10 Best Practices For Data Science | 7wData

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For quite some time now, data science has enjoyed a reputation as the next big revolution in the tech and business landscape. The number of businesses employing the applications of data science has only increased in the recent few years. According to Statista, as of 2021, nearly 60 percent of companies are housing at least fifty data scientists in their teams.

However, if looked at objectively, the results offered by data science do not match the noise it is surrounded with. A lot of organizations applying data science methods to their data often observe their data science strategies prove unfeasible.

A prominent reason behind this, as Gartner suggests, is the lack of proper execution of data science projects. Other reasons commonly include the lack of understanding of business problems, project design inconsistencies and inadequacies in converting data insights into actionable results.

Data science is a complex topic composed of several elements. Thus, companies need to use certain best practices for data science to better implement data science projects.

In this article, we will discuss some such best practices that organisations can incorporate to improve the success rate of their data science efforts. But first, let us gather some information on data science as a concept.

Data science has inadvertently taken on the reputation of an IT buzzword similar to Bitcoin, NFTs, Crypto, etc. However, if we filter through the hype, we will see a multi-layered field incorporating various aspects of mathematical reasoning and computer programming to understand data.

Contrary to what it seems, data science is not a new IT phrase. Its earlier uses, especially in the late 20th century, indicate its proximity to statistics, a word signifying the organized documentation of data.

Data Science is fundamentally an augmentation and conjugation of disciplines such as big data, data mining and Machine Learning. Today, it essentially refers to the collection and analysis of loads of unstructured data of an organization.

Data scientists, professionals who record and demystify bulky and noisy data, use mathematical aptitude, coding skills and a range of skillsets concerning databases, computing and communication to process data and derive relevant insights. Companies then use these insights to improve their customer service, product quality, inter-organizational communication and more.

Data science is gradually becoming a coveted asset for several organizations, and as the years pile up, it is bound to gain more traction.

So far, we have gathered information on the definition and purpose of data science. Now let us look at some data science best practices that companies can abide by to better leverage the advantages of data science.

One of the primary reasons companies cannot fully utilise their data science projects is the absence of specialised data science infrastructure. Commonly, companies consist of data science teams of two or three who work on different undertakings concurrently. They have no documented modus operandi and lack the metrics required to measure the success of each task they accomplish.

Also, in many cases, these teams are devoid of the necessary technical support required to furnish their potential. As such, the value these teams offer to a business’s overall growth does not amount to much.

To better employ the under-utilised capabilities of its data science team, every business needs to encourage the establishment of a data science plan which will include:

A unicorn refers to a mythical being that resembles a horse with a horn on its forehead. In popular culture, this word is used as a metaphor to describe anything that many people crave but can only obtain with difficulty. 

In the context of data science, the term unicorn carries virtually the same meaning. It refers to a person, a data scientist to be more specific, who possess or can acquire virtually all the data science skills a business desires.