Time is money — and data is money, too. Many of you are gathering data, either officially through a dealership management system or unofficially through notes and spreadsheets. A recent story on Inc.com shares these statistics about why you need to do more than gather and store data: According to The Harvard Business Review, high-performing companies that employ data-driven decision-making are 5% more productive and 6% more profitable than their competitors ... Today's consumers have high expectations and a whopping 72% expect companies to understand their needs and expectations."
According to the story, here are 5 ways to turn data into intelligence and intelligence into revenue.
1. Set goals that match your business objectives.
Before digging into your data, define what's most important to your business. For instance, do you need to improve sales of a particular line or product group? Drill down to those metrics to help guide what you need to do next.
2. Use the right tools.
The decisions you make from data are only as good as the data itself. Bad data comes in many forms — duplicates, missing or inaccurate info, outdated sources. Invest in tools and technology that will help you focus on quality collection, aggregation, and analysis. Research ways to verify and consolidate your data.
3. Expand access.
Once you consolidate and organize your data into a central view, make it actionable by sharing it with the broader team. Business growth is inhibited when departments operate in silos; when everyone has the same view of the business, you can truly start making data-driven decisions and keep everyone moving in the same direction.
4. Make it a habit.
When you need to make decisions, start asking, "what does the data tell us?" Create a culture where data becomes part of the discussion and decisions. The best way to start applying data analysis is to use your intuition to guide the process, then use data to tell you whether your gut is true or false.
5. Stay nimble.
Small businesses have the advantage of agility. Look at your data and formulate tests and hypotheses. Then apply those actions as tests. These isolated tests can help drive more productive outcomes that you can rollout on a larger scale.