What is the Information Value Chain?
The first mention of the value chain was in Michael Porterâ€™s 1985 book Competitive Advantage: Creating and Sustaining Superior Performance. In the book, Porter, who has continued to write on the subject, offered the idea that a value chain (sometimes referred to as a supply chain), when properly managed, can help a company or organization create a higher quality product or service. Porter believes that the value chain is made up of primary and support activities, and that these activities can be described as either direct, indirect, or quality assurance-related. While Porterâ€™s value chain was designed primarily for the production of a product or, in some cases, a service, we can take what he posited and apply it to data management, as well. In a data management context, we could refer to it as an information value chain, ensuring that the quality of our information is of the highest quality at every transitional point. Before discussing how the value chain can be applied to data management, itâ€™s important to first understand the chain and its activities.
Primary activities are those that Porter defines as being most integral to the successful creation of value. Without these critical activities, the value chain would cease to exist. However, even if these activities are in place but are being done improperly or quality isnâ€™t being assessed at each step, the results can be insufficient value to the end user or consumer. These are also activities that Porter would likely describe as being â€œDirect Activitiesâ€, or those that add value on their own.
Inbound logistics are the processes related to receiving, storing, and distributing inputs. In the business world, this might refer to supplies such as the bedding, First Aid equipment, and medications that a hospital might need. Warehousing items is also an inbound logistics task, as is the distribution of supplies to manufacturers.
In a value chain, operations refer to the activities that transform inputs into outputs. Returning to our hospital example, utilizing the supplies and equipment you purchase and receive in order to deliver healthcare would be an operation. Itâ€™s an example of taking an input and turning it into an output, in this case the service of healthcare. Operations can be more straightforward, however, and can also apply to production. For instance, if a company uses wood to make furniture, the process of turning that raw supply of wood into beautiful furniture falls under the operations step of the value chain.
Marketing and Sales
These activities are fairly straightforward and likely do not require a definition for most people. Marketing and sales are the processes that an organization or company uses to convince clients or customers to purchase from them instead of the competition. For instance, a Facebook ad for a clinic or a commercial for a prescription drug are both forms of marketing and sales activities.
After-sales services are, as the name implies, any of the services you administer after the sale has been completed. In short, this would be where activities like customer service take place. After-sales services could be anything from following up with a customer, asking a patient to fill out a survey, or checking in on a patient after a surgery or medical procedure to see how theyâ€™re doing.
Support activities help to facilitate value by assisting the primary activities in one way or another. Procurement aids with inbound logistics, human resources functions help to facilitate operations and marketing and sales, and so on. These activities each play a role in the development of the product or service that a particular organization is seeking to create. Therefore, quality should be checked during each of these actions.
Procurement is what the organization or company does to get the resources it needs in order to operate. Activities that qualify as procurement include finding vendors, negotiating prices, and so on. In a healthcare context, procurement could include sourcing technology, negotiating service fees, and deciding on where to purchase medical equipment.
Human Resource Management
As most people know, human resource management deals with how well a company recruits, motivates, rewards, and trains its employees. It also has to do with finding and hiring talent. Staff training, hiring and firing, disciplinary measures, and benefits all fall under human resource management activities.
In short, technology-related activities are those that relate to managing and processing information. Of course, technology plays an important role in almost every area of healthcare. From medical equipment to administrative systems and everything in between, technology is enmeshed with the healthcare system at every level.
Finally, Porter lists infrastructure as the final support activity. Infrastructure is the companyâ€™s support systems and includes the processes and functions that allow it to successfully operate on a day-to-day basis. Legal, administrative, accounting, and general management tasks all fall under infrastructure-related activities.
Direct, Indirect, and Quality Assurance
Direct activities are those that create value by themselves. Indirect activities are those that help other activities create value. Quality assurance activities are, of course, those that relate to ensuring quality at every stage in the process, including ensuring the quality of the final product or service.
Applying the Value Chain to Data Analysis
Similar to, and likely adopted from, Porterâ€™s value chain is the Big Data Value Chain as described by Currey, et al. Their value chain consists of five major functions: data acquisition, data analysis, data curation, data storage, and data usage. We might also include some of the pre-data collection activities, like asking clear research questions.
Asking questions is an important part of the data management process. Without a clear research question, itâ€™s nearly impossible to be efficient or successful in your data analysis project. While asking a clear question isnâ€™t necessarily related to a particular value chain activity, itâ€™s necessary to ensure thereâ€™s value at all and that the chain works the way itâ€™s supposed to. Just as you canâ€™t create a brand without knowing the company, product, and target audience, you canâ€™t build a value chain or conduct data analysis or data management without knowing what types of data youâ€™re looking for in the first place. Formulating a clear research question with objective and explicit goals is the foundation upon which the rest of your project is based.
Collecting data is most closely related to inbound logistics activities. Instead of receiving, storing, and distributing goods, youâ€™re receiving, categorizing, and organizing information so you can work with it more efficiently.
Analyzing data most closely resembles Porterâ€™s description of operations-related activities. If operations activities are defined as those actions that change inputs into outputs, analyzing data fits perfectly. Data analytics, when properly conducted, takes raw data and information and helps researchers and other healthcare professionals arrive at actionable conclusions that can be used to create products and services aimed at improved healthcare for the users or customers. In this way, data analysis is transformative and falls into the operations category.
Curating data is where a lot of the quality assurance happens, although quality assurance in itself should be an ongoing activity. Curation might also be concerned with activities like annotation, data validation, automation, and curation at scale.
Storing data is closely associated with tasks like inbound logistics, since storing supplies or materials — which in this context data is — exists in that umbrella of activities. Storing data is important because without having a sufficient way to store the data set youâ€™re working with, the entire process will be disorganized, inefficient, and ultimately unsuccessful. Ensuring security is important, as well, when storing data. NewSQL databases, NoSQL databases, cloud storage, and standardization activities also occur here.
Finally, this process is useless if youâ€™re not using the data youâ€™ve collected, analyzed, curated, and stored. Using data is most like the operations activities that porter described because itâ€™s where you take the â€œraw materialsâ€, or the raw data, and start to create your end product or service that will add value to the lives of the end user or consumer. Prediction, decision support, visualization, and modeling are all activities that fall under â€œdata useâ€ activities.
Support Activities in a Data Analysis Context
When it comes to the support activities mentioned in Porterâ€™s value chain, it stands to reason that, since most of them are organization-specific, only a few apply directly to data analysis. For instance, human resource management doesnâ€™t directly correlate to data analytics. However, a case could be made that managing the human resources involved in handling and working with the data is extremely important, and in this case it does apply, just not specifically the way Porter likely intended. The infrastructure of Porterâ€™s value chain would most closely relate to any of the functions that allow data analysis to occur. These are too varied to include here, but itâ€™s easy to see how data management and data analysis requires an infrastructure of sorts in order to maintain consistency, accuracy, and efficiency. Technology is obvious since data itself is technological in nature, and procurement could mean anything from obtaining the right devices to finding vendors for lab equipment or securing data storage solutions.
While Porter intended his value chain to be utilized in a business sense, we can extrapolate from that structure and apply it to data analysis, as well. Porter hoped that his value chain would create not only a checklist but also a framework within which businesses could ensure quality and create more efficient and higher quality products and services. As data analytics professionals and others who work with big data on a regular basis begin to adapt Porterâ€™s value chain to the digital world, more efficient systems for data management and implementation are being developed. Why bother? Because, as Rayport and Sviokla discussed in their 2000 article Exploiting the Virtual Value Chain, the world is becoming increasingly digital, and weâ€™re going to need systems of management, quality assurance, and productivity that help us become as efficient and successful in the digital realm as weâ€™ve become in the physical. Eighteen years later, the need for digital infrastructures and systems of value creation are even more necessary, opening the door for more variations and adaptations of Porterâ€™s value chain in the digital world.