Considering enterprise data in terms of supply, demand, and transaction costs enables companies to craft more effective data strategies and increase return on data capital.
Learn how the unique capabilities of Oracle technology can put these ideas into action.
Data is a kind of capital. This isn't a metaphor like data is the new oil or data is the new gold. Data fulfills the literal, economic textbook definition of capital.
Capital is a produced good, not a natural resource. You have to invest to create it, not just dig it out of the ground. More importantly, capital is a necessary input into other goods and services. It's what economists call a factor of production. Without that factor, you can't make the product or deliver the service you have in mind.
For example, if you design a skyscraper but lack the financial capital to pay for its construction, you can't build it. Similarly, you can't deliver that service if you create a fraud detection algorithm but lack the data to feed it.
But, the way it creates value in your firm is through a data economy, hiding in plain sight inside your company.
This hidden data economy has a supply-side where applications, sensors, and devices originate data in a wide variety of shapes and structures. And it has a demand-side where analytics and AI try to use that data in a wide variety of shapes and models.
The data economy in your organization most likely started as a supply-driven command data economy. The purpose of enterprise applications was to execute business processes. They created data as a side-effect. Some of that data would be packaged up for pre-determined questions in the form of reports and dashboards. This was the dominant internal data economy until the mid-2000s.
But this left a lot of questions unanswered on the business side. This latent demand led to an informal data economy. Departmental IT staff created data stocks of their own, sometimes extracted from central systems. Departmental business analysts bought their own analytical tools to build new interactive dashboards and exploratory environments to satisfy the latent demand.
At the same time, new sources of supply emerged as company websites and mobile apps increased, providing the informal data economy with a diversified pool of data, enabling them to create new value outside the command data economy.
Through the 2010s, corporate IT attempted to respond by creating places to pool large amounts of diverse data and sanctioning a growing array of different analytical tools and frameworks to work on it. But this remained a largely informal data economy alongside the command economy.
Meanwhile, out on the Internet, something completely different happened. A market data economy arose. This market data economy was the polar opposite of the command economy in two critical ways. First, it completely decentralized both data supply and demand. The proliferation of websites, mobile apps, and internet services led to an explosion in available data, and APIs led to a chaotic blossoming of new uses of that data. Second, the market data economy blurred the distinction between supply and demand, between points of creation and points of use. Embedding analytics and AI into internet apps and services meant that using data created new data, driving a flywheel effect and, in some cases, a winner-take-all outcome.
Why does this matter to you? Because all three of these data economies are operating in your firm, usually separately.
Let us take a big step back. The key is a data strategy that reinforces competitive strategy.
And to define strategy, we turn to Michael Porter, who is the godfather of competitive strategy. Porter described strategy as "creating unique value in a unique way."
It's not enough to create value your customer can only get from you. You have to deliver this value in a way your rivals cannot easily copy.
A firm's data strategy should reinforce its competitive strategy. Data strategy comes down to creating unique data assets and using them to enhance your firm's uniqueness. The way you use proprietary data assets should strengthen your differentiation, improve your cost position relative to rivals, or both.
But there is one more piece to a successful data strategy. It must also protect the observer, usually the company itself, and the observed, including customers, partners, and employees.
Because companies must balance data innovation with data compliance, any data strategy must consider the constraints of legislation, regulation, business ethics, and cultural norms when planning what data to capture and how to use it.
But how do you plan a data strategy that reinforces competitive strategy? The key is to examine the firm's activities.
Everything a firm does consists of activities. The firm's activities are an organization designs its products or services, the way it markets them, distributes them, and services them. Sustainable competitive strategy comes from creating a unique system of activities.
Use data to enhance uniqueness. Activities unique to you. Linkages among activities. Using data to make data
Create unique data assets. Activities special to you. Interactions with customers and partners where you're competing for data. Capturing more attributes, not just more instances
Protect observer and observed. Gather consent where necessary. Profile, aggregate, and anonymize. Control, track, and audit
So, how can a data strategy reinforce this competitive strategy?
Distributed ownership with teams close to the business domains responsible for creation or use
Passive-data that is something
Data Sets - data collections in different shapes/formats
Models - domain objects, data models, ML features
Libraries - inert algorithms, technical definitions of business semantics
Analytics - historical/real-time reports & dashboards
Algorithms - ML models, scoring, business rules
Data Services - docs, payloads, topics, authorization
Quality based on lineage
Quality relative to each job to be done
Take Southwest Airlines in the late 1990s. Southwest was a low-cost, no-frills, short-haul carrier that shook up the airline industry. A unique set of interconnected activities allowed them to stake out a profitable position. Porter summarized the core activities in their system like this:
Southwest Airlines used data to answer a tpyical business problem: Can we use predictive maintenance to get to 10-minute turnarounds?
These activities fit together in a unique system that made Southwest's tagline, "Southwest, the low-fare airline." not just a marketing slogan, but a defensible strategic position in a highly competitive industry.
To create a unified data economy, enterprises need a new data backbone. You need an enterprise architecture that helps you shift more activity into your market data economy, connect to critical back-office and financial data assets from your command data economy and bring the informal data economy into the fold.
Ease of data reuse and recombination
(see note 1)
Value created as a result of data usage per unit of work, dollar invested or resource consumed
Protections against external and internal threats
Assurances of data quality, compliance, ethics
Notes
1. https://cisr.mit.edu/publication/2021_0501_DataLiquidity_WixomPiccoli
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