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Requirements for Becoming a Strategic Chief Risk Officer

The proliferation of chief “something” officer (CxO) titles over the past decades recognizes that there’s value in having a single individual focused on a specific critical problem. A CxO position can be strategic or it can be the ultimate middle management role, with far more responsibilities than authority. Many of those handed such a title find that it’s the latter. This may be because the organization that created the title is unwilling to invest the necessary powers and portfolio of responsibilities to make it strategic – a case of institutional inertia. Or it may be that the individual given the CxO title doesn’t have the skills or temperament to be a “chief” in a strategic sense.

In business, becoming a chief anything means leaving behind most of the hands-on specific skills that made one successful enough to receive the promotion. This is often the hardest requirement, especially for those coming from an administrative or a highly technical part of a business. Take the chief financial officer position. The person who gets that job often was a controller – an individual who must be able to manage the minutiae of a finance organization. Most of the detailed skills required of a great controller are counterproductive for a CFO, who must focus on the big picture, work well with all parts of the business and be the face of the company to bankers and investors. People who can’t leave the details behind are by definition not strategic CFO material. Similarly, the job of the chief information officer ultimately is not about coding, technical knowledge or project management. It’s about understanding and communicating how the most important issues facing the business can be addressed with technology, ensuring that the IT organization understands the needs of the business and delivering value for the money spent on IT.

The same distinction applies to newer C-level titles. For example, since the financial crisis a few years ago, there has been a growing recognition that banks must manage risk more comprehensively. In response, a number of banks have created the position of chief risk officer or, if they already had one, have invested a broader range of responsibilities in that office. Managing risk strategically has gained importance in financial markets as rising capital requirements and increased regulation force banks to structure their asset portfolios and manage their assets more carefully to maximize their return on equity (ROE). In most banks, optimizing risk – getting the highest return at any given level of risk – and managing risk more dynamically over a credit cycle requires a strategic CRO to lead the effort. Even so, in many organizations the office of the CRO doesn’t have the weight it needs to make such a difference. Here are the most important requirements for chief risk officers who want to transform a middle management job into something more strategic.

Approach risk management as if it were a four-dimensional chessboard. Having the proverbial “seat at the table” (a hackneyed business phrase that’s shorthand for being taken seriously by the senior leadership group) means being able to bring something of value to the table. While an appreciation of the overall business and its strategy is necessary as one rises through the ranks, a purely functional position usually doesn’t require an especially deep understanding of the other parts of the business. For a chief risk officer to play more than a titular role, however, he or she must have a solid understanding of all the major operating pieces of the business on both sides of the balance sheet and a knowledge of the industry’s competitive dynamics – three dimensions of the chessboard. This is particularly important because risk is just a constraint, not the sole consideration in decision-making. That is, the role of the CRO is not simply to enforce constraints that minimize risk – it’s about optimizing risk within the context of the corporate strategy. Stiffer capital requirements are a defining characteristic of today’s banking industry, especially in the United States. Optimizing risk is a necessary condition for optimizing return on equity and the long-term success of the bank. Moreover, the role requires thinking ahead several steps and understanding the dynamics of the business – that’s the fourth dimension. A solid grasp of credit and financial market cycles is essential in leading a risk organization. The ability to use past experience to forecast the consequences of even disparate sets of actions makes the risk organization strategic.

Learn another language. Understanding of other parts of the business goes a long way toward being able to work more effectively, and a CRO should be to translate risk jargon into words and concepts that are relevant to specific parts of the business. It works both ways, too. Understanding the objectives, objections and concerns of other executives means being able to grasp the nuances of their questions and comments. It also helps in explaining the thinking behind the trade-offs necessary to optimize a balance sheet to achieve an optimal ROE for the level and structure of the risk. It’s also essential to be able to communicate the essence of risk management to laymen, for example, by distilling the complexities of a black-box risk strategy into an elevator pitch. All risk models are translatable into easy-to-comprehend concepts. A CRO must be able to do this and even develop an institutional shorthand within the organization that everyone understands – the functional equivalent of describing a feature film as “a car-chase buddy movie.”

Assert leadership when it’s needed. Some leaders are born, but everyone else needs to unlearn habits that detract from their effectiveness as a leader. People in risk or compliance roles may have a harder time than others because the basic skills necessary to excel in this area tend to be found in less introspective souls. Those who work in a compliance function can fall into the trap of using “the rules” as a cudgel for wielding power rather than persuading and gaining assent. Joining the senior leadership team, though, transforms the CRO from a simple enforcer to one who works with others to find solutions.

Beyond these three personal and interpersonal requirements, appropriate use of information technology – data and software – is essential to strategic risk management in banks (and other financial services companies). Successfully exploiting the advantages that can be had with advanced IT is fundamental requirement of making the role of a CRO strategic. SuccessfulCROs must weigh the make-or-break information technology issues of mastering data quality and using the right software tools.

Data is the lifeblood of risk management. The credibility of the risk organization is based on accuracy and availability of data. Bad data drives bad decisions and undermines the authority of the risk organization. As data sets proliferate, grow larger and increasingly incorporate external data feeds (not just market data but news and other unstructured data), the challenge increases. The proverbial garbage-in-garbage-out (GIGO) becomes Big GIGO, as I have writtenvr_infomgt_06_data_fragmentation_is_an_issueData quality must be built into all of the systems. Speed in handling data is essential. The pace of transactions in the financial markets and the banking industry continues to increase, and their risk systems must keep up. Our benchmark research shows that financial services has to deal with more sources of data than other industry sectors.

Yet beyond these maxims is the reality that all large financial institutions fall short in their ability to handle data. “You can have your answers fast or you can have them accurate,” is often said in jest, but it reflects the business reality that analyses often are not black-and-white – utterly reliable or completely false. They may have to be based on information that to varying degrees is incomplete, ambiguous, dated or some combination of these three. Adapting to this reality, new tools utilizing advanced analytical techniques can qualify the reliability of a bit of analysis. It’s better to get some assessment and see that it’s 33 percent reliable than to get no answer or – worse – get an answer without qualification. In most cases, it’s better to get an approximate answer now than to wait for an ironclad answer in a day or two. The decision-makers have an idea of the risk they’re taking if they act on the result, or they can take a different approach to look for a way to get an answer that is more reliable.

Software is essential to risk management and optimization. Technology can buy accuracy, speed, visibility and safety. Many banks ought to do more dynamic risk management. Analytical applications using in-memory processing can substantially reduce the time it takes to run even complex models that utilize very large data sets. This not only improves the productivity of risk analysts but it makes scenario analysis and contingency planning more accessible to those outside the risk organization. If you can run a complex, detailed model and immediately get an interactive report (one that enables you to drill back and drill around), you can have a business conversation about its implications and what to do next. If you have to wait hours or days as you might using a spreadsheet, you can’t.

Desktop spreadsheets have their uses, but in risk management the road to hell begins in cell A1. Spreadsheets are the right tool for prototyping and exploratory analysis. They are a poor choice for ongoing risk management modeling and analytics. They are error-prone, lack necessary controls and have limited dimensionality. The dangers of using spreadsheets in managing risk exposure were laid bare by the internal investigation conducted by JP Morgan, which I commented on at the time. There are many alternatives to desktop spreadsheets that are affordable and require limited training. For example, many financial applications for planning and analysis have Excel as their user interface. There are more formal tools, such as a multidimensional spreadsheet, that are relatively easy for risk modelers to use and offer superior performance and control compared to desktop spreadsheets.

Automate and centralize. Information technology delivers speed, efficiency and accuracy when manual tasks are automated. The payoff from automating routine reporting and analytics may seem trivial, but this is usually because people – especially managers – underestimate the amount of time spent as well as the routine errors that creep into manual tasks (especially if they are performed in a desktop spreadsheet). The need for automation and centralization especially applies to regulatory and legal activities, such as affirmations, attestations, signoffs and any other form of documentation. Especially in highly regulated industries such as financial services, there is no strategic value in meeting legal requirements, but there is some in doing so as efficiently as possible and limiting the potential for oversights and errors. Keeping all such documentation in a central repository and eliminating the use of email systems as a transport mechanism and repository for compliance documentation saves time of highly compensated individuals when inevitable audits and investigations occur and limits the possibility that documents cannot be found when needed.

Senior executive sponsorship is also a critical need if the chief risk officer is to be a strategic player. If the CRO has done all of the above, that’s not going to be a problem because the CRO’s objectives and the CEO’s objectives will be largely aligned. True, that’s not always a given. Some organizations will not embrace the notion that managing risk can be strategic. CROs who find themselves in an organization where their aspirations to serve a strategic role are not met should find another one that appreciates the value they can bring to the table.

Regards,

Robert Kugel – SVP Research


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


 

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