science is not limited to extracting data. It affects every business function in the following ways.
1. Decision making at every level in the organization
Decisions are made at every level in the organization. Human resource makes decisions at different levels. It involves searching recruitment databases for the right candidate. Along with it, recruiting the right candidate and on-boarding is also done. Finance teams ponder over the budget allocation, payments, and dues. Operation teams allocate resources, optimal utilization of workforce and also manage inventory. Top management spends considerable time on taking critical decisions. These can impact the organization. In a traditional context, these would work as individual units that function independently. The rise of data science as an attractive career option and enterprises realizing the potential of it, and real-time data has lead employees to make data-backed decisions.
2. Storytelling and visualization for decision making
Most often employees are not comfortable with data. It is often seen as cumbersome and time-consuming. This can make it difficult to use data backed information across the organization. Storytelling can make this an interesting topic for employees. In turn, this can help get their inputs. Forbes takes it a step further where is shares the growing importance of data storytelling for strategic decision making. Visualization presents difficult to understand information in an easy to use format. It builds on storytelling and adds to the simplicity of the information presented.
3. Factual data without bias
Decision making can at times favor a particular group and often lead to team conflicts. As per research, cognitive bias can cloud the perception of the data scientist and lead to skewed errors. For e.g. confirmation bias on marketing data on developing countries can be skewed. It can result in low sales and hence low profits. Without actually relying on data, this bias can yield inaccurate results. It is often seen in reporting where positive or negative reporting shows a bias to the subject. Use of positive words is few in controversial topics. On the other hand news on development and growth often has positive words in it.
4. Makes calculated risks through the use of past examples
Companies are using past data as a useful tool to understand and provide a host of options for users. The best example of this is Amazon which provides users with past purchase data. It takes the guess work out of purchasing. It also helps the user to know what purchase would be helpful. In gaming, the user experience is enhanced through the use of past move data which tracks the next move. Based on this the opponent or machine player would know how to counter the player. It helps to improve the strategies and also improve user experience. Calculated risks backed by data analysis can work well to prove the decision making is valid.
These are some ways in which data science helps not to be blindsided in decision making. It contributes to understanding the different techniques that can be used in decision making. It also provides a clear path through which decisions can be taken. Thus, data science can not only help analyze data but also provide enough evidence. This can help in decision making. Are there any other data science benefits that can help in decision making? Let us know in the comments!
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