Oracle's Modern Data Strategy

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.

Shining A Light On The Hidden Data Economy

DATA IS CAPITAL

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.

SEPARATE DATA ECONOMIES INSIDE YOUR ORGANIZATION

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.

Command Data Economy

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.

Informal Data Economy

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.

Market Data 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?

Why does this matter to you? Because all three of these data economies are operating in your firm, usually separately.









DATA STRATEGY REINFORCES
COMPETITIVE STRATEGY


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.

TYING EVERYTHING TOGETHER


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?

BUILDING BLOCKS OF DATA STRATEGY


Domain ownership

Distributed ownership with teams close to the business domains responsible for creation or use

Variety of data product types

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

Active-data that does something

Analytics - historical/real-time reports & dashboards
Algorithms - ML models, scoring, business rules
Data Services - docs, payloads, topics, authorization

Variety of implementations

Data products may be implemented using databases, lakes, and warehouses

Multiple quality dimensions

Quality based on lineage
Quality relative to each job to be done

A TEXTBOOK EXAMPLE


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:

  • - Limited passenger service
  • - Short-haul, point-to-point routes between midsize cities and secondary airports
  • - Very low ticket prices
  • - High aircraft utilization
  • - Lean, highly productive ground and gate crews
  • - Frequent, reliable departures


Southwest Airlines used data to answer a tpyical business problem: Can we use predictive maintenance to get to 10-minute turnarounds?

  • - Sensor data from engines, subsystems
  • - Operational data - aircraft utilization, repair, maintenance, arrivals, and departures
  • - Anonymize, mask employee (crew, engineers) records
  • - Monitor model for performance as planes ages


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.

A UNIFIED DATA ECONOMY REQUIRES A NEW DATA BACKBONE


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.

Learn more about building a new data architecture in the cloud





Shining a Light on the Hidden Data Economy

Paul Sonderegger
Senior Data Strategist


Considering enterprise data regarding supply, demand, and transaction costs enable companies to craft more effective data strategies and increase return on data capital. Join us to learn how the unique capabilities of Oracle technology can put these ideas into action.


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Automating Data Management for All

Andy Mendelsohn
Executive Vice President, Database Server Technologies


Oracle Autonomous Database has simplified the management of operational and analytical database workloads. This keynote discusses how the innovation continues to improve analyst, data scientist, and developer productivity.


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Rethinking Database Architecture to Embrace Modern App Dev

Juan Loaiza
Executive Vice President
Mission Critical Database Technologies


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Oracle Database: What's New, What's Next

Jenny Tsai-Smith
Vice President, Product Management


Learn about recent Oracle Database news and developments and get a sneak peek of what's coming next from the Oracle Database development team.


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Oracle Cloud Infrastructure GoldenGate: Elastic, Real-Time Data Integration Service

Denis Gray
Senior Director


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Oracle Autonomous Database: Simplify Your Database Management

Yasin Baskin
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All About Graphs in Oracle Database

Melliyal Annamalai
Distinguished Product Manager
Spatial and Graph


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Oracle Autonomous Data Warehouse and the Data Lake

Marty Gubar
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Big Data


Learn how to easily combine insights from your data lake and your data warehouse. Use Oracle Cloud Infrastructure Data Catalog to manage, search for, and understand all your data assets. Then, use Oracle Autonomous Database to analyze those assets at scale with no additional management.


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Self-Service Data Warehousing

Patrick Wheeler
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Streamlining Security for Your Databases with Oracle Data Safe

Bettina Schaeumer
Senior Principal Product Manager, Database Security


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In-Database Machine Learning: Automate, Scale, Deploy

Mark Hornick
Senior Director, Data Science / Machine Learning


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Superfast Event Stream Processing with a Converged Database

Tirthankar Lahiri
Senior Vice President
Data and In-Memory Technologies


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Oracle Exadata: New Features and Functionality

Kothanda (Kodi) Umamageswaran
Senior Vice President
Exadata Development
Subban Raghunathan
Vice President


Oracle Exadata Database Machine continues to pull away from the competition. Join Oracle product management experts for a discussion on the latest innovations and technologies used to make Oracle Exadata the world's fastest Oracle Database platform.


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Futuristic SQL: Making the impossible possible

Connor McDonald
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Oracle Database


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Get into the Zone with Spatial in Oracle Autonomous Database

David Lapp
Product Manager
Oracle Spatial


The integration of maps and locations are expected in modern applications. How far is the nearest flood zone? Which assets are in a projected storm path? Learn how Spatial, a feature of Oracle Autonomous Database, answers location-based questions.


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All about spatial in Oracle Database

David Lapp
Product Manager
Oracle Spatial


As part of Oracle's converged database, developers can use Oracle tools, APIs, and SQL to add maps and location analysis in any application. Learn about these industry-leading capabilities and see new, easy-to-use Spatial Studio in Oracle Database.


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You Have a Machine Learning Model. Now What?

Marcos Arancibia
Senior Principal Product Manager,
Oracle Machine Learning


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Automating the Machine Learning Modeling Process

Mark Hornick
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Oracle Essbase Integration with Oracle Autonomous Data Warehouse

Ashish Jain
Senior Product Manager,
Essbase and Autonomous Database


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Collect, Connect and Correct: Data Integration in Autonomous Database

Jayant Mahto
Senior Principal Product Manager,
Oracle Autonomous Database
Bud Endress
Senior Principal Product Manager,
Oracle Autonomous Database

Learn how Oracle Data Warehouse's Data Load and Transform tools make loading data from various apps, databases, and files a breeze. Quickly and easily profile, repair, and prepare your data for analysis and insights with these built-in tools.


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Introducing Oracle Data Integrator Web Edition

Jayant Mahto
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Oracle Autonomous Database


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Operational Data Lake for Capacity Planning and Analytics

Sriram Vrinda
Product Management
Observability and Management Group


Curate a data lake of telemetry from Oracle's Automatic Workload Repository (AWR), Enterprise Manager, Exadata, and more. Then, use OCI Operations Insights for long-term AWR and SQL performance analysis, capacity planning, and forecasting.


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Oracle Autonomous Database High Availability and Disaster Recovery

Nilay Panchal
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Oracle Autonomous Database


Peek under the hood at the highly available infrastructure that powers Oracle Autonomous Database while learning to shield your users from planned outages using Application Continuity, and unplanned, unpredictable outages with Autonomous Data Guard.


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Top Tips for Security and Networking in Oracle Autonomous Database

Can Tuzla
Principal Product Manager
Oracle Autonomous Database


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Database Activity Monitoring Made Simpler

Angeline Janet Dhanarani
Principal Product Manager
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Oracle Database provides the industry's most comprehensive auditing capability. Learn to create effective audit policies for monitoring database activity that are selective, targeted to your needs, and supportive of reliable threat detection.


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Hands-on labs - explore the building blocks for a new data architecture



Important Tools for Everyone Using Oracle Autonomous Database Tools

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Boost Analytics Performance with Oracle Database In-Memory

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Getting Started with Oracle Autonomous Database

Keith Laker
Senior Principal Product Manager

Nilay Panchal
Principal Product Manager


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Oracle Cloud Data Lake House

Scott Wiesner
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Massimo Castelli
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Get Started with Graph Analytics on Oracle Autonomous Database

Melliyal Annamalai
Distinguished Product Manager


Learn how Graph Studio makes it easy to explore relationships and discover connections in data to detect fraud or hidden dependencies. Get acquainted with its support for graph structures, comprehensive analytics, and intuitive


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Oracle Data Integrator - Basics Workshop

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Database Security - Unified Auditing Workshop

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Principal Product Manager


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Introduction to Oracle Spatial Workshop

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Python Users: Build intelligent applications faster with Oracle Machine Learning Workshop

Mark Hornick Lapp
Senior Director, Data Science / Machine Learning


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Oracle Data Safe Fundamentals Workshop

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Send Data from Oracle GoldenGate to OCI GoldenGate Workshop

Denis Gray
Senior Director


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Watch the Hidden Data Economy session from Oracle Database World



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