How to run a data-driven organization

Let’s get back to the basics.

Once the province of super geeky internet companies like Google, LinkedIn and Facebook, data analytics has since spread rapidly throughout the business landscape. And as data-driven teams start to learn about all the cool things data can do, it’s no wonder that more and more organizations, from small businesses to large universities, are jumping on board.

But with so much talk about the power of big data, are you up to date on data analytics basics?

How it works

Data analytics is simply the process of examining enormous amounts of information to uncover trends, hidden patterns and correlations that organizations can use to improve operations. Using 21st century computing technology, data scientists can gather billions of data points and spot patterns that would have been invisible to traditional intelligence systems in the past.

Analytics can help data-driven organizations understand problems, improve productivity, save money, use resources more efficiently and gain insights about their processes.

Here are a few impressive examples:

• Macy’s used inventory and demand data to adjust pricing in real time for 73 million items.

• British grocery store chain Tesco Plc used predictive analytics to save 100 million pounds of food from going to waste.

• Express Scripts identified patients most likely to forget to reorder their prescriptions and added customized reminders to their pill-bottle caps. Google used its search data last year to identify flu outbreaks around the world.

• By analyzing data from its trucks, UPS eliminated 5.3 million miles from its routes, reduced engine idling time by almost 10 million minutes, saved 650,000 gallons of fuel, and reduced carbon emissions by more than 6,500 metric tons.

Getting started

So how can you make your information work for you?

First, take a look at your operations and processes, especially in areas where you face complex decisions. Analytics can uncover new angles and help you make informed decisions.

Assemble a team and choose a technology system to begin leveraging data wisely; you don’t need an army of data scientists. Be sure to get the green light from stakeholders, and then appoint a project manager to oversee the process. Using data analytics doesn’t have to be prohibitively expensive. Most organizations use the cloud for fast big data processing, but integrate results with existing legacy systems.

Many cheap or reasonable tools are available for analytics, and your IT department can use open source programming for statistical analysis, predictive modeling and geographic mapping. To manage data, they can use databases like MySQL, PostgreSQL and Hadoop.

If you’re just beginning, start with less complex projects, such as analyzing typical behaviors. After gaining and applying your insights, you may decide to do more ambitious data analytics projects. In time, you’ll be a data master, able to predict the success or failure of products, and making complex decisions about adding new organizational lines.