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.
Decoding The Anatomy of Data Science
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.
10 Impactful Best Practices for Data Science
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.
1. Build a dedicated program for data science in the organization
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:
- the purpose of its data science initiatives
- equipping itself with the necessary data science infrastructure (trained experts, mandatory equipment, etc.)
- a delivery roadmap
- performance metrics
2. Create a capable team instead of hunting for unicorns
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.
And as it is with the definition of unicorns, data science unicorns are a rare find but in high demand due to the nature of their portfolio.
This best practice states that a company should prioritise building cross-functional data science teams instead of looking for an all-rounder.
A typical cross-functional or interdisciplinary data science team consists of the following profiles:
- Data engineer(s) to collect, convert and pool unrefined data into accessible and usable information for the rest of the team members.
- Machine Learning Expert(s) to create ML data models for recognising patterns in collected data
- DevOps engineers to deploy and maintain the ML data models.
- Business Analyst(s) to understand the requisites of the company as well as the market(s) it is targeting.
- A team leader to properly usher the team.
Cross-functional teams are a better alternative to unicorns as they can:
- share a workload
- offer varying perspectives when solving a problem
- improve overall decision-making
3. Thoroughly define a problem before embarking on the journey to arrive at its solution
One cannot stress enough the need to describe data science problems in their entirety, covering even the most minute details.
Unfurling the specifics of a problem allows data scientists to examine each of its constituents and measure them up against concrete parameters such as priority, clarity, usable data and ROI. It also allows them to identify the primary and secondary stakeholders required to work on that problem. Once the problem is defined, data scientists can work on systemizing data collection, analysis and interpretation.
However, this seemingly fundamental proposition is one that not many companies focus on while carrying out their data science operations. They instead vaguely explain the problem that complicates the efforts of the data scientists even further.
Hence, before attempting to solve a problem, companies need to dissect it to the bones and lay bare all its components and requirements.
4. Ensure that a POC is run on a definitive use case
POCs or (Proof of Concept) are crucial to any data science project as they determine whether a data model or a data science solution will prove feasible. It is essentially a test case of a wider data science solution that determines whether a company’s data science initiatives will address its needs.
Running a POC, first and foremost, requires a use case. And it is the choice of the use case that can make or break a POC’s prospects of seeing the production phase. Hence, data scientists should choose the most appropriate use case that can offer quantifiable results when the POC is run.
Also, the use case should signify a critical business issue or a range of issues to offer specific and relevant measurement criteria to the POC.
5. Determine and list all the KPIs
What decides whether a company’s data science efforts are delivering adequate results? It is the Key Performance Indicators (KPIs) they are juxtaposed with.
Now, while most companies implementing data science have a set of business goals, they lack the relevant demarcated KPIs to monitor their progress towards these goals.
Thus, businesses need to set aside certain measurable KPIs such as ROI, revenue increase per consumer in percentage, CSAT score, etc., to determine the viability of their data science projects.
For example, if a business deploys an optimisation algorithm to boost revenue, it can use performance indicators such as monthly sales numbers, number of website visitors, etc.
6. Emphasise proper management of stakeholders
As per the data science jargon, stakeholders are individuals who employ the data offered by the data scientists. They can be internal – such as business analysts who use the data for business growth, or external – clients who approach data scientists for interpreting data.
Now, data science primarily deals with data. But, keeping in mind the individuals who plan to use it – stakeholders – is also necessary.
Doing so ensures that data scientists analyse not only data but the human elements associated with it. In other words, managing stakeholders enables data scientists to work with people and not just data.
To effectively manage stakeholders, data scientists should implement strategies such as:
- establishing transparent communication channels
- conveying all the possible outcomes of a project to them
- asking for feedback
- initiating collaborative efforts
7. Base data science documentation on stakeholders
Documentation is critical to any data science project. And no, we do not want to think otherwise.
Properly documenting all the facets of a project allows the stakeholders to comprehend and utilise its data better.
However, no matter how good the documentation is, if you cannot communicate the specifics of the DS project to the right stakeholder, the project might not turn out to be as effective.
Hence, you should document a project according to the requirements and specializations of the involved stakeholders and not take the “one for all” approach.
8. Learn to match a data science job with appropriate tools
This one might seem obvious, but pairing the right data science project with the right tools requires great skills and an aptitude for data science.
This account by Tim Bohn on the need for using appropriate tools for a data science project signifies the need for this best practice.
Choosing tools for a data science job can refer to:
- picking the right data visualization software
- gauging the amount of cloud storage capacity for the project
- picking the apt programming language
- assessing the scalability of the current data science infrastructure
- determining the right approach to the problem at hand and more
The premise of this data science best practice is that readying the tools required for a job assists the data scientists in working on the data faster and more efficiently.
9. Incorporate the agile methodology
If stripped of all its decorations and oversimplified, the agile methodology states that software development should take place in pieces, with communication and interaction being key.
Each of the pieces should be a <insert time frame> long, and developers should prioritise the product instead of a theoretical explanation.
Now, while some might disagree, applying the agile methodology to a data science project works wonders.
The agile framework essentially divides a project into sprints – time constraints usually a few weeks long in which data scientists work on specific aspects of the project.
Each sprint kicks off after interacting with the stakeholders to outline its requirements, determine the budget of the stakeholders, offer them a delivery timeline and prioritise the tasks to be done.
At the end of every sprint, a review is conducted to assess the work done so far.
10. Keep track of data ethics
Data models are objective in their execution, but data scientists are not. Hence, data scientists must build models that do not violate data collection, analysis and interpretation ethics and potentially cause harm to people.
Failing to abide by data ethics can seriously impact the credibility and reputation of a firm in more ways than one. If you know about the Cambridge Analytica scandal, you understand what we mean.
Conclusion
So there you have it, a list of 10 data science best practices to supplement your data science undertakings.
Data science is a rapidly growing field, with its scope of application widening with each passing day. If implemented correctly, data science can be a valuable component for a business and boost its growth significantly. The only catch is that organisations should equip themselves with adequate data science infrastructure, hire the right people, collaborate extensively and follow the above-mentioned best practices to make the most out of their data science efforts.
