4 Data science best practices for your business

According to a 2019 survey by McKinsey, 47 percent of organizations believe that data analytics has fundamentally or significantly transformed how their industries compete. Data science rose to prominence around the year 2008 and has since gained momentum to become a prominent trend in the IT world.

The popularity and acceptance of data science providers have grown over time as a result of its ability to help businesses of all sizes uncover patterns in data, allowing them to explore new markets, manage expenses, improve operational efficiency, and gain a competitive advantage. Here are a few best practices to succeed in data science:

Choose the right tools and metrics

Plan an infrastructure that fits your business plan, such as whether you require a multi-cloud infrastructure or if you want to stay behind a firewall. Plan for the amount of data you’ll need to scale, as well as the compute power you’ll need. Consider the appropriate methods and algorithms for the task at hand. In terms of metrics, it’s critical to pick the correct ones to link data science outcomes to business objectives. 

Include IT and developers in the POC phase

Involving IT and software developers early in the process, especially for security protocols to be met early on, is one method to ensure that the models ultimately make it into the hands of end-users and deliver value to the business.

Early reviews evaluations of the software components that will be used to develop a model ensure that data will be safely managed once the model is in production. IT teams can also assist in securing the necessary infrastructure for model training and production, while developers can assist in providing a better end-user experience for the final product.

Adopt an agile mindset

Implement an Agile Data Science methodology to maintain constant progress. This includes dividing your project down into 2–3 week sprints, with sprint reviews at the conclusion of each cycle to provide demos of results, as well as Agile task planning. To reduce uncertainty, manage risks, gain shared understanding, and keep the scope of the project in check, invite all stakeholders to the sprint reviews and sprint planning sessions.

Go beyond predictions and insights to make better decisions

Traditional business intelligence tools can help you understand the current state of business, but they won’t tell you what might happen in the future. (Check out Data Science Roadmap 2022). You’ll need predictive analytics for that. Predictive analytics, on the other hand, can help you understand what might happen in the future, but it won’t provide you recommendations on what to do about it; you’ll need prescriptive analytics for that.

You should make use of Prescriptive Analytics and Decision Optimization (a term that describes mathematical programming and constraint programming methods used for decision-making solutions). It is the only technology capable of weighing various trade-offs among thousands, if not millions, of simultaneous alternatives and business limitations, and recommending the optimal future action or plan.

Gartner predicted in 2018 that 85 percent of Data Science projects would fail through 2022. It is critical to maintain the data-to-analysis-to-insight-to-action chain in order to succeed in Data Science. You may apply these best practices to succeed whether your company is creating a data science practice, building and growing their data science expertise, or upgrading and innovating an already mature data science organisation.

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