vr_infomgt_obstacles_to_information_management_updatedWhen applying information technology to drive better business performance, companies and the systems integrators that assist them often underestimate the importance of organizing data management around processes. For example, companies that do not execute their quote-to-cash cycle as an end-to-end process often experience a related set of issues in their sales, marketing, operations, accounting and finance functions that stem from entering the same data into multiple systems. The inability to automate passing of data from one functional group to the next forces people to spend time re-entering data and leads to fragmented and disconnected data stores. The absence of a single authoritative data source also creates conflicts about whose numbers are “right.” Even when the actual figures recorded are identical, discrepancies can crop up because of issues in synchronization and data definition. Lacking an authoritative source, organizations may need to check for and resolve errors and inconsistencies between systems to ensure, for example, that what customers purchased was what they received and were billed for. The negative impact of this lack of automation is multiplied when transactions are complex or involve contracts for recurring services.

Our benchmark research shows that data fragmentation, consistency, availability, usability and timeliness are key issues for companies.  The information management issues in process design and execution are similar to those at work for analytics.   However, addressing them effectively requires a different approach than just creating a separate data store to be the “single version of the truth.” Careful consideration is required to determine the best method to manage data throughout a core business process, particularly when multiple applications are required to automate and support the execution of the process. Software application platforms offered by some vendors make it far easier to integrate niche software applications into processes in a way that may eliminate the need for an operational data store.

The information dimension is usually overlooked in designing business systems because data is viewed as a given, is not explicitly considered (“we’ll work out the details later”) or is considered only an afterthought. This may occur because the information dimension of systems engineering is treated as being of secondary importance to defining the best process and determining the required applications capabilities. But we think making data an afterthought is a mistake. Ventana Research uses a framework that explicitly calls out information (all forms of data) and technology (software, hardware and networks) as separate elements in addressing business issues, rather than lumping the two together as “technology.” Explicitly taking the data perspective into account provides a broad perspective that frames process and technology requirements. We assert that treating data as a core consideration can result in better process design and clarify the issues companies must consider to select the appropriate systems to support the people and process aspects of business operations.

Quote-to-cash is a useful example of where an end-to-end process requires more than just workflow to manage the handoffs as tasks are executed. In some simple cases, an ERP system can handle all of the details. In others, automating the process and data flows may require multiple systems (such as a CRM system for customer and account information, as well as systems for product configuration, contract management, billing and collection in addition to ERP. Some of the data assembled in a quote-to-cash transaction may have to be transferred to other operational systems to fulfill the transaction. To achieve best results, data must be staged and controlled from start to finish and there must be a single system of record. Deciding on what application (or applications) to use to manage the process and where to locate the system of record physically and logically depends on a company’s specific circumstances.

Engineering quote-to-cash end to end from both process and data flow perspectives can speed its completion (thereby improving customer responsiveness), remove unnecessary manual steps (generating efficiencies) and reduce or eliminate errors at every step (resulting in better customer service and lower costs).

Another example that benefits from a data-driven end-to-end process is requisition-to-pay. It may seem counterintuitive, but accelerating the payment of invoices can improve a company’s bottom line. With interest rates in much of the developed world at historic lows, the greatest return on available cash is taking advantage of early payment discounts. Yet few companies take advantage of these. One important reason why they don’t is deficiencies in the data and technology needed to make early payment practical. Starting the automated process at the point of initial requisition gives the treasury function better visibility into the amounts and timing of future outlays, making cash forecasting more certain. Greater certainty about the corporation’s cash position lowers the amount of cash it needs to hold to meet payment obligations while maintaining an adequate operating liquidity buffer to allow for forecasting errors and unanticipated needs. Companies that have limited visibility will be cautious about making payments and must maintain a larger, more conservative buffer stock of cash. Using automated systems to speed the processing of invoices by eliminating delays in handoffs is only one element needed to make early payment discount feasible. Timely access to accurate data to support processing invoices is necessary, as is data needed by an analytical application that supports the treasury function to handle the complexities of managing cash effectively.

The importance of timely access to reliable data is often overlooked, but it can be the key ingredient to improving the execution of core business and finance department functions. Engineering data and data management into the design of technology-driven processes must not be an afterthought; it must be integral to the decisions about what software is used and how processes are to be performed. Our research shows that data issues plague companies, and the larger the company, the bigger the problem may be. Effective data management is essential to improve corporate performance. We advise companies to review their current processes and take steps to modernize and automate any that are a drag on performance.

Regards,

Robert Kugel – SVP Research


Qlik was an early pioneer in developing a substantial market for a visual discovery tool that enables end users to easily access and manipulate analytics and data. Its QlikView application uses an associative experience that takes  an in-memory, correlation-based approach to present a simpler design and user experience for analytics than previous tools. Driven by sales of QlikView, the company’s revenue has grown to more than $.5 billion, and originating in Sweden it has a global presence.

At its annual analyst event in New York the business intelligence and analytics vendor discussed recent product developments, in particular the release of Qlik Sense. It is a drag-and-drop visual analytics tool targeted at business users but scalable enough for enterprise use. Its aim is to give business users a simplified visual analytic experience that takes advantage of modern cloud technologies. Such a user experience is important; our benchmark research into next-generation business intelligence shows that usability is an important buying criterion for nearly two out of three (63%) companies. A couple of months ago, Qlik introduced Qlik Sense for desktop systems, and at the analyst event it announced general availability of the cloud and server editions.

vr_bti_br_technology_innovation_prioritiesAccording to our research into business technology innovation, analytics is the top initiative for new technology: 39 percent of organizations ranked it their number-one priority. Analytics includes exploratory and confirmatory approaches to analysis. Ventana Research refers to exploratory analytics as analytic discovery and segments it into four categories that my colleague Mark Smith has articulated. Qlik’s products belong in the analytic discovery category. Users can use the tool to investigate data sets in an intuitive and visual manner, often conducting root cause analysis and decision support functions. This software market is relatively young, and competing companies are evolving and redesigning their products to suit changing tastes. Tableau, one of Qlik’s primary competitors, which I wrote about recently, is adapting its current platform to developments in hardware and in-memory processing, focusing on usability and opening up its APIs. Others have recently made their first moves into the market for visual discovery applications, including Information Builders and MicroStrategy. Companies such as Actuate, IBM, SAP, SAS and Tibco are focused on incorporating more advanced analytics in their discovery tools. For buyers, this competitive and fragmented market creates a challenge when comparing offers in the analytic discovery market.

A key differentiator is Qlik Sense’s new modern architecture, which is designed for cloud-based deployment and embedding in other applications for specialized use. Its analytic engine plugs into a range of Web services. For instance, the Qlik Sense API enables the analytic engine to call to a data set on the fly and allow the application to manipulate data in the context of a business process. An entire table can be delivered to node.js, which extends the JavaScript API to offer server-side features and enables the Qlik Sense engine to take on an almost unlimited number of real-time connections  by not blocking input and output. Previously developers could write PHP script and pipe SQL to get the data, and the resulting application is viable but complex to build and maintain. Now all they need is JavaScript and HTML. The Qlik Sense architecture abstracts the complexity and allows JavaScript developers to make use of complex constructs without intricate knowledge of the database. The new architecture can decouple the Qlik engine from the visualizations themselves, so Web developers can define expressions and dimensions without going into the complexities of the server-side architecture. Furthermore, by decoupling the services, developers gain access to open source visualization technologies such as d3.js. Cloud-based business intelligence and extensible analytics are becoming a hot topic. I have written about this, including a glimpse of our newly announced benchmark research on the next generation of data and analytics in the cloud. From a business user perspective, these types of architectural changes may not mean much, but for developers, OEMs and UX design teams, it allows much faster time to value through a simpler component-based approach to utilizing the Qlik analytic engine and building visualizations.

vr_Big_Data_Analytics_06_benefits_realized_from_big_data_analyticsThe modern architecture of Qlik Sense together with the company’s ecosystem of more than 1,000 partners and a professional services organization that has completed more than 2,700 consulting engagements, gives Qlik a competitive position. The service partner relationships, including those with major systems integrators, are key to the company’s future since analytics is as much about change management as technology. Our research in analytics consistently shows that people and processes lag technology and information in performance with analytics. Furthermore, in our benchmark research into big data analytics, the benefits most often mentioned as achieved are better communication and knowledge sharing (24%), better management and alignment of business goals (18%), and gaining competitive advantage (17%).

As tested on my desktop, Qlik Sense shows an intuitive interface with drag-and-drop capabilities for building analysis. Formulas are easy to incorporate as new measures, and the palate offers a variety of visualization options which automatically fit to the screen. The integration with QlikView is straightforward in that a data model from QlikView can be saved seamlessly and opened intact in Qlik Sense. The storyboard function allows for multiple visualizations to build into narratives and for annotations to be added including linkages with data. For instance, annotations can be added to specific inflection points in a trend line or outliers that may need explanation. Since the approach is all HTML5-based, the visualizations are ready for deployment to mobile devices and responsive to various screen sizes including newer smartphones, tablets and the new class of so-called phablets. In the evaluation of vendors in our Mobile Business Intelligence Value Index Qlik ranked fourth overall.

In the software business, of course, technology advances alone don’t guarantee success. Qlik has struggled to clarify the position its next-generation product and it is not a replacement for QlikView. QlikView users are passionate about keeping their existing tool because they have already designed dashboards and calculations using this tool. Vendors should not underestimate user loyalty and adoption. Therefore Qlik now promises to support both products for as long as the market continues to demand them. The majority of R&D investment will go into Qlik Sense as developers focus on surpassing the capabilities of QlikView. For now, the company will follow a bifurcated strategy in which the tools work together to meet needs for various organizational personas. To me, this is the right strategy. There is no issue in being a two-product company, and the revised positioning of Qlik Sense complements QlikView both on the self-service side and the developer side. Qlik Sense is not yet as mature a product as QlikView, but from a business user’s perspective it is a simple and effective analysis tool for exploring data and building different data views. It is simpler because users no do not need to script the data in order to create the specific views they deem necessary. As the product matures, I expect it to become more than an end user’s visual analysis tool since the capabilities of Qlik Sense lends itself to web scale approaches. Over time, it will be interesting to see how the company harmonizes the two products and how quickly customers will adopt Qlik Sense as a stand-alone tool.

For companies already using QlikView, Qlik Sense is an important addition to the portfolio. It will allow business users to become more engaged in exploring data and sharing ideas. Even for those not using QlikView, with its modern architecture and open approach to analytics, Qlik Sense can help future-proof an organization’s current business intelligence architecture. For those considering Qlik for the first time, the choice may be whether to bring in one or both products. Given the proven approach of QlikView, in the near term a combination approach may be a better solution in some organizations. Partners, content providers and ISVs should consider Qlik Branch, which provides resources for embedding Qlik Sense directly into applications. The site provides developer tools, community efforts such as d3.js integrations and synchronization with Github for sharing and branching of designs. For every class of user, Qlik Sense can be downloaded for free and tested directly on the desktop. Qlik has made significant strides with Qlik Sense, and it is worth a look for anybody interested in the cutting edge of analytics and business intelligence.

Regards,

Tony Cosentino

VP and Research Director

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