“The status quo is not a neutral state, but a mindset to uphold the decisions of the past.”
In 2023, effective business planning, budgeting, and forecasting is a necessary capability to keep organizations running. Already this year, we have seen unexpected banking failures, unpredictable labor markets, and continued supply chain and logistics challenges based on geopolitical challenges. In light of these challenges, Amalgam Insights believes that businesses must have a shared version of the truth that they use as they look at their budget and finances.
And, in this case, we specifically talk about a “shared” version of the truth rather than the “single version of the truth” typically associated with data warehouses and enterprise applications. This is because data changes quickly and every stakeholder can potentially make different decisions to define and augment their data, even basic changes such as language and currency translation that can lead to different versions of the truth. In this analytically enhanced and globally complicated world, it makes more sense to have a shared version of the truth that is augmented with personalized or localized data and assumptions. However, this consistently shared version of the truth can be hard to accomplish in organizations where planning is handled in a distributed and personalized manner through spreadsheets. In the enterprise world, finance professionals are inured to the basic realities of auditable data, processes, and results. And they are often asked to provide reports and memos that are used at the executive level or by external investors and public markets to ascertain the health of the market. Given the assumed importance of this formality, why would experienced professionals use spreadsheets in the first place?
Let’s face it; spreadsheets are easy to use. They are the lingua franca of data; a format that every experienced data user has been trained on. And with plug-ins and Visual Basic, spreadsheets can now handle relatively complex analytic use cases. Even if they aren’t quite data science tools, spreadsheets can provide structured analytic outputs. Also, spreadsheets are accessible on every computer through Excel, Google Sheets, or other common spreadsheet software. And with the emergence of cloud-based spreadsheets, it is now possible for two or more people to collaborate within a single spreadsheet.
Spreadsheets also provide users with the ability to customize their own analytic views with their own personalized views of data and the ability to hypothesize by building their own models. Who hasn’t looked at data and wondered “what if the data looked a bit differently?” or “what if we have a drastic scenario that suddenly increases or decreases a fundamental aspect of the business?” In light of COVID, rapid interest rate hikes, global shortages in commodities production, trained labor shortages, and the increasingly unstable banking environment we are in, it is important to be able to test potential extreme assumptions and support a wide variety of scenarios. Between the ease of use, availability, and personalization aspects of spreadsheets, it is not hard to figure out why spreadsheets are still a leading tool for financial planning and analysis. Even so, Amalgam Insights has found that once organizations pass Dunbar’s number (approximately 150 employees), they start to struggle with collaborative tasks simply because it becomes difficult for any one employee to know all of the other employees who need to be involved in the business planning process and spreadsheets have been designed to maximize individual productivity, rather than collaborative work, for decades. From a practical perspective, people tend to work with the people they know best. This is fine for a small company with a dedicated office where everyone knows each other. According to US Census data, the typical 1,000-person company has 19 locations, making it highly unlikely that all of the key budget stakeholders will be in one office. In this regard, Amalgam Insights finds the following challenges in supporting spreadsheet-based planning at scale.
The distributed nature of work also makes spreadsheet governance a challenge, as it is easy for spreadsheets to suffer from version control issues, a structured workflow process, and for file owners to lose control of the inputs and outputs that they are responsible for supporting. The lack of version control, workflow, and activity tracking is especially challenging in industries and geographies that require tracking of any personal data either related to employees or customers.
Spreadsheets also struggle in large data environments, which are quickly becoming commonplace in the business planning world. Although a core enterprise database may only be a few gigabytes, accurate planning now often includes access to sales, operations, and potentially even IT transactional data sources that can quickly expand beyond the memory and data size constraints that spreadsheets are designed to use. From Amalgam Insights’ perspective, the size and variety of data are the biggest technical constraints that spreadsheets face as planning solutions.
Spreadsheets lack advanced analytic and machine learning capabilities. Although algorithmic, statistical, and machine learning tools are increasingly becoming part of the FP&A world, especially in forecasting, Amalgam Insights finds in practice that most organizations have not yet embraced complex analytics as a core part of their FP&A approach. Based on current job site metrics, Amalgam Insights estimates that less than 2% of FP&A professionals currently have a machine learning or data science certification or degree, making this an early innovator capability that has still not crossed the chasm to become a standard job requirement for FP&A.
But perhaps the most significant challenge with spreadsheet models is that they are often fragile: created based on the logic of a single person rather than on defined business logic and with little to no documentation associated with the plans, forecasting algorithms, and multi-tabular complexity that inevitably occurs when a spreadsheet is the primary planning tool for a business, which can also lead to costly data accuracy issues. The model is only as adaptable as the spreadsheet creator’s knowledge of the industry and is dependent on that employee staying employed. Considering that it is unrealistic to expect an FP&A senior analyst to remain in that role for more than five years before either getting promoted or getting a better offer, this human risk is a significant challenge for business planning solutions.
As organizations grow in size to support more than a handful of locations and a set of workers that exceeds Dunbar’s number of 150 colleagues, Amalgam Insights believes that it becomes necessary to adopt a formalized planning solution that supports collaboration, scale, advanced analytics, continuous planning across many scenarios, and advanced forecasting analytics. Otherwise, it is difficult for businesses to maintain a consistent and shared version of the truth across financial planning and analysis personnel that can drive both departmental and executive planning efforts.
Ultimately, the use of spreadsheets as a formal system of record for business planning is a risky one for any organization with a formal corporate structure, governed industry or geography, or any organization that has a significantly distributed business. The ubiquity of the spreadsheet makes it an easy place to start modeling a budget, and the value of the spreadsheet in helping users to structure small datasets will exist for the foreseeable future. But the fragility of the data structure, lack of user and version control governance, inability to scale, and the difficulty of verifying data with other sources while avoiding human error all lead to the need of supporting a more formalized planning solution over time. As organizations face a future of keeping distributed groups focused on a shared version of the truth and collectively consider a variety of scenarios at any given time, the risk of spreadsheet fragility needs to be matched up against the value of using a formalized FP&A solution designed to analyze, govern, and protect all relevant business data, formulas, and outcomes.
Analytics in the Retail and Consumer Packaged Goods (CPG) markets is more complex than the average corporate data ecosystem because of the variety of analytic approaches needed to support these organizations. Every business has operational management capabilities for core human resources and financial management, but retail adds the complexities of hybrid workforce management, scheduling, and operational analytics as well as the front-end data associated with consumer marketing, e-commerce, and transactional behavior across every channel.
In contrast, when retail organizations look at middle-office and front-office analytics, they are trying to support a variety of timeframes ranging from intraday decisions associated with staffing and customer foot traffic to the year-long cycles that may be necessary to fulfill large wholesale orders for highly coveted goods in the consumer market. Over the past three years, operational consistency has become especially challenging to achieve as COVID, labor skill gaps, logistical bottlenecks, commodity shortages, and geopolitical battles have all made supply chain a massive dynamic risk factor that must be consistently monitored across both macro and microeconomic business aspects.
The lack of alignment and connection between the front office, middle-office, and administrative analytic outputs can potentially lead to three separate silos of activity in the retail world— connected only by some basic metrics, such as receipts and inventory turnover, that are interpreted in three different ways. Like the parable of the blind men and an elephant where each person feels one part of the elephant and imagines a different creature, the disparate parts of retail organizations must figure out how to come together, as the average net margin for general retail companies is about 2% and that margin only gets lower for groceries and for online stores.
Analytic opportunities to increase business value exist across the balance sheet and income statement. Even though consumer sentiment, foot traffic, and online behavior are still key drivers for retail success, analytic and data-driven guidance can provide value across infrastructure, risk, and real-time operations. Amalgam Insights suggests that each of these areas requires a core analytic focus that is different and reflects the nature of the data, the decisions being made, and the stakeholders involved.
Facing Core Retail Business Challenges
First, retail and CPG organizations face core infrastructure, logistics, and data management challenges that typically require building out historic analysis and quantitative visibility capabilities often associated with what is called descriptive or historical analytics. When looking at infrastructure factors such as real estate, warehousing, and order fulfillment issues, organizations must have access to past trends, costs, transactions, and the breadth of relevant variables that go into real estate costs or complex order fulfillment associated with tracking perfect order index.
This pool of data ideally combines public data, industry data, and operational business data that includes, but is not limited to, sales trends, receipts, purchase orders, employee data, loyalty information, customer information, coupon redemption, and other relevant transactional data. This set of data needs to be available as analytic and queryable data that is accessible to all relevant stakeholders to provide business value. In practice, this accessibility typically requires some infrastructure investment either by a company or a technology vendor willing to bear the cost of maintaining a governed and industry-compliant analytic data store. By doing so, retail organizations have the opportunity to improve personalization and promotional optimization.
A second challenge that retail analytics can help with is associated with the risk and compliance issues that retail and CPG organizations face, including organized theft, supplier risk, and balancing risk and reward tradeoffs. A 2022 National Retail Federation (NRF) survey showed that organized retail crime had increased over 26% year over year, driving the need to identify and counter organized theft efforts and tactics more quickly. Retailer risk for specific goods and brands also needs to be quantified to identify potential delays and challenges or to determine whether direct store delivery and other direct-to-market tactics may end up being a profitable approach for key SKUs. Risk also matters from a profitability analysis perspective as retail organizations seek to make tradeoffs between the low-margin nature of retail business and the consumer demand for availability, personalization, automation, brand expansion, and alternative channel delivery that may provide exponential benefits to profits. From a practical perspective, this risk analysis requires investment in a combination of predictive analytics and the ability to translate the variance and marginal cost associated with new investments with projected returns.
A third challenge for retail analytics is to support real-time operational decisions. This use case requires access to streaming and log data associated with massive volumes of rapid transactions, frequently updated time-series data, and contextualized scenarios based on multi-data-sourced outcomes. From a retail outcome perspective, the practical challenge is to make near-real-time decisions, such as same-day or in-shift decisions to support stocking, scheduling, product orders, pricing and discounting decisions, placement decisions, and promotion. In addition, these decisions must be made in the context of broader strategic and operational concerns, such as brand promise, environmental concerns, social issues, and regulatory governance and compliance associated with environmental, social, and governance (ESG) concerns.
As an example, supply chain shortages often come from unexpected sources. An unexpected geopolitical example occurred in the United States when the government’s use of containers as a temporary barrier to block illegal immigration checkpoints on the US-Mexico border led to shortages at US ports for delivery. This delay in accessing containers was not predictable based solely on standard retail metrics and behavior and demonstrates one example of how unexpected political issues can affect a hyperconnected logistical supply chain.
Recommendations for Upgrading Retail Analytics in the 2020s
To solve these analytic problems, retail and CPG organizations need to allow line-of-business, logistics, and sourcing managers to act quickly with self-service and on-demand insights based on all relevant data. This ultimately means that to take an analytic approach to retail, Amalgam Insights recommends the following three best practices in creating a more data-driven business environment.
Create and implement an integrated finance, operational, and workforce management environment. Finance, inventory, and labor must be managed together in an integrated business data store and business planning environment or the retail organization falls apart. Whether companies choose to do this by knitting together multiple applications with data management and integration tools or by choosing a single best-in-breed suite, retail businesses have too many moving parts to split up core operational data across a variety of functional silos and business roles that do not work together. In the 2020s, this is a massive operational disadvantage.
Adopt prescriptive analytics, decision intelligence, and machine learning capabilities above and beyond basic dashboards. When retail organizations look at analytics and data outputs, it is not enough to gain historical visibility. In today’s AI-enabled world, companies must have predictive analytics, statistical analysis, detect anomalies quickly, and have the ability to translate business data into machine learning and language models for the next generation of analytics and decision intelligence. Retail can be more proactive and prescriptive with AI and ML models trained to their enterprise data to support more personalized and contextualized purchasing experiences.
Implement real-time alerts with relevant and actionable retail information. Finally, timely and contextual alerts are also now part of the analytic process. As retail organizations have moved from seasonal purchases and monthly budgeting to daily or even hourly decisions, regional and branch managers need to be able to move quickly if there are signs of business danger coordinated revenue leakage, brand damage across any of the products held within the store, unexpected weather phenomena, labor issues, or other incipient macro or microeconomic threats.
We are in a time of transformational change as the awareness of artificial intelligence (AI) grows during a time of global uncertainty. The labor supply chain is fluctuating quickly and the economy is on rocky ground as interest rates and geopolitical strife create currency challenges. Meanwhile, the commodity supply chain is in turmoil, leading to chaos and confusion. Rising interest rates and a higher cost of money are only adding to the challenges faced by those in the global business arena. In this world where technology is dominant in the business world, the global economic foundation is shifting, and the worlds of finance and talent are up for grabs, Workday stepped up to hold its AI and ML Innovation summit to show a way forward for the customers of its software platform, including a majority of the Fortune 500 that use Workday already as a system of record.
The timing of this summit will be remembered as a time of rapid AI change, with new major announcements happening daily. OpenAI’s near-daily announcements regarding working with Microsoft, launching ChatGPT, supporting plug-ins, and asking for guidance on AI governance are transforming the general public’s perception of AI. Google and Meta are racing to translate their many years of research in AI into products. Generative AI startups already focused on legal, contract, decision intelligence, and revenue intelligence use cases are happy to ride the coattails of this hype. Universities are showing how to build large language models such as Stanford’s Alpaca. And existing machine learning and AI companies such as Databricks are showing how to build custom models based on existing data for a fraction of the cost needed to build GPT.
In the midst of this AI maelstrom, Workday decided to chase the eye of the hurricane and put stakes in the ground on its current approach to innovation, AI, and ML. From our perspective, we were interested both in the executive perspective and in the product innovation associated with this Brave New World of AI.
Enter the Co-CEO – Carl Eschenbach
Workday’s AI and ML Innovation Summit commenced with an introduction of the partners and customers that would be present at the event. The Summit began with a conversation between Workday’s Co-CEOs, Aneel Bhusri and Carl Eschenbach, where Eschenbach talked about his focus on innovation and growth for the company. Eschenbach is not new to Workday, having been on its board during his time at Sequoia Capital, where he also led investments in Zoom, UIPath, and Snowflake. Having seen his work at VMware, Amalgam Insights was interested to see Eschenbach take this role and help Workday evolve its growth strategy from an executive level. From the start, both Bhusri and Eschenbach made it clear that this Co-CEO team is intended to be a temporary status with Eschenbach taking the reins in 2024, while Bhusri becomes the Executive Chair of Workday.
Eschenbach emphasized in this session that Workday has significant opportunities in providing a full platform solution, and its international reach requires additional investment both in technology and go-to-market efforts. Workday partners are essential to the company’s success and Eschenbach pointed out a recent partnership with Amazon to provide Workday as a private offering that can use Amazon Web Service contract dollars to purchase Workday products once the work is scoped by Workday. Workday executives also mentioned the need for consolidation, which is one of Amalgam Insights’ top themes and predictions for enterprise software for 2023. The trend in tech is shifting toward best-in-suite and strategic partnering opportunities rather than a scattered best-in-breed approach that may sprawl across tens or even hundreds of vendors.
These Co-CEOs also explored what Workday was going to become over the next three to five years to take the next stage of its development after Bhusri evolved Workday from an HR platform to a broader enterprise software platform. Bhusri sees Workday as a system of record that uses AI to serve customer pain points. He poses that ERP is an outdated term, but that Workday is currently categorized as a “services ERP” platform in practice when Workday is positioned as a traditional software vendor. Eschenbach adds that Workday is a management platform across people and finances on a common multi-tenant platform.
From Amalgam Insights’ perspective, this is an important positioning as Workday is establishing that its focus is on two of the highest value and highest cost issues in the company: skills and money. Both must exist in sufficient quantities and quality for companies to survive.
The Future of AI and Where Workday Fits
We then heard from Co-President Sayan Chakraborty, who took the stage to discuss the “Future of Work” across machine learning and generative AI. As a member of the National Artificial Intelligence Advisory Committee, the analysts in the audience expected Chakraborty to have a strong mastery of the issues and challenges Workday faced in AI and this expectation was clarified by the ensuing discussion.
Chakraborty started by saying that Workday is monomaniacally focused on machine learning to accelerate work and points out that we face a cyclical change in the nature of the working age across the entire developed world. As we deal with a decline in the percentage of “working-age” adults on a global scale, machine learning exists as a starting point to support structural challenges in labor structures and work efforts.
To enable these efforts, Chakraborty brought up the technology, data, and application platforms based on a shared object model, starting with the Workday Cloud Platform and including analytics, Workday experience, and machine learning as specific platform capabilities. Chakraborty referenced the need for daily liquidity FDIC requests as a capability that is now being asked for in light of banking failures and stresses such as the recent Silicon Valley Bank failure.
Workday has four areas of differentiation in machine learning: data management, autoML (automated machine learning, including feature abstraction), federated learning, as well as a platform approach. Workday’s advantage in data is stated across quantity, quality associated with a single data model, structure and tenancy, and the amplification of third-party data. As a starting point, this approach allows Workday to support models based on regional or customer-specific data supported by transfer learning. At this point, Chakraborty was asked why Workday has Prism in a world of Snowflake and other analytic solutions capable of scrutinizing data and supporting analytic queries and data enrichment. Prism is currently positioned as an in-platform capability that allows Workday to enrich its data, which is a vital capability as companies face the battle for context across data and analytic outputs.
Amalgam Insights will dig into this in greater detail in our recommendations and suggestions, but at this point we’ll note that this set of characteristics is fairly uncommon at the global software platform level and presents opportunities to execute based on recent AI announcements that Workday’s competitors will struggle to execute on.
Workday currently supports federated machine learning at scale out to the edge of Workday’s network, which is part of Workday’s differentiation in providing its own cloud. This ability to push the model out to the edge is increasingly important for supporting geographically specific governance and compliance needs (dubbed by some as the “Splinternet“) as Workday has seen increased demand for supporting regional governance requests leading to separate US and European Union machine learning training teams each working on regionally created data sources.
Chakraborty compared Workday’s approach of a platform machine learning approach leading to a variety of features to traditional machine learning feature-building approaches where each feature is built through a separate data generation process. The canonical Workday example is Workday’s Skills Cloud platform where Workday currently has close to 50,000 canonical skills and 200,000 recognized skills and synonyms scored for skill strength and validity. This Skills Cloud is a foundational differentiator for Workday and one that Amalgam Insights references regularly as an example of a differentiated syntactic and semantic layer of metadata that can provide differentiated context to a business trying to understand why and how data is used.
Workday mentioned six core principles for AI and ML, including people and customers, built to ensure that the machine learning capabilities developed are done through ethical approaches. In this context, Chakraborty also mentioned generative AI and large language models, which are starting to provide human-like outputs across voice, art, and text. He points out how the biggest change in AI occurred in 2006 when NVIDIA created GPUs, which used matrix math to support the constant re-creation of images. Once GPUs were used from a computational perspective, they made massively large parameter models possible. Chakraborty also pointed out the 2017 DeepMind paper on transformers to solve problems in parallel rather than sequentially, which led to the massive models that could be supported by cloud models. The 1000x growth in two years is unprecedented even from a tech perspective. Models have reached a level of scale where they can solve emergent challenges that they have not been trained on. This does not imply consciousness but does demonstrate the ability to analyze complex patterns and systems behavior. Amalgam Insights notes that this reflects a common trend in technology where new technology approaches often take a number of years to come to market, only to be treated as instant successes once they reach mainstream adoption.
Chakraborty points out that the weaknesses of GPT include bad results and a lack of explainability in machine learning, bad actors (including IP and security concerns), and the potential Environmental, Social, and Governance costs associated with financial, social, and environmental concerns. As with all technology, GPT and other generative AI models take up a lot of energy and resources without any awareness of how to throttle down in a sustainable and still functional manner. From a practical perspective, this means that current AI systems will be challenged to manage uptime as all of these new services attempt to benchmark and define their workloads and resource utilization. These problems are especially problematic in enterprise technology as the perceived reliability of enterprise software is often based on near-perfect accuracy of calculating traditional data and analytic outputs.
Amalgam Insights noted in our review of ChatGPT that factual accuracy and intellectual property attribution have been largely missing in recent AI technologies that have struggled to understand or contextualize a question based on surroundings or past queries. The likes of Google and Meta have focused on zero-shot learning for casual identification of trends and images rather than contextually specific object identification and topic governance aligned to specific skills and use cases. This is an area where both plug-ins and the work of enterprise software companies will be vital over the course of this year to augment the grammatically correct responses of generative AI with the facts and defined taxonomies used to conduct business.
Amalgam also found it interesting that Chakraborty mentioned that the future of models would include high-quality data and smaller models custom-built to industry and vertical use cases. This is an important statement because the primary discussion in current AI circles is often about how bigger is better and how models compete on having hundreds of billions of parameters to consider. In reality, we have reached the level of complexity where a well-trained model will provide responses that reflect the data that it has been trained on. The real work at this point is on how to better contextualize answers and how to separate quantitative and factual requests from textual and grammatical requests that may be in the same question. The challenge of accurate tone and grammar is very different from the ability to understand how to transform an eigenvector and get accurate quantitative output. Generative AI tends to be good at grammar but is challenged by quantitative and fact-based queries that may have answers that differ from its grammatical autocompletion logic.
Chakraborty pointed out that reinforcement learning has proven to be more useful than either supervised or unsupervised training for machine learning, as it allows models to look at user behavior rather than forcing direct user interaction. This Workday focus both provides efficacy of scale and takes advantage of Workday’s existing platform activities. This combination of reinforcement training and Workday’s ownership of its Skills Cloud will provide a sizable advantage over most of the enterprise AI world in aligning general outputs to the business world.
Amalgam Insights notes here that another challenge of the AI discussion is how to create an ‘unbiased’ approach for training and testing models when the more accurate question is to document the existing biases and assumptions that are being made. The sooner we can move from the goal of being “unbiased” to the goal of accurately documenting bias, the better we will be able to trust the AI we use.
Recommendations for the Amalgam Community on Where Workday is Headed Next
Obviously, this summit provided Amalgam Insights both with a lot of food for thought provided by Workday’s top executives. The introductory remarks summarized above were followed up with insight and guidance on Workday’s product roadmap across both the HR and finance categories where Workday has focused its product efforts, as well as visibility to the go-to-market and positioning, approaches that Workday plans to provide in 2023. Although much of these discussions were held under a non-disclosure agreement, Amalgam Insights will try to use this guidance to help companies to understand what is next from Workday and what customers should request. From an AI perspective, Amalgam Insights believes that customers should push Workday in the following areas based on Workday’s ability to deliver and provide business value.
Use the data model to both create and support large language models (LLMs). The data model is a fundamental advantage in setting up machine learning and chat interfaces. Done correctly, this is a way to have a form of Ask Me Anything for the company based on key corporate data and the culture of the organization. This is an opportunity to use trusted data to provide relevant advice and guidance to the enterprise. As one of the largest and most trusted data sources in the enterprise software world, Workday has an opportunity to quickly build, train, and deploy models on behalf of customers, either directly or through partners. With this capability, “Ask Workday” may quickly become the HR and finance equivalent of “Ask Siri.”
Use Workday’s Skills Cloud as a categorization to analyze the business, similar to cost center, profit center, geographic region, and other standard categories. Workforce optimization is not just about reducing TCO, but aligning skills, predicting succession and future success potential, and market availability for skills. Looking at the long-term value of attracting valuable skills and avoiding obsolete skills is an immense change for the Future of Work. Amalgam Insights believes that Workday’s market-leading Skills Cloud provides an opportunity for smart companies to analyze their company below the employee level and actually ascertain the resources and infrastructure associated with specific skills.
Workday still has room to improve regarding consolidation, close, and treasury management capabilities. In light of the recent Silicon Valley Bank failure and the relatively shaky ground that regional and niche banks currently are on, it’s obvious that daily bank risk is now an issue to take into account as companies check if they can access cash and pay their bills. Finance departments want to consolidate their work into one area and augment a shared version of the truth with individualized assumptions. Workday has an opportunity to innovate in finance as comprehensive vendors in this space are often outdated or rigidly customized on a per-customer level that does not allow versions to scale out in a financially responsible way as the Intelligent Data Core allows. And Workday’s direct non-ERP planning competitors mostly lack Workday’s scale both in its customer base and consultant partner relationships to provide comprehensive financial risk visibility across macroeconomic, microeconomic, planning, budgeting, and forecasting capabilities. Expect Workday to continue working on making this integrated finance, accounting, and sourcing experience even more integrated over time and to pursue more proactive alerts and recommendations to support strategic decisions.
Look for Workday Extend to be accessed more by technology vendors to create custom solutions. The current gallery of solutions is only a glimpse of the potential of Extend in establishing Workday-based custom apps. It only makes sense for Workday to be a platform for apps and services as it increasingly wins more enterprise data. From an AI perspective, Amalgam Insights would expect to see Workday Extend increasingly working with more plugins (including ChatGPT plugins), data models, and machine learning models to guide the context, data quality, hyperparameterization, and prompts needed for Workday to be an enterprise AI leader. Amalgam Insights also expects this will be a way for developers in the Workday ecosystem to take more advantage of the machine learning and analytics capabilities within Workday that are sometimes overlooked as companies seek to build models and gain insights into enterprise data.
2023 is going to be a tough year for anybody managing technology. As we face the repercussions of inflation and high interest rates and the bubble of tech starts to be burst, we are seeing a combination of hiring freezes, increased focus on core business activities and the hoary request to “do more with less.”
Behind the cliche of doing more with less is the need to actually become more efficient with tech usage. This means adopting a FinOps (Financial Operations) strategy to cloud to go with your existing Telecom FinOps (aka Telecom expense) and SaaS FinOps (aka SaaS Management) strategies. And it means being prepared for new spend category challenges as companies will need to invest in technology to get work done at a time when it is harder to hire the right person at the right time. Here is a quick preview of our predictions.
14 Key Predictions for the IT Executive in 2023
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“Innovation distinguishes between a leader and a follower.”
Steve Jobs
In 2023, we face a series of global planning challenges across accounting, finance, supply chain, workforce management, information technology, and data management. Each of these challenges involves a different set of stakeholders, data structures, key performance indicators, and broader economic and environmental drivers.
In light of this increasingly complex and nuanced set of categories that now make up the responsibilities associated with financial performance management (also known as enterprise performance management; corporate performance management; budgeting, planning, and forecasting, and other buzzwords, but all basically coming back to the financial planning and analysis FP&A role that we have known for decades), companies face a technology-related challenge for managing business plans. Is it better to work with a platform-based approach that allows every user to use the same application to support a variety of accounting and finance use cases including consolidation, close, and planning? Or is it better to use a Best-in-Breed application for business planning?
The basic starting point for evaluating this decision starts with a common sense question for enterprises: is it worth spending money on a standalone planning application or is it better to bundle planning with consolidation and transactional accounting such as an ERP or an accounting platform? In making this decision, companies should look at the following considerations:
Is the solution easy to use? In the 2020s, planning apps should be fairly easy to use, including ease of data entry, the ability to analyze data once it is entered, collaborative planning with other colleagues or budget-holding executives, mobile app support, and the ability to drill into planning data to explore specific deltas, outliers, and budget categories that are of specific interest. Ease of use should also extend to model and scenario management as financial professionals seek to bring a wide variety of potential considerations to enterprise forecasting environments. This ease of use is especially important as planning and forecasting exercises have accelerated in the 2020s based on COVID, supply chain challenges, currency value shifts, inflation, and the looming threat of a potential recession. The[1] need to support flexible planning scenarios can be challenging to accomplish within the accounting framework of creating a fixed and defined set of data that is fully consolidated and auditable.
Is the current solution integrated with all of the data – including operational data – that is needed from a planning perspective? If spreadsheets are considered, this immediately leads to potential governance and consistency problems as each individual will probably have their own specific assumptions. Suppose companies are using a planning solution as part of their ERP. In that case, the planning solution will likely have access to the majority of accounting data associated with planning. Still, companies then have to see how much of their semi-structured data, third-party data (such as weather, government, or market-based data), and other external data are integrated into a solution. And do these integrations require significant IT support or can they be supported either by the vendor, line-of-business operations manager or even by the end users, themselves?
Is the current planning solution flexible enough to both provide each department with the level of planning they are trying to perform while providing a consistent and shared version of the truth? Over the past few decades, the worlds of enterprise analytics and business accounting have both focused on the idea of a rigid “single version of the truth,” but the reality is that there is no single version of the truth as each individual and each department typically has specific goals, assumptions, terminology, and performance drivers specific to their specific job roles. And the moment that data is officially published or defined as “clean,” it immediately starts becoming outdated.
Accordingly, planning data needs to be organized so that every person involved in planning is able to access a consistent set of metrics while also having specialized views of the operational benchmarks and drivers associated with their specific goals as well as the ability to explore specific “what-if” hypothetical scenarios related to the variability of business situations that the organization may encounter. The operational data needed to support this level of flexibility is not always included as part of a core ERP suite and may need to come from a variety of transactional, payment, process automation systems, workflow management, and project management solutions to provide the level of clarity needed to support enterprise planning.[2][3]
From Amalgam Insights’ perspective, the answer to this initial question of planning application vs platform is that it is a bit of a red herring. Consolidation, close, and accounting audits are based on the need to lock down every transaction and document what has happened in the past. This historical view provides guidance and can be reviewed as necessary. But planning and forecasting are exercises in constructing the present and future of a business that requires the need to view the company through multiple lenses and scenarios and need to be altered based on possible business or global activities that may never happen. By nature, financial planning and analysis activities involve some level of uncertainty. Organizations seeking to accelerate the pace of planning and to extend planning beyond pure financial planning into sales, workforce, supply chain, information technology, & project portfolio management, will likely find that the need for near real-time analytics and data management increasingly requires an application that combines analytic speed, collaboration, and the ability to experiment within an application in ways that may conflict with or surpass the rate of accounting. Business planning needs to be a Best-in-Breed capability that allows for the flexibility of what-if analysis, the real-time feedback associated with new data and business considerations, the scale of modern data challenges, and the ability to collaboratively work with relevant business stakeholders. Without these supporting capabilities that can help organizations to independently adjust to the future, financial planning is ultimately a compliance exercise that lacks the impact and strategic guidance that executive teams need to make hard decisions.
“The will to win means nothing without the will to prepare”
Boston Marathon champion Juma Ikangaa
2023 is undoubtedly a challenging year to forecast from an economic perspective as the tempest of inflation, stock market volatility, foreign exchange challenges, hiring freezes, supply chain delays, and geopolitical conflicts are creating pressure for companies of all sizes and industries. As companies seek to make sense of a complex world and forecast performance, it is important to take full advantage of planning and forecasting capabilities to provide guidance. Of course, it is important to provide visibility and report to business stakeholders. But beyond the basics, what should you be thinking about as we prepare for a bumpy ride? Here are five key recommendations Amalgam Insights is providing for the business community.
Build a planning process that can be changed on a monthly basis. Even if your organization does not need to plan on a continuous basis, there will be at least one or two unexpected planning events that happen this year that will require widespread reconsiderations of the “annual plan.” The “annual planning cycle” concept is dead at companies after the past three years of working through COVID, supply chain issues, and workforce shortages. This means that planning often has to be updated with new and unexpected data to support a wide variety of scenarios. Locking the plan to a specific structure, schedule, or level of data consolidation is increasingly challenging for companies seeking better guidance throughout the year. If you are not building out a variety of scenarios and tweaking changes throughout the year based on business issues and changes, your business is working at a disadvantage to more nimble and agile organizations.
2. Identify planning anomalies quickly. As businesses review their plans, they will find that they are off-plan more quickly than they have historically been. One example of this is in cloud computing, a spend area that is expected to grow 18-22% in 2023, far above general IT spend or the expected rate of inflation in 2023. Other commodities such as complex manufactured goods and food stocks may fall into this category as well based on production delays, logistical shortages, & new novel diseases interrupt supply chains. The ability to quickly identify spend anomalies that exceed budgetary expectations allows companies to affect spend, procurement, and technologies strategies that may further optimize these environments. By identifying these anomalies quickly, finance can work with procurement both to figure out opportunities to reduce spend and to find alternative providers that can either reduce cost or ensure business continuity to meet consumer demand.
3. Interest rates and the cost of money may incentivize longer sourcing contracts to lock in costs. This lesson comes from the sports world, where baseball players are getting long contracts this year. Why? Because the cost of money is increasing and baseball teams can’t play games without players, leading teams to seek the opportunity to lock in costs. Of course, to do this, companies must budget for the potential upfront costs associated with taking on new contracts. This is a story of Haves and Have-nots where the haves now possess an opportunity to lock in costs for the next few years and take advantage of the value of money over the next couple of years while the Have Nots struggling to visualize their spend may be locked in short-term contracts that will cost more over time. However, this ability to make decisions based on the current cost of money is dependent on the ability to forecast the potential ramifications of locking in cost, especially when those costs represent the variable cost of goods to meet the demand for consumer purchases and services.
4. Cross-departmental business planning requires a data strategy that allows organizations to bring in multiple data sources. Finance must start learning about the value of a data pipeline and potentially a data lake for bringing data into a planning environment, processing and formatting the data properly, and maintaining a consistent store of data that includes all relevant information for modern business planning use cases. In the past, it may have been enough for finance to know that there was a database to support financial and payment information and then an OLAP cube to provide high-performance analytics for business planning. But in today’s planning world where finance is increasingly asked to be a strategic hub based on its view of the entire business, planning data now potentially includes everything from weather trends to government-provided data to online sentiment and even social media. These new data sources and formats require finance to both store and interact with data in ways that exceed the challenges of simply having massive row-based tables of business data.
5. Look for arbitrage opportunities across currencies, geographies, and even internal departments. The valuation of mission-critical skills and resources can be valued very differently across different areas. 2023 is an environment where corporate equity and stock values are lower, the US dollar is strong against the majority of global currencies, and skills and commodities can be hard to find. These are both challenges and opportunities, as they allow FP&A professionals to dig into forecasted costs and see if there are opportunities to go abroad or to look internally for skills, goods, and resources that may be less expensive than the typical markets businesses participate in. Finance can work with sourcing, human resources, information technology, and other departments to proactively identify specific areas where the business may have an opportunity to improve.
As we plan for 2023, it is time to prepare sagaciously so that we are ready to execute when challenges and opportunities emerge. By planning now for a wide variety of potential situations, businesses can make better decisions in critical moments that can define careers and the future of the entire organization.
“If you think it’s expensive to hire a professional to do the job, wait until you hire an amateur.”
Red Adair
In 2023, workforce planning is significantly more challenging and requires a combination of headcount, skills, finance, sourcing, and automation management. We are facing a remarkable confluence of labor trends that force workforce management to be more closely tied to financial management. Workforce volatility is at a peak as the Great Resignation has led to a mass exodus that has been augmented in recent months by layoffs from companies that overstaffed in the now-halcyon days of the favorable market that has defined our economic environment over the past decade. At the same time, the demand for specialized talent continues as the need to market, sell, deliver, produce, and digitize is still there for companies that are still healthy. And all this is happening at a time of global inflation and currency exchange challenges leading to cost constraints and explorations of geographic arbitrage and automation to introduce and scale up skills. This financial uncertainty leads to the increasing need for finance to support the details of workforce planning to build better businesses.
But this combination of volatility and demand has led to a more uneven distribution of talent that must be reconciled. From a practical perspective, this means that it is more important to make a business case for each hire that accurately estimates the value of a new employee’s skills and capabilities with the expected revenue per employee ratio that the company seeks to achieve. New employees must bring a combination of organizational fit and rapidly deployable skills to their companies to create value in a timely fashion. Considering that the cost of finding, onboarding, and ramping up a new employee can range from $15,000 to $50,000 based on Amalgam Insights’ estimates, companies face the challenge of ensuring that new employees are put in a position to succeed. From a planning perspective, this means having hardware, software licenses, data access, training, and relevant employee relationships all defined on Day Zero or Day One rather than a penny-wise, pound-foolish approach of attempting to provide just-in-time access as employees demand it.
Workforce planning may also include investing in training or learning and development resources proactively as skills needs are forecasted, as the cost of training can be lower than the cost of hiring a new employee or finding a new consultant. From a financial perspective, it is important to conduct a cost analysis of skills acquisition based on the future-facing needs of the organization. Even in a cost-conscious environment, it takes money to make money. However, workforce investments must be focused on employees who will both create value quickly and have the mindset to provide long-term value through their problem-solving, self-improvement, and collaborative approaches. And in considering the cost of skills, companies need to consider both the need for hard skills such as process automation and machine learning as well as the need to teach and train soft skills such as effective project coordination and corporate communications skills. By accounting for skills that may only be needed on a short-term basis compared to those that represent long-term commitments for an organization, companies can prioritize workforce planning from a more quantitative and business growth-oriented perspective.
As companies consider the full cost of employee skills, companies also have to consider the fully loaded cost of an employee, including the resources and benefits associated with bringing an employee on board. The accounting for supporting employee productivity has become more complex in the face of COVID and the subsequent reimagining of the overhead associated with employees. At the peak of COVID, an estimated 40% of employees worked from home. Based on this trend, it was not difficult for organizations to start scrutinizing the real estate and other long-term assets and leases that have traditionally been seen as depreciable aspects of employee cost providing tax benefits over time. The increasing willingness to move headquarters and other large offices to more tax or cost-of-living-friendly locations as well as the tradeoffs between depreciation, asset sales, and leases are increasingly relevant to structuring workforce planning. Companies must readjust the cost assumptions of their workforce to reflect the new reality of their organization.
This set of assumptions does not simply mean that companies can assume that an employee will be fully remote or fully on-site, as this discussion is driven by a nuanced set of considerations. Remote workers have struggled to onboard and reach full productivity and younger workers have sought mentorship and leadership that has traditionally been provided on-site. On the other hand, experienced specialists point to greater productivity and efficiency when they work in remote environments where they run into fewer ad-hoc distractions and interruptions and can work more flexibly. [1]
From a planning perspective, this may mean setting up scheduled hybrid assumptions for workforce overhead that include office space and physical resources on a monthly or quarterly basis depending on the roles involved. Real estate and other long-term assets/leases that have traditionally been seen as depreciable aspects of employee cost providing tax benefits over time, but with large office vacancies and the increasing willingness to move headquarters and other large offices, the tradeoffs between depreciation, asset sales, and leases are now increasingly relevant to structuring workforce planning. Amalgam Insights expects that 2023 will be a year where companies are still struggling to find the correct balance of office space and may find themselves overcompensating in ways that affect long-term productivity.
The cost of bringing a workforce to full productivity at scale is seen through a variety of data, including the United States economic census, which shows that small companies under 500 employees make approximately $220,000 per employee while large enterprises with over 5,000 employees make over $375,000 per employee. (SOURCE: United States 2017 County Business Patterns and Economic Census) This difference of over $150,000 speaks to the potential difference in productivity between employees who are fully supported at an enterprise level and their small business counterparts who presumably have less support, brand power, and capabilities to enhance their efforts.
But this increase is ultimately only possible by aligning workforce planning efforts with the financial planning and forecasting efforts that align talent and skills with business strategy and outcomes. Ultimately, workforce planning and business planning must be intertwined to be successful across business demand, skills, onboarding, and overhead.
2022 was a banner year for artificial intelligence technologies that reached the mainstream. After years of being frustrated with the likes of Alexa, Cortana, and Siri and the inability to understand the value of machine learning other than as a vague backend technology for the likes of Facebook and Google, 2022 brought us AI-based tools that was understandable at a consumer level. From our perspective, the most meaningful of these were two products created by OpenAI: DALL-E and ChatGPT, which expanded the concept of consumer AI from a simple search or networking capability to a more comprehensive and creative approach for translating sentiments and thoughts into outputs.
DALL-E (and its successor DALL-E 2) is a system that can create visual images based on text descriptions. The models behind DALL-E look at relationships between existing images and the text metadata that has been used to describe those images. Based on these titles and descriptions, DALL-E uses diffusion models to start with random pixels that lead to generated images based on these descriptions. This area of research is by no means unique to OpenAI, but it is novel to open up a creative tool such as DALL-E to the public. Although the outputs are often both interesting and surprisingly different from what one might have imagined, they are not without their issues. For instance, the discussion around the legal ownership of DALL-E created graphics is not clear, since Open AI claims to own the images used, but the images themselves are often based on other copyrighted images. One can imagine that, over time, an artistic sampling model may start to appear similar to the music industry where licensing contracts are used to manage the usage of copyrighted material. But this will require greater visibility regarding the lineage of AI-based content creation and the data used to support graphic diffusion. Until this significant legal question is solved, Amalgam Insights believes that the commercial usage of DALL-E will be challenging to manage. This is somewhat reminiscent of the challenges that Napster faced at the end of the 20th century as a technology that both transformed the music industry and had to deal with the challenges of a new digital frontier.
But the technology that has taken over the zeitgeist of technology users is ChatGPT and related use cases associated with the GPT (Generative Pre-Trained Transformer) autoregressive language model trained on 500 billion words across the web, Wikipedia, and books. And it has become the favorite plaything of many a technologist. What makes ChatGPT attractive is its ability to take requests from users asking questions with some level of subject matter specificity or formatting and to create responses in real-time. Here are a couple of examples from both a subject matter and creative perspective.
Example 1: Please provide a blueprint for bootstrapping a software startup.
This is a bit generic and lacks some important details on how to find funding or sell the product, but it is in line with what one might expect to see in a standard web article regarding how to build a software product. The ending of this answer shows how the autogenerative text is likely referring to prior web-based content built for search engine optimization and seeking to provide a polite conclusion based on junior high school lessons in writing the standard five-paragraph essay rather than a meaningful conclusion that provides insight. In short, it is basically a status quo average article with helpful information that should not be overlooked, but is not either comprehensive or particularly insightful for anyone who has ever actually started a business.
A second example of ChatGPT is in providing creative structural formats for relatively obscure topics. As you know, I’m an expert in technology expense management with over two decades of experience and one of the big issues I see is, of course, the lack of poetry associated with this amazing topic. So, again, let’s go to ChatGPT.
Example 2: Write a sonnet on the importance of reducing telecom expenses
As a poem, this is pretty good for something written in two seconds. But it’s not a sonnet, as sonnets are 14 lines, written in iambic pentameter (10 syllable lines split int 5 iambs, or a unstressed syllable followed by a stressed syllable) and split into three sections of four lines followed by a two-line section with a rhyme scheme of ABAB, CDCD, EFEF, GG. So, there’s a lot missing there.
So, based on these examples, how should ChatGPT be used? First, let’s look at what this content reflects. The content here represents the average web and text content that is associated with the topic. With 500 billion words in the GPT-3 corpus, there is a lot of context to show what should come next for a wide variety of topics. Initial concerns of GPT-3 have started with the challenges of answering questions for extremely specific topics that are outside of its training data. But let’s consider a topic I worked on in some detail back in my college days while using appropriate academic language in asking a version of Gayatri Spivak’s famous (in academic circles) question “Can the subaltern speak?”
Example 3: Is the subaltern allowed to fully articulate a semiotic voice?
Considering that the language and topic here is fairly specialized, the introductory assumptions are descriptive but not incisive. The answer struggles with the “semiotic voice” aspect of the question in discussing the ability and agency to use symbols from a cultural and societal perspective. Again, the text provides a feeling of context that is necessary, but not sufficient, to answer the question. The focus here is on providing a short summary that provides an introduction to the issue before taking the easy way out telling us what is “important to recognize” without really taking a stand. And, again, the conclusion sounds like something out of an antiseptic human resources manual in asking for the reader to consider “different experiences and abilities” rather than the actual question regarding the ability to use symbols, signs, and assumptions. This is probably enough of an analysis at a superficial level as the goal here isn’t to deeply explore postmodern semiotic theory but to test ChatGPT’s response in a specialized topic.
Based on these three examples, one should be careful in counting on ChatGPT to provide a comprehensive or definitive answer to a question. Realistically, we can expect ChatGPT will provide representative content for a topic based on what is on the web. The completeness and accuracy of a ChatGPT topic is going to be dependent on how often the topic has been covered online. The more complete an answer is, the more likely it is that this topic has already been covered in detail.
ChatGPT will provide a starting point for a topic and typically provide information that should be included to introduce the topic. Interestingly, this means that ChatGPT is significantly influenced by the preferences that have built online web text over the past decade of content explosion. The quality of ChatGPT outputs seems to be most impressive to those who treat writing as a factual exercise or content creation channel while those who look at writing as a channel to explore ideas may find it lacking for now based on its generalized model.
From a topical perspective, ChatGPT will probably have some basic context for whatever text is used in a query. It would be interesting to see the GPT-3 model augmented with specific subject matter texts that could prioritize up-to-date research, coding, policy, financial analysis, or other timely new content either as a product or training capability.
In addition, don’t expect ChatGPT to provide strong recommendations or guidance. The auto-completion that ChatGPT does is designed to show how everyone else has followed up on this topic. And, in general, people do not tend to take strong stances on web-based content or introductory articles.
Fundamentally, ChatGPT will do two things. First, it will make mediocre content ubiquitous. There is no need to hire people to write an “average” post for your website anymore as ChatGPT and other technologies either designed to compete with or augment it will be able to do this easily. If your skillset is to write grammatically sound articles with little to no subject matter experience or practical guidance, that skill is now obsolete as status quo and often-repeated content can now be created on command. This also means that there is a huge opportunity to combine ChatGPT with common queries and use cases to create new content on demand. However, in doing so, users will have to be very careful not to plagiarize content unknowingly. This is an area where, just like with DALL-E, OpenAI will have to work on figuring out data lineage, trademark and copyright infringement, and appropriation of credit to support commercial use cases. ChatGPT struggles with what are called “hallucinations” where ChatGPT makes up facts or sources because those words are physically close to the topic discussed in the various websites and books that ChatGPT uses. ChatGPT is a text generation tool that picks words based on how frequently they show up with other words. Sometimes that result will be extremely detailed and current and other times, it will look very generic and mix up related topics that are often discussed together.
Second, this tool now provides a much stronger starting point for writers seeking to say something new or different. If your point of view is something that ChatGPT can provide in two seconds, it is neither interesting or new. To stand out, you need to provide greater insight, better perspective, or stronger directional guidance. This is an opportunity to improve your skills or to determine where your professional skills lie. ChatGPT still struggles with timely analysis, directional guidance, practical recommendations beyond surface-level perspectives, and combining mathematical and textual analysis (i.e. doing word problems or math-related case studies or code review) so there is still an immense amount of opportunity for people to write better.
Ultimately, ChatGPT is a reflection of the history of written text creation, both analog and digital. Like all AI, ChatGPT provides a view of how questions were answered in the past and provides an aggregate composite based on auto-completion. For topics with a basic consensus, such as how to build a product, this tool will be an incredible time saver. For topics that may have multiple conflicting opinions, ChatGPT will try to play either both sides or all sides in a neutral manner. And for niche topics, ChatGPT will try to fake an answer at what is approximately a high school student’s understanding of the topic. Amalgam Insights recommends that all knowledge workers experiment with ChatGPT in their realm of expertise as this tool and the market of products that will be built based on the autogenerated text will play an important role in supporting the next generation of tech.
The biggest US-based travel story at the end of 2022 was the absolute collapse of Southwest Airlines. The United States was hit by a sudden cold snap just as Christmas approached, leading to a massive travel delay across almost all travel modes including airlines, trains, and road-based transit. However, after a couple of days, most US-domestic airlines seemed to have recovered with the exception of Southwest, which suddenly and unexpectedly canceled nearly all of its flights in the last week of 2022, just as people were traveling from or to locations for Christmas, Hanukkah, New Year’s Eve, and other holidays. The timing was horrible and inexplicable. And with little to no official explanation, travelers stranded across the country could only guess whether this was due to an unannounced strike. Were there problems with Southwest’s airplane fleet? Were there problems with a specific airport?
It turns out that the problem was with Southwest’s internal scheduling tool, an in-house software application built in the 1990s and held together over the years as Southwest roughly doubled in size across passengers, planes, trips, employees, and number of destinations supported. This complexity ended up being especially challenging because Southwest’s model as a regional airline meant that it did not use a central hub as most other large airlines in the United States use. Rather, each plane flies from point to point leading to a combination of possibilities that grew exponentially rather than linearly. Although Southwest does not fly every plane from each location to every other location, the complexity of operations from roughly 45 locations in the late 1990s to roughly 100 domestic locations today is not a doubling of complexity but more along the lines of N*(N-1)/2, as long-time analytic advisor Neil Raden pointed out. This means the complexity increase is more akin to (45*44/2) = 990 vs. (100*99/2) = 4950. This level of complexity is multiplied by the challenges of organizing the thousands of pilots and flight attendants traveling from point to point every day.
The orders of magnitude in complexity associated with this scheduling system had already been strained in previous years but met a critical breaking point at the end of 2022 due to a lack of investment and modernization. This failure is a textbook example of the concept of “technical debt.”
Technical debt is often described as a concept that is difficult to articulate for a business audience, but the concept is actually very straightforward from a business perspective. Just as with financial debt, which must be paid back with interest or risk a default that threatens business assets, technical debt is an act of borrowing against the future. Like financial debt, technical debt either requires future investment (the “interest”) to fix the technology over time or to accept that the technology will fail (“default”) and lead to breaking down any processes dependent on the technology.
The lessons from this breakdown are straightforward but are potentially challenging to follow in 2023, a year where companies will be tempted to cut costs by any means possible.
Ensure that executive stakeholders are clear both on the concept of technical debt and the labor associated with current technical debt. It may not be possible to put an exact dollar amount on the technical debt that currently exists in the organization, but it should be possible to provide some guidance on the current labor and resources assigned to managing outdated technology as well as the potential points of failure associated with, say, being unable to find a FORTRAN developer quickly or the use of applications no longer supported by a vendor or by in-house developers.
Document every technology associated with each mission-critical process. With the cliché that “every company is a technology company” having been fully realized in today’s web, mobile, and automated world, IT’s job is to provide proactive guidance on the hardware, software, and skills that must either be supported or upgraded. The business value propositions of IT asset and service management are unlocked when assets are specifically aligned to business dependencies, projects, and processes.
Identify technologies where business growth lead to exponential technology demand. Southwest’s scheduling system needed to grow exponentially and eventually failed based on its legacy design. Look at the mathematics associated with key processes to see if growth is logarithmic, linear, exponential, or unpredictable. Simply assuming that a process grows linearly with revenue, employee growth, or business traffic can be a job-ending mistake.
Ensure that legacy technologies have the capacity to support forecasted business complexity or business growth. Any time technology growth needs to expand faster than overall IT spend or overall operational spend, it should serve as a warning sign to either change the technological approach or to invest in the necessary capacity.
We face a challenging year as inflation, foreign currency challenges, geopolitical issues, and supply chain bottlenecks still threaten the spectre of recession. But as executives seek to cut costs, Southwest serves as a reminder that businesses must still futureproof their technology approaches, evaluate the scalability of their processes, and invest in service delivery commensurate with their brand promise or risk lasting revenue and market capitalization losses.
Yesterday, IBM filed suit against Micro Focus for claims of copying part of the z/OS for data mapping in the web services implementation of Micro Focus Enterprise Suite. To understand this suit, I think the most relevant excerpts of claims in the suit are:
26. CICS® TS (Customer Information Control System Transaction Server) Web Services uses a “web service binding file,” known as a WSBIND file, to expose CICS® TS programs as web services and maps data received.
40. Micro Focus’s Enterprise Suite offers a web services implementation (“Micro Focus Web Services”) that includes a WSBIND file for mapping data • Micro Focus’s WSBIND file uses IBM internal structures that are not available outside of IBM. • The Micro Focus utility processing reflected in the log file exhibits the same configuration, program sequence, program elements, program optimizations, defects, and missing features as the corresponding CICS® TS utility programs. • Micro Focus’s WSBIND file is encoded in EBCDIC—like IBM’s—yet, Micro Focus has no need for using that encoding as it uses an ASCII environment.
(Analyst’s note: I think this is probably going to be one of the key hinges of the lawsuit. EBCDIC is really an IBM-specific format at this point while ASCII is everywhere. A bit weird to use IBM’s specific encoding for characters.)
42. …no legitimate reason for Micro Focus to have copied IBM’s computer program. Without copying from IBM, Micro Focus had a broad range of design and architectural choices that would have allowed it to create software that offers the same features as the Micro Focus Enterprise Suite.
It’s no secret that IBM has bet the farm on modernization and digital transformation (see Red Hat). The ability to manage IBM customer technology evolution is core to the future of the business. If nothing else, this suit sends a strong message: Don’t Mess with the zSeries. I’m interested to see how this suit will reference Google vs. Oracle: this isn’t the same, but I’d imagine Micro Focus will try to make it sound that way.
