Your Team Can Leverage Data Science Today Using These Four Easy Practices
Becoming a data-driven organization can increase the effectiveness of your decisions and operations, as long as you cultivate a company-wide data culture. In this article we’ll discuss four easy data science practices that you can quickly leverage to extract value for your organization.
Many organizations pursue the goal of becoming “data-driven” by attempting to accumulate massive data stores, building complex infrastructure, or hiring sought after data scientists. While all of these are helpful in increasing data usage, they will not alone quality a company as “data-driven”. The biggest hurdle companies face is building a data centric culture, where data and data science is seen as the method for answering complicated business questions. A strong data culture is the foundation for successful data-driven companies.
If you can achieve this, the benefits of being a data-driven company and leveraging data science at all scales are innumerable. Here are four easy practices you or your company can adopt today, which will allow you to begin extracting value from your data immediately.
Use concise data visualizations to communicate complex insights quickly and easily
Interactive data visualizations allow a diverse range of stakeholders to easily access information in an accessible and clear way. Visualizations, like a dashboard, are extensible and flexible, so it’s no mystery why they have become the preference at many organizations.
A good data visualization can allow an organization to scale their decision making efforts, often with little to no code if you’re using a tool like Tableau or AWS QuickSight. If you need a more complex or nuanced visualization, leveraging programming languages like Python or JavaScript allow you to have complete control over how your data is displayed and how internal users can interact with it.Leverage historical data for decision making
Let data guide your strategic business and operational decisions. Start with summarizing or describing your organizations historical performance in even one key area, and then expand out from there to create a rich understanding of how your company truly operates. This will allow your organization to double-down on what works (with confidence!), and let go of what doesn’t work. Looking at the historical data you already have will allow your organization to create robust strategic plans, and pinpoint target areas for improvement.
Some well known historical analyses are:
- market basket analysis, which helps retailers optimize their physical or digital spaces to create a better shopping experience.
- time series analysis, which is used for a wide range of cases including budget planning, sales forecasting, user trend identification
If you have a small amount of data, historical data analyses like the ones mentioned above can be done with relatively little work using tools like Excel. When you have more data to analyze, using an open-source programming language like Python or R will allow your organization to build reusable analyses and data pipelines to keep the insights coming on a regular schedule.Statistics + subject matter expertise = a winning combination
The foundation of statistics are familiar summary metrics like mean, median, and mode, as well as metrics that show deviations from the average like variance or standard deviation. These are the foundational tools from which more advanced analyses are built. If you can understand addition, subtraction, multiplication, and division you can perform a basic statistical analysis (don’t let the greek letters scare you!). Even basic statistics, like correlation which measures the strength of the linear relationship between two variables, can be used to succinctly summarize large amounts of data. For example, e-commerce retailers can calculate the correlation between the purchase of their entire product catalog to optimize cross-selling or other marketing campaigns.
More advanced analyses involving controlled experiments can help organizations determine the effect of a decision or design, establishing a clear picture of the value (or cost) of a decision. Controlled experiments are the gold standard in statistics, and provide a simple yet robust method for verifying the recommendations of a subject matter expert. You may have already heard of controlled experiments - sometimes they’re called A/B tests. They have many use cases - everything from deciding website design and email subject lines to deciding which machine learning model produces more acceptable recommendations.Leverage Machine Learning to make predictions and discover new patterns
Companies who want to make predictions about the future or uncover hidden patterns in data can leverage machine learning. Supervised learning techniques, where an algorithm classifies data or predicts outcomes with labeled data, can be used by organizations that want to predict any number of outcomes - including whether or not a customer will buy a product, what a stock will cost 90 days from now, or even whether a client will renew their contract.
In contrast, unsupervised learning can be used when you have little knowledge about the raw data and want to explore the patterns that exist even if you don’t know what you’re looking for. For example, if you want to sort data into categories but you don’t know what the categories are, there are clustering algorithms that will uncover the groups that naturally exist in your dataset. Clustering is commonly used for things like customer segmentation, topic modeling, or even which products often go together. Such knowledge can form the backbone of an effective business strategy.
Building a data-driven organization
Taking the first step towards becoming a data driven organization has many potential benefits. Many of the analyses we’ve discussed in this article can be applied today by teams within your organization.
Still not sure where to start? Need in depth answers or walkthroughs of how to implement data science workflows in your organization today?
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