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Four Ways Your Team Can Start Leveraging Data Science

Becoming a data-driven organization can significantly help you make more effective resource allocation decisions, but this requires nurturing and fostering a company-wide data culture. Here’s a rundown of various data science practices that you can quickly adapt to start extracting value for your organization

Driven by desires to reach the elusive goal of becoming data-driven, organizations have been arduously accumulating data, building infrastructure, and attracting highly skilled talents. Considering how data is often touted as the silver bullet to a myriad of problems, these organizations are moving in the right direction.

However, the virtue of having the right tools and talents does not automatically qualify a company as data-driven. The biggest obstacle towards becoming data-driven lies in building a data culture, where data science is seen by everyone as a methodology for solving business problems. Indeed, any organization aspiring to be data-driven needs to first nurture a data culture by fostering organization-wide data fluency, equipping everyone with the skills to perform the data tasks they need to excel at their roles. Such a fundamental shift in mindset presents a sizable challenge in a company’s goal of becoming data-driven. In fact, according to the 2018 Gartner Chief Data Officer Survey, 35% of CDOs believe low data fluency is the biggest challenge towards extracting value from data at scale.

The benefits of becoming data-driven and leveraging data science at scale are numerous and widely documented. Here’s a run down of four areas of data science that organizations can quickly adopt to start extracting value from their data, illustrated with specific use cases.

1. Communicating complex insights through data visualization, quickly and concisely.

Interactive data dashboards have become increasingly ubiquitous as they allow stakeholders to easily access up-to-date information.

Dashboards are incredibly versatile. They can be a telescope for a company’s long-term north star metrics or a microscope that intently focuses on its short-term operational details. Either way, they allow organizations to scale their data driven decision making. Today, many no-code business intelligence tools like Tableau and PowerBI boast intuitive drag-and-drop interfaces for building powerful dashboards. On the other hand, open-source packages built on popular programming languages, like Python’s plotly and R’s Shiny, provide organizations a low barrier to entry when building highly customizable interactive visualizations.

2. Make better decisions by analyzing historical data

A data-driven company describes, summarizes, and understands its historical performance and leverages it to guide decisions that enable it to double-down on what works and course-correct what doesn’t.. A survey by Deloitte found that about half of the companies assert that the most significant benefit to analytics is to enable better data-driven decision-making. Also, 62% of the companies said data is important to drive business strategy. Clearly, data is becoming an increasingly irreplaceable instrument in the corporate toolbox.

Data-driven decision making can maintain and enhance an organization’s competitive advantage over its competitors. For example, market basket analysis can help physical retailers better optimize their physical spaces and create better shopping experiences for their customers. Marketers can analyze historical email click through rates to evaluate the success of email marketing campaigns. Financial analysts can apply time-series analysis to their historical data to optimize budget planning.

Such analysis of historical data can be done efficiently on spreadsheet tools like Microsoft Excel and Google Sheets when the size of the data is small. As the data grows, analytics can be done most effectively using open-source programming languages like R and Python.

3. Supplement Existing Subject Matter Expertise with Statistical Thinking

Statistics can be used to succinctly summarize large amounts of data. At the very foundation of statistics are summary statistics that represent averages (like mean, mode and median) and deviations from average (like standard deviation and variance). These summary statistics are the building blocks of tools like the box plot and metrics like correlation coefficient (a measure of the strength of the relationship between two variables). For example, retailers can calculate the correlation between the purchase of products to optimize cross-selling campaigns. Market researchers can leverage conjoint analysis to determine the ideal mix of customer preferences before launching a new product feature or changing the pricing of a product.

Controlled experiments can help companies isolate the effect of a change, establish a clear cause-and-effect relationship between two metrics, and make better decisions. With foundations rooted in statistics, controlled experiments provide a relatively simple yet rigorous method of verifying or rejecting the gut instincts of subject matter experts. A simple illustration of a controlled experiment includes A/B testing email subject lines and choosing the one with the highest click through rate. At AirBnB, only when an experiment conclusively shows that a change in the maximum price filter causes conversion rate to increase was the change implemented to all users. At Shutterstock, the idea to remove watermarks on its search page images was rejected when experiments provided strong evidence that such a change decreased conversion rate.

4. Make predictions and discover new knowledge with machine learning

Companies can use past data to make predictions about the future with machine learning. For example, by leveraging supervised learning, a technique that trains algorithms to classify data or predict outcomes with labeled data, any organization can predict which customers are the most likely to churn based on their historical activity. A trading firm can use historical prices of a stock to predict future prices in financial modelling.

In contrast with supervised learning, unsupervised learning is a technique that can be used by a company with little knowledge about a raw data set to explore its data—even if it does not know what it is looking for.Clustering is an example of an unsupervised learning algorithm that sorts data into categories that are not predetermined. For instance, a telecom company that has valuable data about its customers’ data and has yet to find a way to segment its customers can use unsupervised learning to identify customer clusters with distinctive characteristics.

Unsupervised learning is also superb at finding associations. In e-commerce, product recommendations in the form of ‘people who like product X also like product Y’ are often a result of unsupervised learning techniques that uncover such hidden linkages. Such product recommendations can form the backbone of an effective cross-selling strategy.

Building a Data-Driven Organization

Evidently, making the steps towards becoming data-driven unlocks many potential benefits for an organization. There are many low-hanging fruit data use-cases that functional teams can start applying together, requisite they have the necessary skills to get started. That is why DataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering, and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.