When done right, Data Science delivers a lot of measurable values like improved products and services, enhanced customer experiences, sales growth, new business developments channels, and overall business efficiency. However, according to most reliable industry publications, most Data Science projects fail because the Data Science best practices are not followed.
Why Do Businesses Need Data Science Best Practices?
A simple answer is that Data Science best practices bridge the wide gap between a Data Science project’s expectation and its reality. The primary reasons reported behind this wide gap between Data Science project expectations and reality are:
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- Absence of a clearly defined problem
- Inability to arrive at a solution
- Inability to transform data-driven insights into actions
- Absence of code review
Back in 2018, while discussing the prospects of AI implementation in global businesses, Gartner pointed out:
- Most organizations were not prepared for AI and lacked internal manpower expertise in Data Science
- Between 2018 and 2022, 85% of AI projects were likely to fail due to “bias in data, algorithms, or the teams responsible for them”
- 53% of organizations in the CIO survey “rated their ability to mine and exploit data as limited”
This is where the Data Science best practices come in. These best practices can be defined as a collection of rules or guidelines that can help Data Science projects to succeed, even when the team members are not that skilled or the Data Quality is suspect. The webinar on How to Avoid the 10 Big Data Analytics Blunders serves as a good eye-opener for realizing the worth of best practices in Data Science activities.
Data Science Best Practices
With the length and complexity of Data Science best practice lists doing the rounds, the current industry literature can easily confuse and clog the memory banks of an average Data Science enthusiast or even of a practitioner. So, to make it somewhat easy to digest for new DS entrants, the very basic best practices, which encompass most talked-about best practices within the realm of Data Science, are listed below.
The author of this article explains these five basic DS best practices, which also feature prominently on all published Data Science best practice lists:
- Understanding Business Requirements
The first and a major step in any Data Science project is understanding a given business requirement and defining a use case for a model. True that the data scientist must work closely with team members to begin this step, but ultimately the data scientist will be responsible for converting the required business problem into a mathematical problem to solve through ML and other advanced technological aids.
- Communication with Team Members
Effective communications with a business is a Data Science best practice to follow, but this also has its down side. Communicating highly complex technical concepts to less qualified team members may be a serious challenge. For example, explaining how a machine learning model can achieve a specific business goal in layman’s terms is a sought-after skill that data scientists need to work on and improve over time. Developing a combination of skills not only helps the DS team to develop solutions, but also helps to arrive at customer-friendly solutions through constant communications and give-and-take with the customer.
- Data Quality for Data Analysis
Nowadays, advanced technology platforms and tools have made it relatively easy for data scientists to get the data they want, when they want it, and in the exact formats they want. So, automation of Data Science tasks has left the data scientists with free time to explore and dive into the ready-made data for “deep analysis.” Data Quality determines the outcome of data analysis, so two things are involved here: First: the quality of the data, which must be above a need for scrutiny; second: the relevance of the data for solving the business problem.
- The Experimentation Mindset
Every data scientist knows that a given project must adapt to changing business requirements. This mindset is crucial for the success of any DS project. When the DS team works on real-life projects, they are times when they alter or rebuild their models based on changing business goals. An example of this mindset is “shifting behaviors of organizations,” and other stakeholders during the recent pandemic. Models build prior to COVID-19 had to be modified or reengineered to serve the needs of the new business scene.
- Selecting the Right Metrics and Tools
Data scientists typically take the help of coding languages, modeling tools, and other BI tools to drive their projects through completion. This is a long list containing Python, SQL, BigML, R, R Studio, and Apache Spark. The chosen set of tools, along with the set KPIs, can make or break the project.
In this context, reviewing The Top 5 Data Science Practices may be worth your time.
Here’s what Gartner recommends for DS Project Success:
- Collect business requirements to help models to perform and also to help establish “Proofs of Concept.”
- Strike a balance between data accuracy and data value with “Minimum Viable Models.”
- Sell the business case with the help of “data storytelling.”
Another closely related consideration for the success of all DS projects is data security. KD Nuggets shares some Data Security Best Practices, which includes minimal data stores, masked data, communication channels, data encryption, data protection, and security of cloud-hosted data. No Data Science project can succeed without fool-proof data security measures, so this article is a handy guide for project teams.
Data Science Best Practices for Startups
Startups work on an accelerated timeframe for most of their business activities, and product delivery is no exception to this rule. Here are the best practices currently being followed in Ravelin, a model startup company in the U.K. Founded in 2014, Ravelin is a global fraud-prevention startup that traps fraud activities through “real-time behavioral analysis, graph networks, and machine learning.”
Here are the best practices that this company has adopted and likes to promote:
- Production models are built, trained, and deployed within the first week of project execution.
- A new hire is assumed to be knowledgeable about Big Data.
- The code test checks human features-engineering skills.
- Automation is reserved for detecting fraud.
- They actively promote a reliable ML infrastructure.
Ravelin also offers some nuggets of wisdom to take back, so don’t forget to review the links in this section. Startups – are you getting hints?
In the last few years, Data Literacy and data monetization have been consistent themes in all business conferences and webinars. This article from DATAVERSITY® talks about analytics best practices for converting data into an asset. The novel concepts discussed are “Data Quality as a moving target,” and the probable solutions; the importance of Data Literacy; and the possibility of creating new revenue streams with data.
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