Data science, like Bitcoin, NFTs, and other cryptographic buzzwords, has accidentally become an IT term. Nevertheless, if we can get past the hype, we’ll find a multi-layered profession that uses numerous parts from math and programming to analyze data.
Big data, data mining, and machine learning are fields that makeup Data Science. Today, it primarily refers to the collecting and analysis of loads of unstructured data of an organization.
Data Science Best Practices That Will Change Your Life
1. Create a specialized application
The lack of specialized data science infrastructure is one of the key reasons firms cannot effectively utilize their data science initiatives. Commonly, firms consist of data science teams of two or three who engage in multiple tasks concurrently.
They don’t know how they work and don’t have the criteria necessary to evaluate how well they do their jobs.
Businesses must support the development of a data science plan that includes the following elements better to utilize their data science team’s underutilized capabilities:
The objective of its data science initiatives or sequipping itself with the appropriate data science infrastructure (trained specialists, requisite equipment, etc.) (trained experts, mandatory equipment, etc.)
- A delivery roadmap
- Performance metrics
2. Create A Skilled Team Instead Of Chasing Unicorns
A unicorn refers to a legendary entity that resembles a horse with a horn on its forehead. In popular culture, this word is used as a metaphor to represent something many people seek yet can only attain with effort.
The name “unicorn” has nearly the same connotation in data science. One who obtains nearly all of the business’s desired data science capabilities, namely a data scientist, is referred to as the “data unicorn.”
And as it is with the definition of unicorns, data science unicorns are a rare discovery but in great demand owing to the nature of their portfolio.
3. POC – Proof Of Concept
The feasibility of a data model or a data science solution can only be determined through POCs (Proof of Concept). It’s a kind of trial run to see if a company’s data science projects are going to meet its needs or not.
Running a POC, first and foremost, requires a use case. And it is the decision of the use case that can make or break a POC’s prospects of hitting the production phase. Hence, data scientists should identify the most relevant use case to deliver measurable findings when the POC is executed.
Also, the use case should symbolize a significant business issue or a spectrum of concerns to give explicit and meaningful measuring criteria to the POC.
4. Determine And List All The Kpis
What decides if a company’s data science initiatives are providing enough results? It is the Key Performance Indicators (KPIs) they are juxtaposed with.
There are business objectives in place for most firms using data science, but there are no specific KPIs to track how close they are getting to achieving those objectives.
Thus, organizations need to set aside specific, quantifiable KPIs such as ROI, revenue gain per consumer in percentage, CSAT score, etc., to assess the profitability of their data science initiatives.
For example, if a firm uses an optimization algorithm to enhance revenue, it might utilize performance metrics such as monthly sales statistics, the number of website visits, etc.
5. Data Science Documentation On Stakeholders
Documentation is crucial to every data science effort. And no, we don’t want to entertain the possibility that we could be mistaken. Stakeholders can better understand and utilize project data when all aspects of the project are thoroughly documented.
However, even if the documentation is excellent, the DS project may not be as successful if its specifics cannot be communicated to the appropriate stakeholder.
It’s essential to tailor the documentation of a project to the needs and expertise of the many stakeholders involved rather than using a “one size fits all” approach.
6. Keep Track Of Data Ethics
Data models are objective in their implementation, but data scientists are not. Hence, data scientists must create models that do not breach data collecting, processing, and interpretation ethics and potentially cause harm to individuals.
Failing to comply with data ethics may adversely influence the credibility and reputation of an organization in more ways than one. You understand what we mean if you know about the Cambridge Analytica affair.
Conclusion
The subject of data science is expanding at a rapid pace, and its potential applications are becoming more diverse by the day. Data science may be a powerful tool for a company’s growth if properly utilized. However, to get the most out of their data science activities, organizations must set up suitable infrastructure, recruit qualified personnel, foster collaboration, and adhere to the aforementioned best practices.