The folks of the government agency recognized this problem of centralized vs. decentralized information management, and they went about crafting a strategy that just solved this problem. And data needs this language but observing what they did, I view it as sort of a divide and conquer problem. In this large state there are so many organizations and so many subject areas and data sets, you could only really solve so much.
So a big part of this was a lot of communication between the various departments in the state, and they essentially had what I would call constitutional conventions, where they brought everyone together from other parts of the state and agreed upon different subject areas in the data. And then this divide and conquer approach was basically saying okay well what information needs to be globally consistent across the enterprise.
Some of our payroll data, our ERP, our workforce, time and attendance data, that all needs to be globally consistent so let’s make that the domain of the centralized team, and they created a very competent centralized team to be able to build out BI solutions for those particular subject areas.
Promote Successful Reporting Examples
So, basically our application, as well as the Qliks and Spotfires and Tableaus of the world are having a very strong disruptive impact on this market. So we will talk about that as the second key issue and we will discuss how you might want to go about using data discovery along side some of the data warehouse and governance programs you already have in place. We’re not suggesting replacing them, but augmenting them. And then the third sort key aspect of your strategy really has to be having the right organizational model, to have put the right team structures in place with the right rules and have that balance of centralized and decentralized just right for your company.
So those three keys issues we are going to cover; strategy, technology and organization. I think if you nail those three, I think you will be well on your way to a strong self-service BI program. I should say there will be time for questions at the end. You should be able to use this webinar system to just enter in any questions you have in the questions tab and then we will get to as many of those as we can at the end of the presentation.
Okay let’s dive into the first key issue on strategy. I want to start with a case study that I wrote several years ago but I thought it was just a good example of what the folks of a government agency did in their self-service BI program. And I think with most problems the first step is recognizing and understanding the problems. You can read through on the left hand side of this slide, the current state and challenges at the time. Number one was just a lack of understanding of which reporting tools to use and when, and then number two, there was just a lack of knowledge of which reports existed and both of those things resulted in number, which was this uncontrolled propagation and replication of data. You can see what that did in terms of the business impact, the cost and the complexity and the increased business risk there.
 |
“Flexible product with great training and support. The product has been very useful for quickly creating dashboards and data views. Support and training has always been available to us and quick to respond.
- George R, Information Technology Specialist at Sonepar USA
|
So I think that probably sounds very familiar to a lot of organizations. I have talked to a lot of companies and I pretty much see this everywhere I go. A lot of people use their BI tool as their own personal ETL tool, just to get data out of the data warehouse and then they go and put it in their own excel spreadsheet or access database or SAS tool so that they have autonomy and they can control it, and they can mash it up with other data sources, just like what the number 3 challenge is saying there.
How Do You Get People to Stop Exporting Data Via a BI Tool In Order to Analyze It in Their Own Spreadsheets?
Getting people to stop exporting data from a BI tool to analyze it in their own spreadsheets is a common challenge faced by organizations. This practice can lead to issues such as data fragmentation, loss of data integrity, and security risks. Here are several strategies to address this problem and encourage the use of BI tools for data analysis:
1. Understand the Reasons Behind Data Export
Before implementing any measures, it's crucial to understand why users prefer exporting data to spreadsheets. Common reasons include:
- Familiarity: Users are more comfortable using spreadsheets.
- Perceived Flexibility: Spreadsheets offer a perceived flexibility that users believe BI tools lack.
- Specific Functionality: Certain analytical functions or formatting options might be more accessible in spreadsheets.
- Custom Reporting: Users might need to create custom reports or visualizations that they think are not possible within the BI tool.
2. Enhance Training and Support
- Comprehensive Training: Offer thorough training programs to help users become more comfortable and proficient with the BI tool. Highlight the advantages of using the BI tool over spreadsheets, such as real-time data updates, data integrity, and advanced analytics.
- Ongoing Support: Provide ongoing support through help desks, FAQs, and user groups. Make sure users know they have access to assistance when they encounter issues or have questions.
3. Improve BI Tool Functionality and User Experience
- Customization Options: Ensure the BI tool provides sufficient customization options for reports and dashboards. Users should be able to create the same or better visualizations and analyses that they can in spreadsheets.
- Ease of Use: Continuously improve the user interface to make the BI tool as intuitive and user-friendly as possible.
- Feature Parity: Identify the specific features that users are missing in the BI tool and work to incorporate these features if feasible.
4. Demonstrate the Value of BI Tools
- Showcase Success Stories: Highlight case studies and success stories where the BI tool has been effectively used to drive business decisions and improve outcomes.
- Data Integrity and Security: Emphasize the importance of data integrity and security. Explain how the BI tool ensures data consistency and protects sensitive information, which might be compromised when exporting to spreadsheets.
- Real-Time Data Access: Demonstrate how the BI tool provides real-time data access, which is often not possible with exported spreadsheets that can quickly become outdated.
5. Create Incentives for Using the BI Tool
- Recognition Programs: Establish recognition programs for teams or individuals who effectively use the BI tool.
- Performance Metrics: Incorporate the usage of the BI tool into performance metrics and evaluations. Reward employees who leverage the BI tool to generate insights and make data-driven decisions.
6. Restrict Data Export Capabilities
- Access Controls: Implement role-based access controls to limit who can export data. Ensure that only those with a legitimate need have this capability.
- Export Limits: Restrict the amount or type of data that can be exported. For example, allow summary data to be exported but restrict access to detailed transactional data.
- Audit Trails: Use audit trails to monitor data export activities. This can help identify patterns and address unauthorized or excessive exporting.
7. Develop Trust in BI Data
- Data Accuracy: Ensure the data in the BI tool is accurate, timely, and reliable. Users are less likely to export data if they trust the data available within the BI tool.
- Transparency: Maintain transparency in how data is collected, processed, and presented within the BI tool. Make it easy for users to understand data lineage and transformations.
8. Promote Collaboration and Sharing
- Collaborative Features: Highlight and promote collaborative features within the BI tool that allow users to share dashboards and reports easily. This reduces the need for data exports for sharing purposes.
- Centralized Reporting: Encourage the use of centralized reporting and dashboarding within the BI tool to ensure everyone is working from the same data and insights.