Data Integration & Quality
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Recent Blog Posts
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Unlocking Business Success through Data Quality and Trust
Data Quality and Trust: The Business Impact The quality and trustworthiness of data significantly influence how and whether it is used, impacting vari... Show MoreData Quality and Trust: The Business Impact
The quality and trustworthiness of data significantly influence how and whether it is used, impacting various aspects of a business, including:
- Decision-making
- Compliance
- Customer satisfaction
- Market efficiency
- Competitive advantage
- Overall business growth
For example, a retail company found that its loyalty program failed to identify duplicate customer records, resulting in a 30% increase in marketing costs due to duplication. This also led to customer frustration, as they received conflicting information and promotions.
Maintaining high standards for data quality and trust is crucial for building a reliable and successful business. By ensuring this data is made available through a data marketplace, organizations can offer consumers a trusted, single version of the truth.
Data Quality: Accuracy and Reliability
Accurate, high-quality data is essential for making informed decisions. When data is inaccurate or unreliable, it can lead to poor decision-making, damaging the credibility and success of both the data product** and the data marketplace.
For instance, a supply chain company suffered significant losses due to outdated and inconsistent inventory data. This resulted in accepting orders that couldn't be fulfilled on time, leading to contract breaches, financial penalties, and a loss of customer trust, ultimately costing the company millions in long-term contracts.
The value of data products in a marketplace is directly linked to their quality levels for a domain-centric use-case. High-quality data products are more likely to deliver value and meet consumer needs, strengthening the marketplace’s reputation.
**A data product is like a cake, created from ingredients like raw data, algorithms, and analytics. The result is a fully "baked" product, ready to deliver value—whether as insights, dashboards, or predictions. Unlike raw data, it’s complete and easy to use.
Trust: User Confidence in the data
Consumers need to trust that the data they are purchasing or accessing is accurate, reliable, and suitable for their needs. Without trust, consumption decreases and there is no incentive for the data producers to produce these data products, hindering overall data marketplace growth. Sellers need to ensure that their data products are of high quality to build a positive reputation. Consistent delivery of high-quality data helps establish credibility and fosters long-term relationships with buyers.
A best-in-class organization achieves consistent delivery of high-quality data products by combining clear objectives, strong data governance, and robust development processes. They define standards for data quality, adopt agile methodologies, and utilize scalable technology to ensure reliability and adaptability. Multidisciplinary teams collaborate to build products tailored to user needs, while continuous feedback and performance monitoring drive improvement. By fostering a culture of quality and investing in skilled teams and modern tools, they create data products that are accurate, user-friendly, and impactful.
As an example, an e-commerce platform allowed sellers to post product reviews without verifying their authenticity, leading to a loss of customer trust due to fabricated or exaggerated ratings. This resulted in declining sales, reputational damage, and increased regulatory scrutiny, forcing the company to make significant investments to overhaul its systems, policies, and practices.
A company would need to invest in review verification tools, identity checks, and moderation systems to ensure authenticity. It must implement stricter policies, comply with regulations, and educate users on ethical practices. Additional efforts include hiring moderators, rebuilding trust with PR campaigns, and using analytics to improve review quality. These investments aim to restore trust and reputation.
Examples and Use Cases
The following examples and use cases explore the business impact of data quality and trust.
Regulatory Compliance
As shown in the example above, poor quality and untrusted data can lead to regulatory scrutiny. Adherence to data quality standards and regulations (such as GDPR, CCPA) is necessary to ensure compliance. Non-compliance can lead to legal issues, fines, and damage to the marketplace's reputation.
For example, a pharmaceutical company in a highly regulated market faced severe consequences due to poor data management. Inaccurate and incomplete reporting led to hefty fines, delayed drug approvals, and temporary production halts, causing significant revenue loss and allowing competitors to gain an edge. The company also suffered reputational damage and was placed under stricter regulatory scrutiny, forcing costly investments in compliance and data governance systems.
Ensuring data quality often involves implementing robust data security measures to protect sensitive information and maintain data integrity.
Customer Satisfaction
Customers expect data products to meet certain quality standards. If data products are unreliable or inaccurate, customer satisfaction will decrease, leading to negative reviews and potential loss of business.
At a financial services firm, employees were dissatisfied with the data quality available through the new analytics platform, which was inconsistent and inaccurate. This led to increased manual work, reduced productivity, and low adoption of the platform, causing delays in decision-making, frustrated clients, and higher turnover rates. The company faced significant costs in recruiting and training new staff to replace those who left.
High-quality data products reduce the need for extensive support and troubleshooting, leading to better overall customer experiences.
Market Efficiency
High-quality data allows for more accurate matching between buyers and sellers, enhancing the efficiency of transactions in the marketplace.
As an example, an online marketplace for data products used advanced algorithms to accurately match buyers with relevant sellers, significantly improving efficiency. Buyers saved time by being directed to the most appropriate data products, while sellers increased transaction volume by reaching the right customers quickly. The matching system encouraged high-quality data offerings, reduced transaction costs, and facilitated better decision-making for buyers, ultimately leading to increased business outcomes for both parties.
Quality data is easier to integrate and use across different systems and platforms, increasing the utility and effectiveness of data products built from it.
Competitive Advantage
A high-quality and trusted data marketplace generates competitive advantages for data product consumers by providing access to reliable, accurate, and up-to-date data that enhances decision-making, innovation, and operational efficiency.
As an example, a retail company that invested in high-quality, trusted customer data gained a significant competitive advantage by offering personalized shopping experiences. By leveraging accurate insights into customer preferences and behaviors, the company was able to tailor promotions, recommend products, and optimize inventory in real-time, leading to increased customer satisfaction and loyalty. By feeding this high-quality, curated data into an AI/ML model, the business was able to closely predict trends, respond faster to dynamic market demands, and outpace competitors who were using less reliable data, ultimately driving higher sales and market share.
Reliable data enables innovation and the development of new data products and services, driving growth and advancement.
Scalability and Growth
Ensuring data quality supports the scalability of the marketplace by maintaining consistency and reliability as the volume of transactions and participants grows.
As an example, in a large multinational corporation, different departments acted as both buyers and sellers of data. For example, the marketing team sold customer insights to the product development team, while finance shared budget and performance data with both marketing and operations. This internal data marketplace improved collaboration, ensured efficient data sharing, and enabled faster, data-driven decision-making, leading to optimized resources and a more agile business model.
A focus on quality helps to build a strong, trustworthy brand, which is essential for attracting new users and expanding the marketplace.
Conclusion
Data quality and trust are foundational to the success of any data product marketplace. By ensuring high-quality, reliable data, businesses can enhance decision-making, maintain compliance, improve customer satisfaction, and gain a competitive edge. As the marketplace scales, maintaining data integrity ensures continued growth and long-term success.
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Qlik Replicate and Snowflake: Mandated Multifactor Authentication (MFA) starting...
Hello, Qlik Replicate admins, Beginning in Q3 2025, Snowflake will mandate Multifactor Authentication (MFA). For detailed information and a timetabl... Show MoreHello, Qlik Replicate admins,
Beginning in Q3 2025, Snowflake will mandate Multifactor Authentication (MFA). For detailed information and a timetable, see FAQ: Snowflake Will Block Single-Factor Password Authentication by November 2025.
How will this affect Qlik Replicate?
Unless MFA has been set up, this change will impact connectivity to Qlik Replicate.
How do I prepare for the change?
To mitigate the impact, switch to Key Pair Authentication. Key Pair Authentication is available by default starting with Qlik Replicate 2024.05.
For more information, see Setting general connection parameters.
If an upgrade is currently not feasible, review How to setup Key Pair Authentication in Snowflake and How to configure this enhanced security mechanism in Qlik Replicate for a possible workaround to apply Key Pair Authentication.
If you have any questions, we're happy to assist. Reply to this blog post or take similar queries to the Qlik Replicate forum.
Thank you for choosing Qlik,
Qlik Support -
Qlik Alerting - Security Patch available
Edit 31st of March 2025: Added CVE number. Hello Qlik users, A security issue in a version of Qlik Alerting Windows has been identified. This issue wa... Show MoreEdit 31st of March 2025: Added CVE number.
Hello Qlik users,
A security issue in a version of Qlik Alerting Windows has been identified. This issue was resolved in a later patch, which is already available. Details can be found in the Security Bulletin Critical Security fix for Qlik Alerting (CVE-2025-31509.
- The impacted Qlik Alerting Windows version is July 2023 SR1.
- No workarounds can be provided. Customers should upgrade to Qlik Alerting July 2023 Service Release 2 or higher.
All Qlik software can be downloaded from our official Qlik Download page (customer login required). Follow best practices when upgrading Qlik Alerting.
The information in this post and the Security Bulletin Critical Security fix for Qlik Alerting (CVE-2025-31509 are disclosed in accordance with our published Security and Vulnerability Policy.
The Security Notice label is used to notify customers about security patches and upgrades that require a customer’s action. Please subscribe to the ‘Security Notice’ label to be notified of future updates.
Thank you for choosing Qlik,
Qlik Global Support -
My wines
My winesC40 CitiesAnalyzing my wine tastes and ratings just for fun! (And to get hints on what wines to try next).DiscoveriesI can see how my ratings ... Show MoreMy winesC40 Cities
Analyzing my wine tastes and ratings just for fun! (And to get hints on what wines to try next).
Discoveries
I can see how my ratings have become more strict compared to the market average as time has passed.
Impact
Not my business, but this is my grain of sand for the winery industry.
Audience
About every month, or when I want to try something differene
Data and advanced analytics
I make more informed wine purchases and know which wines I would like to go back to.
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U.S. Presidential Elections 🗳️
U.S. Presidential Elections 🗳️ AnyChart Analyze the results of U.S. presidential elections from 2016 to 2024 — with plans to go all the way bac... Show MoreU.S. Presidential Elections 🗳️AnyChartAnalyze the results of U.S. presidential elections from 2016 to 2024 — with plans to go all the way back to George Washington — in depth.All through an interactive lens that brings clarity to voting patterns, candidate performance, and third-party impact.Delivering actionable insights into electoral dynamics with county-level granularity.Discoveries
1) Explore candidate performance & popular vote trends in depth — at national, state, and county levels.
2) Identify how key swing geographies influenced outcomes.
3) Examine the effects of third-party & independent candidates — and see how Daenerys Targaryen, Donald Duck, Frank Underwood, Harrison Ford, and others fared on real ballots.Impact
Streamlines the exploration of complex election data, enabling faster insights and deeper analyses.
Audience
Political analysts, journalists, researchers, educators, and anyone interested in uncovering trends and patterns in U.S. presidential voting.
Data and advanced analytics
Built on data from the MIT & Harvard, the app uses Map, Decomp Tree with AI Splits, Circular Gauge, Bar Chart, Treemap, and KPI visualizations — unlocking insights at every level.
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Butterfly Bar Chart
Last week a new presentation option for the bar chart was introduced in Qlik Cloud. The Butterfly presentation format displays two measures that mirro... Show MoreLast week a new presentation option for the bar chart was introduced in Qlik Cloud. The Butterfly presentation format displays two measures that mirror one another along the axis based on a single dimension. In the past, there have been methods used to generate the butterfly chart but now, it is a property option in the bar chart. Below are examples of butterfly charts. In the first example, the butterfly chart is comparing the average salary for men and women by country. In the second example, game stats are being compared for two selected college basketball teams.
Let’s look at how easy it is to create a butterfly chart. In the Human Capital Management example, the butterfly chart is comparing the average salary for men and women by country. The butterfly chart requires one dimension and two measures. In this example, Country is the dimension, and the two measures are as follows:
One measure for women and one measure for men. Both measures in a butterfly chart must return positive values to be displayed. If you are like me and used the old trick of creating butterfly charts by making one of the measures negative, you can simply remove that part of the expression to update your chart. In the app, both measures are master items, and a master color is applied to the measures so that males and females are different colors consistent with the rest of the app. Now, the only thing left to do is change the presentation to butterfly. This can be done from the properties of the bar chart in the Presentation > Styling section.
In both examples, the bar charts are horizontal, with mirroring measures on the y-axis. You also have the option to display the bar chart vertically. In this case, the mirroring measures will be on the x-axis.
Simple, right? As long as there are two items to be compared like male/female or team 1/team 2, a butterfly chart makes a nice alternative to the standard grouped or stacked bar chart. Try it for yourself and learn more at Qlik Help.
Jennell
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LinkedIn Analytics Dashboard for Personal Profils
LinkedIn Analytics Dashboard for Personal ProfilsEvaco GmbHUntil now, it was only possible to analyze personal LinkedIn posts directly on LinkedIn or ... Show MoreLinkedIn Analytics Dashboard for Personal ProfilsEvaco GmbHUntil now, it was only possible to analyze personal LinkedIn posts directly on LinkedIn or by exporting the data manually. The LinkedIn Analytics Dashboard for Personal Profiles now offers a centralized solution: it allows unlimited exports to be collected within one app, making it possible to conduct analyses over periods longer than 365 days. In addition to the main dashboard, which displays metrics such as impressions, reached users, followers, and interactions, there are several additional analysis sheets available. These provide detailed insights into engagement, individual posts, and the demographic and temporal distribution of users viewing the content. An impression breakdown by content format is also included.Discoveries
It is now possible to track the long-term development of the reach of LinkedIn posts. With integrated forecasting features, you can also assess whether your goals are being met. Another key advantage: Qlik offers significantly more interactivity and flexibility compared to LinkedIn’s built-in analytics tools.
Impact
The monitoring and performance analysis of LinkedIn posts becomes significantly faster and more comprehensive with the new dashboard. Data can be analyzed efficiently, trends identified early, and posts optimized in a targeted way.
Audience
Perfect for anyone looking to analyze one or more personal LinkedIn accounts – quickly, clearly, and across extended time periods.
Data and advanced analytics
The app makes it possible to analyze LinkedIn data directly within the familiar Data & Analytics platform, using the tools and capabilities you already know.
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Welcome back Marisa Sánchez: Transforming Data Education with Qlik as Educator A...
We are excited to announce Marisa as a returning Qlik Educator Ambassador for 2025! Marisa continues to make a profound impact at Universidad Nacional... Show MoreWe are excited to announce Marisa as a returning Qlik Educator Ambassador for 2025! Marisa continues to make a profound impact at Universidad Nacional del Sur in Bahía Blanca, Argentina, where she teaches Business Intelligence for the Business Administration bachelor’s degree. Her course equips students with essential skills in data visualization, data mining, and simulation models, preparing them for data-driven decision making in their future careers.
Marisa’s dedication to staying current is unmatched. “Each year we update the course,” she shares. Last year, she revamped the evaluation process, ensuring students are assessed in ways that reflect real-world applications. This year, she’s going even further by introducing Generative Artificial Intelligence (GAI), preparing students to leverage this cutting-edge technology for automation and data augmentation.
“Generative AI opens new possibilities. Unlike predictive AI, GAI uses extensive datasets to generate new content, making it vital for future professionals to master this technology,” Marisa explains. Her foresight ensures her students are prepared for the rapidly evolving digital landscape.
The course’s data visualization curriculum is built on the Qlik Learning Portal, empowering students to create impactful data stories. Additionally, Marisa plans to integrate Qlik’s functionalities into data mining projects, ensuring students gain hands-on experience with industry-leading tools.
To foster a community of data enthusiasts, Marisa organizes an annual seminar on Data Visualization using Qlik, open to students, researchers, and professionals. This event not only showcases the power of Qlik but also creates networking opportunities that bridge academia and industry.
Although the university does not track students’ career progress, Marisa has noticed a remarkable trend: “Many students decide to take more courses on data analytics after graduation because of their experience with Qlik,” she proudly shares. Her teaching doesn’t just educate, it inspires lifelong learning and career growth.
Marisa recognizes the growing challenges of teaching in the digital age. “Teaching is increasingly challenging because of the rapid technological advancements, but it’s rewarding to offer impactful content for students’ professional lives,” she reflects. Her passion for education and innovation drives her to continually enhance her curriculum.
Being a Qlik Educator Ambassador is a source of pride for Marisa. “It is a great honor to be part of the Qlik Academic Program. It allows me to see how other educators use Qlik, get inspired by their achievements, and learn from customer success stories,” she explains. The program has also increased her visibility within the university community, helping her share the benefits of the Qlik Academic Program more widely.
Congratulations, Marisa, on your continued journey as a Qlik Educator Ambassador! Your commitment to innovation and passion for empowering students are truly inspiring.
For more on our Educator Ambassador Program, visit: qlik.com/academic-program/ambassadors
University educators and students can get access to free Qlik software and training resources, qualifications, and certifications by applying to the Academic Program today: qlik.com/academicprogram
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Unlocking the Power of Iceberg Lakehouses with Qlik Talend Cloud Pipelines and S...
Today, Qlik Talend Cloud (QTC) offers an end-to-end enterprise-grade solution that delivers rapid time to insight and agility for Snowflake users. Qli... Show MoreToday, Qlik Talend Cloud (QTC) offers an end-to-end enterprise-grade solution that delivers rapid time to insight and agility for Snowflake users. Qlik’s solution for Snowflake users automates the ingestion, design, implementation, and updates of data warehouses and lakehouses while minimizing the manual, error-prone design processes of data modeling, ETL coding, and scripting.
As a result, customers can speed up their analytics and AI initiatives, achieve greater agility, and reduce risk — all while fully realizing the instant elasticity and cost advantages of Snowflake’s cloud data platform.
Now, as organizations continue to scale their data operations, modern architectures like Iceberg-based open lakehouses are emerging as the go-to solution for flexibility, performance, and cost efficiency. To support this evolution, Qlik Talend Cloud Pipelines introduces two new powerful capabilities designed to simplify and enhance the process of building open lakehouses with Snowflake: Lake landing for Snowflake and support for Snowflake-managed Iceberg tables.
Lake-Landing Ingestion for Snowflake Pipelines
A key challenge for customers in cloud data management is balancing rapid data ingestion with optimized compute resources in Snowflake. Qlik Talend Cloud’s new lake-landing ingestion feature for Snowflake addresses this by allowing users to land their data into a cloud-object store first, before consuming it in Snowflake. With this, customers can replicate data from diverse sources into a cloud storage of their choice (Amazon S3, Azure Data Lake Storage, or Google Cloud Storage) with low latency and high fidelity, instead of ingesting data directly into Snowflake’s storage layer. Ingestion into cloud storage is fully managed by Qlik and doesn’t require the use of Snowflake compute.
In addition, Qlik Talend Cloud allows you to configure the frequency at which Snowflake will pick up the data from the cloud storage: While you can replicate source data changes in real-time to a cloud object store, the Snowflake storage task can read and apply those changes at a slower pace (could be once every hour or once every 12 hours for example).
For ingestion use-cases where low latency replication into Snowflake is not a requirement this reduces Snowflake warehouse uptime requirements and ultimately optimizes costs.
Support for Snowflake-Managed Iceberg Tables
In addition to lake-landing ingestion, Qlik Talend Cloud Pipelines now supports Snowflake-managed Iceberg tables. This new feature allows Qlik Talend Cloud pipeline tasks (Storage, Transform, and Data Mart) to ingest and store data directly into Iceberg tables utilizing external cloud storage (S3, ADLS, or GCS). Those externally stored Iceberg tables are fully managed by Snowflake, meaning they benefit from Snowflake performance optimizations and table lifecycle maintenance. Moreover, this new feature is fully integrated with Snowflake’s Open Iceberg Catalog (based on Apache Polaris) to ensure full interoperability with any Iceberg compatible query engine.
These two capabilities described above can be used independently or in combination, offering greater flexibility in how data is ingested, stored, and queried.Example implementation
Below is a diagram showing simple implementation of both of these capabilities together.
It features a pipeline built on Qlik Talend Cloud, composed of 3 successive tasks (lake-landing, storage and transform) that takes care of:
- Replicating data changes from a MySQL source to an S3 object store.
- On a defined schedule, applying the changes onto a Snowflake-based bronze layer. The bronze layer materialized as Iceberg tables that are managed by Snowflake and stored on S3.
- Creating a cleansed, standard table structure, as Iceberg tables as well. In our example, this is the data consumption layer that can be consumed in both Snowflake and in any Iceberg-compatible technology, thanks to a synchronization with Snowflake Open Catalog.
Here is a video shows how to create the above example pipeline:
Why This Matters
With these new capabilities, Qlik Talend Cloud empowers data teams to build Iceberg-based open lakehouses with Snowflake in a more efficient, scalable, and cost-effective manner. Whether optimizing for low-latency ingestion or ensuring seamless interoperability, these enhancements bring significant advantages to modern data architectures. Some of the key benefits of these enhancements include:
- Enhanced Interoperability: Leverage Snowflake-managed Iceberg tables for open data formats that integrate with multiple analytics engines.
- Optimized Compute Efficiency: Reduce compute burn by decoupling ingestion and storage consumption.
- Scalable and Cost-Effective Data Management: Streamline data workflows with flexible ingestion and storage strategies.
Get Started Today
Ready to take advantage of these new capabilities? Explore how Qlik Talend Cloud can help your organization build next-generation open lakehouses with Snowflake.
- Replicating data changes from a MySQL source to an S3 object store.
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Welcome Back Angelika Klidas – Qlik Educator Ambassador Class of 2025!
We are thrilled to welcome back Angelika Klidas to the Qlik Educator Ambassador Program for another exciting year! A long-standing and deeply valued m... Show MoreWe are thrilled to welcome back Angelika Klidas to the Qlik Educator Ambassador Program for another exciting year! A long-standing and deeply valued member of our Academic Program, Angelika has been an Educator Ambassador since 2021, and her passion for Qlik and commitment to teaching data literacy continues to inspire students and fellow educators alike.
Angelika is based in the Netherlands and teaches at the University of Applied Sciences in Amsterdam (HVA). She remains the driving force behind the Minor in Data & Analytics, where she seamlessly embeds Qlik into her curriculum. Her mission is clear: to empower students with the tools, knowledge, and confidence they need to find their own path in the ever-evolving data and AI landscape.
In 2024, Angelika focused on developing various trainings through her work with the BDC Academy, an initiative she’ll also be integrating into the Minor this September. Her teaching philosophy continues to reflect her signature motto: “Think big, act small, scale fast”—encouraging her students to start smart and aim high.
Angelika’s impact speaks for itself—nearly 40% of her students are now working in data-related roles, with some even leading IT departments where Qlik plays an integral role. Her classroom is a launchpad for real-world careers, and her students’ success stories are a testament to the practical and inspiring education she provides.
A true data advocate, Angelika believes that Data & AI Literacy is more vital than ever. In her words:
“Analytics is no longer a 'nice to have'—it’s a must. Data strategy, trusted data, and strong governance must be part of every organization’s roadmap. Only then can Data & AI thrive and deliver meaningful insights.”
She continues to challenge her students and organizations and her students to look at AI as a subset (advanced) of Data Literacy, as true AI Literacy requires Data Literacy, therefore it can't be seen as separate areas.
Angelika's dedication goes far beyond the classroom. She recently collaborated with her customer Van Oord on a powerful Qlik app that was spotlighted on the qlik.org platform by Julie Kae. This same project earned Van Oord the Transformation Award at Qlik Connect—a real-world success story that she proudly shares with her students as a source of inspiration and motivation.
In her personal life, Angelika also has a lot to celebrate:
Her eldest daughter was promoted to Sergeant in the Royal Dutch Navy, her son secured an exciting role in airport security at Schiphol, and her youngest just got her driver’s license and is on the verge of graduating from her nursing program. As a proud mom, educator, and mentor, Angelika’s dedication shines through every facet of her life.When asked why she continues to serve as an Educator Ambassador, Angelika answered simply and sincerely:
“I just love the program. It gives my students the opportunity to get excited about Qlik Sense—just like I did—and bring that knowledge into the companies they work for.”
We’re proud to have Angelika as part of our community and look forward to another impactful year of collaboration. Her work continues to bridge academia and industry, building the next generation of data-literate leaders.
For more on our Educator Ambassador Program, visit: qlik.com/academic-program/ambassadors
📢 University educators and students can get access to free Qlik software and training resources, qualifications, and certifications by applying to the Academic Program today:
👉 qlik.com/academicprogram -
Geomap human body
Geomap human bodyPastifício SelmiO gerente da Segurança do Trabalho me pediu para criar um relatório com os acidentes na empresa, e em especial, um gr... Show MoreGeomap human bodyPastifício SelmiO gerente da Segurança do Trabalho me pediu para criar um relatório com os acidentes na empresa, e em especial, um gráfico do corpo humano com os locais das lesões. Foi uma grande experiência e um baita conhecimento adquirido para resolver esse simples gráfico.Discoveries
.
Impact
.
Audience
.
Data and advanced analytics
.
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【オンデマンド配信】データ分析の魅力を底上げ！映えるダッシュボード作成術
データ分析用に無味乾燥な KPI と表ばかりのダッシュボードを作っていませんか？データ活用やデータ民主化を推進したくても、そんなダッシュボードではユーザーに敬遠されてしまいますよね。 Qlik のビジュアライゼーションはここ最近大きな進化を遂げました。Qlik Sense の最新機能を使えばユーザー... Show More -
Spotify Personal Usage
Spotify Personal UsagePersonalThis Spotify analytics dashboard visualizes my personal streaming data over a year (May 2022-2023), providing insights i... Show MoreSpotify Personal UsagePersonalThis Spotify analytics dashboard visualizes my personal streaming data over a year (May 2022-2023), providing insights into my listening habits. It displays key metrics including total plays, unique songs, artists, along with detailed breakdowns of listening patterns by time of day, favorite tracks, and popular artists. The heatmap reveals when I'm most active on Spotify throughout the week, while the top 10 songs chart and artist word cloud highlight my music preferences. The dashboard serves as a personal music consumption analyzer, allowing me to discover patterns in my listening behavior, identify peak listening times, and recognize my most-played artists and tracks. Its value lies on transforming raw streaming data into visual insights that help me better understand my music consumption habits and discover trends I wouldn't otherwise notice."Discoveries
Key discoveries: 1. Peak listening occurs at 5-7 PM on weekdays, especially Mondays (500+ plays at 8 PM) 2. Daily average of 2:45 hours of music, totaling 1,003 hours for the year 3. Strong song preferences despite accessing 8,482 unique tracks 4. Highest engagement on Mondays and Thursdays, minimal weekend listening 5. Clear artist preferences (Niño Prodigio, Dudu Tassa) among 2,958 artists explored 6. Unexpected night-time listening pattern between 10 PM-12 AM on Thursdays
Impact
This personalized Spotify dashboard has transformed how I understand my music consumption patterns, enabling more intentional listening habits and better playlist curation. By visualizing my listening data, I've optimized my music selection for different activities and times of day, significantly enhancing my overall Spotify experience.
Audience
The Spotify Personal Analytics dashboard is primarily used by me as an individual music enthusiast to track and analyze my listening patterns, though I occasionally share insights with friends who are music aficionados.
Data and advanced analytics
This Spotify dashboard has enhanced my relationship with the platform by providing deeper insights into my listening habits, leading to more intentional engagement and increased usage of premium features.
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Breaking Change in Qlik Application Automation JIRA blocks (May 1st 2025)
The Qlik Application Automation JIRA blocks List issues, List issues by project and List new and updated issues incrementally, have been updated to th... Show MoreThe Qlik Application Automation JIRA blocks List issues, List issues by project and List new and updated issues incrementally, have been updated to the latest API URL.
The changes come into effect on the 1st of May 2025.
What has changed?
The following changes in the blocks:
Input Parameter changes
List issues block:
- Current (Old) Version: Input parameter "Jql" is optional
- New Version: Input parameter "Jql" is a required parameter
List issues by project:
- Current (Old) Version: Input parameter "Project" is optional
- New Version: Input parameter "Project" is a required parameter
List new and updated issues incrementally:
No changes
Response changes
- Old Response: By default, all navigable fields are returned.
If a list of specific fields should be returned, specify the names of the fields in the "Fields" input parameter.
For an example of the raw response, see the attached .json (JIRA-old-response.json) file. - New Response: By default, the resource returns IDs only.
If a list of specific fields should be returned other than “id”, specify the names of the fields in the "Fields" input parameter.
Example of the response:[
{
"id": "157638"
},
{
"id": "156647"
}
]Example of the response if a user has specified the names of the field (such as "Summary") in the "Fields" input parameter:
[
{
"expand": "",
"fields": {
"summary": "Main order flow broken"
},
"id": "10002",
"key": "ED-1",
"self": "https://your-domain.atlassian.net/rest/api/3/issue/10002"
}
]
How do I prepare for the change?
- Update your automations to use the new updated blocks:
- Select the old block
- Then click Upgrade to latest version
Follow the same steps for all remaining blocks, such as List issues by project and List new and updated issues incrementally.
-
Since the response has been updated, the "Id" field will be returned by default if no fields are specified in the "Fields" input parameter.
After upgrading any of the List issues, List issues by project, and List new and updated issues incrementally blocks, add the relevant fields to the input parameter in the block, which is referenced by other blocks.Additionally, modify the output references in other blocks if necessary. If this is not done, it will result in a null value for the input parameter, causing referenced blocks to fail with the error "Missing mandatory parameter" whenever the input parameter is required.
Example error:
{
"response": {
"status": 400,
"body": {
"errors": [
{
"code": "HTTP-400",
"title": "Invalid Request.",
"detail": "Missing mandatory parameter \"Summary\"."
}
]
}
},
"external error": false
}
If you have any questions, we're happy to assist. Reply to this blog post or take similar queries to the Qlik Application Automation forum.
Thank you for choosing Qlik,
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Sheet and Object-level Access Control in Qlik Cloud
Hey guys, it's been awhile since we had a guest blogger on, so today I am pleased to introduce you to Daniel Pilla. Daniel is a Master Principal Ana... Show MoreHey guys, it's been awhile since we had a guest blogger on, so today I am pleased to introduce you to Daniel Pilla. Daniel is a Master Principal Analytics Platform Architect at Qlik and is part of the Presales organization. He has been with Qlik for 8 years, and specializes in integration, architecture, embedding, and security. Take it away Dan!
Sheet and object-level access control in Qlik Cloud
This is a relatively common request, especially from customers coming from Qlik Sense Enterprise Client-Managed. The use case is when organizations want to show/hide specific assets in an application based on the group membership of the current user that is accessing the application. Note that this is in no way a strategy or solution for data security (which is handled with section access), but rather serves as a potential design pattern for custom tailoring apps for specific groups of users.
Example Scenario
Let’s assume a customer has a global sales application. That application contains sheets that are designed for specific product group sales that not every sales representative sells. The customer wants to show the product-specific sheets only to the sales representatives that sell those respective products. If the user contains the group “Product Group A” then they should see the “Product Group A Analysis” sheet, and likewise if the user contains the group “Product Group B” then they should contain the “Product Group B Analysis” sheet.
Solution
To achieve this in Qlik Cloud, we can use the Advanced Analytics connector, which in essence is a RESTful server-side extension. This connector offers the ability to connect to RESTful services in real-time from both the load script and from the front-end (charts and expressions). We can use this connector to connect directly to the Qlik Cloud APIs to fetch the groups of the current user, return those groups as a pipe-delimited string, and then use those groups in a show condition expression.
Setup
Prerequisites:
- The Advanced Analytics connector is going to be making API calls to Qlik Cloud on behalf of the logged in user. This means that you must have a user with the “Developer” role assigned as well as an API key issued to that user.
- The Creation of groups setting must be enabled in the Console under Settings > Feature control
- Ensure that there are groups available to the user that you are testing for in the tenant. To check this, you can enter the following into the browser, replacing {tenant} and {region} accordingly: https://{tenant}.{region}.qlikcloud.com/api/users/me -- There, you will find the assignedGroups array which contains the groups that are assigned to the logged in user.
Connector Setup:
- Import the sample application attached to this page.
- Open the application and navigate to the load script.
- Under Data connections, select Create new connection and select Advanced Analytics.
- For URL, fill in your own tenant for URL followed by ‘/api/v1/users’
- https://{tenant}.{region}.qlikcloud.com/api/v1/users
- Change the Method to GET.
- Under Query Parameters, add a parameter with the Name of ‘filter’ and the Value should resemble the following, where {subject} is an existing user subject for the filter so you can test whether the connection is operational:
- {subject co “{subject}”}
- In this example, the user I am using has a REALM value. Note that you will have to escape the backslash with an extra backslash, e.g., QLIK-POC\dpi needs to be QLIK-POC\\dpi
- Within Authorization, change the Authorization Method to Bearer Token.
- Under Token Scheme, select Bearer.
- For Bearer Token, enter in the API key mentioned as a prerequisite above.
- Within Response Table, for Name of Returned Table enter the value of ‘data’. Note that this value is only really relevant for the load script, but the field is required to be populated nonetheless.
- Under Table Path (JMESPath), enter in the value of ‘data’. Note that this is the name of the JSON object containing the data returned from `api/v1/users` and contains source of data that we require from the payload.
- Within Response Fields, deselect Load all available fields. This is so we can customize the value that is returned.
- Within Response Table, under Table Fields (JMESPath), enter a new Name value as ‘groups’, then enter the Value of ‘join(‘|’,assignedGroups[*].name)’. This will concatenate all of the values of the ‘assignedGroups’ array returned by the API into a pipe delimited string. This function is a part of the JMESPath query language that is supported by the Advanced Analytics connector. To learn more, you can refer to: https://jmespath.org/tutorial.html.
- Leave the remaining settings untouched.
- Set the Name of the connection as ‘Get Groups’. Note in this case this is important because the name of the connection is directly referenced in the expression of the accompanying sample app.
- Test the connection and ensure that it is operational.
Sample App Testing:
The sample application includes three sheets:
- Get Current User Groups – this sheet displays the current groups of the logged in user.
- Product Group A Analysis – this sheet has a commented out calculation condition to only show the sheet if the user contains the group ‘Product Group A’.
- Product Group B Analysis – this sheet has a commented out calculation condition to only show the sheet if the user contains the group ‘Product Group B’.
The application transforms the OsUser() result into the subject format, looks up the user, gets the groups, and returns them as a pipe-delimited string. You can find this process defined in the vUserSub and vUserGroups variables.
To test the application, first confirm that the first sheet returns your user groups. If it does, you can modify the sheet calculation conditions on the latter two sheets to your desired group names that you want to show based on.
Modify the expression by uncommenting it and adding in your desired group name (ensured it is enclosed by pipes so as to not partially match another group name):
In my example, I am a member of the group `Product Group A’ and not `Product Group B’, so while in edit mode, I see the following, confirming the ‘Product Group B Analysis’ is hidden from my view:
Exiting edit mode, I now see:
Additional Notes
- If users have edit-level access or greater to the application where this method is deployed, when in “Edit” mode, they will be able to see that the hidden sheet(s) exist. The user could then duplicate that sheet and remove/alter the show condition so that they could see the sheet. This is not a data security risk, as this technique only focuses on cosmetic app design, however it should be noted that if it is desired that the users cannot have access to these sheets then they must not have any roles that allow for edit-level access including “Can manage”, “Can contribute”, and “Can edit”.
- This solution relies on API calls being made by the owner of the API key.
- This user must:
- Continue to be exist
- Continue to have the “Developer” role
- Ensure that the API key does not expire and/or is rotated to prevent downtime
- This user must:
- As this solution is making calls on behalf of a single user as users are leverage the application, there is the potential for rate-limiting at the Users API. The current rate limit of the Users API is 1,000 calls per minute.
- The Advanced Analytics connector function call in the sample app leverages the "ShouldCache":"False" setting in the vUserGroups variable. This ensures that the user’s groups are not cached by the engine, however it makes more calls to the APIs. If you are experiencing or are concerned about rate limiting, this setting can be removed.
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Knowledge Nuggets - 2025-Q1
Knowledge Nuggets - 2025-Q1Insight ConsultingThis Qlik Sense app curates and provides links to Qlik-related content published since Qlik Connect 2024.... Show MoreKnowledge Nuggets - 2025-Q1Insight ConsultingThis Qlik Sense app curates and provides links to Qlik-related content published since Qlik Connect 2024. Its purpose is to highlight and celebrate contributors from around the world for their valuable contributions to Qlik's body of knowledge. Additionally, it serves as a go-to reference tool for Qlikkies, supporting their continuous learning and professional growth.Discoveries
The app provides insight into the frequency of contributions and subject matter from various content creators
Impact
The app can be used as a reference tool to help Qlik developers to find learning material to support their continuous professional development.
Audience
Can be used by Qlik Developers as well as other stakeholders
Data and advanced analytics
Can be used by Qlik Developers as well as other stakeholders as a reference tool
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Qlik and Talend Subprocessors - Version 5.8 - March 2025
Table of Contents Third party subprocessors for Qlik offeringsThird party subprocessors for Talend offeringsAffiliate Subprocessors for Qlik and Talen... Show MoreTable of Contents
- Third party subprocessors for Qlik offerings
- Third party subprocessors for Talend offerings
- Affiliate Subprocessors for Qlik and Talend
Version 5.7. Current as of: 20th January 2025
Qlik and Talend, a Qlik company, may from time to time use the following Qlik and Talend group companies and/or third parties (collectively, “Subprocessors”) to process personal data on customers’ behalf (“Customer Personal Data”) for purposes of providing Qlik and/or Talend Cloud, Support Services and/or Consulting Services.
Qlik and Talend have relevant data transfer agreements in place with the Subprocessors (including group companies) to enable the lawful and secure transfer of Customer Personal Data.
You can receive updates to this Subprocessor list by subscribing to this blog or by enabling RSS feed notifications.
Third party subprocessors for Qlik offerings
Third party subprocessors for Qlik Cloud
Third Party
Location of processing (e.g., tenant location)
Service Provided/Details of processing
Address of contracting party
Contact
Amazon Web Services
(AWS)
If EMEA region is chosen:
Ireland (Republic of); &
Paris, France (back-up); or
Frankfurt, Germany; &
Milan, Italy (back-up); or
London, UK; &
Spain (back-up);
or
UAE (back-up also in UAE).
Qlik Anonymous Access:
Stockholm,
Sweden.
If AMER region is chosen:
North Virginia, US; &
Ohio, US (back-up).
If APAC region is chosen:
Sydney, Australia; &
Melbourne, Australia (back-up); or
Singapore;&
Seoul, S. Korea(back-up); or
Tokyo, Japan;&
Osaka, Japan(back-up); or
Mumbai, India;
Hyderabad, India (back-up).
Qlik Cloud is hosted through AWS
Amazon Web Services, Inc. 410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A
MongoDB
See Qlik Cloud locations above Any data inputted into the Notes feature in Qlik Cloud
Mongo DB, Inc.
229 West 43rd St.,
New York, NY 10036
USAThird party subprocessors for Qlik Support Services and/or Consulting Services
The vast majority of Qlik’s support data that it processes on behalf of customers is stored in Germany (AWS). However, in order to resolve and facilitate the support case, such support data may also temporarily reside on the other systems/tools below.
Amazon Web Services
(AWS)
Germany
Support case management tools
Amazon Web Services, Inc.
410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A.
Salesforce
UK
Support case management tools
Salesforce UK Limited
Village 9
Floor 26 Salesforce Tower
110 Bishopsgate
London, UK
EC2N 4AYGrazitti SearchUnify
United States
Support case management tools
Grazitti Interactive
Plot 164
Industrial Area Phase 2
Panchkula, Haryana
India
134113Microsoft
United States
Customer may send data through Office 365
Microsoft Corporation
One Microsoft Way,
Redmond, WA
98052
USAChief Privacy Officer
One Microsoft Way,
Redmond, WA
98052
USAAda
Germany
Support Chatbot
Ada Support
371 Front St W,
Unit 314
Toronto, Ontario
M5V 3S8
CANADAPersistent
India
R&D Support Services
2055 Laurelwood Road
Suite 201
Santa Clara, California 95054
USAAtlassian
(Jira Cloud)
Germany, Ireland (Back-up)
R&D support management tool
350 Bush Street
Floor 13
San Francisco, CA 94104
United StatesAltoros
United States
R&D Support Services
Altoros Americas, LLC
830 Stewart Drive, Suite 119
Sunnyvale, CA 94085
United StatesIngima
Israel
R&D Support Services
Ha-Khilazon St 3, Ramat Gan, Israel
Galil
Israel
R&D Support Services
Galil Software and Technology Services Ltd.
Industrial Park, Mount Precipice,
2015 St, 1610102 Nazareth, IsraelThird party subprocessors for Qlik mobile device apps
Google Firebase
United States
Push notifications
Google LLC
1600 Amphitheatre Parkway
Mountain View
California
United StatesThird party subprocessors for Talend offerings
Third party subprocessors for Talend Cloud
Third Party
Location of processing (e.g., tenant location)
Service Provided/Details of Processing
Address of contracting party
Contact
Amazon Web Services (AWS)
Talend Cloud
AMERICAS:
Virginia, US; &
Oregon, US (backup).
EMEA:
Frankfurt, Germany; &
Ireland (Republic of)(backup).
APAC:
Tokyo, Japan; &
Singapore (backup); or
Sydney, Australia; &
Singapore (backup); or
Singapore;&
Seoul, S. Korea(back-up);
Stitch
AMERICAS:
Virginia, US; &
Oregon, US (backup).
EMEA:
Frankfurt, Germany; &
Ireland (Republic of) (backup).
These Talend Cloud locations are hosted through AWS
Amazon Web Services, Inc.
410 Terry Avenue North, Seattle, WA 98109-5210, U.S.A.Microsoft Azure
United States:
California; Virginia (backup)These Talend Cloud locations are hosted through Microsoft Azure
Microsoft Corporation
1 Microsoft Way, Redmond, WA 98052, USAMicrosoft Enterprise Service Privacy
Microsoft Corporation
1 Microsoft Way
Redmond, Washington 98052 USAMongoDB
See Talend Cloud locations above Mongo DB, Inc.
229 West 43rd St.,
New York, NY 10036
USAThird party subprocessors for Talend Support Services and/or Consulting Services:
In order to provide Support and/or Consulting Services, the following third party tools may be used.
Sub-processor
Location of processing (e.g., tenant location)
Service Provided/Details of processing
Address of contracting party
Contact
Atlassian
France
United States
Project management; support issue tracking
Atlassian Pty Ltd 350 Bush Street Floor 13
San Francisco, CA 94104 United States
Atlassian (Jira Cloud) Germany, Ireland (Back-up)
R&D support management tool
Atlassian Pty Ltd 350 Bush Street Floor 13
San Francisco, CA 94104 United StatesMicrosoft
United States
Email provider, if the Customer sends Customer Personal Data through email.
Microsoft Corporation
1 Microsoft Way, Redmond, WA 98052, USAMicrosoft Enterprise Service Privacy
Microsoft Corporation
1 Microsoft Way
Redmond, Washington 98052 USA
Salesforce
United States
CRM; support case management
Salesforce UK Limited
Floor 26 Salesforce Tower
100 Bishopsgate
London
EC2N 4AYAffiliate Subprocessors for Qlik and Talend
Affiliate Subprocessors
These affiliates may provide services, such as Consulting or Support, depending on your location and agreement(s) with us. Our Support Services are predominantly performed in the customer’s region:
EMEA – France, Sweden, Spain, Israel; Americas – USA; APAC – Japan, Australia, India.
Subsidiary Affiliate
Location of processing (e.g., tenant location)
Service Provided/Details of Processing
Address of contracting party
Contact
QlikTech Netherlands BV, Talend Netherlands B.V.
Netherlands
These affiliates may provide services, such as Consulting or Support, depending on your location and agreement(s) with us. Our Support Services are predominantly performed in the customer’s region: EMEA – France, Sweden, Spain, Israel; Americas – USA; APAC – Japan, Australia, India.
Evert van de Beekstraat 1-122
Building B, 6th Floor
1118 CL SchipholDPO QlikTech Netherlands BV (Belgian branch)
Belgium
Culliganlaan 2D
1831 DiegemBlendr NV
Belgium
Bellevue Tower Bellevue 5, 4th Floor, Ledeberg 9050 Ghent Belgium
QlikTech UK Limited, Talend Ltd.
United Kingdom
1020 Eskdale Road, Winnersh, Wokingham, RG41 5TS United Kingdom
Qlik Analytics (ISR) Ltd.
Israel
1 Atir Yeda St, Building 2 7th floor 4464301, Kfar Saba Israel
QlikTech International Markets AB (DMCC Branch)
United Arab Emirates
AB (DMCC Branch)
JBC 3 Building, Cluster Y
3rd Floor, Office 301
P.O. Box 120115
Jumeirah Lake Towers DubaiQlikTech Inc.
United States
211 South Gulph Road Suite 500 King of Prussia, Pennsylvania 19406
QlikTech Corporation (Canada), Talend
Canada
1133 Melville Street Suite 3500, The Stack Vancouver, BC V6E 4E5 Canada
QlikTech México S. de R.L. de C.V.
Mexico
c/o IT&CS International Tax and Consulting Service San Borja 1208 Int. 8 Col. Narvate Poniente, Alc Benito Juarez 03020 Ciudad de Mexico Mexico
QlikTech Brasil Comercialização de Software Ltda.
Brazil
51 – 2o andar - conjunto 201 Vila Olímpia – São Paulo – SP Brazil
QlikTech Japan K.K., Talend KK
Japan
105-0001 Tokyo Toranomon Global Square 13F, 1-3-1. Toranomon, Minato-ku, Tokyo, Japan
QlikTech Singapore Pte. Ltd., Talend Singapore Pte. Ltd.
Singapore
9 Temasek Boulevard Suntec Tower Two Unit 27-01/03 Singapore 038989
QlikTech Hong Kong Limited
Hong Kong
Unit 19 E Neich Tower 128 Glouchester Road Wanchai, Hong Kong
Qlik Technology (Beijing) Limited Liability Company, Talend China Beijing Technology Co. Ltd.
China
51-52, 26F, Fortune Financial Center, No. 5 Dongsan Huanzhong Road, Chaoyang district, Pekin / Beijing, 100020 China
QlikTech India Private Limited, Talend Data Integration Services Private Limited
India
“Kalyani Solitaire” Ground Floor & First Floor 165/2 Krishna Raju Layout Doraisanipalya Off Bannerghatta Road, JP Nagar, Bangalore 560076
QlikTech Australia Pty Ltd, Talend Australia Pty Ltd.
Australia
McBurney & Partners Level 10 68 Pitt Street Sydney NSW 2000 Australia
QlikTech New Zealand Limited
New Zealand
Kensington Swan 40 Bowen Street Wellington 6011 New Zealand
In addition to the above, other professional service providers may be engaged to provide you with professional services related to the implementation of your particular Qlik and/or Talend offerings; please contact your Qlik account manager or refer to your SOW on whether these apply to your engagement.
Qlik and Talend reserve the right to amend its products and services from time to time. For more information, please see www.qlik.com/us/trust/privacy and/or https://www.talend.com/privacy/.
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【オンデマンド配信】リニューアル公開！Qlik Sense の基本操作と実践アプリ開発技術を習得
Qlik Sense を初めて操作する方、アプリ開発の基礎から応用を習得したい方向けに、Web セミナーをリニューアル公開しました。最新の操作画面での開発手順や使い方をアップデート機能も含めて解説しています。アプリ開発では、チャート関数や SET 分析を駆使した集計、データ変換を実現するロードスクリ... Show MoreQlik Sense を初めて操作する方、アプリ開発の基礎から応用を習得したい方向けに、Web セミナーをリニューアル公開しました。最新の操作画面での開発手順や使い方をアップデート機能も含めて解説しています。アプリ開発では、チャート関数や SET 分析を駆使した集計、データ変換を実現するロードスクリプトまで、実際のリアルな現場で使える分析アプリの作成を習得できます。
※参加費無料。パソコン・タブレット・スマートフォンで、どこからでもご視聴いただけます。
Qlik Sense 入門 ハンズオン Web セミナーを視聴する -
"Data Whisperer & HR Innovator: Turning Numbers into Powerful Stories" - HR Turn...
HR Turnover App Intelco This Qlik Sense app was created with the goal of creating interactive dashboards on turnover and absenteeism rates in ... Show More
HR Turnover AppIntelcoThis Qlik Sense app was created with the goal of creating interactive dashboards on turnover and absenteeism rates in HR feld.Turnover (or turnover rate) refers to staff turnover within an organization. It indicates how often employees leave the company and are replaced by new hires.Absenteeism refers to the frequency with which employees are absent from work, whether for excused (e.g., illness, vacation) or unexcused (unauthorized absences) reasons.The Employee Turnover dataset is a real dataset used to predict an Employee's risk of quitting (with a Survival Analysis Model). It explained that "Survival Analysis" is one of the most importance but it's not the most popular algorithm to predict employee turnover. By inspiration, dataset containing the data was taken from Kaggle: https://www.kaggle.com/datasets/davinwijaya/employee-turnover
Discoveries
Absenteeism rate, Turnover rate, Anticipation of leaving employment, Industries with higher turnover rate
Impact
It is possible to predict the abandonment of staff employment, it is possible to predict the rate of staff absenteeism
Audience
The application can be used by HR departments (such as management control manager, finance manager, etc.) of companies to make informed decisions.
Data and advanced analytics
KPIs are used with statistical and predictive analysis. Time-based and behavior-based analysis.
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HITs Analytics
HITs AnalyticsPersonalEvery week, my colleagues participate in a contest where we each choose the best song proposed on a specific topic. In the app, ... Show MoreHITs AnalyticsPersonalEvery week, my colleagues participate in a contest where we each choose the best song proposed on a specific topic. In the app, we manage all the contest's KPIs, progress, and results reports. You can even listen to the songs. It's the highest-rated app.Discoveries
Tracking of the song contest among peers, where the evolution of points, positions, who gives more or less points to whom, who guesses another's song, etc. is managed. You can see from which position each contestant usually bets on to watching the videos of the songs.
Impact
It serves to create cohesion and a greater group atmosphere within the company.
Audience
The app's users are the competition participants and other members of the company following the event.
Data and advanced analytics
Contest tracking data, YouTube access to share and view videos of the songs bet on. Tests have also been conducted with ChatGPT to send the contest history and weekly bets so that users can also place bets on which song belongs to each contestant.
