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Big Data Analytics Require Best Practices in Using Technology

Organizations should consider multiple aspects of deploying big data analytics. These include the type of analytics to be deployed, how the analytics will be deployed technologically and who must be involved both internally and externally to enable success. Our recent big data analytics benchmark research assesses each of these areas. How an organization views these deployment considerations may depend on the expected benefits of the big data analytics program and the particular business case to be made, which I discussed recently.

According to the research, the most important capability of big data analytics is predictive analytics (64%), but among companies vr_Big_Data_Analytics_08_top_capabilities_of_big_data_analyticsthat have deployed big data analytics, descriptive analytic approaches of query and reporting (74%) and data discovery (64%) are more readily available than predictive capabilities (57%). Such statistics may be a function of big data technologies such as Hadoop, and their associated distributions having prioritized the ability to run descriptive statistics through standard SQL, which is the most common method for implementing analysis on Hadoop. Cloudera’s Impala, Hortonworks’ Stinger (an extension of Apache Hive), MapR’s Drill, IBM’s Big SQL, Pivotal’s HAWQ and Facebook’s open-source contribution of Presto SQL all focus on accessing data through an SQL paradigm. It is not surprising then that the technology research participants use most for big data analytics is business intelligence (75%) and that the most-used analytic methods — pivot tables (46%), classification (39%) and clustering (37%) — are descriptive and exploratory in nature. Similarly, participants said that visualization of big data allows analysts to perform faster analysis (49%), understand context better (48%), perform root-cause analysis (40%) and display multiple result sets (40%), but visualization does not provide more advanced analytic capabilities. While various vendors now offer approaches to run advanced analytics on big data, the research shows that in terms of big data, organizational capabilities still revolve around more basic analytic access.

For companies that are implementing advanced analytic capabilities on big data, there are further analytic process considerations, and many have not yet tackled those. Model building and model deployment should be manageable and timely, involve specialized personnel, and integrate into the broader enterprise architecture. While our research provides an in-depth look at adoption of the different types of in-database analytics, deployment of advanced analytic sandboxes, data mining, model management, integration with business processes and overall model deployment, that is beyond the topic here.

Beyond analytic considerations, a host of technological decisions vr_Big_Data_Analytics_13_advanced_analytics_on_big_datamust be made around big data analytics initiatives. One of these is the degree of customization necessary. As technology advances, customization is giving way to more packaged approaches to big data analytics. According to our research, the majority (54%) of companies that have already implemented big data analytics did custom builds using big data-specific languages and interfaces. The most of those that have not yet deployed are likely to purchase a dedicated or packaged application (44%), followed by a custom build (36%). We think that this pre- and post-deployment comparison reflects a maturing market.

The move from custom approaches to standardized ones has important implications for the skills sets needed for a big data vr_Big_Data_Analytics_14_big_data_analytics_skillsanalytics initiative. In comparing the skills that organizations said they currently have to the skills they need to be successful with big data analytics, it is clear that companies should spend more time building employees’ statistical, mathematical and visualization skills. On the flip side, organizations should make sure their tools can support skill sets that they already have, such as use of spreadsheets and SQL. This is convergent with other findings about training needs, which include applying analytics to business problems (54%), training on big data analytics tools (53%), analytic concepts and techniques (46%) and visualizing big data (41%). The data shows that as approaches become more standardized and the market focus shifts toward them from customized implementations, skill needs are shifting as well. This is not to say that demand is moving away from the data scientist completely. According to our research, organizations that involve cross-functional teams or data scientists in the deployment process are realizing the most significant impact. It is clear that multiple approaches for personnel, departments and current vendors play a role in deployments and that some approaches will be more effective than others.

Cloud computing is another key consideration with respect to deploying analytics systems as well as sandbox modelling and testing environments. For deployment of big data analytics, 27 percent of companies currently use a cloud-based method, while 58 percent said they do not and 16 percent do not know what is used. Not surprisingly, far fewer IT professionals (19%) than business users (40%) said they use cloud-based deployments for big data analytics. The flexibility and capability that cloud resources provide is particularly attractive for sandbox environments and for organizations that lack big data analytic expertise. However, for big data model building, most organizations (42%) still utilize a dedicated internal sandbox environment to build models while fewer (19%) use a non-dedicated internal sandbox (that is, a container in a data warehouse used to build models) and others use a cloud-based sandbox either as a completely separate physical environment (9%) or as a hybrid approach (9%). From this last data we infer that business users are sometimes using cloud-based systems to do big data analytics without the knowledge of IT staff. Among organizations that are not using cloud-based systems for big data analytics, security (45%) is the primary reason that they do not.

Perhaps the most important consideration for big data analytics is choosing vendors to partner with to achieve organizational objectives. When we understand the move from custom technological approaches to more packaged ones and the types of analytics currently being implemented for big data, it is not surprising that a majority of research participants (52%) are looking to their business intelligence systems providers to supply their big data analytics solution. However, a significant number of companies (35%) said they will turn to a specialist analytics provider or their database provider (34%). When evaluating big data analytics, usability is the most important vendor consideration but not by as wide a margin as in categories such as business intelligence. A look at criteria rated important and very important by research participants reveals usability is the highest ranked (94%), but functionality (92%) and reliability (90%) follow closely. Among innovative new technologies, collaboration is important (78%) while mobile access (46%) is much less so. Coupled with the finding that communication and knowledge sharing combined is an important benefit of big data analytics, it is clear that organizations are cognizant of the collaborative imperative when choosing a big data analytics product.

Deployment of big data analytics starts with forethought and a well-defined business case that includes the expected benefits I discussed in my previous analysis. Once the outcome-driven framework is established, organizations should consider the types of analytics needed, the enabling technologies and the people and processes necessary for implementation. To learn more about our big data analytics research, download a copy of the executive summary here.

Regards,

Tony Cosentino

VP & Research Director


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


 

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