From big picture marketing strategies to cost-cutting initiatives to resource allocation, companies are constantly facing big decisions. Many of which contribute to the overall health, success, and sustainability of a firm.

When contemplating a critical business decision, it’s easy to rely on assumptions and natural inclinations. But intuition and gut feelings can only take you so far. In most cases, it’s more prudent to leverage metrics and research.

Using data to find patterns and trends bridges the gap between speculation and an informed business decision. That’s why incorporating data analysis into your decision-making process is crucial to not only maintain, but also grow as a company.

Data driven decision making (DDDM) is the process of making decisions based on hard data, factual findings, and the insights gleaned from this information – as opposed to acting on intuition and general observations.

Driven by the development of data science (and business intelligence software), the process of collecting and analyzing big data is no longer a mountainous task. With the right team and software in place, you can begin to utilize data to make the right choices for your business.

Why is DDDM important?

Because business has become digital – which means companies must constantly adapt to a fluid environment.

Data analysis helps identify new revenue opportunities, improve operational efficiency, predict future areas of need, and determine under-targeted consumers. So, it’s imperative to employ data and use it to your advantage not only for development and growth but also consistency and sustainability.

When is it useful?

One example: marketing campaigns. Whether it’s direct mail or digital marketing, companies can leverage data to answer critical questions and determine key differentiators of a campaign.

Such as, when is the best time to launch? What are the best distribution channels for this type of campaign? Who is this most likely to resonate with?

Another example: Allocating resources. DDDM can help identify areas of need, bottlenecks, or even over-staffing.

Here are some DDDM tips to implement in order to maximize results:

  1. Protect against bias by including reliable, knowledgeable teammates in your data analysis discussions. It’s natural to frame data to your perspective and desires – which can be counterproductive.
  2. Simply being aware of potential bias and calling attention to it can be an effective guard against bias. According to a past study by McKinsey, running an unbiased decision making process can have significant benefits – like improving the ROI of one’s business investments by about 7%.
  3. Implementing data analysis without direction is worthless. It’s important to determine your company’s goals and the steps required for reaching them before bringing data analysis into the mix. Setting clear and relevant Key Performance Indicators (KPIs) can help you accomplish this. So too can figuring out areas of need and determining what questions your company needs to answer.
  4. Start collecting data as soon as possible. The sooner you take this step, the sooner you’ll build a quality, reliable database to analyze for trends and patterns.
  5. Identify where your data is coming from. Gathering a list of data sources puts parameters on your analysis and ensures you’re not missing any critical information.
  6. Keep it clean. With unorganized data, you risk creating inefficiencies – when the obvious goal is to do the opposite. According to the New York Times, data scientists spend anywhere from 50-80% of their time sifting through disorderly data. This inefficient use of time is known as the 80/20 rule: 80% of data work is spent locating and organizing data, while only 20% is spent analysis the data.
  7. Your data driven decision making process should continue to adapt to your business’s strengths and needs.