The Role of Data Analytics in Shaping Your Tech Stack Optimization

In today’s business climate, a company is only as strong as the tech stack it deploys. It’s the software, hardware, and infrastructure management systems that enable businesses to produce the best possible value for their customers and stakeholders. Utilizing technology for data collection, storage, and processing, automating workflows, and monitoring business performance metrics is and always will be a valuable investment for success.  

Ironically, the technology stack that helps a business scale performance also becomes exponentially challenging to manage as that business grows. More users and technology resources in the mix mean a more extensive network, which means newly-added technical issues, difficulty obtaining visibility, and an influx of data to comprehend. So how exactly can your organization remove these blind spots and optimize its tech stack?  

The quick answer is employing a data-driven strategy that lets you decide what software tools to invest in, which to remove, and how to manage them for peak efficiency. Here we cover data analytics’s various roles in optimizing a business’s technology stack. They include: 

1. Data analytics to identify software needs
2. Data analytics to proactively uncover IT issues
3. Data analytics to reduce wasted technology costs
4. Data analytics to see the value of your tech stack

1. Data Analytics to Identify Software Needs

Data analytics plays a key role as a technology advisor that can recommend software tools to deploy within your operation. By understanding your unique business needs, processes, and goals, a business intelligence (BI) tool can identify improvement opportunities that a particular software or hardware system could solve.  

For example, let’s say you’re a manufacturer looking to boost production line efficiency. After running data analysis on your operational process, you discover that your biggest bottleneck is processing inventory orders, as you’re currently handing them manually using personnel. Based on these insights, you see the need for a software solution that can automate this stage by receiving orders from a customer portal and auto-pushing them to the production line—prompting you to begin working with your DevOps team. 

2. Data Analytics to Proactively Uncover IT Issues 

If an IT manager can spot slowdowns or technical irregularities in the technology stack before it impacts business operations, they can proactively fix the problem without worrying about lost production. Here’s where data analysis plays the role of the “watcher.” Taking large quantities of network activity and system performance data and analyzing it in a BI tool allows you to uncover IT issues for fast remediation.   

Similarly, DevOps teams can use data analytics to understand the performance of internally-built software within an organization’s tech stack. Whether it’s one component overloaded with user requests or the entire server, data analytics allows you to pinpoint the issue, run diagnostics, and patch it before it hinders the customer experience.  

3. Data Analytics to Reduce Wasted Technology Costs 

In addition to helping decide which technology to invest in, data analytics is also a catalyst for the opposite; determining what to remove from the stack. If you run the numbers and find a specific tool isn’t fully adopted by company users or is falling short of return on investment (ROI) objectives, your data analysis can be the strong justification for removing it altogether. Analytics can also provide deeper insights into the wasted costs of your technology stack. 

For example, let’s say there’s internally-built customer relationship management (CRM) software deployed in your business that has helped automate sales and slightly increase revenue. After running a deeper cost analysis, however, you found that it’s taking up much of your cloud storage space and causing an influx of support ticket submissions as users constantly have technical issues. These insights could prompt you to cut the platform from the stack and shop for a software-as-a-service (SaaS) CRM solution with fewer maintenance requirements.  

4. Data Analytics to See the Value of Your Tech Stack 

Whether it’s to receive a budget approval, for an internal audit, or simply out of curiosity, there will be a time when you need to prove the business value generated by your technology stack. It could be determining efficiency metrics, costs saved, or new revenue produced thanks to software or hardware. Regardless, data analytics is what lets you see these numbers.   

This use case is particularly useful for adjusting to market dynamics and digital transformations within your tech stack. For example, with recent pressure to deploy artificial intelligence (AI) across business functions, companies must justify its use and see AI tools improve performance and competitiveness in their marketplace through its automation capabilities. Data analytics can help decipher tech optimization in the context of business value added.        

Final Thoughts 

A company’s technology stack helps drive peak performance and adapt to dynamic market changes. Optimizing that stack, however, only gets more complicated as a business grows because of challenges in gaining visibility and interpreting what your mass influx of data is telling you. Data analysis using a robust BI platform gives you the insights to make critical decisions related to your technology stack and ensure it meets your business goals.  

Bhupesh Malhotra

Bhupesh is a Global Product Manager at wynEnterprise. He loves to tell the story behind data through data visualization techniques. He likes to hear from customers and talk about their experiences with reporting and analytics. When he isn’t thinking about data, he spends time with his family and enjoys traveling to new places with them. You can connect with Bhupesh on Twitter