Robert Kugel's Analyst Perspectives

Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the application. In tech speak, this means the semantic layer is optimized for the intended audience. It is stocked with data gathered from multiple sources and immediately available for analysis, forecasting, planning and reporting. This does away with the need for analysts to repeatedly perform data extraction, enrichment or transformation motions from the required source systems, all but eliminating the substantial amount of time analysts and business users routinely spend on data preparation.

Topics: Continuous Planning, Business Intelligence, Data Management, Business Planning, Data, Financial Performance Management, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain, data operations, digital finance, profitability management, Analytics & Data, Streaming Data & Events

The need for a COVID-19 vaccination “passport” has prompted some to suggest using blockchain technology as a means of reliably verifying an individual’s status at an international level. There are precedents: for example, until smallpox was eradicated, all international travelers were obliged to carry an immunization record for that disease on a standard paper form to gain entrance to a country. With the likelihood that COVID-19 will remain endemic for many years, a reliable digital record with universal accessibility would be a boon to everyone, especially to international travelers. Vaccination records are just one part of the broader topic of using blockchain technology for medical identity management.

Topics: Data Governance, Information Management, Data, blockchain

Environmental, social and governance reporting by public corporations has become a top-of-mind issue for senior executives and boards of directors as countries increasingly consider or mandate its implementation in some form. The fundamental rationale for ESG reporting is rooted in the inability of purely financial measures to capture externalities (such as greenhouse gas emissions) or provide metrics that enable an objective assessment of management’s ability to properly determine trade-offs between short-term results and long-term sustainability. And, while in the United States the Sarbanes-Oxley Act mandates that auditors assess governance, the focus of this assessment is on preventing financial fraud as opposed to broader objectives that may be important to the functioning of the company as a sustainable entity.

Topics: Human Capital Management, Office of Finance, Business Intelligence, Data Governance, Data Preparation, Data, Financial Performance Management, ERP and Continuous Accounting

An important recent development in software designed for the Office of Finance is the addition of what we’re calling a data aggregation device (DAD) for analytical applications. A DAD automates the collection of data from disparate sources using, for example, application programming interfaces (APIs) and robotic process automation (RPA). With a DAD, users of the analytical application have immediate access to a much broader data set; one that incorporates operational as well as financial data from internal and external sources. The larger data set enables a much more expansive set of analyses than has been feasible in the past because the process of acquiring the data is automated, and the data aggregation is handled in a controlled manner. This control means that data in the system is authoritative, accurate, consistent, complete and secure. The difference between a DAD and a finance data mart is that the former is prebuilt for the specific application, and therefore eliminates this source of implementation costs and offers faster time to value.

Topics: Office of Finance, Analytics, Business Intelligence, Data, Financial Performance Management, Price and Revenue Management, robotic finance, Predictive Planning, AI and Machine Learning

I’ve written before about blockchain’s significant potential. A lot of the current discussion on the topic centers on cryptocurrencies and financial trading platforms, both of which are already in operation. However, my focus is on its applicability to business generally, especially in B2B commerce, where I believe there is significant potential for it to serve as a universal data connector. There’s also a great deal of potential for blockchain to provide individuals with greater power in managing their identity and greasing the wheels of trade. That noted, those designing and planning to implement commerce-related blockchains must address fundamental issues if blockchain technology is to achieve its potential.

Topics: Sales, Human Capital Management, business intelligence, Business Collaboration, Internet of Things, Data, Product Information Management, Digital Commerce, Enterprise Resource Planning, blockchain, candidate engagement, collaborative computing, continuous supply chain

By itself, data isn’t useful for business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competence of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, Finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights. Analysis thus should include, for example, data from the systems that manage sales operations, human resources and field service and that data must be available to all departments and applications that need it.

Topics: Customer Experience, Human Capital Management, Marketing, Voice of the Customer, business intelligence, embedded analytics, Learning Management, Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, ERP and Continuous Accounting, blockchain, natural language processing, robotic finance, Predictive Planning, candidate engagement, Intelligent CX, Conversational Computing, Continuous Payroll, AI and Machine Learning, revenue and lease accounting, collaborative computing, mobile computing, Subscription Management, agent management, extended reality

What’s the easiest way to completely immobilize a 500,000-ton ship?

Lose a sheet of paper.

The paperwork that accompanies international trade is a serious source of friction, inefficiency — and therefore cost — in supply chain execution. Trade documentation requires massive amounts of paper that today can be replaced by digital data. In 2018, Maersk, the world’s largest shipping company, teamed up with IBM to create TradeLens, a digital platform that utilizes blockchain technology as a secure, unified source of trade transaction data used by businesses, financial institutions and government authorities. TradeLens is designed to enable all participants to connect, share information and collaborate, providing them with a comprehensive view of the data they need to transact trade. The system makes it possible to digitally collaborate in handling their global supply chains.

Topics: Office of Finance, Continuous Planning, Internet of Things, Data, Operations & Supply Chain, Enterprise Resource Planning, blockchain, continuous supply chain

Identity management is an old problem that has taken on new dimensions in the digital world. In 1993, at the dawn of the World Wide Web (WWW), The New Yorker ran a cartoon featuring two dogs talking, one perched in front of a computer. The caption reads: “On the Internet, nobody knows you’re a dog.” The phrase quickly evolved into a meme highlighting the issue of identity uncertainty in the new digital environment.

Topics: Human Capital Management, Office of Finance, Learning Management, Internet of Things, Data, Workforce Management, Digital Technology, ERP and Continuous Accounting, blockchain, candidate engagement

I recently attended BlackLine’s annual user conference. The company aims to automate time-consuming repetitive tasks and substantially reduce the amount of detail that individuals must handle in the department. The phrase “the devil is in the details” certainly applies to accounting, especially managing the details in the close-to-report phase of the accounting cycle, which is where BlackLine plays its role. This phase spans from all the pre-close activities to the publication of the financial statements. The non-practitioner is likely unaware of the hair-curling amount of essential detail that the finance and accounting organization must handle in the close-to-report. Beyond its toll on efficiency, the time and attention involved in performing this work manually bedevils departments’ attempts to become a more strategic partner to the rest of the business.

Topics: automation, close, closing, Consolidation, control, effectiveness, Reconciliation, CFO, compliance, Data, controller, Financial Performance Management, FPM, Sarbanes Oxley, Accounting, reconcile

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.

Topics: Big Data, Data Science, Planning, Predictive Analytics, Social Media, FP&A, Office of Finance, Operational Performance Management (OPM), Budgeting, Connotate, cryptic, equity research, Finance Analytics, Human Capital, Kofax, Statistics, Analytics, Business Analytics, Business Intelligence, Customer Performance Management (CPM), Data, Datawatch, Financial Performance Management (FPM), Kapow, Sales Performance Management (SPM), Supply Chain Performance Management (SCPM), Strata+Hadoop