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Recent Blog Posts
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Qlik Insight Bot - an AI-powered bot for conversational analytics built on Qlik’...
Yet, in the field of business intelligence, making information more usable--more available, understandable, and digestible--is often an exercise in si... Show MoreYet, in the field of business intelligence, making information more usable--more available, understandable, and digestible--is often an exercise in simplifying the experience. We summarize information because most raw data is too detailed for us to process. We distill huge amounts of complex data into simply displayed KPIs so that humans don’t have sift through rows and columns of data for insight. And we process data through machine learning algorithms to discover patterns that would take humans years to discover. All of these efforts are intended to make the process of generating actionable insights from data simple.
Human beings prefer what is simple.
Even business users with access to nicely designed but complex dashboards often prefer the simplicity of interfacing with a knowledgeable and delightful data analyst. It's simpler to type questions into a chat window than it is to navigate content from a BI portal, where oceans of data can feel like mountains of sophisticated analytic content. For everyday business users, it's easier to ask content creators for simple answers than to search for an answer like the proverbial needle in a haystack.
This is why at Crunch Data (acquired by Qlik earlier this year), we created we created CrunchBot.ai—an AI-powered bot for conversational analytics built on Qlik’s Associative Engine.
We built what is now called Qlik Insight Bot with the idea of providing conversational access to data insights as easily as conjuring trivia and music playlists from Alexa. We believe conversational analytics will be the key to delivering value to users who struggle to adopt analytics in general, users who prefer to work with data analysts as intermediaries, and even users who only sometimes struggle to grasp new analytic content.
I have spent years working with customers to drive value from data, first as a Qlik employee, and then as a co-founder of a small consultancy specializing in Qlik-related data engineering projects. It was clear to me that no matter how well an application suited a particular set of users, there would be another set of users for whom our solutions lacked the accessibility to deliver maximum value.
At one of my favorite companies, a giant multinational corporation known worldwide for its excellent software for small business and personal tax software, we built a set of Qlik apps on data from Hyperion and other sources, and we were broadly successful rolling out this sophisticated capability and content to the finance and HR groups. Both application sets worked beautifully for their respective audiences, with a relatively high degree of data literacy and domain expertise. But when providing access to a wider contingency, specifically to budget owners and people managers, we (predictably) found that broad simplifications were required to make the applications usable.
Budget owners just wanted answers generated in a simple manner:
- What is my budget?
- How much budget is remaining for this quarter? How about this year?
- Which employees are spending the most on travel?
- Show me spend for each sales rep.
People managers wanted to ask simple questions such as:
- What is my net promoter score?
- How does my net promoter score compare to the entire company?
- Which employees with a high retention rating are at risk for attrition?
- Predict attrition be over the next 6 months?
Predictably, it was easier for new users to get answers from their finance and HR business partners than it was to find them in an application built for Finance and HR.
Instead of asking developers to create custom views tailored to the specific needs and skill sets of different user groups, the advent of AI now allows organizations to offer a conversational analytics experience to complement visual dashboards, creating a new paradigm in which users can ask any questions about their data using natural language. We designed the Qlik Insight Bot to fulfill this exact purpose--to act as an analytic intermediary, in the same way that an analytics expert might intermediate on behalf of a casual business user.
The Qlik Insight Bot is now available, allowing users to type in natural language (NLP) questions consisting of every possible combination of dimensions, measures, and visualizations. It applies machine learning algorithms to find patterns, root causes, and anomalies, then responds in natural language (NLG). It frees the data analyst from constantly serving their less analytically inclined users, it and empowers the casual business user with an easy ramp into data literacy. Users can ask precise questions directly to the Insight Bot, and the Insight Bot responds in sub-seconds with answers and analysis that might take a human analyst several hours to create. Users have access to any answered available from Qlik Sense application, and they access it in the most natural, simple way possible.
This is not to say that simplicity is enough or desirable. Users rarely lack follow up questions, and a simple question and answer session with most any other "chat bot" available is a pale, half comparison to the conversational analytics enabled by the Qlik Insight Bot.
The difference is that other chat bots are limited to superficially answer questions. The Qlik Insight Bot engages users in meaningful dialog, inviting casual users not just to ask questions, but to discover and explore a depth of insight behind answers. When a user asks a question of the Qlik Insight Bot, getting a direct answer is only the beginning of the conversation. After delivering a concise answer, Qlik Insight Bot uses the Qlik Associative Engine and AI (Augmented Intelligence) to deliver a visual depiction of the answer based on what it has learned from users asking similar questions. It then provides a natural language (NLG) interpretation from machine learning (ML) algorithms. The pattern detection and causality inferences are embedded into the response, suggesting what (i.e. what products, employees, or external factors) could be causing changes in the subject performance attributes. Notably, we are also able to infer cause from what is conspicuously absent from a trend, this capability being uniquely possible with Qlik's associative engine, a with no analogous feature in competing products
But I argue, that even this isn't enough. The unknown pundit who pontificated that "if something is made so simple that fools can use it, only fools will use it" remains unknown for good reason, but he or she did have a point.
Conversational analytics as delivered by the Qlik Insight Bot are possibly the best way, and certainly the simplest way, of engaging users in meaningful data analysis; but ultimately, depth in understanding comes from personally exploring data visually and performing one's own analysis. Expediency and data literacy may prevent some users from immediately taking the dive into visual exploration, but it's important for them to have the option. Because the Qlik Insight Bot runs on the Qlik Associative Engine, which understands and retains context, the same engine drives both visual and conversational analytics and creates a seamless experience across them. Being able to “bridge the gap”, users can enter conversationally, and the seamlessly dive into visual dashboards – in context – to explore further. The Qlik Insight Bot to persistently augments the conversation with links to the underlying Qlik apps, gently reminding its users that further insight is just a click away.
Charney Hoffmann
Director, Consulting Offerings & Enablement -
Conditional Show/Hide Dimensions and Measures in a Chart
Conditional show or hide is available in line and bar charts giving the user the ability to toggle dimensions or measures on or off in a single chart.... Show MoreConditional show or hide is available in line and bar charts giving the user the ability to toggle dimensions or measures on or off in a single chart. This allows developers to customize line and bar charts and save space by using one chart to show various metrics and dimensions. Let’s look at a simple way of using this feature to show or hide lines in a line chart. In the Overall Equipment Efficiency demo found on the Demo Site, there is a line chart accompanied by buttons that are used to toggle the lines on and off in the line chart.
This is done by using variables. When each button is clicked, the respective variable is toggled from 0 to 1 or 1 to 0 depending on its current value. See the value expression in the image below.
In the measure expression in the line chart, this variable is checked to determine if the expression should be evaluated and displayed or if the measure should be set to null.
This is a perfectly good way to toggle the lines, but with the ability to use conditional show and hide in line and bar charts, this process can be simplified. First, in the measure expression, we no longer need to use an if statement which can help reduce calculation time. We can simply use our normal expression and the “Show measure if” setting, with the respective variable, to evaluate if a line should be shown in the visualization or not.
The “Show measure if” and “Show dimension if” settings evaluate the expression and will show the line if the expression evaluates to true. In my example, vShowOEE will be either 1 or 0. If it is 1, the line will be displayed. If it is 0, then it will not be displayed. We can continue to use the buttons to toggle the respective variable (from 1 to 0 and vice versa) for each line.
My example is basic, but more complex expressions can be used as well. For example, you may want to show/hide lines based on a selection or a calculated value or you may want to use some business logic to determine which dimension or measure should be displayed. The expression can be as simple or complex as needed, as long as it returns a true or false value. Keep in mind, that this show setting is optional and can be left blank. When no expression is entered, the line (or bar) is displayed.
There are a few limitations of this new feature to be aware of: 1) Custom tooltips are disabled when using a conditional dimension, 2) Time series forecasting is not available when using conditional dimensions or measures. While the “Show measure if” and the “Show dimension if” can both be used in the same chart, it is recommended that you use only one at a time. Check out Qlik Help to learn more and test this new feature out in your next line or bar chart.
Thanks,
Jennell
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Now Live: Qlik Sense SaaS Simplified Authoring – Analytics Creation for Everyone
At Qlik, we continually look for ways to improve the user experience and make it easier for you and your organization to make data-driven decisions an... Show MoreAt Qlik, we continually look for ways to improve the user experience and make it easier for you and your organization to make data-driven decisions and take action. Feedback from customers like you combined with technology from our 2020 Knarr Analytics acquisition drove the features of Simplified Authoring, which include:
Assets Panel: Classifies field types and, when fields are expanded, shows values; also displays master items (dimensions and measures), which can be selected for your chart or visualization.
Properties Panel: Includes a Data area; Autochart, which provides more than 30 visualization choices; allows you to include or exclude dimension values by ticking boxes; and Presentation area, where you can choose sorting, coloring, labeling, styling, tooltips, and axis options.
SmartGrid: Acts as a placeholder for your visualizations, which automatically adjusts the size, so they fit within your sheet. You can easily add charts by clicking on “+” to the right and below the grid.
Data Table: Explore your data and inspect fields and values from tables loaded within Qlik Sense SaaS. Make selections within the table to pinpoint areas of interest and analysis.
If you prefer the classic look and feel or require the full set of properties for your use cases, simply click on the toggle in the upper right-hand corner for Advanced Authoring.
Check out the following Simplified Authoring resources for more information:
We will continue to build out more capabilities as we obtain additional feedback on Simplified Authoring.
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Qlik Sense Enterprise for Windows - New Security Patches Available Now
Edited August 30th, 15:55 CET: Added clarification on older Qlik Sense Enterprise on Windows versionsEdited August 31st, 13:10 CET: Added clarificatio... Show MoreEdited August 30th, 15:55 CET: Added clarification on older Qlik Sense Enterprise on Windows versions
Edited August 31st, 13:10 CET: Added clarification on possible workarounds (none exist) as well as information regarding what authentication methods (all) are affected and that HTTP and HTTPS are impactedHello Qlik Users,
Two security issues in Qlik Sense Enterprise for Windows have been identified and patches made available. Details can be found in Security Bulletin Critical Security fixes for Qlik Sense Enterprise for Windows (CVE-2023-41266, CVE-2023-41265).
Today, we have released five service releases across the latest versions of Qlik Sense to patch the reported issues. All versions of Qlik Sense Enterprise for Windows prior to and including these releases are impacted:
- May 2023 Patch 3
- February 2023 Patch 7
- November 2022 Patch 10
- August 2022 Patch 12
All prior versions of Qlik Sense Enterprise on Windows are affected, including releases such as May 2022, February 2022, and earlier. While no patches are currently listed for these versions, Qlik is actively investigating the possibility of patching older releases.
No workarounds can be provided. Customers should upgrade Qlik Sense Enterprise for Windows to a version containing fixes for these issues. August 2023 IR released today already contains the fix.
- August 2023 Initial Release
- May 2023 Patch 4
- February 2023 Patch 8
- November 2022 Patch 11
- August 2022 Patch 13
This issue only impacts Qlik Sense Enterprise for Windows. Other Qlik products including Qlik Cloud and QlikView are NOT impacted.
All Qlik software can be downloaded from our official Qlik Download page (customer login required). Follow best practices when upgrading Qlik Sense.
The information in this post and Security Bulletin Critical Security fixes for Qlik Sense Enterprise for Windows (CVE-2023-41266, CVE-2023-41265) are disclosed in accordance with our published Security and Vulnerability Policy.
Clarifications and Frequently Asked Questions:
What can be done to mitigate the issue?
No mitigation can be provided. An upgrade should be performed at the earliest. As per Qlik's best practices, the proxy should not be exposed to the public internet, which reduces the attack surface significantly.
What authentication methods are affected?
All authentication methods are affected.
Are environments with HTTP disabled impacted?
Environments will be affected regardless if HTTP or HTTPS are in use. These vulnerabilities affect the HTTP protocol overall, meaning even if HTTP is disabled, the environment remains vulnerable.
These attacks don’t rely on intercepting any communication, and therefore, are indifferent whether the HTTP communication is encrypted or not.
Kind regards, and thank you for choosing Qlik,
Qlik Global Support
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Qlik Sense Enterprise for Windows - New Security Patches Available Now
Hello Qlik Users, A security issue in Qlik Sense Enterprise for Windows has been identified, and patches have been made available. Details can be foun... Show MoreHello Qlik Users,
A security issue in Qlik Sense Enterprise for Windows has been identified, and patches have been made available. Details can be found in the Security Bulletin Critical Security fixes for Qlik Sense Enterprise for Windows (CVE-pending).
Today, we have released eight service releases across the latest versions of Qlik Sense to patch the reported issues. All versions of Qlik Sense Enterprise for Windows prior to and including these releases are impacted:
- August 2023 Patch 1
- May 2023 Patch 5
- February 2023 Patch 9
- November 2022 Patch 11
- August 2022 Patch 13
- May 2022 Patch 15
- February 2022 Patch 14
- November 2021 Patch 16
No workarounds can be provided. Customers should upgrade Qlik Sense Enterprise for Windows to a version containing fixes for these issues. The listed fixes also address CV-2023-41266 and CVE-2023-41265 (link).- August 2023 Patch 2
- May 2023 Patch 6
- February 2023 Patch 10
- November 2022 Patch 12
- August 2022 Patch 14
- May 2022 Patch 16
- February 2022 Patch 15
- November 2021 Patch 17
This issue only impacts Qlik Sense Enterprise for Windows. Other Qlik products including Qlik Cloud and QlikView are NOT impacted.
All Qlik software can be downloaded from our official Qlik Download page (customer login required). Follow best practices when upgrading Qlik Sense.
Qlik provides patches for major releases until the next Initial or Service Release is generally available. See Release Management Policy for Qlik Software. Notwithstanding, additional patches for earlier releases may be made available at Qlik’s discretion.
The information in this post and Security Bulletin Critical Security fixes for Qlik Sense Enterprise for Windows (CVE-pending) is disclosed in accordance with our published Security and Vulnerability Policy.
Clarifications and Frequently Asked Questions:
What can be done to mitigate the issue?
No mitigation can be provided. An upgrade should be performed at the earliest. As per Qlik's best practices, the proxy should not be exposed to the public internet, which reduces the attack surface significantly.
What authentication methods are affected?
All authentication methods are affected.
Are environments with HTTP disabled impacted?
Environments will be affected regardless if HTTP or HTTPS are in use. These vulnerabilities affect the HTTP protocol overall, meaning even if HTTP is disabled, the environment remains vulnerable.
These attacks don’t rely on intercepting any communication, and therefore, are indifferent whether the HTTP communication is encrypted or not.
Kind regards, and thank you for choosing Qlik,
Qlik Global Support
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FROM_FIELD() - Is very powerful! It helped me parse JSON data stored in a field!
This is a simple example that works with flat json, meaning non-nested json hierarchies. However, you can use a combination of other functions like J... Show MoreThis is a simple example that works with flat json, meaning non-nested json hierarchies. However, you can use a combination of other functions like JSONGET() and JSONSET() to extract needed data. More examples on this to follow.
Playlist: https://www.youtube.com/playlist?list=PLW1uf5CQ_gSqF5bcmbBrk1q7Q4-h899V1
Qlik Help:
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Yes! It's That Easy - AI-Generated Goal Management and Tracking with Insight Adv...
Insight Advisor Insight Advisor is your AI assistant that provides AI-generated charts and dashboards based on your needs, using capabilities such as:... Show More
Insight AdvisorInsight Advisor is your AI assistant that provides AI-generated charts and dashboards based on your needs, using capabilities such as:
- Natural language search
- Field point and click exploration
- Pre-configured Analysis Types
Analysis Types
Analysis Types are displayed as a list of tiles which represent predefined analyses. These analyses can answer a variety of questions and are categorized which include:
- Ranking
- Breakdown
- Comparison
- Geospatial
- Smart Sheets: (pre-defined dashboards)
- and so much more
Did you know you can jump start your analytics creation OR even get additional answers to business questions by using Insight Advisor Analysis types? Insight Advisor capabilities are not just for initial creation of dashboard content.Video
See how AI-enhanced Insight Advisor Analysis Types: Smart Sheets can automatically generate an interactive performance management dashboard to keep track of your goals in just a few clicks. Yes it is that easy!
Feature: Insight Advisor Analysis Types
Analysis Type: Smart Sheet - Period changes (detailed)
Required: 1 Measure, 1 Dimension, 1 Time Period (defined in Calendar periods (Business Logic))
Options:
-Target - values are set using whole numbers to represent percent of previous period
-Colors: RED = Missed, Yellow=Almost, Green=Met.
-Analysis Periods - calendar periods defined in business logicCan't see the video? YouTube blocked by your region or organization? Click here.
Qlik Help: Analysis Types
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「ハイブリッドクラウド」とは？（Qlik Blog 抄訳）
ブログ著者：Adam Mayer （本ブログはLittle Fluffy Hybrid Clouds の抄訳です） 今日、マルチクラウドの導入、カスタマイズされたクラウドアーキテクチャ、クラウドプラットフォームとの連携など、様々なクラウドで溢れているようです。ところで、ハイブリッドクラウドの本当... Show Moreブログ著者：Adam Mayer （本ブログはLittle Fluffy Hybrid Clouds の抄訳です）
今日、マルチクラウドの導入、カスタマイズされたクラウドアーキテクチャ、クラウドプラットフォームとの連携など、様々なクラウドで溢れているようです。ところで、ハイブリッドクラウドの本当の意味とは何でしょうか？このブログでは「ハイブリッドクラウド」という言葉について考えてみます。
まず、パブリッククラウドとプライベートクラウドの違いについて理解する必要があります。クラウドのデプロイメントは、パブリックかプライベートのどちらかになる傾向があり、パブリッククラウドは基本的にマネージドサービスで、多くの組織によって共有されています。インフラからデータストレージ、Office 365 のようなアプリケーションまで、ベンダーが提供するすべてのサービスについて、あなたは目に見えないところで何が行われているかを知る必要はなく、クラウドがあなたのために機能していることを知るだけでいいのです。
プライベートクラウドは、通常は利用者が自分で管理する環境です。これはデータセンターであったり、インフラサービスであったりします。覚えておかなければならないのは、これらはその利用者にとってプライベートなものだということです。またクラウドベンダーはすべて、何らかの形でプライベートクラウドも提供しています。
ところで、これまでデータは石油や電気に例えられてきました。しかし、雨を降らせる「雲」もあれば、雪を降らせる「雲」もあることを考えると、私は以下の理由からデータを水に例えたいと思います。
- 組織は生き残るためにデータを消費する必要がある
- 水と同じように、データは継続的に流れている方が良い
- 非常に大量のデータを扱う場合、気をつけないと溺れる危険性があり、圧倒されることがある
- 海と同じように、適切な機器がないとその表面の底に眠っていることに気づかない未発見の宝物（「インサイト」と呼ぼう）が隠されている可能性が大量のデータにはある
データを水に喩えれば、水を手に入れる方法は2つあることに気づくでしょう。
- 自分の所有地にある井戸から。そこからどのように水を手に入れるかはあなた次第です。バケツで少しずつ水を汲むこともできるし、自分で配管インフラを構築し、それを維持することもできます。
- 水道会社が所有し維持管理している巨大なインフラから水が送られてくる水道の蛇口。あなたは水道会社に料金を支払えば、必要な時に蛇口を捻るだけで水を得られます。
最初の方法は、自分で管理するプライベートクラウドに近く、次の方法は、パブリッククラウドと同じ利点を持ちます。つまり、サービスがあなたに提供されるのです。
では、ハイブリッドクラウドはどこに当てはまるのでしょうか。実は、ハイブリッドクラウドは、クラウドベンダーが提供するサービスによって異なり、業界標準の定義がありません。一方、ハイブリッドクラウドは、2つのテクノロジーを統合し、シームレスに連携させたものであり、そのうちの少なくとも 1つはクラウド環境である、という点では共通しています。
ここでは、ImagineThat社という会社を想定して、いくつかの例をみてみましょう。
ハイブリッドクラウドの最も一般的な例は、オンプレミス（自社所有・管理）とクラウドが混在した環境です。ImagineThat社では、絶対に外に出すことができず、社内に留まらなければならないデータがあります。それ以外の機密性の低いデータについては、パブリッククラウドベンダーの計算能力（CPU パワー）とスケーラビリティ（必要なだけ使用できる）を活用します。
ハイブリッドクラウドのもう一つの例は、複数のクラウドベンダーにまたがる環境です(マルチクラウドとも呼ばれます）。ImagineThat社では、IT のほとんどでMicrosoftを利用しており、電子メールやファイル共有などの生産性アプリケーションには Office365 を使用していますが、データストレージには Google Cloud も使用しています。
最後の例は、両方の例をより複雑に組み合わせたもので、異なる国や地域にまたがっています。
ImagineThat社では、LeastExpected社と Well.i.Never社の2社を買収し、世界中にオフィスを展開していています。ImagineThat社と Well.i.Never社の両社は、オンプレミスとプライベートクラウドに、数カ国にまたがる独自のデータセンターを持っています。さらに、Well.i.Never社は Amazon AWS を使ってデータを保存し、LeastExpected社はMicrosoft Azure 上にデータストレージを構築している。これ以上のハイブリッド、マルチクラウドはないでしょう！
多くの場合、ハイブリッドクラウドのアプローチは、適切な戦略とツールによって、柔軟性をもたらし、すべてを同じベンダーに依存しなければならないというベンダーロックインの罠を回避し、利益をもたらすことができます。
しかし、一般的にハイブリッドクラウド戦略は移行期にとられるものです。重要なのは、レガシーな環境やシステム（多くの企業にとって難点となっている古いやり方を思い浮かべてください）から、以前よりも迅速な拡張とパフォーマンスが可能なアーキテクチャとデプロイメントのモダナイズへと移行し、あなたとあなたの組織のパフォーマンスを最大化することです。
ハイブリッドクラウドという用語について、少しはご理解いただけましたでしょうか。
ハイブリッドクラウドについてもっと知りたい方は、John Sands 氏と私がこのトピックについて詳しく解説したビデオをご覧ください。
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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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Branching out with Treemaps
In our journey to explore the vast selection of chart options that Qlik has to offer we have covered most of the well-known offerings, but now we begi... Show MoreIn our journey to explore the vast selection of chart options that Qlik has to offer we have covered most of the well-known offerings, but now we begin to explore those that might be new to you. While these charts might be lesser known, they are still a powerful tool for users to display their data and create visualizations. Today we will be exploring the lesser known, Treemap charts.
What is a Treemap?
Treemaps show hierarchical data as a series of rectangles. Each data rectangle’s size is proportionate to the specific variable. Treemaps are wonderful for showing a vast amount of data without taking up much room in your sheet. Let’s take a look at a few examples to better understand.
What information do we gain from this example?
Here we have treemap chart from an app called Overall Equipment Efficiency. For context: the purpose of this app is to show performance and potential inefficiencies in a sheet metal manufacturing factory. The manufacturing process is divided into four stages, Tapping, Shaving, Forming and Rolling, all of which are displayed in our chart above. Each of these stages have various machines that handle that stage of the process. Our treemap is showing the efficiency of each of the machines and giving us a visual of which machines are most efficient and least efficient.In application
Let’s think of this from the viewpoint of a plant manager, why would this visualization be useful to us, and how could we use it? Quickly at a glance we’d notice the distribution of machines for each stage, Tapping being the biggest followed by Shaving, Forming then Rolling.
With this information we might concentrate on training operators, accordingly, assigning most to Tapping and the least to the specialization of Rolling. This information could be used when ordering replacement parts, ordering parts for the Tapping machines because the efficiency is so low on half of the machines in that area, while doing the same for Rolling. Even though there are less machines for Rolling, 1 of the 2 machines has a low efficiency, requiring repairs often. Additionally, we see that the Shaver, while having many machines, does not lose efficiency often, thus not requiring repairs often. We’re able to display this information in only a 12x6 portion of our sheet leaving room for additional visualizations.So that is treemaps and how you can use them. Do you have any ideas that I might have missed pertaining to treemaps? Can you think of a better way to use them? Have you been able to write an expression to get a treemap to achieve a certain idea of yours? If so, write it in a comment down below and share it with the community.
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Connector Factory – September 2023 releases
Qlik Cloud Analytics New generic ODBC connector Qlik Cloud Analytics can now access any data repository that uses standard ODBC drivers. Using the ... Show MoreQlik Cloud Analytics
New generic ODBC connector
Qlik Cloud Analytics can now access any data repository that uses standard ODBC drivers. Using the Direct Access gateway, your apps can now utilize many more data stores in the cloud or on-premises. This is in addition to the many data source and web service connectors that are already available to Qlik Cloud Analytics. Simply select “ODBC (via Direct Access gateway)” from the list of connectors in the Qlik Sense Add data connection or Data load editor dialogs to begin adding a new connector.
Details on this connector can be found here.
Qlik Application Automation
Supported by Qlik Cloud Government
The following connectors can now additionally run within Qlik Cloud Government, which is an edition of Qlik Cloud that’s designed for government agencies who have additional required security protocols:
- Databricks Connector
- API Key connector
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Service Release - QlikView May 2023 SR1
QlikView May 2023 users can now upgrade to Service Release 1 (SR1) by logging into Qlik Community with their Qlik Account and visiting our downloads s... Show MoreQlikView May 2023 users can now upgrade to Service Release 1 (SR1) by logging into Qlik Community with their Qlik Account and visiting our downloads site to install the latest version.
This upgrade includes:
- Multiple resolved defects, such as fixes with drill-downs on calculated dimensions, and issues with quoting characters in CSV files, and more!
Check out all the product upgrade details (including a list of fixes) in our Release Notes QlikView May 2023 SR1.
Thanks for choosing Qlik!
Qlik Global Support
P.S. use the below filters to find the latest QlikView Service Release on our Download's site!
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Focusing on Scatter Plots
Throughout my blog entries, we have taken a tour through the catalog of charts that Qlik has to offer. We have covered bar charts, line charts, pie an... Show MoreThroughout my blog entries, we have taken a tour through the catalog of charts that Qlik has to offer. We have covered bar charts, line charts, pie and Sankeys, today we’re going to be diving into one of the lesser known, but still powerful, charts: Scatter Plots.
Scatter plots are used to show the relationship between two quantitative variables. The scatter plot is usually made of three elements, the X axis, the Y axis, and a point to show a data point shared between the two axes. Additional information can be shown on the chart in the form of the size of the data points, in Qlik Sense these data points are called ‘bubbles'.
How can you use a Scatter plot chart to visualize your data?
To demonstrate the capabilities of a scatter plot, we’ll look to an example found in the CRM app. This app was developed to showcase data including sales, numbers of customers and opportunities. This app would help a manager of a company see what parts of their business are doing well, and which need improvement.
Above we have the scatter plot built for this app, as well as the view of the ‘Advanced options’ of the chart to give a clearer view of which data is being shown and how. Beginning with our X axis, # of Customers and the Y axis which is Opportunity Amount. With the interaction of these axes, we’re shown that the higher and more to the right that a data point would be, the better for the company that data point would be (more money, more customers), and the opposite for down and to the left (less money, less customers). Additionally, the name of the Sales Person is assigned to the ‘Bubbles’ in this chart. Finally, the size of the bubbles shows the Amount of Opportunities won.
What information can we gain from this example?A manager looking at this chart would quickly be able to determine who are the top and bottom performers and in which way. At a glance, the manager could see two outliers, Gonzalo Geary and Val Conforto, for two different reasons. According to our chart Gonzalo is adept at gaining customers, close to around 230 (double that of their closest competitor), with a larger number of opportunities won compared to his fellow salespeople. Val conversely shows that while she does not have as many customers as Gonzalo does, she makes the most out of the customers she does have, ranking highest in the amount of her opportunities.
That is the power of the scatter plot giving users insight into data points between two metrics. If the manager had only looked at Opportunity Amount, they might think Gonzalo as an average salesperson, while the same could be said for Val if looked at through the lens of # of Customers. Instead, the scatter plot allows for the manager to see how these individuals excel, and where they require additional assistance or training.
Hopefully this blog entry has led to a few ideas of how you can use scatter plots to visualize your own data. How can you use scatter plots to help you or your company? Is there something I might have missed? Leave it in a comment down below. -
Direct Query now available in Qlik Sense SaaS!
Differing from our industry leading analytics engine, Direct Query generates SQL queries on the fly against the source and executes the query and comp... Show MoreDiffering from our industry leading analytics engine, Direct Query generates SQL queries on the fly against the source and executes the query and compute at the database level. From big data to near real time use cases, Direct Query empowers users of any level to easily explore and analyze the most recent data right from the database, increasing the speed to insight and decision-making.
Direct Query complements our best-in-class analytics engine and extends the range of consumption techniques for analyzing data from cloud databases. The first connector rolling out for Direct Query will be for Snowflake with Databricks next on the roadmap and others planned, so stay tuned.
Can't see this video? - View it on the Qlik Video Page - Direct Query Part 1 - Overview and Operation
Can't see this video? - View it on the Qlik Video Page - Direct Query Part 2 - Building Hybrid Analytic Solutions
How does Direct Query work?
Direct Query can be activated at the database connection level. So, using Snowflake as the example, when building a new application and starting with a data connection, you will have the option to use Direct Query versus our analytics engine.
The user can then select certain components of the source database –in the Snowflake connector users will see the options for role, database, and schema. Authentication for Direct Query is governed at the database level so users will only have access to what is granted from the source database.
The user can then select a single view or multiple tables with the option of choosing the type of relationship between the data sets like full outer join or inner join.
Once the relation is designated, an import live statement is inserted into the script in the load editor on the back-end and the visualizations can begin being built on the front-end. Since this is a SQL pushdown tool, users will need to use the function library of the underlying database. As of this blog, we will support the major 5 aggregations of sum, avg, min, max, and count. Also, some simple forms of set analysis will be available for filtering within measures of charts.
After the charts and objects have been built, users can filter and interact with data in real time as Direct Query generates SQL on the fly and pushes the query and compute down to the source database. Keep in mind that every selection or filter within the app from a user, results in a query pushed down to the database and compute.
Direct Query will also feature a seamless transition into our analytics engine for advanced interactivity and the full suite of capabilities. Check out Part 2 ‘Building Hybrid Solutions’ from the videos below.
To learn more, check out these two videos on Direct Query:
Part 1: Overview and Operation
Part 2: Building Hybrid Analytics Solutions with On Demand App Generation (ODAG)
To learn more about how Qlik and Snowflake together can optimize your investment into data and analytics, read our corporate blog here.
Join us for the webinar series, Do More with Qlik, on August 24th where our very talented Mike Tarallo will be showcasing Direct Query for Snowflake.
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Capacity Pricing and Usage Telemetry in Qlik Cloud – available today for Analyti...
What does this mean for you? Capacity pricing removes restrictions around data sources and users, allowing for greater value around data loading and... Show MoreWhat does this mean for you?
Capacity pricing removes restrictions around data sources and users, allowing for greater value around data loading and analysis. This new pricing model uses Data as the primary value metric and is designed to empower you to better leverage your accessible data sets and support more analytics use cases with the use of AutoML and GenAI capabilities in Qlik Cloud. The structure of our new model is based on extensive market research and customer feedback, with the goal of providing greater flexibility and predictability when using Qlik Cloud. You can now subscribe to pre-defined data packs at a fixed monthly cost while making it easier to adopt the full set of cloud capabilities. All of this, with embedded telemetry, organizations can understand and monitor their data usage.
Capacity pricing offers the following benefits:
- Simplified pricing with data as the primary value metric that is aligned with customer needs and ease of doing business with Qlik
- Expand user access to support more analytics use cases within your organization
- Drive Capability Adoption: access to key cloud capabilities and platform value drivers for faster time-to-value
- Provide Data Usage Transparency: insights into data utilization and telemetry within Qlik Cloud
Can't see the video? Watch it here
What are the new pricing tiers?Understand the details of Qlik’s capacity pricing and packaging options on a new pricing page on Qlik.com showing the list prices of each plan. New customers can choose from three distinct plans when doing business with Qlik: Standard, Premium, and Enterprise. And existing customers can move to the new pricing model on their own timeline and can decide what tier is best for their needs. Work with your account team to explore migration to this capacity model.
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Complete your Qlik Nation challenges in Qlik Community
Transformational. Innovative. Powerful. These are just a few of the inspiring terms customers use to describe Qlik and our product suite. Customer fee... Show MoreTransformational. Innovative. Powerful. These are just a few of the inspiring terms customers use to describe Qlik and our product suite. Customer feedback and input are critical to Qlik’s success, and we regularly hear directly from our most active users through Qlik Nation, our gamified customer engagement hub.
Through interactive challenges and activities, Qlik Nation members connect, learn, and engage with Qlik and with each other. By completing challenges in the platform, members can receive early notification of new product features, boost knowledge through quality educational content, and have opportunities to influence the product roadmap. Qlik Nation also allows customers to demonstrate skills, network with peers, and amplify their personal brand. All while having fun and being rewarded!
And now, Qlik Nation members will be able to complete challenges while visiting Qlik Community. Current members will see a new carousel on the Qlik Community homepage inviting them to complete challenges, making it even easier for you to engage and earn points.
Customers are at the heart of everything we do, and Qlik Nation offers an exclusive experience for our most dedicated and passionate fans (Qlik Nation is a complementary platform to Qlik Community, which has open membership). What do our members say about their experience in Qlik Nation?
- “It’s a fantastic opportunity to link up with technologists and data enthusiasts across a wide array of industries to learn and promote the best use of the Qlik product suite.” – Howard Teall, Enterprise Architect, HSBC & Qlik Luminary
- “Qlik Nation brings together all the news and knowledge in one place, keeping me up to date with Qlik and the world of data & analytics” – Mark Little, Senior Business Intelligence Specialist, Mayborn Group
- “Qlik Nation has provided me with a platform to keep me motivated and try and do better with our analytics platform and capabilities. The community joins you with like-minded individuals to share and promote everything Qlik.” - Willem Jacobs, Head of Analytics, OneLogix
If you’re a Qlik end-user and want to learn more about how to join Qlik Nation, please email QlikNation@qlik.com. We’d love to hear from you!
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Totals in Charts
The total in a chart is not the sum of the individual rows of the chart.Instead, the total and the subtotals are calculated using the expression – but... Show MoreThe total in a chart is not the sum of the individual rows of the chart.
Instead, the total and the subtotals are calculated using the expression – but on a larger subset of the data than for the individual row.
Usually, the two methods result in the same numbers, but sometimes there is a huge difference. One example of this is if you use a non-linear function, e.g. Count(distinct …) as expression. The example below clearly shows this.
The source data to the left assigns a country to each state, and if you count the number of countries per state using a Count(distinct Country), you will get the chart to the right: Each state belongs to one country only, and the total number of countries is 2, also if the chart has four rows.
A second example is if you have a many-to-many relationship in the data. In the example below, you have three products, each with a sales amount. But since each product can belong to several product groups, the sales amounts per product group will not add up: The total will be smaller than the sum of the individual rows, since there is an overlap between the product groups. The summation will be made in the fact table.
Another way to describe it would be to say that a specific dollar belongs to both product groups, and would be counted twice if you just summed the rows.
In both cases, QlikView will show the correct number, given the data. To sum the rows would be incorrect.
So, how does this affect you as an application developer?
Normally not very much. But it is good to be aware of it, and I would suggest the following:
- When you write your expression, you should have the total line in mind. Usually, the expression will automatically be right also for the individual rows.
- Always use an aggregation function. This will ensure that QlikView is able to calculate the total correctly.
- If you want an average on the total line, you should most likely divide your expression with Count(distinct <Dim>). Then it will work both for the individual rows (where the count is 1) and the total lines. Example
Sum( Amount ) / Count( distinct Customer )
- For cases where you want to show something completely different in the total line, you should consider the Dimensionality() function, that returns 0, 1, 2, … depending on whether the evaluation takes place in a total, subtotal or row. Example:
If( Dimensionality() = 0, <Total line expression>, <Individual line expression> )
But If I want to show the sum of the individual rows? I don’t want the expression to be calculated over a larger data set. What do I do then?
There are two ways to do this. First, you can use an Aggr() function as expression:
Sum( Aggr( <Original expression> , <Dimension> ) )
This will work in all objects. Further, if you have a straight table, you have a setting on the Expressions tab where you can specify the Total mode.
Setting this to Sum of Rows will change the chart behavior to show exactly this: The sum of the rows.
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Engaging and fruitful visit to Hyderabad!
Last month, I had the opportunity to visit Hyderabad and meet some of the Academic Program Partner Universities there. Hyderabad, as a city in partic... Show MoreLast month, I had the opportunity to visit Hyderabad and meet some of the Academic Program Partner Universities there. Hyderabad, as a city in particular has been quite engaged towards the academic program. Its also where the first Centre of Excellence in Analytics of the Academic Program was set up in VJIT.
This time around plan was to visit VJIT, Anurag University, NMIMS Hyderabad and BVRIT.
After I landed at the airport, I headed straight to VJIT and like I said, this is a special relationship on account of the fact that the first CoE was set up and also we've had hundreds of students registering into the academic program and getting qualified as Qlik Sense Business Analysts and Data Architects from here. I met with the educators who are active in teaching students data analytics and related subjects and thereafter, addressed over 100 students in an auditorium
Thereafter, I proceeded towards the campus of NMIMS which is one of top private universities in India with multiple campuses across the country, including Hyderabad. A very special reason to visit NMIMS was Dr. Siddhartha Ghosh with whom the association began when he was with VJIT and instrumental in setting up the CoE and is now the Director of the NMIMS campus. After meeting him, it was nice to address a student crowd of around 100 who are pursuing their MBA studies. Various initiatives were discussed and the immediate one was to conduct a small hands on session on Qlik Sense for the students which is being organised on October 2nd. .
On the next day, I visited Anurag University and the Educators along with members of the Data Analytics Club. The University had organised a session with students to encourage their participation into the academic program. The Data Analytics Club which comprises of student members is a wonderful initiative to support the exchange of ideas in analytics and foster this subject among students. It was nice to address a large gathering of students and talk about various developments in analytics and also about the free resources of the academic program.
Next was a visit to BVRIT which is an all girls Engineering Institution. With BVRIT, we' ve had a special engagement due to the fact that a couple of years back, few students were recruited from the campus by a Qlik Partner, Diagonal. These students had completed the Qlik Sense Business Analyst Qualification and basis that, they were hired by Diagonal. During this visit, I interacted with the Director and other Educators and addressed a group of students. Most of them were learning data analytics as a specialised subject in their course.
Overall, an engaging and fruitful visit to Hyderabad! Look forward to embarking on the various initiatives discussed.
To know more about the Qlik Academic Program, visit qlik.com/academicprogram
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New Process for Submitting a Feature Request for All Talend Customers and Partne...
Effective October 2, 2023, all feature requests will be submitted via the Qlik Community. Requests are entered as an “Idea” and are evaluated by Produ... Show MoreEffective October 2, 2023, all feature requests will be submitted via the Qlik Community. Requests are entered as an “Idea” and are evaluated by Product Managers. Product Managers will communicate with you via the idea throughout the lifecycle. With this change, feature requests are no longer required to be submitted via the Talend Support Portal.
What you will need to do:
- Navigate to Qlik Community and Sign In
- If you are not a Qlik Community member, you will have to register first
- Once signed in to the Qlik Community, look for Ideation in the navigation
- You can learn all about the ideation process by choosing “About Ideation”
- Learn how to submit an idea and browse existing ideas
- Check out the Process Overview so you will understand what to expect after your idea is submitted
If you have any questions about this new process, please comment below or email QlikCommunityAdmins@qlik.com.
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Techspert Talks - Visualization Day
Hi everyone, Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand insi... Show MoreHi everyone,
Want to stay a step ahead of important Qlik support issues? Then sign up for our monthly webinar series where you can get first-hand insights from Qlik experts.Next Thursday, October 5th Qlik Support will host another Techspert Talks session and this time it's Visualization Day!
But wait, what is it exactly?
Techspert Talks is a free webinar held on a monthly basis, where you can hear directly from Qlik Techsperts on topics that are relevant to Customers and Partners today.In this session, we will cover:
- Future Visualization Updates
- New Layout Container
- Dynamic Linking with Bookmarks
Click on this link to choose the webinar time that's best for you.
The webinar is hosted using ON24 in English and will last 30 minutes plus time for Q&A.
Hope to see you there!!
