Every day, organizations have the opportunity to gain insight into customer behavior, identify problems and opportunities, and improve efficiencies. The best way to inform these areas is through evaluating data.
Gathering data can be a time-consuming and confusing process, but businesses can learn a wealth of information when equipped with the right tools.
Introducing business intelligence software can enable organizations to collect, report, visualize metrics and make data-driven decisions. Navigating how to get from point A — a jumble of data — to point B — informed decisions based on measurable insights — is the outstanding question. With the right software, strategy, and decision-making process, your organization will be ready to face the evolving, data-driven nature of the business world.
How to Choose the Right BI System
The first step in establishing an effective BI strategy is understanding your requirements before selecting the right system. Emerging BI models can solve many problems, but because each software is customizable and unique, it is critical to begin the search with a solid idea of your organization’s needs. Before making decisions, be sure you thoroughly understand your deliverables, involve vital users who will utilize the software, and engage stakeholders who visualize the reports.
Tip: Be prepared and remain flexible as your expectations may change while you learn more about your available software choices. The must-have and nice-to-have features are likely to evolve as you progress through the evaluation process.
After considering and comparing features and tools, continue the evaluation by reviewing cost, ease of implementation, customization options, and maintenance. The chosen system should be able to integrate with your legacy systems and serve long-term organizational goals. You may also want to bring in a BI expert to help you determine the best fit for your company.
Consider the following questions:
- How user-friendly is the system? Are the reports easily accessible and understood?
- How up-to-date is the data, and how often does it refresh? Is it instant? Is there a wait time?
- What is the pricing model? How often are payments required? Is this subscription-based?
- How long will it take to implement the system and train end-users?
- How customizable is this solution, and what is the cost associated? Will an expert need to make these custom requests, or can an in-house team change it?
Successfully Implement a BI System
Once you have made a business intelligence software selection, the implementation process will involve several critical steps.
- Data cleaning is a must to ensure the records are accurate, consistent, and up to date. Identify any errors or corruption in the current data and correct them.
- Establish a team to lead the implementation. Team structure will vary depending on the needs of your organization. If your in-house resources lack the experience to carry out a BI implementation, consider partnering with an expert consultant who can fill any knowledge gaps on your team. Key members often include:
- Application Lead Developer
- BI Infrastructure Architect
- Data Administrator
- Data Mining Expert
- Data Quality Analyst
- Database Administrator
- Project Manager
- Subject Matter Expert
- Based on your timelines, run a test or pilot on a small group of individuals to ensure the system is performing the necessary tasks as expected. Review the pilot results, and then fine-tune the processes and procedures until your organization achieves the desired results.
Gathering and analyzing data, also called data mining, is not so simple. All data points will need to be integrated and configured with the new system based on organizational goals.
- Define KPIs
The easiest way to define KPIs is to ask questions. What is your business trying to learn from your data? How can this data inform your future decisions? Ensure the KPIs are measurable, clear, and concise. Include potential problems to solve or takeaways to gather. Finally, KPIs should result in some action. Remember, KPIs are not necessarily about hitting targets but measuring progress. Start simple, with clearly defined objectives, measurements, and timelines, and determine when you will update each one.
- Source Data
Data collection can occur either from existing sources (i.e., databases, previous surveys, etc.) or new sources. In some cases, your data must be adequately organized and logged as it is collected to allow for easier analysis down the line. However, if your data is sourced from a data lake, it might be unstructured but can still be analyzed. Again, a dedicated BI expert on your team can help determine the best method for your organization.
- Analyze Data
Out of the box BI visualization tools should represent the data in several different formats such as charts, graphs, and raw data. Review them to determine which structure best represents information, but you may find throughout your analysis that custom dashboards need to be built. If your team lacks the required expertise, bring in a Data Analyst, Data Scientist, Data Engineer, or Business Intelligence Analyst to help customize and create dashboards for you to utilize.
- Make Decisions
Once you have a firm grasp of the conclusions drawn from your data, it’s time to make some decisions. Is there something you can change or improve now that you have gathered since this new insight? Does your data address any potential objections to business processes that other stakeholders may have? These questions can help you determine your next best step.
As you become more comfortable gathering and analyzing data, be careful to avoid common pitfalls such as growing too comfortable with your current processes and procedures. Adaptability and flexibility are two components of an effective BI strategy. Ensure you are staying updated with trends in BI software as new technologies are constantly emerging. Confirm that your BI strategy is meeting your business needs.
With a well-chosen BI software implemented and a thorough strategy in place, your organization can expect to make more informed decisions and solve problems using strategic, data-driven logic.