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September 25, 2016 in Business Analytics, Business Collaboration, Business Intelligence (BI), Business Performance Management (BPM), Cloud Computing, Customer Performance Management (CPM), Financial Performance Management (FPM), Human Capital, Mobile Technology, Operational Performance Management (OPM), Social Media, Supply Chain Performance Management (SCPM) | Tags: Accounting, Analytics, big data, Budgeting, CEO, CFO, CIO, close, Continuous Accounting, Continuous Planning, ERP, Financial Performance Management, Forecasting, FPM, Governance, in-memory, Planning | by Ventana Research | Leave a comment
Like many other industry observers I’ve heard overblown claims for information technology for decades. However, I’ve also observed that – eventually – reality catches up with vision. Finance and accounting departments are particularly resistant to change, yet because almost no corporations use adding machines or typewriters any more, it’s clear that transformative change can happen. Nonetheless, because users of business computing systems are inundated with “it’s better than ever” promotions by vendors, journalists and industry analysts, may have grown jaded and disbelieving. In the case of ERP systems that help run many organizations, that is too bad because we are finally at the point of a fundamental change in this business-critical software category.
ERP systems themselves have been undergoing transformation, enabled by the growing availability of technologies that can address the shortcomings of established systems and an increasing appetite for multitenant, cloud-based ERP systems. As I noted in my research agenda for the Office of Finance, the demographic shift taking place in the ranks of senior executives and managers – from the baby-boom generation to those who grew up with computer technology – will create demand for more capable software. ERP systems are evolving to deliver a better user experience, greater flexibility and agility, as well as an optimized mobile experience and lower total cost of ownership. The transformation has already started for some vendors and to some degree. The pace of change will increase over the next two years as new releases become available. However, I don’t expect companies to buy a brand-new ERP system just to acquire next-generation features. Our Office of Finance benchmark research finds that on average companies replace their ERP systems only every 6.4 years, mainly because of the cost and difficulty of implementing the software. Moreover, many of these capabilities will be available under maintenance contracts for on-premises systems and incorporated automatically in upgrades of cloud-based systems.
The new generation of ERP systems will be able to support a more effective approach to managing the functions I call continuous accounting that will benefit finance and accounting departments. By eliminating batch data processing and by supporting analytic as well as transactional operations in a unified system, the next generation of ERP systems will enable companies to provide executives and managers with immediate information, alerts and guidance. It will enable departments to spread workloads more evenly across months and quarters, rather than having to wait until the end of the period. In so doing, many companies will be able to accelerate their close, as I have discussed. Continuous accounting can contribute to providing a strategic focus for the finance organization – a change that organizations will welcome. In our research on finance innovation, nine out of 10 participants said that it’s important or very important for finance departments to take a strategic role in running their company.
In many respects, today’s ERP systems are exactly what people don’t want any more. They are notoriously time-consuming and expensive to set up, maintain and modify. In our ERP research only 21 percent of larger companies said that implementing new capabilities in ERP systems is easy or very easy while one-third characterized it as difficult. For this and other reasons, the current generation of ERP software acts a barrier to innovation and improvement.
To be sure, more than any other type of enterprise software, ERP systems are a challenge because of the complexities of business organizations. This isn’t going to change. I’ve spent decades examining all sorts of businesses from multiple perspectives – from strategic, high-level business models to footnotes in financial statements and the execution of specific manufacturing and financial processes. To the uninitiated, everything about business appears simple until they get into the details. Then, even when you strip out inessential elements, it’s still complicated. ERP is complicated because the underlying business requirements are complicated. For example, in any organization there are competing demands and priorities at work when an ERP system is set up.
Although some aspects of ERP will always be complex and require experienced assistance to design and maintain, techniques for mass customization can make it easier to implement and change, thereby eliminating a significant portion of the cost of ownership. To be sure, software companies have tried to minimize deployment costs. For a couple of decades, ERP vendors have offered packages aimed at specific industries such as aerospace and pharmaceuticals. Those addressing midsize companies, which have tighter budgets than large ones, offer out-of-the-box configurations aimed at even more specific types of business, such as steel service centers, manufacturing job shops or brewers. For more generic businesses, today’s cloud-based ERP systems are one solution to the problem of costly updates and reconfiguration. However, this option still may not be attractive if an organization is in a business that has very specific customization requirements that more generic ERP systems cannot support well (for instance, process-manufacturing industries such as specialty chemicals manufacturing).
One positive development in the ERP category is the increasing attention vendors have been paying to the user experience in the design of screens and workflows. The dull, cluttered and difficult-to-navigate interfaces that have been the norm up to now were the result of inexperience in design and constrained computing resources. The next generation of ERP systems is being designed with decades of experience and far more powerful computing platforms and tools than the current ones. In the 1930s, Raymond Loewy and others revolutionized the design of everyday objects, from soda fountains to locomotives and automobiles so that form and function combined to produce a better product. Today, it’s even more important to apply basic concepts of industrial design and ergonomics to creating user interfaces. This goes beyond making old code bases pretty. Largely because of tablets and mobile computing platforms, people now work with multiple types of interfaces and use a wider range of methods and gestures to interact with their devices. The next generation of ERP software must incorporate these advances and ensure that the screens and their flows are optimized for the device. The emerging generation of finance executives and ERP users won’t put up with the inconveniences and awkwardness that their predecessors reconciled themselves to.
It’s also clear that ERP systems will be faster in the future, as redesigning the software’s underlying data structure and utilizing technology such as in-memory processing will eliminate nearly all batch routines. Faster systems enable shorter cycle times, which promote corporate agility because up-to-date information is available sooner. Another important change that is already under way is the ability to do analytics in real time or near real time on data held in an ERP system. The business intelligence (BI) software category was invented two decades ago to enable companies to get useful information from newly implemented ERP software. While BI addressed this shortcoming, it also added to the cost and complexity of a company’s IT operations.
Another focus of new ERP systems will be collaboration. In-context collaboration provides an important set of capabilities that can improve performance. Rather than following a general broadcast model, social collaboration capabilities in ERP and other business applications understand that individuals belong to multiple groups. For example, people in a company typically have a general role (“I’m in Finance”) and one or more task-specific ones (“I’m the director of financial planning and analysis”). Some relationships are persistent while others begin and end with a project. Issues that arise may be open to all or confined to specific groups, subsets of groups or a private dialogue. Queries or comments may be general, specific or anywhere in between. Some conversations, especially in finance and tax departments, must be tightly controlled. Software that understands the context of the work performed and automates the process of managing the who, what and when of communications will support more effective collaboration, faster completion of tasks, greater situational awareness within the organization and as a result better decision-making. Over the past three years, ERP vendors have been introducing more in-context collaboration capabilities in their software.
Mobile enablement is already an important capability of some ERP systems. However, it’s important that ERP vendors focus on those elements where mobility is important and optimize the user experience for the task and platform. Unlike CRM and sales force automation systems, where sales and service information must be accessible anytime and anywhere, mobility’s importance in ERP depends on who uses it and why. Certain tasks such as data entry are not well suited to mobile devices, while routine reviews and approvals are. These must be simple to configure and deploy as well as use.
More generally I am convinced that the worst aspect of today’s ERP systems is that they inhibit change in corporations. The lack of adaptability in these systems has infused a “set it and forget it” mindset that inhibits companies from making necessary changes in processes and stifles innovation. The inability to make changes easily to an ERP system inhibits improvements in corporate functions that run on ERP. This is ironic, since one of the factors driving corporations to buy the first ERP systems in the 1990s was their desire to do business process re-engineering, a business strategy of the time. More useful is developing a culture of continuous process improvement, one of the pillars of continuous accounting, in the finance organization. Making ERP more easily configurable by business users supports continuous process improvement efforts.
As the business software market, including ERP, increasingly moves to the cloud, a major challenge facing software vendors is designing their applications for maximum configurability. By this I don’t mean offering the ability to select modules from a menu, but enabling only moderately trained line-of-business users to make granular adjustments to process flow and data structures in a multitenant setting. This lack of flexibility is an important barrier inhibiting adoption of cloud-based ERP. Although user organizations that are more able to adapt to an as-is version of an ERP system are more likely to take the cloud-based option, this covers only some of the potential market. The cloud ERP vendors that offer greater flexibility in allowing individual customers to modify their implementation to suit their specific needs will have a competitive advantage. Multitenant cloud ERP vendors already have had to pay attention to configurability, and on-premises ERP vendors also would benefit from enhancing the configurability of their systems.
Today’s corporations have been willing to put up with the deficiencies in their ERP systems because everyone was in the same boat. That won’t be the case much longer. The cost and complexity of ERP systems has meant that IT departments, not business users, have had the fullest involvement in managing them. This, too, will change. Business users and finance departments in particular will need to be involved in periodic assessments of how well their ERP system supports their responsibilities and objectives. Finance executives in particular should begin this process now by understanding how the application of new technologies can drive fundamental changes in the way they manage their department. Vendors that offer ERP systems that are much easier to configure, use and update, support in-context collaboration and mobility and provide timely, reliable analysis and reporting will survive. Those that excel in these areas will win market share.
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February 19, 2016 in Big Data, Business Analytics, Business Intelligence (BI), Customer Performance Management (CPM), Financial Performance Management (FPM), Human Capital, Operational Performance Management (OPM), Sales Performance Management (SPM), Social Media, Supply Chain Performance Management (SCPM) | Tags: Analytics, big data, Budgeting, Connotate, cryptic, data, Data Science, Datawatch, equity research, Finance Analytics, Forecasting, FP&A, Hadoop, import.io, Kapow, Kofax, Office of Finance, Planning, predictive analytics, Statistics | by Robert Kugel | Leave a comment
Using information technology to make data useful is as old as the Information Age. The difference today is that the volume and variety of available data has grown enormously. Big data gets almost all of the attention, but there’s also cryptic data. Both are difficult to harness using basic tools and require new technology to help organizations glean actionable information from the large and chaotic mass of data. “Big data” refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially those related to human behavior and interaction. The challenges in dealing with big data include having the computational power that can scale to the processing requirements for the volumes involved; analytical tools to work with the large data sets; and governance necessary to manage the large data sets to ensure that the results of the analysis are accurate and meaningful. But that’s not all organizations have to deal with now. I’ve coined the term “cryptic data” to focus on a different, less well known sort of data challenge that many companies and individuals face.
Cryptic data sets aren’t easy to find or aren’t easily accessed by people who could make use of them. Why “cryptic?” As a scuba diver, I donate time to Reef Check by doing scientific species counts in and around Monterey Bay, Calif. Cryptic organisms are ones that hide out deep in the cracks and crevices of our rocky reefs. Finding and counting them accurately is time-consuming and requires skill. Similarly, it’s difficult to locate, access and collect cryptic data routinely. Because it’s difficult to locate or access routinely, those who have it can gain a competitive advantage over those who don’t. The main reason cryptic data is largely untapped is cost vs. benefits: The time, effort, money and other resources required to manually retrieve it and get it into usable form may be greater than the value of having that information.
By automating the process of routinely collecting information and transforming it into a usable form and format, technology can expand the range of data available by lowering the cost side of the equation. So far, most tools, such as Web crawlers, have been designed to be used by IT professionals. Data integration software, also mainly used by IT departments, helps transform the data collected into a form and format where it can be used by analysts to create mashups or build data tables for analysis to support operational processes. Data integration tools mainly work with internal, structured data and a majority have little or no capability to support data acquisition in the Web. Tools designed for IT professionals are a constraint in making better use of cryptic data because business users are subject matter experts. They have a better idea of the information they need and are in a better position to understand the subtleties and ambiguities in the information they collect. To address this constraint, Web scraping tools (what I call “data drones”) have appeared that are designed for business users. They use a more visual user interface design and hide some of the complexity inherent in the process. They can automate the process of collecting cryptic data and expand the scope and depth of data used for analysis, alerting and decision support.
Cryptic data can be valuable because when collected, aggregated and analyzed, it provides companies and individuals with information and insight that were unavailable. This is particularly true of data sets gathered over time from a source or combination of sources that can reveal trends and relationships that otherwise would be difficult to spot.
Cryptic data can exist within a company’s firewall (typically held in desktop spreadsheets or other files maintained by an individual as well as in “dark” operational data sets), but usually it is somewhere in the Internet cloud. For example, it may be
- Industry data collected by some group that is only available to members
- A composite list of products from gathered from competitors’ websites
- Data contained in footnotes in financial filings that are not collected in tabular form by data aggregators
- Tables of related data assembled through repetitive queries of a free or paid data source (such as patents, real estate ownership or uniform commercial code filings).
Along these lines, our next-generation finance analytics benchmark research shows that companies have limited access to information about markets, industries and- economies.Only 14 percent of participants said they have access all the external data they need. Most (63%) said they can access only some of it, and another 14 percent said they can’t access any such data. In the past, this lack of access was even more common, but the Internet changed that. And this type of external data is worth going after, as it can help organizations build better models, perform deeper analysis or do better in assessing performance, forecasting or gauging threats and opportunities.
Cryptic data poses a different set of challenges than big data. Making big data usable requires the ability to manage large volumes of data. This includes processing large volumes, transforming data sets into usable forms, filtering extraneous data and code data for relevance or reliability, to name some of more common tasks. To be useful big data also requires powerful analytic tools that handle masses of structured and unstructured data and the talent to understand it. By contrast, the challenge of cryptic data lies in identifying and locating useful sources of information and having the ability to collect it efficiently. Both pose difficulties. Whereas making big data useful requires boiling the ocean of data, cryptic data involves collecting samples from widely distributed ponds of data. In the case of cryptic data, automating data collection makes it feasible to assemble a mosaic of data points that improves situational awareness.
Big data typically uses data scientists to tease out meaning from the masses of data (although analytics software vendors have been working on making this process simpler for business users). Cryptic data analysis is built on individual experience and insight. Often, the starting point is a straightforward hypothesis or a question in the mind of a business user. It can stem from the need to periodically access the same pools of data to better understand the current state of markets, competitors, suppliers or customers. Subject matter expertise, an analytical mind and a researcher’s experience are necessary starting capabilities for those analyzing cryptic data. These skills facilitate knowing what data to look for, how to look for it and where to look for it. Although these qualities are essential, they not sufficient. Automating the process of retrieving data from sources in a reliable fashion is a must because, as noted above, the time and expense required to acquire the data manually are greater than its value to the individual or organization.
Almost from the dawn of the Internet, Web robots (or crawlers) have been used to automate the collection of information from Web pages. Search engines, for example, use them to index Internet pages while spammers use them to collect email addresses. These robots are designed and managed by information technology professionals. Automating the process of collecting cryptic data requires software that business people can use. To make accessing cryptic data feasible, they need “data drones” that can be programmed by users with limited training to fetch information from specific Web pages. Tools available from Astera ReportMiner, Connotate, Datawatch, import.io, Kofax Kapow and Mozenda are great examples on where you can get started for leveraging cryptic data. I recommend that everyone who has to routinely collect information from Internet sites or from internal data stores that are hard to access or who thinks that they could benefit from using cryptic data investigate tools available for collecting it.
Robert Kugel – SVP Research