Data Driven Decision Making Series - Part 3
10 skills required for data driven decision making
- Data Science
- 5 min read
“Without data, you are just another person with an opinion”. This quote by W.Edwards Deming resonates vibrantly in an era where data plays the most critical role in the life of any decision maker. As human beings generate data to the tune of quintillions (1 followed by a staggering 18 zeroes!) of bytes on a daily basis, it’s imperative that this data should be utilized effectively. Data driven decision making has gained traction in recent times and even smaller businesses are trying to tap into the full potential of data.
As companies transition from an intuitive decision making process to a data driven decision making process, there are certain skills employees have to imbibe in order to gain the maximum out of its data. This set of skills can be broadly categorized into two camps – technical and soft skills. These skills go hand-in-hand and should be prioritized equally.
Technical Skills
There is no doubt that your organization will have personnels with significant technical skills, but it might be concentrated in small pockets around technical departments. To utilize data effectively, these skills should permeate to all levels of the organization as data is extensively available and lies in every quarter of the organization. The below skills are considered highly essential for an employee to make data driven decisions:
- Data extraction : Data can be stored in multiple platforms in an organization. Once you’ve decided the outcome you want to achieve, the relevant data should be pulled to do the analysis. This is the initial process of any analytical project and a basic skill required for any employee to start working on a project.
- Data transformation and standardization : Data in its raw form is seldom in a condition to directly work on. It needs to be cleaned, transformed and standardised to be ready to work on. It is estimated that almost eighty percent of an analyst’s time is spent on cleaning and transforming data. With so much time and resource getting expended for such a mundane task, it is crucial that organizations should have the right skills to deal with it astutely.
- Basic math and understanding of data : Basic mathematical skills are essential to derive good decisions from data analysis. As the result of most analyses are numerical in nature, you should have the basic understanding of mathematics to interpret the results. Also the roadmap of any analyses depend on the type of data we use. This includes a foundational understanding of data, including data types (categorical or continuous), distributions and attributes.
- Foundational statistics : Statistics help an organization predict outcomes from existing sets of data. An understanding of basic probability, correlation, regression, along with inferential statistics are relevant skills for a person to make data-informed decisions.
- Data science : While data science is a broad field and includes machine learning and artificial intelligence, basic understanding of the field is necessary to turn data into insights which helps you make decisions.
Soft Skills
It might seem irrelevant to consider technical skills and soft skills in the same bracket, but having these less tangible skills enhances your effectiveness in this digital era. Being able to communicate, empathise and connect with others is often the difference between your decision being accepted or rejected.
- Systems and enterprise thinking : Systems thinking helps decision makers understand the collective mindset and why people behave the way they do. Viewing the organization from this perspective helps identify causes vs symptoms easier and make decisions accordingly.
- Critical thinking : Data-driven decision making involves the ability to think critically about data and discern the complexity of data and weigh in on the pros and cons of the decisions made. Decision makers need to understand and accept that there can be limitations in the data and the effect of her/his own personal biases while arriving at a decision, and in turn mitigate them. Critical thinking helps in tackling these roadblocks and arriving at the right conclusion.
- Active listening : Decision makers are exposed to different types of information from various sources before arriving at a decision. It is basic human nature that the information received might be perceived in a different meaning and the purported outcome of the decision changes accordingly. Active listening helps decision makers overcome this challenge.
- Relationship building : For a person to make an effective decision, she/he needs to communicate with multitudes of people in the organization to gather information and understand the premise of the problem at hand. Good relationships between the stakeholders act as a catalyst for this process and contribute to good decision making.
- Communicating with data : Any decisions made on the basis of data should attract collective acceptance in the organization. For the stakeholders (employees, investors or customers) to accept the decision, you should be able to validate your decisions on the basis of the data on which you arrived at it. Communicating your findings based on the underlying data helps in acceptance of your decisions.
To put it all together
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