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The developed world has an embarrassment of riches when it comes to information technology. Individuals walk around with far more computing power and data storage in their pockets than was required to send men to the moon. People routinely hold on their laps what would have been considered a supercomputer a generation ago. There is a wealth of information available on the Web. And the costs of these information assets are a tiny fraction of what they were decades ago. Consumer products have been at the forefront in utilizing information technology capabilities. The list of innovations is staggering. The “smart” phone is positively brilliant. Games are now a far bigger business than motion pictures.

VR Logo Bug Square BufferYet few business users are tapping the full potential of today’s systems. Most organizations have been slow to integrate IT innovation into their core processes. Companies have made considerable investments in information technology, but their business methods have been slow to adapt to the resources available. For instance, software for incentive compensation software and for planning and budgeting has made it possible to improve these processes, but most companies manage compensation, budget and plan in much the same way they did decades ago. To be sure, it’s much easier for individuals to adopt new tools for themselves than it is to align groups and executives in corporations to change proven approaches – even mediocre ones. But it’s also the case that business software must make it easier for individuals to realize more of the potential of information technology. And this part of the evolution of business software is only beginning. This is the context in which I took note of two emerging capabilities of IBM’s business software. One is its Concert user experience software and the other its emerging application (not yet officially named) designed to make advanced analytics more consumable. These are two important capabilities IBM highlighted at its recent Insight user group meeting and Big Data and Analytics analyst summit.

Integrating processes and data across a business has long been a challenge for IT departments. Some decades ago the issue of “islands of automation” emerged as companies implemented stand-alone business applications one by one to perform some function but then realized that it would be handy if these could share data and manage processes from start to finish. Initial progress toward this goal was made in the form of applications such as ERP that offer integrated functionality and enterprise data stores, although these often were difficult to implement and complex to maintain. Lately, software vendors have been refocusing to provide users with ways of facilitating end-to-end process management and making data more accessible.

IBM Concert is such an attempt. Announced in November 2013, it is a user interface IBM designed to be the central touch point across multiple applications and data stores. (It’s possible to link Concert to other vendors’ software, but it’s unlikely that a company would buy it on its own to link other applications.) It’s meant to replace menu-driven interactions between the user and the system with “I want to do this process” and a “day in the life” approach to organizing how individuals access applications and data. IBM Concert is an example of how we are only now beginning to achieve the longstanding objective of having IT systems conform to the user’s needs rather than the opposite. On a single screen Concert organizes personal task lists and presents metrics, conditions and dashboard elements configured to an individual’s preferences so the user can easily monitor conditions and enable more management by exception. Users can organize the data they need to support a given process right in front of them rather than having to go to some other application to fetch that data.

IBM Concert also has a social component that provides the ability for users to collaborate in context. Social applications generally have improved organizations’ connectedness. They offer greater immediacy than “copy all” email, greater inclusiveness than chat software and better communication in a mobile and geographically dispersed workforce. Initially, social applications took a broadcast approach similar to an unfiltered Twitter feed but as I pointed out at the time, that wasn’t a useful approach. As anyone who has used Twitter during an event can attest, the volume of messages quickly exceeds one’s ability to pick out the important ones. Moreover, not everyone wants to share information broadly, especially, for example, finance departments. Concert by contrast “understands” the area in which the individual is working and connects him or her to the conversations of others who are part of the group that needs to collaborate on that specific task. Users also apply hashtags to add a specific context to the message.

I think that Concert has the potential to become the nexus of business people’s computing environments and a sidekick that helps them stay organized and informed, get alerts, collaborate, find answers and explore their workday world.

Both at Vision and again at the Big Data and Analytics analyst summit, IBM previewed Project Catalyst Insight which is a not-yet-named application that is a significant advancement from its SPSS Analytic Catalyst software. Making big data and analytics more useful and consumable by the white-collar workforce (and even some of the blue collars) would be provide a major boost to organizational performance. By itself, a mass of data is not especially useful, and there are significant challenges to teasing out insights from large data sets, especially when that requires sophisticated analytical techniques. Another as-yet-unnamed application from IBM is designed to make big data and analytics more consumable and more useful. Typically large volumes of data are now accessible mainly to those with Ph.D.s in statistics or otherwise highly trained. The main objective of this project is to package advanced analytics routines for use, after limited training, by ordinary business analysts working in any department in any industry.

Big data has always been with us; it is just a question how much “big” is. Today the term refers to data sets so large and complex that organizations have difficulty processing them using standard database management systems and applications. Technology for handling big data has crossed a threshold, becoming more capable and cost-effective. Companies can now to tap into much larger amounts of structured and unstructured data. Big data has potential – and potential pitfalls – for improving a company’s performance, as I have noted. Big data is of little use unless organizations have the ability to use analytics to achieve insights not available through more conventional techniques. The ability to sift through large quantities of business-related data rapidly could set in motion fundamental changes in how executives and managers run their business. Properly deployed, big data can support a more forward-looking and agile management style even in very large enterprises. It will allow more flexible forms of business organization. It can give finance organizations greater scope to play a more strategic role in corporate management by changing the focus of business reviews from backward-looking assessments of what just happened to emphasis on what to do next.

vr_NG_Finance_Analytics_14_innovative_companies_adapt_betterThe challenge for many companies is that big data and advanced analytics are not readily consumable. Our research on finance analytics finds that fewer than one-third (29%) of companies use big data to support their finance analytics, even though this technology can handle the flood of data into today’s businesses and can help produce more useful analytics and advanced techniques. Although analytics is essential to finance departments, their focus remains on the basics. Fewer than half (44% each) use the proven newer techniques of predictive analytics and leading indicators. Nearly three out of four (73%) do not assess relevant economic or market data and trends, and fewer than half assess customer and product profitability; any of these could make analyses more relevant to the overall success of the company. The ability of finance organizations to master analytical techniques – especially advanced ones – ought to be a priority for senior executives because our research shows a correlation between competence in utilizing big data and analytics and the ability to adapt quickly to changing business and economic conditions.

IBM SPSS Analytic Catalyst Insight is designed to make it easier for business users who are not trained statisticians to create predictive analytical models just by answering a few preliminary questions about what they want to accomplish using the data. The new incarnation with IBM Project Catalyst Insight aims to simplify the process even further to bring it into the reach of a wider set of business users. It does so by packaging a range of standard routines that would be applied to data sets and providing even more guidance to analysts and even some business managers that know what they want to know but have a limited grasp of the analytical techniques necessary to find meaning in a mass of data. If IBM can create an application that enables more business users to utilize predictive analytics and other advanced analytical techniques, it would represent a big step forward in making big data a useful tool for many more functional areas than it is today.

Both IBM Concert and the new business analytics tool called Project Catalyst Insight “to be officially named later” reflect IBM’s strategy of achieving product differentiation in a rapidly evolving software market. The first decades of packaged business applications were characterized by a race to create new categories and load them with distinguishing features and functions. In the next decade competitive advantage will fall to software vendors that – in addition to features and functions – can provide business people with a user experience that is easily molded to how they naturally work. IBM Concert is a useful first step that is likely to be further refined. The new analytical environment derived from IBM SPSS Modeler and SPSS Analytic Catalyst Insight looks and sounds like a good idea, and it will be interesting to see how it develops when it is generally available.

Regards,

Robert Kugel – SVP Research

All the hubbub around big data and analytics has many senior finance executives wondering what the big deal is and what they should do about it. It can be especially confusing because much of what’s covered and discussed on this topic is geared toward technologists and others working outside of Finance, in areas such as sales, marketing and risk management. But finance executives need to position their organization to harness this technology to support the strategic goals of their company. To do so, they must have clarity as to what big data can do, what they want it to do, and what skills and tools they need to meet their objectives.

Big data has always been with us, just on smaller scales: The term refers to data sets so large and complex that organizations have difficulty processing them using on-hand database management systems and applications. It has become a popular buzzword because technology for handling big data has crossed a threshold, making it at the same time more capable and cost-effective. Companies now can tap into huge amounts of structured and unstructured data using advanced data processing technologies, analytics and visualization tools to achieve insights not previously available using more conventional techniques. In a recent research analysis, I covered some of the potential benefits (and potential pitfalls) of big data as it relates to company management. Increasingly, the ability to analyze large quantities of business-related data rapidly holds the promise of fundamental changes in how executives and managers run their businesses. Properly deployed, big data analytics enables a more forward-looking and agile management style, even in very large enterprises. Because it allows more flexible forms of business organization, it can give finance organizations greater scope to play a more strategic role in corporate management.

Big data and analytics are a natural combination. By itself, a mass of data is not especially useful, and there are significant challenges to teasing out insight from such large data sets. However, information technology has evolved to make assembling and working with extremely large amounts of data far more practical. As well, routines involving advanced analytics that were once the domain of people with Ph.D.s in statistics are increasingly usable by business analysts, as new analytical software packages are designed to hide the complexity of the underlying statistical work. My colleague Tony Cosentino recently summarized the progress to date in adoption of big data analytics, covering some of the existing uses (already numerous) and emerging trends.

Keep in mind that it’s not just a matter of learning how to master new software and munge data. Finance departments must sharpen their skills in determining how to best utilize big data analytics. And it’s even more important that finance executives understand vr_oi_goals_of_using_operational_intelligencehow to make practical use of big data analytics: In some cases users may want to consume only the output of the analytics created by other parts of the organization (such as demand forecasts by product families), while in others the organization may want to purchase applications that use or embed big data analytics (such as continuous monitoring for ERP systems governance) or enable price and profit optimization.

Our benchmark research on operational intelligence, a technology-driven discipline that has been using big data operating across networks and systems has been using analytics for years, shows that the most common reasons for using such applications (cited by almost three in five companies) are to manage performance, detect fraud, comply with regulations and manage risk. These areas are broadly applicable for finance organizations, but I assert that as well as governance and control, initially the three main applications of big data analytics are planning, reviews and alerts. Here’s how.

Companies do a lot of planning, so it’s useful to segment the activity. One way is by time. There are three main planning time frames in which big data analytics plays a role.

  1. Short-term tactical planning is used, for example, to project demand for specific products or create offers that might spur incremental demand. Especially in consumer products and business-to-consumer marketing, these models are statistically and computationally challenging, as they must be continually updated and adjusted. However, this is not an area of business where Finance has taken a role.
  2. Long-term and strategic planning can help determine the impact of a confluence of factors on markets and costs. Decades ago, the largest companies maintained strategic planning staffs to generate long-term forecasts to inform senior executives of important market trends. Except at companies that have very long cycles with specific demand and supply requirements, those staffs have disappeared or have been substantially reduced as corporations switched to third-party sources.
  3. In the time horizon between short- and long-term planning there are techniques for improving the accuracy of forecasts of revenue and costs using large sets of historical data, which enable organizations to better understand the various factors that influence demand. This sort of advanced modeling using predictive analytics can be useful in improving the accuracy of corporate business planning and budgeting, which is at the core of financial planning and analysis. Predictive analytics uses techniques from statistics, modeling and data mining that weigh multiple current and historical facts and their interactions to predict outcomes. Good predictive models can identify the most important factors driving outcomes, and because of this, they often can be more accurate than simple extrapolation. For example, by examining large sets of historical data, a fast-food chain can predict with reasonable accuracy demand for certain menu items at specific locations at a given hour of a given day by taking into account factors such as the day of the week, time of the year, sales patterns over the past three weeks, advertising spend and special offers.

As useful as predictive analytics are for forecasting, they may be even more valuable when applied to reviews and alerts. Predictive analytics can provide a baseline against which to compare actuals. This, in turn, enables an organization to get an earlier warning when results diverge meaningfully from what was expected, so executives and managers can react immediately rather than in days or weeks. For example, in business-to-business relationships that involve many routine purchases (any sort of supplies, for example) a divergence from established trends could generate an alert to the sales organization. Embedded analytics in an order-entry system could highlight late or smaller-than-usual orders. These might indicate a competitive threat or some other issue that would benefit from a timely interaction with the customer. This is just one of the ways that data captured by the financial systems can be used to improve the effectiveness of other business units, enabling the department to play a more strategic role in supporting the company.

Another use is in accounts receivable, where predictive analytics can promote customer satisfaction. To illustrate, a company that does a routine analysis of payment patterns can have a good idea of when specific customers will pay. If one that routinely pays its invoice between the 16th and 19th day of the month has not paid by the 23rd day, the analytics system generates an alert. A call to the customer or an automated email notes the delayed payment, asking if there was an error in the billing or some other point in dispute. There are a couple of advantages to this approach. If nothing else, if there is an issue, it is likely to be resolved more quickly. Moreover, from a customer satisfaction perspective, it’s a far superior form of customer interaction than waiting several weeks and then sending out a dunning notice demanding payment. Resolving any issue sooner improves cash flow, and if the company did make a mistake, asking for payment will only annoy the customer. Another use of big data in receivables is to automatically identify customers that are routinely tardy in paying. This can kick off an internal company discussion about what ought to be done about the situation, such as limiting credit or finding ways to accelerate payments.

Governance is another area where big data analytics are already at work, with companies using it for fraud detection and alerting. For instance, software packages can monitor a company’s financial systems for evidence of suspicious activities such as payments to bogus vendors or top-level alterations to financial statements. Such systems are designed to be high-level controls that reduce the need for manual internal and external audit work. And even more is possible. As I noted earlier, in the not-too-distant future it may be possible to have an “auditor in a box” – a forensic system that continuously identifies and lists all suspicious activities, transactions and conditions and weighs their materiality. Such a system would permit more timely responses to the risk of material errors or fraud and facilitate examinations by external auditors. In addition to being far more efficient than periodic manual effort, the auditor-in-a-box concept is potentially more reliable because it examines everything rather than relying on sampling.

However, there are challenges. Staffing and training are significantvr_bigdata_obstacles_to_big_data_analytics issues for Finance in dealing with big data analytics. Our research into the challenges of utilizing big data shows that nearly four in five companies find staffing and training to be an obstacle in utilizing big data. Despite the fact that analytics is an inherent element of the finance function, it almost always involves the broad application of basic approaches employing simple math (ratio and margin analysis, for example). Few departments have applied advanced analytics: Our finance analytics research finds that only 13 percent of finance departments employ predictive analytics.

To be able to handle these staffing and training needs, finance executives must understand their department’s big data analytics competence requirements. A useful place to start is to become familiar with the five personas Tony Cosentino developed to describe the people working with business intelligence and analytics. These personas illustrate the various objectives, skills and interests that individuals bring to the discipline. Adapting his approach to big data analytics to this discussion, at the top of the list are highly skilled statisticians who do exploratory work and create purpose-built analytics and analytical models to address specific tasks. These people usually have advanced degrees in statistics and understand how to use sophisticated analytical software and data sets to their fullest. Few finance organizations need this level of capability. A second type of user includes business analysts who have in-depth knowledge of the business and finance issues, know how to access and apply available data relevant to the issue, and have the ability and commitment to master software that requires training but not an advanced degree in statistics. Depending on a company’s size, finance organizations will need a person or a group of people with this level of competence. A third type is the knowledge worker. This description includes executives, managers and directors who need to interact with – not just consume – advanced analytics. These types of users should not be expected to learn how to create or structure analytics, but they need to know how to employ analytics embedded in dashboards or applications as well as visual discovery tools, which are increasingly user-friendly. This level is where the need is broadest, so finance executives must focus most of their efforts in terms of developing these skills.

Big data analytics is an important development that will challenge finance organizations to use new capabilities to improve their effectiveness and enhance their company’s competitiveness. There are many ways organizations can begin to address the challenge. At least, CFOs and senior finance executives should create a steering committee to identify opportunities to apply big data analytics; identify gaps in skills, processes, data availability and software; and establish timelines and goals. Moreover, if CFOs are serious about exploiting the potential of big data analytics, they must communicate its importance to their department and demonstrate a commitment to a plan of action.

Regards,

Robert Kugel – SVP Research

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