Tagetik provides financial performance management software. One particularly useful aspect of its suite is the Collaborative Disclosure Management (CDM). CDM addresses an important need in finance departments, which routinely generate highly formatted documents that combine words and numbers. Often these documents are assembled by contributors outside of the finance department; human resources, facilities, legal and corporate groups are the most common. The data used in these reports almost always come from multiple sources – not just enterprise systems such as ERP and financial consolidation software but also individual spreadsheets and databases that collect and store nonfinancial data (such as information about leased facilities, executive compensation, fixed assets, acquisitions and corporate actions). Until recently, these reports were almost always cobbled together manually – a painstaking process made even more time-consuming by the need to double-check the documents for accuracy and consistency. The adoption of a more automated approach was driven by the requirement imposed several years ago by United States Securities and Exchange Commission (SEC) that companies tag their required periodic disclosure filings using eXtensible Business Reporting Language (XBRL), which I have written about. This mandate created a tipping point in the workload, making the manual approach infeasible for a large number of companies and motivating them to adopt tools to automate the process. Although disclosure filings were the initial impetus to acquire collaborative disclosure management software, companies have found it useful for generating a range of formatted periodic reports that combine text and data, including board books (internal documents for senior executives and members of the board of directors), highly formatted periodic internal reports and filings with nonfinancial regulators or lien holders.

vr_fcc_financial_close_and_automation_updatedTagetik’s Collaborative Disclosure Management automates the document creation process, eliminating many repetitive, mechanical functions and reducing the time needed to administer the process and ensure accuracy. Automation can shorten finance processes significantly. For example, our benchmark research on trends in developing the fast, clean close finds that companies that use little or no automation in their accounting close take almost twice as long to complete the process as those that fully automate it (9.1 days vs. 5.7 days). Manually assembling the narrative text from perhaps dozens of contributors and combining it with data used in tables and elsewhere in the document is a time-consuming chore. Regulatory filings are legal documents that must be completely accurate and conform to mandated presentation styles. They require careful review to ensure accuracy and completeness. Complicating this effort recently are increasingly stringent deadlines, especially in the U.S. Anyone who has been a party to these efforts knows that there can be frequent changes in the narratives and presentation of the numbers as they are reviewed by different parties, and those responsible need to ensure that any change to a number that occurs is automatically reflected everywhere that amount is cited in the document; to use the depreciation and amortization figure as an example, that would include the statement of cash flows, income statement, the text of the management discussion and analysis and the text or tables of one or more footnotes. Moreover, automated systems afford greater control over the data used. They make it possible to answer the common question of where a number came from quickly and with complete assurance. While inaccuracies in other types of financial documents may not have legal consequences, mistakes can have reputational or financial consequences.

Those managing the process also spend a great deal of energy simply checking the document to ensure that the various sections include the latest wording, that the numbers are consistent in the tables and text, that amounts have been rounded properly (which can be really complicated) and that the right people have signed off on every part of the filing. Automation obviates the need for much of these tasks. Tagetik’s CDM workflow-enables the process, so handoffs are automated, participants get alerts if they haven’t completed their steps in timely fashion, and administrators can keep track of where everyone is in the process. Workflow also promotes consistent execution of the process, and the workflows can be easily modified as needed.

In designing Collaborative Disclosure Management, Tagetik took advantage of users’ widespread familiarity with Microsoft Excel and Word to reduce the amount of training required to use its product. CDM’s workflow design makes it relatively easy for business users to define and modify business process automation. Typically, individuals or small groups work on different sections of the document. CDM enables multiple contributors from finance, accounting, legal, corporate and other functions to work with their part of the document without being concerned about other contributors’ versions. Work can proceed smoothly, and those administering the process can see at any time which components have been completed, are in progress or have not even started. Tagetik’s software can cut the time required to prepare any periodic document, since once a company has configured its system to create what is in effect a template, it’s relatively easy to generate these documents on monthly, quarterly or annual bases. The numbers relevant to the current period are updated from the specified controlled sources, and references to tabular data within the text are automatically adjusted to tie back to these new figures. Often a large percentage of the narrative text is boilerplate that either must not be updated or requires only limited editing to reflect new information. Starting with the previous edition of the report, contributors can quickly mark up a revised version, and reviewers can focus only on what has changed. Other important automation features are data validation, which reduces errors and revisions, and the system’s ability to round numbers using the appropriate statutory methodology.

CDM also handles XBRL tagging, which is essential for all SEC documents and necessary for an increasing number of regulatory filings around the world. The software specifically handles tagging for the two main European prudential regulatory filings for banks and other credit extending institutions, COREP (Common Reporting related to capital) and FINREP (Financial Reporting performed in a consistent fashion across multiple countries).

VR-BUG-WEBCompanies can gain several key benefits by automating the production of their periodic regulatory filings and internal or external financial reports that combine text and data. One of the most important is time. Automation can substantially reduce the time that highly trained and well-compensated people spend on mechanical tasks (freeing them to do more productive things), and the process can be completed sooner. Having the basic work completed sooner gives senior executives and outside directors more time to review the document before it must be filed or made public. Time that can be devoted to considering how best to polish the narratives or if necessary lengthen upstream deadlines to handle last-minute developments and consider options for how best to treat accounting events. Automation can also reduce the chance of errors, since the numbers tie directly back to the source systems and (if properly configured) ensure that references in the narratives and footnotes to items in tables and the numbers in those table agree completely. Restatements of financial reports caused by errors are relatively rare but when they occur are exceptionally costly for public companies’ reputations.

Disclosure management systems are an essential component for any financial performance management (FPM) system. All midsize and larger corporations should be using this software to automate the production of their periodic mandated filings and other documents that combine text and data. They will find that they are useful in cutting the time and effort required to produce these documents, provide senior executives and directors more time to review and craft the final versions, and reduce the chance of errors in the process. Companies that are using older FPM software should investigate replacing it with an FPM suite to gain the additional capabilities – including disclosure management – that newer suites offer. Tagetik’s should be among the financial systems evaluated for office of finance.

Regards,

Robert Kugel – SVP Research


At its annual industry analyst summit last month and in a more recent announcement of enterprise support for parallelizing the R language on its Aster Discovery Platform, Teradata showed that it is adapting to changes in database and analytics technologies. The presentations at the conference revealed a unified approach to data architectures and value propositions in a variety of uses including the Internet of Things, digital marketing and ETL offloading. In particular, the company provided updates on the state of its business as well as how the latest version of its database platform, Teradata 15.0, is addressing customers’ needs for big data. My colleague Mark Smith covered these announcements in depth. The introduction of scalable R support was discussed at the conference but not announced publicly until late last month.

vr_Big_Data_Analytics_13_advanced_analytics_on_big_dataTeradata now has a beta release of parallelized support for R, an open source programming language used significantly in universities and growing rapidly in enterprise use. One challenge is that R relies on a single-thread, in-memory approach to analytics. Parallelization of R allows the algorithm to run on much larger data sets since it is not limited to data stored in memory. For a broader discussion of the pros and cons of R and its evolution, see my analysis. Our benchmark research shows that organizations are counting on companies such as Teradata to provide a layer of abstraction that can simplify analytics on big data architectures. More than half (54%) of advanced analytics implementations are custom built, but in the future this percentage will go down to about one in three (36%).

Teradata’s R project has three parts. The first includes a Teradata Aster R library, which supplies more than 100 prebuilt R functions that hide complexity of the in-database implementation. The algorithms cover the most common big data analytic approaches in use today, which according to our big data analytics benchmark research are classification (used by 39% of organizations), clustering (37%), regression (35%), time series (32%) and affinity analysis (29%). Some use innovative approaches available in Aster such as Teradata’s patented nPath algorithm, which is useful in areas such as digital marketing. All of these functions will receive enterprise support from Teradata, likely through its professional services team.

The second part of the project involves the R parallel constructor. This component gives analysts and data scientists tools to build their own parallel algorithms based on the entire library of open source R algorithms. The framework follows the “split, apply and combine” paradigm, which is popular among the R community. While Teradata won’t support the algorithms themselves, this tool set is a key innovation that I have not yet seen from others in the market.

Finally, the R engine has been integrated with Teradata’s SNAP integration framework. The framework provides unified access to multiple workload specific engines such as relational (SQL), graph (SQL-GR), MapReduce (SQL-MR) and statistics. This is critical since the ultimate value of analytics rests in the information itself. By tying together multiple systems, Teradata enables a variety of analytic approaches. More importantly, the data sources that can be merged into the analysis can deliver competitive advantages. For example, JSON integration, recently announced, delivers information from a plethora of connected devices and detailed Web data.

vr_Big_Data_Analytics_09_use_cases_for_big_data_analyticsTeradata is participating in industry discussions about both data management and analytics. As Mark Smith discussed, its unified approach to data architecture addresses challenges brought on competing big data platforms such as Hadoop and other NoSQL approaches like that one announced with MongoDB supporting JSON integration. These platforms access new information sources and help companies use analytics to indirectly increase revenues, reduce costs and improve operational efficiency. Analytics applied to big data serve a variety of uses, most often cross-selling and up-selling (for 38% of organizations), better understanding of individual customers (32%) and optimizing price (30%) and IT operations (24%). Teradata is active in these areas and is working in multiple industries such as financial services, retail, healthcare, communications, government, energy and utilities.

Current Teradata customers should evaluate the company’s broader analytic and platform portfolio, not just the database appliances. In the fragmented and diverse big data market, Teradata is sorting through the chaos to provide a roadmap for largest of organizations to midsized ones. The Aster Discovery Platform can put power into the hands of analysts and statisticians who need not be data scientists. Business users from various departments, but especially high-level marketing groups that need to integrate multiple data sources for operational use, should take a close look at the Teradata Aster approach.

Regards,

Tony Cosentino

VP & Research Director

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