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Big Data Engineering

Multidisciplinary Analytics | A Practical Guide

Navdeep Singh Gill | 18 January 2022

Multidisciplinary Analytics | A Practical Guide

Introduction to Multidisciplinary Analytics

"Big Data years will be a mathematical era." As more and more data is captured and stored, scientific domains move from data loss to richness, and decision-making processes are increasingly driven by data across all industries, science, and government. "

Using the mathematical mindset to get closer to complex and noisy clinical databases can help strengthen decision-making processes while refining new technologies and machine learning programs to focus on key findings.

For example, the American Cancer Society has been working for over 100 years to eradicate cancer worldwide. The organization realized the importance of understanding how customers interact with their website and what they want. It will help them focus on their marketing efforts and achieve their income goals.

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The company did an analysis:

  • The first step was to divide the users of their website into three categories: information seekers, event participants, and contributors.
  • The next stage was to use analytics to determine the goals of each segment. They built a scoring system for each category using custom metrics to ensure they had achieved their goals after knowing each segment's objectives.
  • They also create remarketing campaigns and personalities for these audiences.
Output: Revenue increased by 5.4 percent year over year.

The future of data and business is infusing analytics everywhere and creating analytics experiences for everyone, but new research shows that businesses are not getting much out of their statistics. Analytics does not provide value until it is linked to a business outcome or ecosystem for internal stakeholders, customers, and partners. There are practical barriers that make access to this information potentially difficult, slow, and frustrating.

What are the challenges of Multidisciplinary Analytics?

There are far more failures in the field of big data, data science, and data analysis. Data science failure rates are alarming:

  • Projects involving big data fail 85% of the time (Gartner, 2017)
  • 87% of data science programs are not completed (VentureBeat, 2019)

This raises why so many large data science and analytics efforts fail.  Let's have a look at some of the reasons behind failures.

Solving the wrong problem

Started a project with a hazy objective in mind. Alternatively, a project with a well-stated goal is either unattainable or adds no substantial value.

Using the Incorrect Process

Organizations try to employ approaches that are used in other fields. The most typical blunder is treating data science initiatives like software, which distances analytics from its true nature. On the other hand, the best approaches combine the data science life cycle with a flexible, collaborative architecture.

Ethics are being forgotten

Models are harsh when it comes to optimizing what you instruct them to. It is a positive thing when done appropriately. It is, however, a two-edged blade that can lead to major ethical, marketing, and legal difficulties.

Racist Health Risk Scoring

Healthcare practitioners employed a health risk score to assess whether or not to deliver proactive healthcare therapy to each patient. The model, however, employed healthcare expenses as a proxy for health risks. Inadvertently, this health risk score favored white patients for proactive treatment because black patients tended to have lower health care costs. (2019, sciencemag.org)

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Multidisciplinary Approach for Analysis

In today's fast-paced culture, various socioeconomic problems relating to other disciplines such as politics, anthropology, and psychology have arisen, necessitating a multidisciplinary approach to solving them. Economists, for example, would be interested in cost-effective techniques, sociologists in empowering the poor and tackling social unfairness, and anthropologists in recognizing local customs, practices, and social structures.

As previously stated, numerous elements influence human life; therefore, studying any component in isolation, that is, in the absence of other disciplines, would not provide a clear picture. As a result, we need multidisciplinary analytics. Multidisciplinary analytics is critical for studying all aspects of a problem. It reduces the impact of the issue's partial or one-sided outcome.

Multidisciplinary analytics is a process in which techniques from several fields and disciplines are combined to solve a problem. This collaborative and coordinated study employs professionals from several areas. They all combine their knowledge to solve any problem.

For example, Cyber risk analysis and mitigation face numerous obstacles, including a lack of awareness of risk dimensions and a lack of perception of the scope by particular stakeholders, such as lawyers or computer scientists. However, a solution can be found by coordinating efforts and using a multidisciplinary approach. Every discipline believes they can deal with cyber risk. However, each field has a limited understanding of the issues. For example, a statistician may believe it is the study of the likelihood of an attack. In contrast, a political scientist may believe it is the study of international security in the context of digital threats, although both perspectives are correct. They must be viewed in the context of a bigger picture for cyber risk to be tackled methodically as a separate study topic. So that it may be addressed cooperatively using a multidisciplinary strategy that includes accounting, data science, behavioral science, economics, computer science, management science, political science, and law.’

As a result of the coordination of several disciplines, it will be easier to analyze and mitigate cyber risk in a broad sense.

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The Perks of Adopting A Multidisciplinary Approach

Let's look at some of the benefits.

Abundance of Creativity

Combining such a diverse set of skills can lead to highly effective experimentation and analysis. For example, a group working on vaccine development that requires virology expertise could benefit from dermatological and neurological experts to understand the vaccine's effects on the skin and nervous system.

Innovative Ideas of a Higher Order

Multidisciplinary research also leads to amazing scientific breakthroughs. Much of science has resulted through the successful collaboration of scholars from several fields. Space exploration is a notable example of this. Researchers from various professions, such as mechanical engineering, biomedical engineering, medicine, astronomy, geography, meteorology, and others, come together to secure the success of a forthcoming space trip. Many of today's power tools, such as drills and compressors, result from breakthroughs gained during space exploration research.

Enhance Abilities

This allows people to focus on honing their current skills and learning from specialists they otherwise might not have encountered. Additionally, because you are most likely the lone expert in your field in the group, you can work individually to tackle challenges.

Despite the significance of multidisciplinary analysis in modern life, it is plagued by several flaws. Mainly because all analysts belong to the same organization, but there is a lack of coordination or cooperation. This anomaly causes some practical issues. Some of these include an absence of team spirit, integration challenges, and a superiority complex. Analysts must develop an interest and motivation to learn about the methodologies and methods of different fields. Discipline and teamwork should also be given special consideration.

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The multidisciplinary study makes it easier to see and analyze the complexities of modern life's social and economic factors. It considers all facets of the problem to conduct a thorough and successful investigation. The fundamental advantage of multidisciplinary research is that it operates objectively, without prejudice, bias, or erroneous assumptions. It encourages collaboration between different fields.