120 Embedded Analytics in the Self-Service BI Enterprise

Embedded Analytics in the Self-Service BI EnterpriseA Data Report by George Anadiotis

This report underwitten by: Izenda

Embedded Analytics in the Self-Service BIEnterprise02/10/2015

Table of Contents

1. Executive Summary

2. Why Organizations Need Visual Data Discovery

3. Accessing Data: Where It Lives and Who Sees It

4. Reporting in a Self-service BI World

5. Key Takeaways

6. About George Anadiotis

7. About Izenda

8. About Gigaom Research

9. Copyright

Embedded Analytics in the Self-Service BI Enterprise 2

1 Executive SummaryProduction reporting has been around for a long time, but its requirements change as much asthe tech industry itself does. Delivery expectations have shifted from quarterly to hourly, so theentire business intelligence stack must now be user-driven and flexible. Licensing by number ofusers is obsolete, since there is no way to predict who will need the reports or who will createthem. And in the mobile era, sending users to IT for their reports is doomed to failure, anddesktop apps are legacy technology.

If the learning curve for reporting isnt low, adoption will be. Reporting and analytics must beembedded inside applications and end-users must be able to use their interfaces not just forrunning reports but also for designing them. Highly complex reports shouldnt take twoexpensive developers to build if one single client services person can do it instead.

This report is for executives, product managers and developers at independent softwarevendors (ISVs), SaaS vendors, solutions providers, or anyone building business applications. Itwill investigate how reporting needs, capabilities, and implementation requirements havechanged, and present a new set of dos and donts for successful embedded analytics.

Key findings in this report include:

It is imperative to embed modern BI (reporting, dashboards, and visualizations) in theapplication users daily work. A generation of users that has been brought up on mobile andbrowser-based applications that are self-contained is unlikely to accept switching to aseparate application to access BI features.

Democratizing access to data is key, and can be achieved by enabling access totransactional databases (or read-only copies) instead of creating separate analyticaldatabases. In doing so, the burden on IT or the data science team is minimized, and usersare empowered to access data on their own.

Democratized access to data should not equal a lack of control. There must be a mechanismfor user authorization and access rights, as access to the reports and data should berestricted to only the appropriate users for any given scenario.

Reporting functionality should have a professional look and feel that is natural for users.Reports should be easy to create, embedded in applications, and have production-levelpolish.


ISVs and solution providers now face a build-versus-buy strategy for BI implementation.Arguments tend to favor buy because these organizations lack core BI expertise and oftenspend too much time and too many resources building and maintaining it in-house.

Thumbnail image courtesy of Sergey Nivens/iStock.


2 Why Organizations Need Visual Data DiscoveryWe live amid a constant deluge of applications, and more and more are finding their way intothe enterprise, whether they are delivered on-premises (the traditional method) or via the cloudas SaaS.

As a byproduct, the data these applications generate is accumulating in an environment thatprovides increasingly vital information for an organizations operations. Organizations mustdecide how to examine and interpret an array of datasets to gain insights. Not only does theamount of accumulated data increase over time, but the sources of data keep expanding, too.

Change in both of these dimensions is happening fast and involves a growing number oforganizations. Data analysis functionality is becoming critical and organizations cannot afford tofall behind the competition. Having an accessible way to explore datasets and discover patternsand outliers is now imperative.

This is precisely the need that visual data discovery and exploration tools aim to address. Thequestion is, What visual data discovery and exploration technology should organizations use?The current generation of users has grown up on mobile and browser-based applications thatare self-contained and has less tolerance for separate applications for specific functions such asreporting and analytics.

The market also has a proliferation of visual data discovery and exploration tools, though mostof them fail to meet the self-containment aspect: They may be good at what they do, but theyrequire their own installation, setup, and operation processes and add to the overall complexityof the visual data discovery and exploration experience. This fact highlights the divide betweenwhat users want from data analysis tools and what most tools offer. The emergence of the cloudand the SaaS paradigm further amplifies expectations around self-contained applications.Application self-reliance is becoming the new normal, with visual data discovery and explorationfeatures included.

Analysts should focus on the datasets, not on the tools, and remember that data analysis is ademanding yet necessary task. Adding layers of software complexity on top of it only increasesthe difficulty. Data analysis should be an integral part of the applications where datasets aregenerated, not something for a different application to perform. Only then will organizations beable to empower all user types to perform data analysis effectively and make better decisions inreal-time without having to rely on data analysts or technical staff who may lack the businesscontext to perform or prepare data for analysis.


Users are coming to expect that they can access information when and how they want.Applications failing to meet their expectations will ultimately fail to increase adoption.Dashboards should enable users to go deeper by accessing broader data sets requiringvisualizations. ISVs and solution providers will not retain customers without addressing thesenew realities.


3 Accessing Data: Where It Lives and Who Sees ItTraditionally, organizations wanting to analyze their data have utilized an extracted replica oftheir database to run the analyses. This is considered good practice, as mixing transactional andanalytical workloads means that the two may conflict with one another. Furthermore,transactional and analytical databases are structured differently because each has a differentfocus and modus operandi.

On the other hand, this process means building and maintaining additional infrastructure, whichincurs additional cost. There is also the associated complexity and delay involved in the processof extracting, transforming, and loading (ETL) the data from the transactional to the analyticaldatabase. So many organizations are now performing analytics directly on their transactionaldatabases or read-only copies of them.

Reporting off normalized/transactional database schemas instead of star schemas found in datawarehouses and OLAP cubes means eliminating the specialized data stores required to supportDWs. It also shortens the process of accessing data considerably and makes it leaner, as thereis no more ETL to be done and no more reliance on a specialized team to perform it. All userscan access data in real time or near-real-time on replicated instances.

An issue of great importance when using a BI reporting framework is user authorization andsecurity. This becomes even more important when reporting off transactional databases, as theintermediate step of preparing data and granting access is now missing and users couldpotentially access data they are not entitled to use. A mechanism, then, must be in place foruser authorization and access rights.

This security and access mechanism must be granular; an all-or-nothing model will not work.Different types of users may have access to the same reports, but with different underlying dataexposed. To avoid the cumbersome solution of employing different security mechanisms,reporting frameworks should be able to inherit or adopt existing security frameworks. This canbe achieved by means of a security API, enabling reporting frameworks to integrate withapplications and have direct access to their access tokens and security roles. The ability tooperate in the cloud can also facilitate security considerations, as potentially broad user groupsno longer have to download data to their desktops. Instead, data remains stored in the cloudand users can access it remotely via reports and dashboards, thus eliminating the risk thatcomes with storing local copies. Users can get an overview of the information they need withoutthe overhead and risk associated with local data storage.


4 Reporting in a Self-service BI WorldThe notion of self-service is transforming the way businesses run. These changes also applyto BI reporting frameworks, too. Modern solutions are based on user empowerment, providingto business users the tools to design their own reports, configure dashboards, and explorevisualizations in real time rather than going through a lengthy process involving the ITdepartment.

Production cycles are now shorter, and reporting on those cycles needs to follow suit. In thepast, it was acceptable to have quarterly reports that were implemented by IT and remainedlargely unchanged throughout their lifetime. Today, agile production cycles may go frominception to release within a few months, so static reporting frameworks are no longeracceptable. The pace of business and modern decision making/value creation dictates real-timeend-user empowerment and a broader set of end users having access to data.

Data scientists and consultants should not be the sole control for consuming and accessingdata. The traditional data team has its strengthsand enables deep and sophisticatedanalysisbut it has its limitations, particularly when it becomes a bottleneck for data access.Data access should be democratized and real-time, without the intervention of a timeconsuming process or a specialized team.

The above needs along with the self-service BI approach involves the IT and the data scienceteams only in the initial database-setup phase. After that, users perform everyday taskssuchas field addition, dashboard creation, and data visualizationon their own.

For the self-service BI approach to work, BI reporting frameworks should be intuitive and easyto learn, so training time can be minimized.

Users should not have to learn how to use a different environment for their reporting needs.Reporting should be embedded in the application, and offer its functionality in a look and feelthat is natural to the users. This can only be fully realized when visualization elements are trulyembedded in the application environment. The use of IFrames is an old technique that somesolutions use, but it does not offer a seamless graphical environment and should be avoided.Truly embedded reports are the ones that offer a look and feel that is inseparable from theapplication that hosts them.

Embedded reports also allow application providers to leverage the cloud. By embeddingreports into their application environment, providers can distribute their applications through thecloud, leveraging the SaaS model, and can offer their users an integrated experience. This is in


stark contrast to the fragmented experience that comes from using a separate BI tool (either onthe desktop or on a different cloud environment) for data exploration and visualization.

Using embedded BI does pose an issue: the need for ad-hoc reporting with production-levelpolish. Traditionally, reporting has come in two variations: One is aimed at quick-and-dirtyreporting, giving users tools to mix and match various datasets to produce ad-hoc reportsaccording to their needs. Typically this kind of reporting is aimed at on-screen consumption andis lacking in terms of pixel-perfect alignment and production-level polish. The other is aimed atproducing reports following internal guidelines or regulatory frameworks. Typically this kind ofreporting is aimed at printed-medium consumption and is focused on looking professional.

These two worlds must converge. Organizations need reports that are easy to create,embedded in applications, and have production-level polish. One way to achieve this is byimplementing a multilevel editing mechanism. New reports can be based on master reporttemplates that enable on-the-fly creation, but they should also be individually editable to enablehigh quality.

In the end, to be successful, self-service BI must combine all of the features mentioned so far:direct access to data for reporting and analysis, easy-to-use BI tools, and simpler andcustomizable end-user interfaces.

This is not an easy goal to achieve for any ISV, so it calls for an important decision: to developthis functionality internally or to bring it onboard by means of purchasing an existing customersolution.

Just like end users, most ISVs see BI functionality as something that brings additional value butfalls beyond their core expertise. The level of sophistication required to develop and maintain aBI solution dictates that a specialized BI team be created within ISVs wishing to implement BI ontheir own. This has cost and time-to-market implications. Putting together a team can bechallenging. Developers with knowledge of BI, particularly emerging capabilities likevisualizations, are in short supply, so non-BI software organizations needing to staff will havedifficulty.

The alternativeutilizing existing personnel to deal with the needs of embedding BIfunctionality into productswill lead to sub-par results and cause disruption in the ISVs corebusiness. Allowing scarce and expensive technical resources to focus on core product andoperations is better than continually trying to maintain and enhance BI functionality.

Even if a specialized BI team can be assembled, the cost and the time required to develop a BIsolution will be substantial. Embedding an off-the-shelf BI platform solution can be integratedfaster than developing BI functionality (reports, dashboards, visualizations) or trying to integratedisparate BI tools that lack a common architecture, and it should also end up costing less. In


addition, embedded solutions have become more modular and more easily customized,reducing the need for building a custom BI solution.


5 Key TakeawaysApplications are ubiquitous, and the need to access the data they generate for analyticalpurposes grows more and more pronounced. An alternative to traditional BI solutions isembedded BI, as it gives users the opportunity to access data directly from the applicationenvironment.

Examining the features of user-driven, embedded BI solutions, here are some takeaway points:

Reporting, dashboard, and analytical solutions should be embedded in applications toachieve maximum usability by a generation of users that is accustomed to self-reliantapplications.

BI solutions should aim to democratize access to data and lift the burden from IT and thedata science team. Reporting off transactional databases rather than creating specializedanalytical ones facilitates this goal. Both end users and IT stand to benefit from thisdevelopment.

Democratized access to data requires a solid security framework to ensure that the rightpeople have access to the right data. A security framework must not overlay itself onexisting application security, but rather integrate seamlessly so it becomes an organic part ofthe application.

The two separate strands of visual reports, quick-and-dirty reporting and production-levelreports, formatted according to guidelines, must converge. Reports should be easy tocreate, embedded in applications, and have production-level polish.

For most ISVs and solution providers, developing an in-house BI solution for applications isprohibitive and costly in terms of time-to-market and required resources. In most casesadopting a third-party solution for BI is most sensibleas long as it offers the requiredfunctionality and integration.


6 About George AnadiotisGeorge Anadiotis is an Analyst for Gigaom Research. He has been in ICT since 1992, havingworn many hats and juggled many balls. He has moved up the ladder of software engineeringall the way from junior hacker to lead architect, provided consulting services to the likes of KLMand Vodafone, built and managed projects and teams of all sizes and shapes and got himselfinvolved in some award-winning research along the way.


7 About IzendaIzenda is a leading business-intelligence platform purpose-built for ISVs, solutions providers andenterprise users. Its integrated business-intelligence platform allows end users to easily access,visualize, and share valuable business intelligence in real time. Embedded seamlessly intoapplications, Izenda delivers BI directly to the people who need it most. Learn more atizenda.com.


8 About Gigaom ResearchGigaom Research gives you insider access to expert industry insights on emerging markets.Focused on delivering highly relevant and timely research to the people who need it most, ouranalysis, reports, and original research come from the most respected voices in the industry.Whether youre beginning to learn about a new market or are an industry insider, GigaomResearch addresses the need for relevant, illuminating insights into the industrys most dynamicmarkets.

Visit us at: research.gigaom.com.

Giga Omni Media 2015. "Embedded Analytics in the Self-Service BI Enterprise" is a trademarkof Giga Omni Media. For permission to reproduce this report, please contact [email protected].


Embedded Analytics in the Self-Service BI EnterpriseThis report underwitten by: Izenda

Embedded Analytics in the Self-Service BI EnterpriseTable of Contents1 Executive Summary2 Why Organizations Need Visual Data Discovery3 Accessing Data: Where It Lives and Who Sees It4 Reporting in a Self-service BI World5 Key Takeaways6 About George Anadiotis7 About Izenda8 About Gigaom Research

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120 Embedded Analytics in the Self-Service BI Enterprise