How to Make Data Insights Accessible Across Your Organization
Companies have been striving to become more data-driven for years now, in large part because it’s clear that those that leverage data to make better informed business decisions are more likely to succeed, regardless of their vertical or market conditions. Becoming data-driven isn’t always easy, though. It requires sweeping change that’s as much cultural as it is technological. But it also requires deploying business intelligence solutions that enable stakeholders across the organization to make decisions based on current, accurate analytics.
Interest in self-service analytics and data visualization tools like Microsoft PowerBI remains high. There’s good reason for this: these solutions are versatile, easy to learn, and put data insights within reach for business stakeholders across the entire organization.
However, in and of itself, implementing a data analytics solution isn’t enough. To deliver the insights that business users need to make better decisions, your organization also needs a data architecture that can serve up the right data to the right users at the right time. Without this underlying data architecture, business intelligence software won’t be able to provide the insights that users are hoping to discover.
To make data intelligence available in your organization, we recommend adopting a four-step strategy, one that begins with understanding which data insights you want, progresses through building the data pipelines needed to fuel those insights, and culminates in delivering data intelligence to the appropriate business stakeholders.
We’ll take a closer look at what each step in the process entails.
1. Build a data architecture.
The first step in bringing together the data you’d like to use for business intelligence involves understanding where that data is stored, and whether it’s accessible. Of course, in order to find the data you need, you first have to figure out what you’re trying to learn. This means starting with a self-assessment, where you answer questions like:
- What insights are you trying to gather and why?
- What changes can you make that will impact the metrics you’re trying to move?
- What do you want to learn about our business and/or our customers
From there, you can determine which data you’ll need to answer the questions that are most important to you.
Then, you can ascertain whether or not this data is accessible. In most cases, some of it is, and some of it isn’t. Real-world data architectures can be very siloed, with data residing in a variety of customer relationship management (CRM) platforms, enterprise resource planning (ERP) systems, websites and content management systems and other places. The goal is to create a data architecture that will integrate data from all the enterprise systems and processes that produce it so that it can be delivered to a business intelligence solution in usable form.
2. Engage in data engineering.
This stage in the process involves building the pipelines, data models and integrations that bring data from various places into a common repository where it can be used for analytics. In many cases, this will require building a data warehouse, but it’s possible to start with a simple one—which won’t take weeks or months to build.
3 .Implement Business Intelligence.
Only after the underlying data architecture is in place can you use business intelligence software to create graphs and charts. While these visualizations can be powerful and deliver valuable insights, they’re really only the culmination of a data intelligence journey that consists of getting the right information to the right people at the right time.
To that end, data governance should be a key pillar of your data intelligence strategy. Democratizing data insights is an important business goal, but confidential and sensitive information also needs to be safeguarded.
While building your data governance strategy, you’ll need to ask:
- Who in the organization needs access to this data?
- Who is authorized to access it?
- How does it need to be protected?
- Is it accessible in a way that empowers the right people to maximize its value while preventing those that shouldn’t have access from gaining it?
A good data governance strategy goes hand-in-hand with a good data warehousing strategy. Together, they make it possible to deliver access to the people within the organization who need access without exposing data that’s sensitive and should be protected.
4.Move into the realm of data science.
While data analytics and visualization tools like PowerBI are largely descriptive in nature, delivering insights that help users understand which factors are influencing key metrics, they’re rarely able to determine why a change occurred. For this, business intelligence teams typically leverage a combination of analytics software and human insight. Organizations that want to go a step further, to understand what’s likely to happen next—based on the data—and what steps to take in response (predictive and prescriptive analytics) will need to leverage machine learning (ML), artificial intelligence (AI) and analysts with more specific data science skills.
This stage of the data intelligence journey requires additional maturity. Not every organization will want or need to go there right away (or at all).
In every case, though, we advocate taking a “crawl, walk, run” approach as you navigate the data intelligence journey. Start with collecting simple but impactful metrics, for use cases where it’s easy to connect to the source data, deliver quick wins, and build from there. It’s likely that most members of the C-suite already understand the value of data, so demonstrating how smaller steps can deliver almost immediate value paves the way for larger initiatives later on.
Want to learn more about the business intelligence and data analytics solutions that Netrix delivers? Or hear personalized advice from a member of our team of leading experts? Check out our services page or schedule a free consultation today.