Rounding the Bases: How Qlik Helped Us Bring Data Literacy Home
Company Size
1,000+
Region
- America
Country
- United States
Product
- Qlik Sense
- QlikView
Tech Stack
- Data Analysis
- Data Visualization
- Data Governance
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Education
- Healthcare & Hospitals
Applicable Functions
- Human Resources
- Quality Assurance
Use Cases
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Data Science Services
- System Integration
About The Customer
The article does not provide a specific customer description. However, it mentions the author's experience with data journeys at several organizations, including his current role as the manager of education at 2Foqus Data & Analytics. The author is a Qlik Luminary, indicating a high level of expertise in using Qlik for data analysis. The author also shares his experience working on a healthcare service project titled Call To Balloon, which aimed to shorten the time between an emergency call and the performance of treatment. Another example provided is from the author's experience at UWV, where he was charged with writing the plan to centralize the QlikView Competence Center.
The Challenge
The article discusses the challenges faced by organizations in their journey towards data maturity. The first challenge is related to data quality. Organizations often have data quality issues, which become more apparent as they try to utilize the data. Issues can range from misspellings, inconsistent data entry methods, to software compatibility issues. Before any progress towards data-based decisions can be made, the data itself must be trustworthy. This requires creating mechanisms to ensure data quality. The second challenge is related to data literacy. Having great data is just part of the equation. Organizations must also have a genuine curiosity and the ability to ask the right questions. Most organizations are very familiar with asking, “what happened?” but the real power in analyzing data comes from more advanced questions. The final stage of intuitive growth involves reaching a level where insights about data trends lead to an ability to influence outcomes.
The Solution
The solution to the challenges faced by organizations in their journey towards data maturity involves several steps. The first step is to ensure data quality. This can be achieved by creating mechanisms to ensure data quality. For example, Qlik Sense users may find it useful to add a data quality report to the environment. The early steps in the journey must include creating some kind of data governance solution. The second step is to develop data literacy within the organization. This involves training people to analyze and argue with data. Platforms like Qlik Sense can make data reading and manipulation second nature, but people must be trained to analyze and argue. The final step is to develop a predictive analysis. This is where the true value for the company begins to emerge. Analyzing real-time product sales and production data can help a company avoid bare shelves. Traffic patterns and customer addresses can help you make an optimum choice for a new location.
Operational Impact
Quantitative Benefit
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