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Tagetik is a long-established vendor of financial performance management (FPM) software. Its full-featured suite includes planning, budgeting, consolidation, close management, disclosure management, analysis, dashboards and reporting. The software can be deployed on premises or in the cloud as multitenant software as a service or in a private cloud. Tagetik also offers pre-built integration with SAP and SAP HANA, Microsoft SharePoint and Qlik to best support a range of financial management needs.

The current release, Tagetik 5, has a pleasing and productive consumer-style interface. Its design approach aims at enhancing the user experience and making it easier and less time-consuming to perform common tasks in finance and accounting departments. FPM is a mature software category that generally deals with the full cycle of finance department activities as well as the underlying information technology systems that support them. Thus there are limited differences between vendors’ suites in the required features and functions. We find that buyers often select products by how they execute particular tasks and especially how easy it is to perform them. In response FPM vendors have been putting greater emphasis in the design of their software to enhance the user experience, often through a consumer-style interface.

Tagetik addresses the ease-of-use issue in its current release, Tagetik 5, by making it easier for business analysts to create and update basic dashboards without the need for IT department involvement or coding. This facilitates communication and performance monitoring. Users can work in Microsoft Excel, but behind this interface are all of the capabilities of a well-developed software application and database, which eliminate issues that occur when desktop spreadsheets are used in any repetitive collaborative enterprise process such as financial planning and closing. Unlike in desktop spreadsheets, rolling up and consolidating data submissions of any number of participants in a process is almost instantaneous in Tagetik 5. Moreover, unlike desktop spreadsheets, it stores the data with important attributes such as the time period, corporate structure (division or regions, for example), product (anywhere from families down to specific stock keeping units – SKUs – if desired) and currency.

The software also offers comprehensive and easy-to-use administrationvr_fcc_shortening_financial_close_updatedcapabilities, especially in creating and modifying business processes for the full range of financial performance management activities. This capability is more than just a convenience for a few people in the finance department. It can make it easier for whole companies to accelerate planning cycles and facilitates high-participation planning and budgeting processes. Modifying processes also enables companies to tightly manage their accounting close process. Our benchmark research on the fast, clean close shows that companies that successfully shorten their close most often (in 71%) attributed their success to being able to manage the process effectively and consistently. With a limited amount of training and no coding, finance department users of Tagetik can define and manage every step of the close process. It supports a continuous improvement approach to managing the close by making it straightforward for the department to modify these individual processes and subprocesses as they assess sources of delay and inefficiency in their close.

Tagetik also supports more strategic finance processes. For example, it provides a platform that enables all parts of the business to plan in ways that conform to their needs and preferences and while still combining these plans into an integrated view as our next generation business planning research found makes planning processes to work better. For vr_NGBP_02_integrated_planning_works_betteranother, it facilitates the integration of long-term and strategic plans and company budgets. Our research in strategic and long-range planning finds that two-thirds of those that have fully or mostly integrated the two types can respond to changes immediately or soon enough, compared to just 22 percent of companies that have little or no integration.

The suite’s Disclosure Management offering was designed to help corporations manage the creation, editing and publication of external disclosure documents such as those required by the U.S. Securities and Exchange Commission or other regulatory bodies such as bank regulators in European countries for their Common Reporting (COREP)requirements. However, this capability is also useful for automating a range of report creation functions, especially documents for internal or external use that combine text and data. For instance, users can create Microsoft PowerPoint and Word templates with text, numbers and graphics that can be automatically updated with the latest period’s data. Using this capability accelerates production of reports while cutting the staff time required to produce them. It’s easy to achieve bullet-proof accuracy since the numbers in the tables are assembled from a single authoritative source, and references in the text to a specific item in a table (an absolute amount or a percentage change, for example) are always in agreement, even when there are last-minute changes. Thus it is relatively easy to put together a periodic PowerPoint presentation for the senior leadership team or board of directors. It’s especially handy for creating monthly, quarterly and annual reports because once designed, the numbers can be quickly updated to the latest period.

I recommend that companies looking for a financial performance management suite – especially those that are replacing a point solution (such as obsolete financial consolidation software) or those moving away from spreadsheets – add Tagetik to their list of vendors to consider.


Robert Kugel – SVP Research

Optimization is the application of algorithms to sets of data to guide executives and managers in making the best decisions. It’s a trending topic because using optimization technologies and techniques to better manage a variety of day-to-day business issues is becoming easier. I expect optimization, once the preserve of data scientists and operations research specialists will become mainstream in general purpose business analytics over the next five years.

Optimization was first adopted by businesses in the middle of the 20th century, aided by the introduction of digital computers. The first technique that gained broad for a few specific purposes was linear programming, one of the most basic optimization methods. Linear programming enables analysts to quickly determine how to achieve the best outcome (such as maximum unit volume or lowest cost) in a given situation. They do so using a mathematical model that captures the key variables that go into the decision and any constraints that may affect that decision. A food processor, for instance, may use three types of cooking oil to make a product. To maximize its profit, the company needs to determine the exact proportions of the three oils that result in the lowest production cost. However, it can’t just choose the cheapest of the three in every case because for flavor and shelf-life requirements there’s a limit to the maximum percentage of each oil that it can use. Linear programming using the simplex algorithm quickly solves the problem.

As computing capabilities became increasingly affordable, companies could use more complex algorithms to handle ever more difficult optimization problems. For instance, the airline industry used it to determine how best to route aircraft between two cities and to staff flight crews. Not only can softwarevr_Big_Data_Analytics_01_use_of_big_data_analytics find the best solution for scheduling these assets in advance, it also can rapidly re-optimize the solution when weather or mechanical issues force a change in how aircraft and crews are deployed. Airlines were also in the vanguard in the 1980s when they started using revenue management techniques. In this case, the optimization process was designed to enable established airlines to compete against low-cost startups. Revenue management enabled the large carriers to offer low fares to price-sensitive but flexible vacationers without sacrificing the higher fares that the less flexible businesspeople were willing to spend. The same approach was adopted by hotels in pricing their rooms. Starting in the 1990s markdown management software, which I have written about gained ground. It enables retailers to make more intelligent pricing decisions by monitoring the velocity of purchases of specific items and adjusting prices to maximize revenue. To be feasible, each of these optimization problems require large data sets and sufficient raw computing power.

We’re now on the cusp of “democratizing” what I call optimization analytics. Big data technologies are making it feasible and affordable for even midsize companies to work with much larger data sets than they have been able to in the past. Our benchmark research on big data analytics finds that about half of participating companies already use analytics with big data. This is partly the result of more powerful and affordable data processing resources but also because companies have invested in systems to automate many functions. The rich data sets created by these business applications provide corporations with the raw material for analysis. This data has the potential to enable businesses to make more intelligent decisions. From a practical standpoint, though, the value of these large data sets can only be realized by moving optimization analytics out of the exclusive realm of data scientists and into the hands of business analysts. These analysts are the ones who have a sufficient understanding of the business and the subtleties of the data to find useful and repeatable optimization opportunities. Three-fourths of companies in our research said that they need these business skills (“domain expertise”) to use big data analytics successfully.

vr_Big_Data_Analytics_14_big_data_analytics_skillsOptimization analytics is a breakthrough technology with the potential to improve business performance and create a competitive advantage. You can’t do optimization in your head, and it’s not feasible in desktop spreadsheets for anything but the most basic use cases, such as linear programming optimizations on relatively small data sets. This is a good reason for almost any company to consider adopting optimization software.

Another reason why companies will find it attractive to apply optimization analytics broadly is that the results of applying optimization routines may be superior to using common rules of thumb or relying on instinct and experience. One of the most important lessons for executives about optimization analytics is that optimal solutions are sometimes (but – crucially – not always) counterintuitive to established norms. For instance, in markdown management, retailers often have found that smaller, more frequent price reductions maximize profits and produce a considerable improvement in sales over the end-of-the-season price slashing that was once considered to be the best practice. In financial services, charging your best customers more for loans and other services turns out to be the optimal choice for the bottom line of financial institutions. Another important insight from our collective experience with optimization is that while the value of these analytics as realized in a single event or transaction may be small, it can have a measurable impact on profitability and competitiveness when applied broadly in a business.

At this point optimization analytics is in a dual mode. On the one hand, there are proven examples of the narrow application of optimization such as those mentioned above. On the other, bringing optimization analytics to the masses is only beginning. Some vendors have made progress in simplifying their analytics, but mainstream products are only on the horizon. It’s also important to recognize that, as with past breakthroughs in information technology, there are bound to be more duds than success stories in initial attempts at using optimization analytics. Experience suggests that a small number of companies that have strong analytical skills and a rigorous approach to managing company data will prove to be the leaders in finding profitable opportunities for applying optimization technologies and techniques. Others will do well to find these examples and consider how to apply them to their own organizations.


Robert Kugel – SVP Research

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