In December, 2011 InformationWeek published a report called “Outlook 2012” (free to registered users). The report is based on an online survey of 605 business technology professionals (most in IT) in North America conducted in October, 2011. The purpose of the survey was to gauge IT spending, staffing plans, and strategies for 2012.

Report cover image.JPG

Overall, survey respondents expect technology spending to increase in 2012. Fifty-six percent of survey respondents said their companies planned to increase tech spending in the coming year, up from 46% two years ago. In contrast, just 16% of respondents think their companies will cut IT budgets in 2012. A couple of findings are particularly relevant to people involved in BI deployments:

  • Business intelligence is a critical discipline. Almost 60% of respondents said their companies consider it important to do timely analysis of sales and operations data. And mining customer data emerged as one of the top 3 operational and strategic initiatives, trailing only keeping IT systems up and running and analyzing sales and operations. Fifty-four percent of respondents said mining customer data was important. (Interestingly, 21% said it was unimportant.)
  • Tablets are becoming important business tools. Fifty one percent of IT pros in the older 2010 survey “strongly disagreed” that their companies would give tablets to even 10% of employees who normally would get a desktop or laptop computer. In the more recent 2011 survey, just 35% of respondents were that deeply skeptical. And 31% of respondents in the 2011 survey agreed that tablets will become the main device for select employees.

Likely implications for QlikView data wranglers and power analysts, as well as to the IT organizations that support QlikView deployments, (see related blog post, “Self-Service BI: Power to ALL the People”) are that demand for QlikView apps will increase in your organization in 2012, as will demand for support for these apps on mobile devices―particularly tablets.

In November, 2011 Gartner published a report, “Business Intelligence Adoption Trends, 2011” (Gartner subscription required for access). During the four years from 2008 through 2011, Gartner surveyed attendees at its BI summits to track adoption of BI tools by business users. (See the related blog post about the 2011 Gartner BI summit in London, “The Rebirth of BI: The Future Is about Discovery.”) The total number of people who filled out the survey was just shy of 700, with 189 people taking the survey in 2011.

Here are my thoughts about some of the Gartner findings:

  • Self-service BI is the answer to low BI penetration. Gartner is finding that adoption of BI tools by business users has remained static since 2008, with fewer than 30% of potential users within an organization making use of BI. My take: the main reason for this is that most BI tools available to business users are not meeting those users’ needs for exploring data and asking and answering their own streams of questions. In a world of empowered consumers, self-service BI―with a focus on ease of acquisition, ease of use, and ease of creation―is critical to mainstream adoption of BI within an organization.
  • IT and other parts of the business partner closely for best success. Gartner posits that when the business leads the requirement decisions, BI adoption is better compared to when the IT department leads. I'd say that IT support and buy-in is critical for mass adoption of BI tools within an organization—and this is even truer in larger organizations. Widespread adoption of BI tools isn’t about IT versus the business. It’s about the IT part of the business enabling other people in the business with self-service BI. IT can focus on data preparation and governance, security, scalability, while enabling business users to create their own interactive discovery apps.
  • Value is more obvious when adoption is higher. Gartner has found that the higher the adoption of BI within an organization, the more money that organization plans to spend on BI. Organizations reporting strong adoption (>61% of potential users have adopted BI) said they planned to spend 19% of their IT budget on BI software in the coming fiscal year. In contrast, organizations with the weakest adoption (<21% of potential users having adopted BI) said they are planning to spend only 7% of their IT budget in this way. To me, this speaks to the business value organizations get from widespread adoption of BI tools. As more people within an organization use BI software to explore data, derive insights, and make decisions, the ROI (formal or informal) improves and it becomes a no-brainer to continue investments in this area.

While BI software was originally used for core financial analysis, it has moved way beyond that. Easy-to-use, self-service BI software can be used to support an endless array of business decisions, large and small. We have customers who have recently used QlikView to improve customer service, optimize product mixes . . . even to optimize commercial fishing locations and sales points, and catch a serial killer. And we’ve only just begun. It makes me happy to know that 70% of potential users of BI tools still have ahead of them the delightful “a-ha” moment that comes with making their own business discoveries.  It’s a green field ahead!

A couple days ago, we were talking about Siri at lunch. Siri is the intelligent personal assistant that is available on iPhone 4S. It helps you get things done just by asking by using your voice to send messages, schedule meetings, place phone calls, look up information, and more. After that lunch conversation, I started to think how nice it would be to have a personal assistant in QlikView, one that would allow me to use my voice to do Business Discovery. I’d like it to talk back to me and guide me through the data to reveal the informationI need for decision making.

Then I started thinking that QlikView already has all of the pieces needed to get this working. It has extension objects capabilities, which can run custom code from QlikView to get the voice commands working. It also has text box capabilities to automatically generate the voice script with the data for the assistant’s audio. I thought about using free text to speech software to convert the text to audio.

So I did a little research on HTML5 and sat down with QlikView. Within about two days, I built a prototype. I got so excited about it that I gave my personal assistant a name: “Q.” Check out this video. I posted the QlikView application and technical information about creating the speech input and audio extension objects with HTML5 on QlikCommunity.


“Q” is a great example of QlikView’s extensible platform. QlikView provides developers with a comprehensive and integrated set of tools that help them expand the possibilities of a Business Discovery application. If developers are interested in adding speech input capabilities to a QlikView application, they can easily create an extension object by using QlikView Application Programming Interfaces (APIs) and other technologies such as HTML5. Once the extension object is created, it can be used in other QlikView applications, just like any other QlikView object. If you are interested in learning more about QlikView’s extensibility, please visit the QlikView SoftwareDevelopment Kit (SDK) page on QlikCommunity’s Integrations and Extensions Community. The community provides documentations on QlikView SDK and APIs with sample codes.

By the way, if you are wondering what else “Q” can do, I am leaving that to your imagination. With QlikView APIs, it would be very easy to put the pieces together!

  “Cloud” and ”SaaS” (software as a service) are two terms often bundled together and used to describe the same thing. The way we at QlikTech use the term 'cloud' is to refer to the use of off-premise, often distributed, computing environments for the purpose of running applications and managing, storing, and analyzing data. We use the term 'SaaS' to refer to a specific distribution and implementation model for applications running in the cloud (software running on multi-tenant servers in the cloud and vendors charging customers a subscription fee to use the software).


QlikView and The Cloud:

QlikView can be deployed on premise or in the cloud. In fact, QlikTech’s own demo site (http://demo.qlik.com) runs on servers in the Amazon EC2 cloud. Customers that want to put QlikView on servers in the cloud purchase licenses from QlikTech just as they would if they were deploying the software on premise. This is an attractive solution for customers that:


  • Do not want the upfront capital costs of acquiring server hardware
  • Need a flexible environment that can easily scale as their deployment needs scale.



What’s important to note is that in cloud-based environments the data resides in the cloud. This somewhat obvious point is an important one, particularly with respect to business intelligence solutions: The “raw materials” for any successful BI deployment are the data that underlie the deployment. The location of this data will often determine whether a BI solution is brought in house or deployed in the cloud.  If data is on premises and your analytics are in the cloud, then potentially large data sets must be uploaded to the cloud for analysis. If your data is in the cloud, then it still needs to be transferred to your analytic environment. Of course, both of these scenarios are dependent on great bandwidth.


On the other hand, if your data already lives in the cloud, in the same environment as your analytic applications then it makes sense to use cloud analytics.

Through connectors to cloud-based offerings such as Salesforce.com, QlikView can be used to access and analyze data resident in the cloud while maintaining the strict security requirements needed. Therefore, there are no obstacles to QlikView working within a cloud environment: the degree to which it does is determined by how customers choose to deploy QlikView.


QlikView and SaaS:

Current trends in the BI platforms market indicate that BI SaaS offerings are not gaining the type of traction that other cloud-based solutions (such as CRM, for example) are gaining. According to Forrester Research, cloud BI will continue to chip away at on-premises BI, but it’s still a long road ahead. Heavy customization and integration of enterprise BI platforms, tools, and applications done by subject-matter experts and consultants will not go away. Another major reason for this is that most organizations continue to maintain their core operations data on premises. 


Currently, QlikTech has many OEM partners that are using QlikView to deliver SaaS-based BI offerings to their customers. For these partners, their customer base and business model lends itself to providing solutions via a SaaS model. For example, one partner provides a SaaS-based call center transaction solution to their customers. For an additional fee, their customers can avail of an ‘analytics module’ (built with QlikView) where they can analyze and discover insights in their call center data, to help them with resource allocation, RMA trends and so on.


At present, QlikTech does not offer QlikView via a SaaS model. (This would involve QlikView running on multi-tenant servers in the cloud and QlikTech charging customers a subscription fee to use the software). This is due to limited customer demand for such an offering. As the prevalence of cloud-based data increases, so may the demand for SaaS-based BI increase.


We continue to closely watch the growing trends in both Cloud and SaaS environments, and where appropriate to our customers' needs, will continue to provide innovative solutions to meet their Business Discovery requirements.


We are seeing lots of interest in and hype around the topic of “big data” because data volumes are on the rise and strategic thinkers across industries are looking for opportunities to maximize its value. According to McKinsey Global Institute and others, the term 'big data' refers to data sets whose size is beyond the ability of typical database software tools to capture, manage, and process within a tolerable elapsed time. Depending on the industry, this can mean data sets ranging from a few dozen terabytes to multiple petabytes. In addition, the term ‘big data’ is associated not only with the volume of data but also the variety (i.e. the types of data, structured or unstructured etc.) and the ‘velocity’ of data, i.e. the dynamic or changing nature of the data as new data flows into, and old data exits, a system.

During the last two decades, organizations have made significant investments in automating business processes using software applications that generate substantial amounts of data, which must then be manipulated before business professionals can usefully access, explore, and analyze it. This data is in myriad formats and its sheer volume is daunting. Business users are challenged to efficiently access, filter, and analyze the data — and gain insight from it — without using powerful data analytics solutions, which require specialized skills. They need better ways to navigate through the massive amounts of data to find what’s relevant to them, and to get answers to their specific business questions.


The growth in adoption of massively parallel processing (MPP) solutions for handling ever larger volumes of data — whether structured or unstructured —  is driving demand for analysis tools to enable business users to derive insights from the data.


QlikView takes a two-pronged approach to this challenge:


Firstly, QlikView’s approach has always been to understand what it is that business users require from their analysis, rather than to force-feed a solution that might not be appropriate. Providing the appropriate data for the appropriate use case is more valuable to users than providing all the data, all the time. For example, local bank branch managers may want to understand the sales, customer intelligence, and market dynamics in their branch catchment area, rather than for the entire nationwide branch network. With a simple consideration like this, the conversation moves from one of large data to one of relevance. In any organization, the number of people who need to analyze extremely large data volumes is typically relatively small. For example, a retail bank might have thousands of branches, however only about 100 business analysts in a centralized, corporate role. While branch managers only need slices of data that are relevant to their operations, the corporate analysts may need access to much large data volumes. QlikView is designed to accommodate both environments and enables users to focus on the data that is relevant to them and is of the highest value to them and their area of interest. By taking appropriate slices of the data – big or small – QlikView acts as an analytical environment downstream of the data source, to provide business analysts and casual business users alike the insight they need from the data that is most relevant.


Secondly QlikView has been addressing, and continues to address, the big data challenge by ensuring that targeted QlikView applications can address the amounts of data that are needed to ensure the relevancy of the application for business users:


  • Recent trends in large memory spaces available on standard Intel hardware allow QlikView to handle ever-larger volumes of data.
  • QlikView best practices promote an architecture-led deployment when handing very large data sizes, such as making proper use of distributed servers in a clustered environment; constructing appropriate applications for the intended audience; using sophisticated data reload engines; and using document chaining where necessary to allow aggregated views to be coupled with detail-level views while optimizing hardware resources.       
  • QlikView provides an open data protocol (QVX) via a series of API's for developers to allow them to interface with the API's of Hadoop-based data source providers. QlikView's QVX protocol can be used to connect to Hadoop based systems via two different methods
    • Disk based QVX file extracts from Hadoop  - PUSH
    • “Named pipe” QVX connector for Hadoop – PULL
  • A QVX SDK is available to all 3rd party developers who wish to build custom connectors for any system with an open API.  QlikTech has partnered with DataRoket which  has an ETL tool to connect with Hadoop, in addition they have produced a QVX named pipe connector for QlikView to link directly to their ETL tool


In conclusion, the QlikView Business Discovery platform is all about relevance. It’s about putting tools in the hands of business users to enable to them to ask and answer their own streams of questions, without having to go back to IT or business analysts for a new report or a new query every time they come up with a follow-on question.


(My colleague, Elif Tutuk, also wrote a blog post entitled 'An App Model Approach to Big Data' that is well worth a read to learn more about the QlikView approach to Big Data')

This week I did a QlikView demonstration with the purpose of showing QlikView’s self-service BI capabilities. I was tasked with loading a data set and building an analysis user interface. When I opened the demo data file for the first time, I saw that some of the column names were actual data points and needed to be changed to row values to be used in the analysis. The data had a cross table structure and needed to be transposed.

As a former QlikView technical expert, as I started to become familiar with the data, my right brain was already thinking about the data load design and scripting to transform the data but all of a sudden my left brain took over and said, “Stop! There is a wizard to do this!”

After a couple of minutes, I transposed the data by using the crosstable wizard. The following video shows the use of this wizard and shows how to build a QlikView application from start to end in four minutes.

I guess our background always plays an important role on how we use the technology. The reason I loved using QlikView when I was a developer was the power it provided me to transform the data with the scripting capability of the data load editor. QlikView provides a purpose built in-product ETL (extract, transform, load) interface and script API to enable powerful manipulation of data. The interface provides auto-complete, debugging, and wizard functionality. Although there are data load and transformation wizards, I tend not to use them. I guess the developers always aim to write the coolest script without following the steps provided by the wizards as they become familiar with the software. The same is true for other technologies, for example, people use keyboard commands instead of mouse clicks or they use a command line interface instead of a graphical user interface. But I learned my lesson this week: wizards really make development simple and faster, even for experienced QlikView developers!


The bicycle was invented in 1817 by Baronvon Drais. He envisioned it as a walking machine to help him get around the royal gardens faster. It featured two same-size in-line wheels,mounted to a frame which he straddled. He propelled the device by pushinghis feet against the ground, thus rolling himself and the device forward in a sort of gliding walk. The bike was made of wood, which made it very heavy. There were no pedals, so forget about going uphill. And without brakes, going downhill was probably challenging. His invention enjoyed a short lived popularity as a fad, not being practical for transportation in any place other than a well maintained pathway such as in a park or garden.

When I read about this first attempt at building bike, I thought about traditional BI tools. The way traditional BI tools enable data analysis is similar to Baron von Drais’s bike; the data can only be analyzed with predefined drill down paths or by running queries one at a time, so business users are limited to a “well maintained pathway” to do analysis. BI was introduced 25 years ago at a time when storage, memory, and computing resources were scarce. In many cases, the same query -or cube- based technologies are still being used today. In these cases, business users do not have freedom to analyze the data in their way.


Image source: Wikipedia. Link here: http://en.wikipedia.org/wiki/File:Draisine_or_Laufmaschine,_around_1820._Archetype_of_the_Bicycle._Pic_01.jpg

When I thought about today’s modern bicycles and their ease of use to go anywhere, I realized that QlikView is the bicycle of business intelligence! With QlikView’s associative experience, business users can enjoy their own journey through the data. They can literally see the relationships in the data, because with every data point selected in their analysis, an entire network of data, and relationships between them, is implied. An associative search returns data that represent things that are related as well as not related, via various forms of associations that exists in the data. An associative search looks through a network of associations for the things that are connected to users’ selections, and guide them quickly home in to the unknown unknowns that are hidden in the data. With cube or query based technologies on the other hand, each additional search term only provides a linear benefit, there is no exponential amplification using networks that exist in the data.

Two centuries later after the first bike attempt, children all around the world learn to ride a bike and have fun. So my question is: shouldn’t business intelligence be easy and be for everyone just like riding a bicycle? I think so!


* Source: About.com. Link here: http://inventors.about.com/library/inventors/blbicycle.htm

Green lights were flashing all over the place (in my mind) as I read the researchers’ description of the Collaborative path to transformation, in the research report, “Analytics: The Widening Divide,” published by quarterly journal MIT Sloan Management Review in collaboration with the IBM Institute for Business Value. (See related QlikView blog post, “Are You Transformed?”)

At a crossroad.jpg

According to this study, organizations take one of two paths to transformation:

  • The Specialized path can lead to well-defined gains. In organizations that take the Specialized path (slightly >50% of the Experienced organizations surveyed), deep analytic expertise is developed within lines of business or specific functions using a wide array of analytical skills and techniques. They use analytics to improve specific business metrics. Information management is siloed, improvement of analytical skills and tools is a passion, and data-oriented culture will require extra momentum.
  • The Collaborative path crosses organizational boundaries. Organizations that took the Collaborative path (slightly <50% of the Experienced organizations surveyed) create an information platform and enable users to develop and share insights across lines of business. They use analytics to improve enterprise objectives.  Information management is an enterprise endeavor, analytics skills and tools are not fully developed, and a data-oriented culture has emerged.

In organizations on the Collaborative path, people have a willingness to share and accept data and insights from other parts of the organization. They are adept at using visualization techniques and using dashboards that provide snapshot views of performance. People who are not accustomed to working with huge volumes of data can still interact with information and make analytically-based decisions, with user-friendly tools.

How do you know if you are on the Collaborative path? Organizations on this path are twice as likely as organizations that take the Specialized path to provide customer-facing employees with access to data and insights, and are almost 3X more likely to use analytics to guide future strategies. They are twice as likely to provide insights to anyone in the organization who needs them.

In this report, the researchers describe one of the challenges of pursuing the Collaborative path is waiting too long to get the data into its “ideal state” before acquiring tools and skills to analyze it. This is just one of the areas where QlikView shines. For an organization to begin deriving value from QlikView, the data doesn’t have to be in perfect condition yet. In fact, many of our customers use QlikView to assess data quality and identify issues in the data, and QlikView has built-in ETL tools to assist in the cleanup process. It is common for organizations to get up and running with QlikView apps in weeks if not days.

While QlikView is used in all kinds of organizations, at every stage in the MIT researchers’ analytics maturity model, it is a particularly perfect fit for organizations on the Collaborative path to transformation. With its collaborative analytic app creation model and the new collaboration capabilities in QlikView 11, QlikView is well-suited for organizations where people work together to make data-driven decisions. (See related blog posts, “Social Business Discovery with QlikView 11” and “Remixability and Reassembly with QlikView.”)

Are You Transformed?

Posted by Erica Driver Dec 2, 2011

Are you building a business case for further investments in business analytics? If so, the quarterly journal MIT Sloan Management Review, in collaboration with the IBM Institute for Business Value, recently published a research report called, “Analytics: The Widening Divide.” In this study, the researchers identified a growing divide between companies that see the value of business analytics (and are transforming themselves to take advantage of newfound opportunities) and companies that do not. The findings in this report are based on a survey of more than 4,500 managers, executives, and analysts in more than 30 industries and 120 countries; interviews with academic experts and subject matter experts; and IBM case studies.

Cover image MIT Sloan Review research report.JPG

Readers should take some of the results with a grain of salt — particularly those that attribute business success and outperforming peers to the use of analytics — because the results are based on self-reported opinions rather than hard data (e.g., financial filings, quarterly reports, etc.). Still, the findings are interesting, such as:

  • Transformed organizations use analytics in non-traditional ways. The most common uses of analytics are to manage financial forecasting, annual budget allocations, and supply chain optimization, and to streamline operations. In general, companies less often rely on analytics for decisions involving customers, business strategy, and human resources — but half the Transformed organizations use analytics to make decisions in exactly these areas.
  • Transformed organizations are democratizing information. Transformational organizations have mastered three analytical competencies: information management, analytics skills and tools, and a data-oriented culture. As part of this, they make information and insights readily available. Sixty five percent of Transformed organizations make information readily accessible to employees, vs. 21% of Aspirational organizations, and almost as many Transformed organizations (63%) make information readily accessible to all employees, vs. 16% of Aspirational organizations.
  • Analytics adoption is on the rise and companies are seeing benefit. Fifty eight percent of organizations surveyed apply analytics to create a competitive advantage within their markets or industries, up from 37% one year ago. According to self-reported info from the survey respondents (which must be taken with a grain of salt), the same organizations that use analytics to create competitive advantage are more than twice as likely to substantially outperform their peers. All of the gains in competitive advantage have been made by companies in the Transformed or Experienced groups, according to the researchers’ classifications of analytical sophistication — and none of the gains by companies in the Aspirational group.*

This study offers a number of recommendations to help organizations move forward with analytics. At the highest level, the recommendations are to 1) assess your analytics sophistication against the maturity model laid out in the report, 2) improve your analytics competencies, and 3) use an information agenda to connect your path to your competencies. For more detail, you can download the full study here (no charge). 


* In Aspirational organizations (32% of survey respondents), analytics usage is basic. People use analytics to guide decision making in financial management and supply chain management. The primary tool is spreadsheets. In Experienced organizations (45% of survey respondents), analytics usage is moderate. These organizations use analytics to guide future strategies and are increasingly relying on analytics to guide activities in marketing and operations. Enterprise data integration efforts are under way and the organization is expanding its portfolio of analytics tools. In Transformed organizations (24% of survey respondents), analytics users are sophisticated. They use analytics to guide decision making in day to day operations as well as future strategies. The organization deploys a comprehensive portfolio of tools to supported advanced analytic modeling.

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