The 5 steps of data-driven decision making For Business

Experts agree: there is no better place to start making decisions based on data than on the road of ideas and priorities. By asking your team what ideas are most important to pursue and aligning them with company values, you can create clear goals that are prioritized to support company goals and culture. Here are some examples of what this might look like to identify clear and understandable business goals:

The 5 steps of data-driven decision making

Tableau, a data analysis platform, emphasizes the importance of creating a comprehensive understanding of your overall business goals. Then, from there identify specific goals that you want to prioritize. Instead of focusing on promoting all the products that your business sells, focus on one product instead. For example, as software delivery management solution Plutora suggests, a clear goal might be to “increase the sales of product Y by 50% through online advertising.”
If you want to increase your company’s subscriptions and mailings, you can start interested in a broad approach that considers the entire market as your target subscriber. But, as CRM company Super Office suggests, focusing on a specific audience or demographic will be more likely to succeed.
When it comes to best practices for setting business goals, the Asana company recommends using techniques like SMART goals or OKRs. They emphasize the importance of working with senior executives, such as team leaders from other departments, so that your goals align with the values ​​of the entire company. In fact, Forbes continues to support the industry as a whole for the cause. The specific person in the room can vary depending on the business or focus, but in all cases, different groups can support the business goals.

As the ability to collect information and organize it in a meaningful way becomes more and more powerful in our technology-driven world, it becomes more and more important for companies to use the power of data-based decision making. Staying on the cutting edge of data analysis, reporting, and customer insights can help companies stay competitive and meet customer needs. Take BOLD, a company that connects people and businesses. BOLD implemented a business intelligence strategy to help its companies make better data-driven decisions. The results are fast and big — 60x faster development time than before, reduction of work time from one week to 30 minutes, and a significant reduction in the company’s reimbursement costs, helping to save the business $ 30 per reimbursement the back.

Another example is Sysco, which wants to help its customers help themselves. The COVID-19 pandemic has shown that it is important for their customers to be able to identify and meet their product needs instead of relying on a salesperson to help them place their orders. The company started using data monitoring tools to create a dashboard that integrated the various monitoring systems used by the company under one roof. In this way, sales representatives have important information.

“It helped us improve the self-service model,” says Megan Oertel, director of product integration at Sysco LABS, “because we can see from the account or from the geography here and -use our e-commerce platform. , in which customers interact. with, and where they need the help of reps.”

“Our digital transformation is focused on research that ultimately results in a better experience for our customers,” says Oertel. The numbers match. These data visualizations aren’t just fancy new tools that get re-examined from time to time. Instead, they were agents of change for the business, driving a 46% increase in customer orders.

While the results from BOLD and Syscos are impressive, these are just two examples of companies using data to make better decisions and streamline processes, saving both time and money. Every business and every leader can take steps towards data driven business decision making. With the right approach, the right goals, and the right tools at hand, organizations can apply data analysis to improve their performance, support, and value in the market.