The ability to collect and analyze data is transforming the way businesses make decisions. There are very few instances where leaders need to make “gut decisions” now. However, data in its raw form isn’t very helpful — it’s just a bunch of numbers in a spreadsheet that only make sense when you add context and organize them.

Data visualization is simply the process of presenting data in visual formats. For publicly traded companies with reporting obligations, the ability to simplify data helps tell stories, identify compelling trends, and find correlations relevant to decision making. For small businesses and aspiring entrepreneurs, data visualization helps drive transparency around decisions so they can be analyzed, learned from, and duplicated as you scale the business.

If you’re not careful, databases can be overrun by the information they’re aggregating. Without a clear system, associations, and hierarchy, databases are impossible to navigate and query. It would be like trying to build a house without a blueprint.

Enter data modeling: the process of intentionally designing databases to normalize information, making it easily discoverable and digestible.

In my organization, we aggregate operational, financial, and accounting information from multiple sources into databases. Without data models to provide a coherent framework, the information would come in jumbled and disorganized. A good model allows for logical grouping of data to optimize reporting and analytics. For example, if we want to look at labor costs across a portfolio of real estate assets, we can see a collection of properties side by side rather than opening each respective location’s income statement.

Python is a general-purpose programming language, which is another way of saying that it can be used for almost everything: web design, dashboarding, desktop apps, data analysis, you name it. Because it’s one of the easiest languages to read and understand, it is a favorite in the programming world.

In my organization, we leverage Python to prepare data for analysis. This includes statistical analysis, as well as creating data visualizations and foretelling future trends. Possessing this skill can help organizations break down data to find underlying correlations that can be modeled and used for predictive analytics. We’re using these associations to make smarter investment decisions.

To ensure that your website and applications are fulfilling their purpose, and to guarantee that marketing efforts are generating results, you need to leverage web analytics.

Web analytics provide user information and data that can help teams measure the impact of their digital assets and messages. Popular web analytics include metrics like bounce rates, engagement, impressions, and audience demographics.

As part of our internal communications efforts, my team uses web analytics to track which internal stories are trending, where employees are spending the majority of their search time, and what devices they use to access important company updates. All of this information helps us hone our messages and delivery vehicles to ensure efficiency and effectiveness.

Web analytics can be leveraged in any organization that’s looking to maximize it’s website, social, or app engagement.

You can’t talk databases without mentioning SQL, or structured query language — it is the standard for storing, manipulating, and retrieving data from databases.

It doesn’t matter if you’re in sales, finance, or data science, everyone can benefit from learning how to update and retrieve data from a database — especially since data is driving more and more business decisions. SQL is a preferred language because it can be utilized to interface and interact with many different databases. It allows users to manage and query vast amounts of information effortlessly. As a result, companies are far more efficient and well-informed when it comes to reporting.

This information from LinkedIn was a clear indication that the workforce continues to focus on ways to support business decisions with data, and is committed to streamlining the processes with which organizations analyze information critical to their success.