Applying the SCOR Model to the Information Supply Chain

While it sounds straightforward enough, even the most simple supply chains involve a host of moving and tightly coordinated moving parts. The Supply Chain Operations Reference (SCOR) model lays out these processes as:

• Plan— Processes that balance aggregate demand and supply to develop a course of action which best meets sourcing, production, and delivery requirements.
• Source— Processes that procure goods and services to meet planned or actual demand.
• Make—Processes that transform product to a finished state to meet planned or actual demand.
• Deliver—Processes that provide finished goods and services to meet planned or actual demand, typically including order management, transportation management, and distribution management.
• Return—Processes associated with returning or receiving returned products for any reason. These processes extend into post-delivery customer support.
• Enable— Processes for establishing and operating the supply chain procedures, resources, and facilities, including relationships with all stakeholders and other involved parties.

Borrowing from supply chain best practices, information supply chain (ISC) planning should ensure: that the process/flow is integrated from front to back; the ability to simulate what-if scenarios for information production, delivery, and usage; flexibility for demand volatility; and that all individuals and organizations involved understand their downstream and upstream impact. It also involves planning for costs, fulfillment cycle times, return on assets and working capital, demand planning and management, “pull-based” inventory replenishment, inventory recording practices, and dozens of other procedures and considerations, some of which have relevance in in an ISC context.

Sourcing deals with how materials (data) are acquired, how to manage inventory (database, metadata, master data) and supplier network (data source owners), supplier agreements (terms of service with data owners), how to handle payments and revenues (chargeback and payback), and how materials (data) are transmitted and received (accessed and/or streamed, including security controls), and verified (quality controls, data profiling).

Making includes the activities for turning materials (data) into finished goods (information, reports, application inputs). This step may include production activities (cleansing, integration, enrichment), packaging (aggregation, predefined analytics, or reports), staging (data staging areas), and releasing (database loads or updates).

Delivering is the order management (user data/report requests or their actual queries against the data), warehousing (data warehouse, data mart, logical data warehouse architecture), and transportation (query management, API, or web service calls). It also includes product lifecycles (archival, roll-ups, deletion) and import/export requirements (cloud, remote data centers).

Returning refers to the processing of defective product (information, reports), which in an information context may not be so relevant since data is rarely returned. But this process could also include information user support activities.

A few years ago the Supply-Chain Council announced an update to the SCOR model, adding Enable as a sixth process. This process represents the management of business rules (data integration logic, data governance, and security policies and processes), regulatory compliance (data governance) and data management (metadata, master data). Other Enablement processes translate directly into ISC performance management, resource management, facilities management, contract management, supply chain network management, and risk management.

Information Supply Chain Scenarios

The SCOR model also provides a few levels of detail for scoping, configuring, and process/performance attributes. These details enable the handling of specific supply chain scenarios such as “make-to-stock” versus “make-to-order” supply chain configurations for general and custom goods and services, respectively. Differentiating these two configurations for the supply of information can be helpful in designing for:

1. Generalized information uses such as a data warehouse or data lake, or
2. Specified information purposes such as an architected data mart or report, input to a new application, or partner data feed request.

Typically, make-to-stock scenarios are executed not based on supposition, but upon a forecast. In an information context, this could refer to the vision for information described in the previous chapter. Make-to-order scenarios usually require some additional tasks for specing, producing, testing, staging, and releasing in collaboration with an actual customer. The information productization steps enumerated in chapter 4 are representative of a make-to-order scenario.

As supply chains grow more sophisticated, they appear and behave more as networks—complex flows of goods and services among suppliers, distributors, payment processors, and customers. Accordingly, organizations should characterize, architect and document their information life-cycles in this way as well.

Metrics for the Information Supply Chain

The SCOR model includes some 200 key indicators of supply chain operations performance. Although we’ll cover information measurement in part III, these metrics can be useful in understanding and crafting an information supply chain. The high-level metrics framework includes the following attributes, definition, and sample metrics, along with performance measures transposed into sample information supply chain metrics:

A New Supply Chain Model for Information Assets

Certainly the classic product/service supply chain model is useful at a high level, especially to express information management needs to those more familiar with the physical supply chain concept. However, the detailed processes become increasingly unrelated to the specific processes relevant to the management and flow of information. Moreover, I find the product and service-oriented supply chain concept a bit too focused on process and less so on value creation. If infonomics is a concept for treating information as an economic asset, a model or framework for describing its flow perhaps should center more on how each step increases its economic potential.

To that end, let’s consider a range of different kinds of recognized assets, or the stuff organizations manage:

• Material assets (e.g., raw goods, consumer goods, plant, equipment),
• Financial assets (e.g., cash, savings, various investment vehicles, pre-paid expenses),
• Intellectual property (e.g., patents, trademarks, copyrights),
• Human capital (employees, contractors, partners, departments), and
• Information “assets” (e.g., transactions, lists, reference data, content, metadata, etc.).

We will examine the specific processes and standards for each of these in the next chapter.

Financial and material assets are somewhat obvious. It gets a little murkier when discussing people as assets. Today, most organizations consider employees as “human capital.” However, this concept only came about fairly recently. In 1964, University of Chicago economist and Nobel Prize winner Gary Becker published the book Human Capital. Upon the popularity of Becker’s ideas, the term “human capital” became widely used in business, and human resources (HR) executives emerged. Becker and other prominent economists including Milton Friedman then established the economic concept of human capital. So, with employees now considered organizational assets, why aren’t they (we, that is) on the balance sheet? Does your employer actually own you? No. Thankfully, due to anti-slavery laws in civilized countries, we are “at will” employees, even if under contract. Since ownership and control are key asset determinants, you and I show up as an expense on the income statement, not as an asset on the balance sheet.

And we’ve already discussed how information—in most cases—is not a recognized asset according to accounting standards.

For nearly any kind of asset (or proto-asset, as in the case of human capital and information), we execute a similar set of activities on them, or do certain things to them. We collect or otherwise obtain assets. For manufacturers in particular, we produce them and inventory them in the counting, or accounting, sense. Once we have assets, we might also do things to enhance their potential economic benefit, such as enriching them in some way (i.e., employee training), moving them about, or integrating them. And for precautionary reasons, we typically protect them in some way and keep continuous track of them. Organizations do these things to assets to ensure the assets don’t lose value and are able to generate future value.

These lifecycle primitives classify the activities we do to assets, and represent the supply side of the supply chain:

Many of these look familiar juxtaposed against the SCOR framework, but note that they are applicable to any and all classes of asset (or proto-asset). Also note that no sequence of steps is implied. They can be combined and sequenced in any way necessary or applicable. These activities focus on value augmentation alone. They are ways to increase the potential or probable economic value of the asset, but not to realize its value.

Now let’s consider the ways these various assets generate realized economic value. True to the definition of an asset, value realization happens only once we do something with them. These primitives classify the activities we do with assets, and represent the demand side of the supply chain:

(You may have noticed I just used the terms supply and demand. Yes, this is foreshadowing that we’re getting to the thrilling topic of information economics a bit later.)

In the case of financial assets, we typically spend them on other assets (including other kinds of financial assets like securities), or lend them to financial institutions to generate interest income. Actually in doing so, we are giving banks a license to use our money. Material assets are typically sold as finished goods or applied as raw materials to create finished goods. Human assets are applied to business processes. Yes, sometimes we employees feel quite spent, too!

Intellectual property is most often applied to business processes, as well. But because it’s an actual owned asset, it can also be sold or licensed directly, as in the case of a business selling or licensing a patent to another business.

Interestingly, even as an unrecognized asset class, information is at least as flexible on the demand side as any other kind of asset. We can perform any and all of these activities with it. And with a strong information culture, we should be performing all of these activities with it. In the first few chapters you probably started thinking about how much information your organization has that—if packaged and marketed properly—could become a salable (licensable) commodity itself. You may be doing this already, but the opportunities to do so are likely much greater than most people in your organization realize.

This book is not about defining a detailed, 976-page (i.e., SCOR) reference model for an information supply chain. Instead I’ll leave you with a useful illustration based on these supply-side and demand-side primitives, with a few others added specific to information assets. Hopefully, the most enthusiastic of you will look to adopt, adapt, and build on this concept.

Figure 6.1 Information Supply Chain Activities

Figure 6.1 simply shows how information value potential leading to its realization is enhanced along a continuum. Instead of five or six process groupings, it consists of three which I believe represent the main stages of an ISC:

• Acquisition— how you obtain raw data or other information.
• Administration— how the data is enhanced, or what the machinery the “information factory” might include.
• Application— how information is received and used in generating value.

Moreover, as one organization’s ISC intersects with another’s, as in the case of an information supply network, the arrow may loop back on itself. For example: sold, lent, or analyzed information may become the raw data purchased or captured, licensed, or observed by another organization. And so on. This is the nature of our most non-depleting, non-rivalrous, regenerative asset.

Preparing for the Real Information Ecosystem

Speaking of loops, particularly feedback loops, another way to characterize information’s relative place in the world is as an ecosystem. A supply chain may seem too linear, and even a supply network too static and procedural for today’s (and especially tomorrow’s) more dynamic business environment. Indeed, supply chains/networks change and increasingly run pseudo-autonomously on business logic, embedded analytics, and always-on communication among stakeholders. But do they really evolve? Do they really sense and respond and adapt to both immediate and gradual shifts in their larger environment the way biological ecosystems do? Only if someone coded them that way. Someday soon we will witness an entire supply chain driven by a neural network.

Perhaps we should be conceiving and planning for that day. Already, certain players in certain supply chains manage and store much of the information flow. Think: Walmart. Or Coca-Cola. As Don Keough, the former president of Coke once said, “Whoever has information fastest and uses it wins.”³ It’s probably something Sun Tzu once said, too.⁴

In Japan, keiretsus are the renowned corporate ecosystems formed around trust, sharing, collaboration, and coordination. Today, with companies like Walmart and Coca-Cola and Amazon, we’re starting to see the formation of information keiretsus. These ecosystems enable trusted partners to readily share and use one another’s information. Gartner’s Group VP of Data and Analytics, Regina Casonato, has referred to this kind of arrangement as “information without borders.” Whatever we want to call it, the behavior of information within and among these entities resembles something flowing or even thriving within an ecosystem.

This flow of information becomes even more important as businesses turn to ecosystems to fuel their digital growth. The Gartner 2017 CIO Agenda⁵ survey of 2,598 global CIOs found that top performers create or participate in ecosystems and expect to double their ecosystems in two years. As Andy Rowsell-Jones, vice president and research director at Gartner, said, “Many organizations will need to shift their enterprises from a linear, value chain business trading with well-known partners and adding value in steps, to being part of a faster and more dynamic networked digital ecosystem.”

So what we can learn and apply about ecosystems to the discipline of information management? What is an ecosystem exactly?

Lessons from Sustainability

When an ecosystem is healthy, scientists say it is in balance or sustainable. Since we want our information ecosystems to be sustainable and thrive, let’s take a look at how we might incorporate the principles of sustainability or conservation. What was originally known as the “three Rs” of Reduce, Reuse, and Recycle recently have been expanded into “five Rs” which also include Refuse and Repurpose.

Most businesses are compiling information assets so furiously because: 1) they’re easily captured or obtained, 2) they’re increasingly inexpensive to store, and 3) they have nearly unlimited monetization potential. This makes sense, but as gigabytes become terabytes, and terabytes become petabytes, and petabytes become exabytes, the sustainability of an information ecosystem or architecture or infrastructure can be threatened. Even considering the diminishing marginal cost of storing information, conserving its use and production—and thereby the use of information-related resources—can improve the performance, costs, and risks incurred by your information ecosystem.

Information as a Second Language

All of these metaphors for information we’ve devised over the years are not meant to be clever. Rather they’re to help information management professionals, IT professionals, business people, and executives communicate about information more effectively using familiar, non-threatening terminology and frameworks. They can make information seem less ephemeral and abstract, and more tangible, helping us convince our business colleagues, executives, and ourselves that information really is an asset to be treated as such.

To consolidate all of these challenges and potential solutions related to “speaking data” within an organization and among business partners, Gartner analyst Valerie Logan recently has introduced the concept of “information as a second language” (ISL). No surprise, ISL parallels the kind of challenges and training afforded to non-native speakers in the U.S. under the moniker “English as a Second Language” (ESL). Logan’s ISL idea is a fantastic one and still being developed.

Although rectifying our sloppy or geeky information vernacular can help with communicating throughout the organization, to manage information as an actual asset, we still have a lot to learn from other disciplines. In the next chapter we’ll investigate other well-honed, well-articulated, and reasonably well-understood forms of asset management to see what we might borrow from these disciplines to improve and extend information asset management (IAM) capabilities.