How to make data-driven decisions when you’re not a data professional

Data Science helps companies obtain better results. From optimizing their workforce to improving their risk management strategies, data science is the perfect ally, allowing companies to utilize data properly.  

If you’re launching a new product, releasing a new service for your clients, investing in a startup, or designing a marketing campaign, making data-based decisions will improve the rate of success.  

However, making data-based decisions can be challenging, especially if you don’t know what tools to use, how they work, or if you’ll need support from professionals in the field.  

To simplify it, let’s start with Data Science’s Venn diagram. This graphic explains its main components: coding, statistics, and domain knowledge.

The complexities of data science appear when teams do not have statistics and coding abilities. Fortunately, there are many tools to learn how to interpret collected data without being experts.  

Steps for a good decision-making process

1. Data Literacy

Data literacy is a crucial part of the journey. It allows you to understand terms, indicators, numbers, graphics, among other items to guarantee reliable decision-making.

Therefore, at Lean Tech, a division of Lean Solutions Group, we have developed an entire program to provide non-data professionals and their businesses with the information they need to understand basic requirements, how they can implement user-friendly programs, and guide them in the right direction. This program is directed at management and HR members so they can improve decision-making procedures.  

2. Establish Goals and Objectives

Not having clear goals and objectives is like wanting to go on a trip without knowing the destination or how to get there.  

Setting clear objectives is key to defining a strategy, the actions required to achieve it, and how to measure them, including what metrics you need to track so you can notice if it’s working or if you need to make some changes.  

3. Will you Need Data Analysis Experts? How Many?

Here’s an important fact to consider. Having advanced programs and tools doesn’t mean they replace a Data Analyst’s work. Even if those insights can make some requirements more efficient, there are specific assignments of the data life cycle that cannot be automated, such as reviewing and validating the quality of the gathered data to maintain the information's integrity.

Having Data Analysts in your organization guarantees a more detailed understanding of data, strategy, and process development.

At Lean Tech, we have available Data Analysts specialized in data programming and visualization to complement your teams and obtain better results.  

4. Collect, Analyze, and Evaluate Data

Once you’ve determined your goals and metrics, you must conduct market research that includes how your competitors are handling similar situations, what your audience is, their perceptions and behaviors, and what their needs are, among others.  

Afterwards, you must gather that information to build a centralized data repository and analyze it, this includes separating the data that is aligned with your current goals and that one that doesn’t provide any relevance in a visualization dashboard that allows you to have better data visibility.

5. Self-Service Tools

After understanding the data, it’s vital to implement self-service tools to get valuable insights and make data tools a powerful ally. These are just some great examples of self-service tools:

1. TextQL

2. DataBasic

3. The Data Playbook  

6. Make Data-Driven Decisions

Finally, you’ll want to meet with your team and evaluate the information you’ve collected, summarize all the points between data sets, and get to conclusions related to your goals so you can make data-driven decisions and communicate them to your stakeholders.  

Making data-driven decisions is an exciting challenge. If you’re looking for an effective way to reach your company’s goals through data-driven decision-making, we can help you!  

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