Data informed vs Data driven: What is right for your business

Almost without exception, your business needs data in sufficient quantities. When you have metrics to analyze, you’ll either make data-driven or data-informed choices. Learn how these two concepts contrast and which one is better for your decision-making processes. 

What is data-driven decision-making?

In a data-driven model, data is king and is the sole basis for your decisions. Essentially, wherever the data wind blows, that’s where you go. This is an objective and scientifically based approach where the numbers dictate the direction of your marketing, recruiting, product placement, etc.

Let’s say, for example, that you own an e-commerce site, and data reveals the majority of your customers are millennial women. Since this is what the data indicates, you gear your marketing, funnels, and social media outreach towards this specific demographic.

Data-driven key points

• Data is the sole or main driver in decision-making and goal planning
• The more data you have, the better
• This requires a robust customer data platform (CDP) for plotting graphs and databases

What is data-informed decision-making? 

Under a data-informed decision-making cycle, data is treated more as a reference. You let it guide your decisions, but it’s not the sole factor. As a data-informed analyst, you recognize that data alone has its limitations and doesn’t tell the whole story. For a more complete picture, you also introduce a human element to the decision-making to cover additional grounds.

Let’s use the previous example where millennial women are the primary demographic for your e-commerce business. Under a data-driven model, you direct your outreach to this audience segment, and that’s that.

Under a data-informed model, the process isn’t as straightforward. You may investigate further and ask additional questions that cover the what, why, and how:

• Why is my business more appealing to millennial women?
• What adjustments can I make to make my business more appealing to a wider demographic?
• How can I attract a larger demographic while keeping millennial women as the core demographic?

The answers may reveal insights that can’t always be accurately gauged from numerical data. For example, a customer may indicate she chose your product because it resembled a similar product she owned as a child and had a strong nostalgia factor. This experience is personal, subjective, and not exactly easy to translate into a chart or numbers on an excel sheet. Yet, the information is just as usable as numerical data.

Data-informed key points

• Data is informational but not the be-all and end-all
• Data is the premise for answering the what, why, and how
• It takes subjective human experiences into consideration 
• It’s a less rigid and more out-of-the-box approach
• Data may include qualitative data, such as information from open-ended questions in an interview or survey

Data-driven vs. data-informed: Which model is right for my business?

Most businesses adopt a hybrid approach. Both models have their uses depending on the context. Here are situations that warrant a data-driven and data-informed approach.

Data-driven uses

A data-driven model works well for companies that meet the following criteria:

• Have been around for at least a few years 
• Have established a customer base
• Have a CDP in place or plan to invest in one

Companies that meet the above criteria have accumulated enough data and have the tools in place for analyzing their metrics with sufficient accuracy. Data-driven models are also proven reliable for A/B testing. Furthermore, they’re great for number-based initiatives, such as establishing price points, release dates, and revenue estimates. As implied in its name, data-driven means data-backed. This makes it great for proposals that you can back with data and present to sponsors and investors.

Data-driven limitations

The rigid approach may lead to a tunnel vision that glosses over qualitative elements. If you let data drive all of your decisions, you may miss the finer details that drive consumer behavior. Data cannot fully quantify what makes a customer tick and their emotional ties to your product.

Data-informed uses

Young businesses should begin with a data-informed model. During the startup phase, small companies simply haven’t accrued enough data to make data-driven decisions. Established businesses can also benefit from a data-informed strategy. The following scenarios may work better under a data-informed methodology:

• Creating customized customer behavioral profiles
• Developing demographic-specific sales funnels
• Identifying customer pain points
• Creating personalized customer nurturing campaigns

Data-informed limitations

The out-of-the-box approach may lead to an under-reliance on data or taking data for granted. This may lead to hypotheses and analyses that end up being widely off the mark. It’s also harder to pinpoint the precise area(s) that went wrong.

Optimize your data with Lytics

Both data-driven and data-informed approaches require data in sufficient quantities and qualities. You need an organized system of filtering, sorting, and integrating your data sets in real time. At Lytics, Our Cloud Connect makes data collection and metric analysis an intuitive process. Try Lytics risk-free for 30 days today to experiment with our data warehouse.