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Finance Departments Still Lag in Using Advanced Analytics

Business computing has undergone a quiet revolution over the past two decades. As a result of having added, one-by-one, applications that automate all sorts of business processes, organizations now collect data from a wider and deeper array of sources than ever before. Advances in the tools for analyzing and reporting the data from such systems have made it possible to assess financial performance, process quality, operational status, risk and even governance and compliance in every aspect of a business. Against this background, however, our recently released benchmark research finds that finance organizations are slow to make use of the broader range of data and apply advanced analytics to it.

Analytics has long been a tool used by Finance. Yet because analytical techniques for assessing balance sheets, income statements and cash-flow statements are well developed and widely accepted, vr_NG_Finance_Analytics_01_finance_analytics_users_dissatisfiedfinance professionals have had little incentive to do more even as the opportunities available to them have proliferated. Taking a narrow of finance analytics they have largely failed to take advantage of advanced analytics to address the full needs of today’s enterprise and thus to increase their own value to it.

It’s not that finance departments aren’t aware of their shortcomings. For instance, more than half (58%) of participants in this research said that significant or major changes to their process for creating finance analytics are necessary; only 7 percent said no improvements are needed. We found four main reasons for dissatisfaction with their process: it’s too slow; it isn’t adaptable to change; there aren’t enough skilled people to do this work; and data used in it is inaccessible or too difficult to integrate.

Usually, addressing some business issue requires dealing with a combination of the underlying people, processes, information and technology. Companies often fail to address the issue successfully because they focus on just one of these elements. We think it’s important to use the people, process, information and technology framework to isolate the root causes behind the issues. Let’s look at the role of technology – mainly software – in finance analytics.

Our research finds many companies have trouble with the technology aspects. Only 12 percent of organizations are satisfied with vr_NG_Finance_Analytics_02_spreadsheets_arent_right_for_finance_analyticsthe software they use to create and apply analytics; more than twice as many (27%) are not satisfied. That’s probably because 71 percent of them use spreadsheets for analytics, a higher percentage than for any other tool. Two-thirds of these users said that reliance on spreadsheets makes it difficult to produce accurate and timely analytics. In contrast, fewer than half use innovative techniques such as predictive analytics (44%) to assist planning and forecasting, and just 20 percent are employing big data to process the flood of data into today’s businesses.

The research demonstrates a correlation between the technology a company uses and how well its finance analytics processes work. Two-thirds of participants who said their software works well or very well also said their finance analytics process needs little or no improvement. By comparison, just one in four of those that said significant changes must be made to the software they use have a process that needs little or no improvement.

Here again we find that the inappropriate use of spreadsheets is an issue. When asked whether spreadsheets cause problems in their use of analytics, 67 percent said yes. This is because desktop spreadsheets have inherent shortcomings that make them poorly suited for any sort of advanced analytics. In particular, they cannot readily manage analyses involving more than a handful of dimensions. (A dimension is some aspect of business data such as time, business divisions, product families, sales territories and currency.) Many of these dimensions are constructed in hierarchies: Branches roll up into territories which roll up into divisions of companies, for example. Analyzing data usually requires viewing the data from different perspectives (which translates into dimensions) to isolate an issue or opportunity. One such would be looking at sales by product family and region and then drilling down into specific branches or stock-keeping units (SKUs). In doing analysis, it’s difficult enough to manage the dimensions of the purely financial aspects of a business. Spreadsheets are especially ill-suited to analyzing operational and financial data together, such as the delivery method or product configuration details.

Our research data shows that not having the right technology is impedes finance departments’ ability to create and use more advanced analytics. We found several reasons why companies decide not to make these technology investments. The top three are a lack of resources, no budget and a business case that’s not strong enough. The first two may be valid reasons, but not wanting to commit resources and budget to advanced analytics could be a symptom of a poorly constructed business case, as I noted earlier. A lack of leadership and vision on the part of senior finance executives also plays a role. Many may say they want their department to play a more strategic role in running their company yet fail to follow through to adopt new methods and the necessary supporting technology.

But now a new generation of finance department leaders is emerging. These are people young enough to have grown up with technology and to be more demanding in their use of software and systems to produce results. The time is ripe for change, and it’s up them to drive finance departments to be more strategic in their use of analytics.

Regards,

Robert Kugel – SVP Research


SAP Supercharges Business Intelligence with Analytics

SAP recently presented its analytics and business intelligence roadmap and new innovations to about 1,700 customers and partners using SAP BusinessObjects at its SAP Insider event (#BI2014). SAP has one of the largest presences in business intelligence due to its installed base of SAP BusinessObjects customers. The company intends to defend its current position in the established business intelligence (BI) market while expanding in the areas of databases, discovery analytics and advanced analytics. As I discussed a year ago, SAP faces an innovator’s dilemma in parts of its portfolio, but it is working aggressively to get ahead of competitors.

vr_bti_br_technology_innovation_prioritiesOne of the pressures that SAP faces is from a new class of software that is designed for business analytics and enables users to visualize and interact on data in new ways without relationships in the data being predefined. Our business technology innovation research shows that analytics is the top-ranked technology innovation in business today, rated first by 39 percent of organizations. In conventional BI systems, data is modeled in so-called cubes or other defined structures that allow users to slice and dice data quickly and easily. The cube structure solves the problem of abstracting the complexity of the structured query language (SQL) of the database and slashes the amount of time it takes to read data from a row-oriented database. However, as the cost of memory decreases significantly, enabling the use of new column-oriented databases, these methods of BI are being challenged. For SAP and other established business intelligence providers, this situation represents both an opportunity and a challenge. In responding, almost all of these BI companies have introduced some sort of visual discovery capability. SAP introduced SAP Lumira, formerly known as Visual Intelligence, 18 months ago to compete in this emerging segment, and it has gained traction in terms of downloads, which the company estimated at 365,000 in the fourth quarter of 2013.

SAP and other large players in analytics are trying not just to catch up with visual discovery players such as Tableau but rather to make it a game of leapfrog. Toward that end, the capabilities of Lumira demonstrated at the Insider conference included information security and governance, advanced analytics, integrated data preparation, storyboarding and infographics; the aim is to create a differentiated position for the tool. For me, the storyboarding and infographics capabilities are about catching up, but being able to govern and secure today’s analytic platforms is a critical concern for organizations, and SAP means to capitalize on them. A major analytic announcement at the conference focused on the integration of Lumira with the BusinessObjects platform. Lumira users now can create content and save it to the BusinessObjects server, mash up data and deliver the results through a secure common interface.

Beyond the integration of security and governance with discovery analytics, the leapfrog approach centers on advanced analytics. SAP’s acquisition last year of KXEN and its initial integration with Lumira provide an advanced analytics tool that does not require a data scientist to use it. My coverage of KXEN prior to the acquisition revealed that the tool was user-friendly and broadly applicable especially in the area of marketing analytics. Used with Lumira, KXEN will ultimately provide front-end integration for in-database analytic approaches and for more advanced techniques. Currently, for data scientists to run advanced analytics on large data sets, SAP provides its own predictive analytic library (PAL), which runs natively on SAP HANA and offers commonly used algorithms such as clustering, classification and time-series. Integration with the R language is available through a wrapper approach, but the system overhead is greater when compared to the PAL approach on HANA.

The broader vision for Lumira and the BusinessObjects analytics platform SAP said is “collective intelligence,” which it described as “a Wikipedia for business” that provides a bidirectional analytic and communication platform. To achieve this lofty goal, SAP will vr_Big_Data_Analytics_02_defining_big_data_analyticsneed to continue to put resources into HANA and facilitate the integration of underlying data sources. Our recently released research on big data analytics shows that being able to analyze data from all data sources (selected by 75% of participants) is the most prevalent definition for big data analytics. To this end, SAP announced the idea of an “in-memory fabric” that allows virtual data access to multiple underlying data sources including big data platforms such as Hadoop. The key feature of this data federation approach is what the company calls smart data access (SDA). Instead of loading all data into memory, the virtualized system sets a proxy that points to where specific data is held. Using machine learning algorithms, it can define how important information is based on the query patterns of users and upload the most important data into memory. The approach will enable the company to analyze data on a massive scale since utilizing both HANA and the Sybase IQ columnar database which the company says was just certified as the world record for the largest data warehouse, at more than 12 petabytes. Others such as eBay and Teradata may beg to differ with the result based on another implementation, but nevertheless it is an impressive achievement.

Another key announcement was SAP Business Warehouse (BW) 7.4, which now runs on top of HANA. This combination is likely to be popular because it enables migration of the underlying database without impacting business users. Such users store many of their KPIs and complex calculations in BW, and to uproot this system is untenable for many organizations. SAP’s ability to continue support for these users is therefore something of an imperative. The upgrade to 7.4 also provides advances in capability and usability. The ability to do complex calculations at the database level without impacting the application layer enables much faster time-to-value for SAP analytic applications. Relative to the in-memory fabric and SDA discussed above, BW users no longer need intimate knowledge of HANA SDA. The complete data model is now exposed to HANA as an information cube object, and HANA data can be reflected back into BW. To back it up, the company offered testimony from users. Representatives of Molson Coors said their new system took only a weekend to move into production (after six weeks of sandbox experiments and six weeks of development) and enables users to perform right-time financial reporting, rapid prototyping and customer sentiment analysis.

SAP’s advancements and portfolio expansion are necessary for it to continue in a leadership position, but the inherent risk is confusion amongst its customer and prospect base.  SAP published its last statement of direction for analytic dashboard about this time last year, and according to company executives, it will be updated fairly soon, though they would not specify when. The many tools in the portfolio include Web Intelligence, Crystal Reports, Explorer, Xcelsius and now Lumira. SAP and its partners position the portfolio as a toolbox in which each tool is meant to solve a different organizational need. There is overlap among them, however, and the inherent complexity of the toolbox approach may not resonate well with business users who desire simplicity and timeliness.

SAP customers and others considering SAP should carefully examine how well these tools match the skills in their organizations. We encourage companies to look at the different organizationalVRMobileBIVI roles as analytic personas and try to understand which constituencies are served by which parts of the SAP portfolio. For instance, one of the most critical personas going forward is the Designer role since usability is the top priority for organizational software according to our next-generation business intelligence research. Yet this role may become more difficult to fill over time since trends such as mobility continue to add to the job requirement. SAP’s recent upgrade of Design Studio to address emerging needs such as mobility and mobile device management (MDM) may force some organizations to rebuild  dashboards and upscale their designer skill sets to include JavaScript and Cascading Style Sheets, but the ability to deliver multifunctional analytics across devices in a secure manner is becoming paramount. I note that SAP’s capabilities in this regard helped it score third overall in our 2014 Mobile Business Intelligence Value Index. Other key personas are the knowledge worker and the analyst. Our data analytics research shows that while SQL and Excel skills are abundant in organizations, statistical skills and mathematical skills are less common. SAP’s integration of KXEN into Lumira can help organizations develop these personas.

SAP is pursuing an expansive analytic strategy that includes not just traditional business intelligence but databases, discovery analytics and advanced analytics. Any company that has SAP installed, especially those with BusinessObjects or an SAP ERP system, should consider the broader analytic portfolio and how it can meet business goals. Even for new prospects, the portfolio can be compelling, and as the roadmap centered on Lumira develops, SAP may be able to take that big leap in the analytics market.

Regards,

Tony Cosentino

VP and Research Director


Equifax Delivers Insightful Analytics for Compliance with Affordable Care Act

Pressure to comply with requirements of the Affordable Care Act (ACA) is a looming challenge for most organizations today. Many go through numerous manual iterations such as running reports and compiling data into spreadsheets from benefits, payroll and HR systems to calculate whether their employees are eligible. As my colleague Stephan Millard explains in “Is Your Organization Technology Ready for the Affordable Care Act?”, the ACA applies to organizations with 50 or more full-time employees who work more than 30 hours a week; individuals not covered by an employer can get insurance through the government. There are a great many details for employers to address in the ACA, and most HR departments lack a smooth process and effective technology to generate the information to determine compliance.

I was reminded of these issues at Equifax’s FORUM 2014 where the workforce management software provider described its efforts to help organizations with compliance and employer assurance processes including I-9 and W-2 forms management. Among its Workforce Solutions is the ACA Management Platform, which Stephan covered at its launch. Now Equifax says that more than 100 major organizations have adopted the platform, which according to our analysis makes it the most widely adopted dedicated software for ACA compliance.

The product provides verifications, eligibility tracking and employmentvr_HCA_04_dissatisfaction_with_human_capital_analytics notifications on top of modeling and reporting. While many software companies in the HR and payroll management segment provide compliance reports, Equifax has the capability to integrate data sources and model data through analytics that can generate the metrics, reports and dashboards businesses need to determine compliance. This information can be automated and distributed to the appropriate parts of the organization and help avoid fines and penalties for late or inadequate compliance. Equifax has built a library of predefined reports and dashboards that can save time otherwise spent on creating them manually. These capabilities can alleviate what our human capital analytics research finds are the largest points of dissatisfaction: data not readily available (cited by 63%), not enough skilled people (45%) and analytics that are hard to build and maintain (42%).

Equifax also makes it possible to maintain histories of compliance and to look back at the data in detail; these features help prevent falsification of hours worked to show that the company does not avoid providing healthcare as required, which can incur significant penalties and potentially damage the company’s credibility and brand. On the reverse side it also can ensure that the minimum number of hours are worked for healthcare eligibility and notify employees through workforce scheduling. Equifax encourages organizations to evaluate the effectiveness of their technology for supporting the ACA compliance process and its integration with underlying HR systems and processes. Users should consider how well their technology automates the process and whether it can support notifications and management reviews, which are necessary not just to stay in compliance but to assess policy changes and track approvals. We have found confusion in HR and benefit teams as to how and where to generate analytics that produce these and other insights. Our latest research on payroll management optimization found the capability most often seen as very important (by 42%) is to perform auditing or compliance for adherence to policies and procedures; this indicates that many professionals think payroll is the logical place to address this need, but in fact they need detailed employee and work data also from HR, workforce management and even benefits systems for both historical and real-time data related to employment and healthcare. It is clear that organizations have to collect and store worker information from all of these sources and model them with analytics, which is not a capability of legacy HR and payroll management systems.

Equifax has experience serving a variety of industries where the details about types of workers and times worked can be challenging to track and calculate, let alone have the right set of metrics and reports. It has built industry-specific versions of its application for staffing and for higher education that can accelerate the time to value for users of its human capital analytics.

These steps to provide ACA compliance build on a key advance in the use and benefits of human capital analytics that we have vr_HCA_01_issues_driving_human_capital_analytics_investmentresearched. Our research shows that the issues most often driving investment in these systems are about improving efficiency and productivity (for 63%), overcoming a lack of analytical process (41%) and collecting scattered information (37%). The research shows that a dedicated human capital analytics system can provide a foundation to manage compliance at any level and address the broader aspects of people, performance, process and risk metrics that are essential for employer assurance. Equifax’s ACA Management Platform is built on robust analytics that handle a range of data and offer visual discovery and exploration that it acquired several years ago from eThority. This technology continues to advance in support for business users as well as analysts; its latest release provides access to metrics and key indicators on mobile devices through dashboards that can be easily assembled and published.

Equifax is dedicating itself to the range of compliance needs to help employers be sure they are doing everything possible to meet their responsibilities regarding regulations like the Affordable Care Act. If your organization is not confident in how it manages these tasks, we suggest evaluating how Equifax can help it establish and maintain compliance.

Regards,

Mark Smith

CEO & Chief Research Officer


 

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