May 4, 2023
3 Keys to Managing Data and Maximizing Business Intelligence
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Businesses today collect more data than ever before. Companies now have access to huge warehouses of data about every aspect of business, from revenue patterns to risk management to customer analysis. The problem is that data storage solutions often squirrel that data away in separate applications or departments, and they don’t have reliable ways to collect, combine, manage, and take advantage of information.
The results? Compromised data quality and the inability to access or report on relevant data.
When these problems arise, businesses struggle to actually use data to drive better business decisions. If you can’t use data to effectively measure the operations of your business, you won’t have the information you need to move the needle on improvement goals.
Business intelligence (BI) initiatives promote better data gathering to facilitate historical and predictive analysis of business operations. With the right BI tools, you can identify and address problems across the spectrum of your business, from finance to productivity to risk to trust.
But this will only happen if you’re serious about doing it right.
So let’s talk about doing it right.
Establish Goals for Your BI Technology Upgrade
If you’re running multiple applications or relying on spreadsheets to generate analytics reports, it’s time for a technology upgrade. As you consider your options, keep these goals in mind:
Complete Data Gathering
You can’t perform effective analysis without quality data. It may take some negotiations and ROI calculations to convince management, but making an investment in comprehensive data gathering will position you for more successful BI initiatives.
Data Integrity
Data errors can occur during data entry, processing, mapping, or calculations. It’s vital to root out these errors in the data set and the code that powers it, or these problems will undermine the integrity of your analysis.
Trustworthy Analysis
Data integrity, relevant reporting, business needs, and efficient data integration all play a role in trustworthy analysis. Your BI platform should address each of these areas with efficient, usable tools.
Predictive Capabilities
Predictive analytics modeling identifies future impact of initiatives and provides actionable insight rather than simply analyzing decisions that have already been made. Developing your business process model and data governance strategy at the outset of the project is a key to effectively framing predictive analytics efforts.
11 Ways to Improve the Value of Your Business Intelligence Investment
It’s not always easy to secure management buy-in for business intelligence investments or upgrades. But the truth is that most companies aren’t satisfied with their current business analytics processes. One study found that in the manufacturing sector, just 12% of companies function at a high level of analytics maturity. Almost two-thirds of the 2,600 companies surveyed still used spreadsheets for analytics—which is a common but prehistoric method of utilizing data.
Even among those companies that do use business intelligence tools, many don’t have platforms that meet expectations for delivering value or improving business outcomes.
But it doesn’t have to be that way.
Let’s look at 11 ways your company can use BI tools to improve your business intelligence function with improved analytics:
1. Build a compelling business case for new technology
Your company may need to invest in new data warehousing solutions, business applications, or upgrades to existing technology. But often, companies fail to move forward in this area because of a lack of resources caused by a failure to prioritize data initiatives. In order to secure stakeholder buy-in, you’ll need to develop a compelling business case for this kind of software investment.
2. Focus on usability and flexibility
Analytics solutions should provide a range of presentation options, from standard charts and reports to online portals and downloadable reports. The functionality should fit your business’ particular framework, and it should be easy to access and use. Carefully consider the usability of the tool. Will it require extensive training? Are the reports intuitive? Can employees find what they need quickly or customize dashboards to display relevant information?
3. Customize interfaces for different viewers
Different roles within the company need access to different kinds of information. The CEO, for example, should see different KPIs than a district sales manager or the CFO. Before you move forward with the project, identify the metrics that each role needs to improve job performance. Target your applications to those needs.
4. Avoid restricting data for no reason
The flip side of customization is that over-restriction of data can prevent employees from accessing information they need. Your data portal should have access defined by user role, and you should have flexibility to change these role definitions as necessary to meet your business’ needs.
5. Don’t blindly recreate tools the same way they are now
When you upgrade, consider what information is really required and how you can use your new capabilities most effectively. Just because a report exists in the old system doesn’t mean you have to recreate it the same way in the new system. Consider what information you really need on the report and how you can present it more effectively. Your business has changed since you created that report or spreadsheet, so use this opportunity to upgrade both in terms of technology and in terms of the actual data output. Your new tool may offer additional capabilities that weren’t available before, so take advantage of opportunities to streamline your processes.
6. Eliminate reports you don’t use
When it comes to Software-as-a-Service data solutions, it’s easy to get lost of the clutter. While these systems offer a wide array of reports, most companies use only a fraction. Focus what your data warehouse displays so your employees can work efficiently. One way to weed out unused reports is to turn off the capability for a period of time and see if anyone notices. If not, out it goes!
7. Design for the business, not for the technology
As you develop your business analytics platform, start with the language the business already uses and design the tools around functionality and business needs. In many cases developers work backward, designing from the technology to the business rather than from the business to the technology. The end result of that approach is a cumbersome platform that is hard to use and slow to gain adoption.
8. Aim for more relevant data
Pare down the number of reports you create to make content more relevant and timely, at least in terms of what you display at first glance. When users juggle thousands of different reports, it’s difficult to find the information they need to make data-driven decisions.
9. Embed intelligence into daily operations
Don’t view BI as an entirely separate activity. Instead, build it into the daily operations of your business. Easily accessible reporting and analytics gives employees the data they need to make better decisions the first time. By embedding these functions into current processes, you can promote data-driven decision-making as part of your culture.
10. Integrate data across applications and departments
Using multiple applications and BI tools can create data integrity problems. Failure to integrate this data can frustrate cross-departmental analysis efforts. While it may not always be possible to house every piece of data in one central system, it’s beneficial to create integrations so you can have one common portal or dashboard where employees can find what they need.
11. Invest in training and celebrate success
No matter how user-friendly your BI platform claims to be, employees will need training so they can use it effectively. Without proper training, the system will seem intimidating and users may not know how to access the data they need. As you train, celebrate data-driven decisions and foster an analytics-based culture from the C-suite on down.
At Worthwhile, we create intuitive, powerful business intelligence applications designed around the needs of your business. Using Django and Python, we can help you plan and build a custom solutions that meets your deadlines and budget restrictions.
Data for data’s sake will never move your business forward or deliver the return on investment you’re looking for. But when you understand the business case for BI tools—increased revenue, higher productivity, and better risk management—you can use the data to make better business decisions and ultimately grow your business.
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