Best practices should govern your organization’s data analytics. Use these four steps to get started.
Through collecting, organizing, and analyzing data sets—a process known as data analysis—organizations can make intelligent decisions based on the story their data tells. From marketing teams to digital transformation initiatives, every aspect of a business' operations can benefit from data analysis.
Despite this, according to Gartner research, only 20% of analytic insights through 2022 will deliver business outcomes[1]. Why is it that with all the data available to organizations today, there is still a lack of insightful data analysis?
Often, the culprit is a lack of best practices governing the use of data in an organization. Teams run in silos, data is messy, and IT teams are left strained as they attempt to keep up with analysis demands.
This is where IT pros should implement data analytics best practices to create a streamlined approach to data management of data, resulting in cleaner data, optimized resources, and insightful reporting.
In the guide below, we present a step-by-step approach to implementing data analytics best practices across your organization. With each step, you can transform the way your business approaches data and deliver new value to your organization.
1. Determine your organizational approach
One of the first things to establish when setting up data analytics best practices is how your organization will approach data collection, organization, and analysis. Throughout your company, there are large data sets in numerous locations.
Without a congruent approach, this data can be lost, difficult to access, or stored without proper parameters. The end result is that when it comes time to analyze your data, a significant amount of time is wasted simply trying to locate and clean up data sets.
By determining how your organization will approach data, you set everyone on the path to success. There are three common styles of approaching data to consider:
Decentralized: A decentralized approach allows individual teams or departments to handle their own data management. When you take this approach, it's critical to set parameters around how data will be collected, stored, and analyzed. You’ll also need to ensure there are clear guidelines in place for how cross-departmental data analysis will occur.
Centralized: Many large organizations invest in a centralized data management system, where data from every business unit is stored in a singular data lake. From here, analysis is performed by dedicated data teams. This approach facilitates cleaner data sets, as the data team is responsible for maintaining and cataloging data. However, it can have limitations if the team becomes backlogged with data analysis requests. Without ample staffing, this approach can quickly become a bottleneck, slowing down the analysis and reporting process.
Hybrid: For some businesses, balance is found in a hybrid approach. This strategy includes centralized data management while teams still retain their own data sets and have the ability to run departmental analysis. This approach can help empower teams to solve their own data needs while still ensuring data access across an entire organization.
Keep in mind that regardless of which data strategy approach your organization takes, data security requirements should always be centralized. This is critical to protecting your organization and ensuring data compliance.
The best approach for your organization heavily depends on its size, as well as specific business use cases.
2. Define clear goals and prioritize data accordingly
Data analysis should always be driven by clear business goals. Without clear goals, your organization might miss out on gathering critical data. Information can be lost as teams are unclear about what data furthers the goal at hand.
On the other hand, you can wind up buried in too much irrelevant data, which can lead to massive amounts of wasted resources when it comes time to clean up the data.
To avoid wasting time and money, work with key stakeholders to determine the end goal for your organization’s data. From here, you can identify and implement the tools needed to gather this data, organize it, and ultimately provide insightful analysis.
At the end of the day, data analysis is all about solving problems. Having your teams define the problems they wish to solve and what data they need to perform this analysis up front can ensure your team is operating efficiently from the start.
3. Ensure cross-departmental buy-in
Too often, organizations rely on their IT department and data scientists alone for data management and analysis. This siloed approach is fraught with issues.
It's hard to see the bigger picture when teams are requesting singular analysis without collaborating with related teams. Additionally, this approach can waste time as siloed teams are often submitting requests for or completing the same analysis.
For better cross-departmental buy-in, empower every person in your organization to approach their day-to-day work with a hypothesis-based methodology. Every team should consider the problems they need to solve and how data could help them find the answers they seek. Beyond this, teams should work collaboratively across departments to pull together big-picture analysis.
Building this type of data-driven culture begins with education, and IT teams should start at the top. Ensuring buy-in from C-suite members helps create organizations where initiatives are driven by data. When management pressures their teams to back strategies and efforts with data, it has a trickle-down effect.
Instead of relying on IT teams and data scientists alone, every team can work together to contribute to a culture where clean data and intelligent analysis is a priority.
4. Choose the right tool for the job
Different data tools are built with different goals in mind. Choosing the right tool will make a critical difference in how easy it is for your team to implement best practices across your organization.
As you compare data analysis software options, ask yourself the following questions:
Can this tool handle the complexity of our data?
In many cases, you will need to pull numerous data sets into one central location. It's important to choose a tool that will be able to handle the amount of data your organization uses and one that will help you organize that data in a meaningful way.
Is the tool scalable?
As your business grows, your data analysis needs will grow. Not only that, but as your team helps create a data-driven culture, the amount of data being gathered, organized, and analyzed across the business will increase. Make sure you choose a tool that not only meets your needs today, but is capable of handling your needs down the road.
What visualization tools does the solution include?
To turn data analysis into a powerful tool, you will need an effective way to visualize the analysis. Charts, graphs, and other easy-to-digest reports can help you present data across your organization. Check what visualization offerings each tool includes.
Is the tool customizable?
Rarely will an out-of-the-box solution meet every specific need of your organization. Instead, the data analysis tool you choose should allow you to customize the tool to fit your specific requirements. This will help immensely as you establish parameters around data management, leading to improved efficiencies in analysis.
Data analysis software can help you implement and maintain these best practices
Implementing data analytics best practices can be a daunting task, but with the right tools, you can transform the way your organization approaches data. The end result is a team that works together more efficiently and data that leads to actionable insights.
Explore top-rated, popular data analysis software with Capterra Shortlist.