The concept of data-driven decision-making is nothing new for tech teams. Big Data has been hailed as the answer for decades at this point, even as many companies struggle with organizing and cataloging the information they collect. However, no matter what stumbling blocks lie ahead, data remains the most important ingredient in your final decisions. 

Data-driven decision-making starts with understanding its importance. We’ll look at how a tech team can harness data and the best tools.

Power of data in tech decision-making

Data is the key to accurately answering the most detailed questions about your business. So, instead of explaining a dip in sales as a byproduct of a weakened economy, you can take a closer look at what’s happening and why. Tech teams need to understand how staff and customers respond to initiatives, platforms, mobile apps, etc., or risk wasting valuable resources.

Here are a few ground rules for getting started:

• Use it all: You should gather data from all viable sources, ensuring it can all be processed and analyzed for more accurate interpretations.
• Create dynamic data products: If your data is currently organized in relationship databases, it’s time to create multipurpose data products that reduce data engineering.
• Assign roles: Ideally, data is owned by a team or individual. It’s their job to secure, engineer, and implement analytics tools instead of having data sprawl across environments.

Once you have the information at your fingertips, you’ll start to see the patterns that dictate the bottom line. As AI increases, the goal is to integrate the technology into your current data collection, storage, and analytics strategies. With data that has been scrubbed and parsed, you can start to see why certain decisions worked and why others failed. You can also make smarter predictions about what’s to come. 

While you never want to assume that you can see the future, you can use the past to make stronger decisions about what’s ahead.

Real-time analytics for quick responses

When fads come and go, and consumer preferences can change on a dime, real-time analytics can be the key to helping tech teams respond to the current conditions. So, if more customers begin using the mobile app chat feature rather than the website chat to ask questions, it’s easier to pivot your strategy to meet the new demand.

Real-time analytics are the key to keeping up with how people want to interact with the company. In addition to quantifying their behavior with your current technology, you can also prioritize what needs improvements. This could mean adding new features, dismantling programs, or perfecting your most popular applications.

Real-time analytics has been a non-negotiable for security protocols for some time now, but the capacity has rapidly expanded recently. From personalizing content to catching hackers in their tracks, these analytics can spot the anomalies that can turn into full-blown headaches without intervention. 

Tech teams that want to help their companies implement personalized experiences for the consumer will have an easier time when they really drill down on the data.

Data-driven culture in tech teams

A data-driven culture in a tech team is critical to the company’s success. Despite stereotypes in the industry, though, not everyone looks at the numbers before making decisions. While we can’t discount the importance of instinct or soft skills, tech teams don’t have the luxury of ignoring the data.

Establishing a data-driven culture in a tech team is more complex than a top-down implementation. In other words, just because a CTO makes a few hyperbolic statements, the philosophy must be embraced at every level of the hierarchy.

Doing this starts with proving the efficacy behind the approach. Once everyone starts to see how their efforts pay off, it’s easier to make data-driven decision-making a way of life. This is where your homework needs to begin. 

As long as you have the right technology, you’ll have a better chance of establishing a culture where everyone is on the same page. Ideally, you’ll want to work with people who can add to the conversation based on the insights derived from the data.

Tools for data analysis

Data-driven decision-making in tech is fueled by the right tools. If you’re looking for the best technology, you must first identify your most significant pain points. Whether you’re having a hard time sticking to a development schedule or your current analytics only provide basic insights, it will all need to be taken into consideration.

The right tools can help you shift the team’s culture, speed up tech projects, and adapt to consumer behavior. However, they cannot be used as a cure-all either. If you’re going to invest company time and money into finding tools, there must be a strategy behind it. 

How StackSpot helps foster data-driven decision-making in your teams

Tools like StackSpot introduce intelligence for decision-making in your company’s software development cycle. With the customizable and automated platform data dashboard, you can see everything from the engagement of your developers to technology utilization.

If you want to spend less time on manual controls and more time on optimizing your operations, you need to look for providers that have thought through how your team works and what will help them be more efficient.

Want to see other ways StackSpot can help your technology teams become more data-driven? Check out this tutorial using StackSpot AI to create complex dashboards without data science knowledge.

Finding the Right Path Forward

There’s no doubt that your tech team is currently using data as a way to streamline your workflow. However, if you find you’re less efficient than you’d like, it might be time to switch things up. The pivotal role of data-driven decision-making has been proven time and again.

The more you can harness the power of the current tools, the easier it is to whip your current team into shape for the benefit of all departments. It’s the key to unlocking more productivity and innovation, especially if the company is on the precipice of scaling up.

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