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In today’s day and age, it is common to be overwhelmed by the unimaginable amount of information made available to us. Data might seem intimidating because the average person does not have readily-accessible tools to analyse and make sense of the information presented.
With the help of data science consulting, large amounts of data are being studied by professionals through the employment of a mix of scientific methods, processes, algorithms and systems. With more structure and definition, consultants are able to communicate the results to the stakeholders involved to make better, more-informed decisions.
To effectively answer the question “What is data science consulting?” would be to break the process down and explain how individuals and companies are able to find value in the troves of information.
Questions are asked
Consultants in the data science consulting industry get large and complex data sets by asking the questions that matter – What business goals do I want to achieve? Who should I approach and study to get information? What parameters should I set so that I can successfully make sense of the data? Asking the appropriate questions allows data scientists to accurately work towards collecting the necessary information required.
Data gets collected
The next step in the process of data science consulting is the collection of information pertaining to the questions asked. In most cases, finding data that is specific and relevant to the questions at hand is not readily available. Primary research of both qualitative and quantitative nature is often required to kickstart the study.
Aside data collection, it is important to consider a variety of factors before diving into it immediately. Conducting primary research can be a costly and time-consuming process. While it is easy to get carried away with research methodologies, taking too much time in asking
different respondents for their opinion may lead to spending more than the allocated budget. Long data collection periods and delays in getting different departments to be involved can also cause data obsolescence. As such, data science consultants must be clear regarding budget restrictions and be reasonable in allocating their time and resources.
Data is given structure through modelling and analysis
After collecting the required data, data scientists or analysts will need to review the data for any oddities such as false entries that arise from question misinterpretation; or anything that might affect the accuracy of the data. Ensuring the data is stored in a structured manner will allow those involved in the data science consultancy process to be able to revisit the information for any other purposes in the future. Once the data has been organised coherently, it is then used to answer the big questions set in place at the beginning.
The process of modelling and data analysis may seem straightforward, but it is rarely a linear one. In the data science consultancy field, research techniques may be revisited to ensure that the most appropriate methods are being used during the study.
Insights are derived to make better decisions
At this stage, popular techniques such as regression testing and machine learning will be applied to derive meaning from the data before translating it into visual representations. However, this is not the challenging part. What is challenging is the ability for data science consultants to convey and present the results to stakeholders involved such that they are able to make sound business decisions – that is the role of a data science consultant.
Data science consulting is more than just a process to gather information and derive meaning from it. Its true essence lies in the ability to bring math, science, business and technology together to allow for better decision-making when unlocking the true value behind the plethora of data.