The data that our businesses own has become the name of the game when it comes to what ultimately lies at the core of economic value today, now seemingly more than ever before.
We appear to be entering the era of the knowledge economy, where most of what we do in business (about 50% of where the value in developed countries comes from) is tied to the creation and management of knowledge. Thus, data -- especially the actionable knowledge derived from it -- has now, at least on average, become the dominant supply-chain component of whatever products and services we provide to the marketplace.
One way of thinking about it is considering what would happen if we lose the software that we currently use to run our businesses. Chances are pretty good that we'll be able to find new applications to replace the old ones. But lose your data and you'd soon go out of business. Data has become the economic lifeblood of our organizations, whether that's intellectual property, critical methods, business records, operational data, and especially the higher-order knowledge that drives an organization's strategic activities (both explicit and tacit knowledge).
Major Trends in Data-Centricity
The infrastructure we use to create and manage data has transformed considerably this century, even as new ones emerge. I will remind you of at least three major examples of how the way we look at data has changed -- including how it's created, collected, exchanged, managed, and consumed -- in the last ten years:
First was the the advent of service-oriented architecture (or SOA), whose purpose was to meaningfully interoperate, trade, and reuse data between IT systems and trading partners. This has had varying success but it remains the dominant mode for data integration.
Second was the rise of Web 2.0, where user-generated content and peer production systems often proved to be (often by far) the richest source of data -- new or existing -- that most businesses could create and tap into.
And third has been the recent return of master data management (MDM) as mergers and acquisitions created countless new data silos and new IT systems continued proliferate. The amount of content and raw data that organizations are sitting on is now a major issue requiring a dedicated discipline, even if it's largely nascent and piecemeal in most organizations today.
There are other drivers of the increasing focus on data creation and consumption as well, but these are some of the leading ones. So as data itself has become the central asset of our organizations, what we seem to be learning is that we still focus far too much on the creation and capture of it, and too little on actively leveraging it and driving consumption.
A good data-centricity litmus test: When was the last time you tried to SEO (search engine optimize) a piece of enterprise data? Never is probably the answer and that's a key part of the problem. I'd put it to you that the 21st century data-centric organization will make data accessibility and discovery one of their top priorities.
Eight Key Insights for Data-Centric Organizations
Here then are some important things that organizations should keep front of mind if they want to capitalize on and succeed in the new knowledge economy:
- Open business models produce higher levels of value creation. With many of the early lessons coming from open source, but also the scientific community with open data, open business models cost-effectively marshal untapped resources from interested parties to drive productive outputs. These new data production models are only possible because of advent of 1) a fundamentally pervasive, cheap global network and 2) mature interaction techniques that actively enable and elicit participation. Just one example: I recently covered Innocentive as a leading example of this in my recent exploration of the rise of sophisticated, 2nd generation crowdsourcing. Another good example Jigsaw vs. Dun and Bradstreet, the first uses an open business model, and the 2nd doesn't (chart). The bottom line is the best inputs (and therefore data) will often come from where you least expect it and usually over the network (Web or intranet). The lesson: We are now augmenting centralize production of data with peer production and it creates more value.
- Inviting and incorporating inputs from outside, early and often, creates the best data. We learned this with agile development processes and we're starting to learn this with businesses processes in general. The more feedback loops we have and the earlier we have them allows more accurate and useful course correction. Social computing, especially with Enterprise 2.0, is a simple and effective way of opening existing processes and turning all business activities into agile activities. This leads naturally to social business processes that allows anyone that's interested or has a stake to comment, critique, and participate in business activities. To be sure, it's a major cultural shift for many workers and business leaders but an increasingly natural one as more and more daily activities, both personal and professional, turn social. Sameer Patel has some interesting examples of this both internally and externally to organizations from Chevron and Nike. More formally, we call this peer production.
- Building and maintaining control of a hard-to-recreate dataset confers the strategic upper hand. This was the signature lesson of the Web 2.0 era: The new golden rule is the organization that has the best data makes the rules. And has the market leading position. It's simple economics enabled by the same cheap, pervasive global networks: Why use the second best source of data, when you can just as easily get the best? Today's market leading (and data-centric) organizations deeply understand the strategic value of the data that they are paying a fortune to accumulate. They don't let them lie fallow in under-used data silos or locked up behind the firewall (most business data you can leverage in this way, even if it's just aggregated). Instead, they exploit them as high value products in and of themselves, exposing them via open supply chains (Web APIs) and other strategic means that include tightly integrated business models.
- Richer outcomes occur when data consumption is balanced with creation. Underutilization of the data a business owns today means significant market underperformance. Our vital business data is submerged in IT systems and databases when instead it should be available across an organization and often from the greater marketplace. Investment in data creation, storage, and management is not being met by improvements in access and consumption, directly resulting in driving down the actual ROI of IT systems and beneficial business outcomes. Data must be discoverable, reachable, and consumable for it to have any real, long-term use to the business. Data-centric businesses will lower the barrier to consumption as far as possible while meeting critical requirements such as security, scalability, and where appropriate, integrity (such as the classical ACID requirements.)
- Collaboration based on social computing models can generate order of magnitude increases in results. This seems like a bold statement but the math supports it and growing evidence in the field validates it. Central to this concept is the creating of network effects. Social tools deliver this in particular by taking advantage of Reed's Law, peer production, self-service, and other existing or recently understood power laws for network-based systems. An order of magnitude sounds unrealistic until you look at the results out on the Web or at case studies in early adoptor organizations. In my previous post I talked about the information explosion that can result from persistent, open collaboration and how data-centric organizations will have to develop competencies around such information abundance.
- Data-as-a-Service is the most powerful business model of all the types of"as-a-service". Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) are two potent models for delivering capabilities over the network and they form an important element of a larger and even more strategic discussion, cloud computing. Unfortunately less well known, Data-as-a-Service (DaaS) is, however, likely the most strategic aspect of creating business value over the network, more than SaaS and possibly even more than PaaS. Creating a best-of-breed set of data, wrapping a business model around it (advertising, metering, internal chargebacks, build a network effect, etc), defining an SLA, and opening it up internally or to the world is how to both generate consumption as well as becoming in itself the new lock-in. While visions of getting internal departments and/or external businesses "hooked" on your data can appear sinister at first, I would note that all business must have a healthy relationship with their suppliers, including sourcing the data they need to function. In the knowledge economy, whoever has the best data will end up with the best customers and can eventually become uncatchable in the market because the best customers also co-create and build yet more data (product), creating a self-perpetuating cycle.
- Those who understand that ease-of-consumption is the initial trigger for all downstream outcomes of data-centric businesses will have a head start. Data-centricity is just lip service if business data isn't actively made accessible and consumable. The more time and resources are required to reach the data, the more that alternatives will be sought, driving duplication in both effort and data. Remember that networks route around impedance (censorship, poor interoperability, difficulty of integration) naturally. We've learned a lot about how to make data highly consumable and discoverable via networks and Web-Oriented Architecture provides some of the most informed perspective on this.
- Get your "clock" started. You can get a late start to data-centricity, but the later you start the less likely you will ever catch up. The new sustainable competitive advantage is owning and providing strategic access to the best set of data. Using all the tools at an organization's disposal (central production, peer production, sensors, etc) to generate data continuously drives a dataset to grow in a virtuous cycle of creating more data, which drives more participation, which creates more data, and so on. Who has the best data will have an inordinate effect in a nearly frictionless knowledge economy in terms of marketshare to the extent that your network effect is the new marketshare. Not sure about this? Just look at popularity of open data sources for mapping data, ads, classifieds, auction (eBay), customer data (Jigsaw or Salesforce), etc.
It is true that not every organization will be dominated by the data that they own. Certain industries have much more emphasis on capital assets that they do in knowledge, but even these organizations, data is still growing into one of their largest sources of value across the business. So while software and physical IT as well as the physical ability of organizations are all important, in the end the pre-eminence of data has becoming the driving force in modern business.
Important Note: I don't want to de-emphasize the importance of people in this view of business value. But I also recognize that they are ultimately a temporary, transient resource in a business and are obviously not an "owned" by it. In this way, while effective workers are what makes it all work operationally, data very much is a genuine, tangible, and long-term strategic asset of businesses today.
Related: David Linthicum's Why Web API Directories Drive Data-as-a-Service
Data-centricity is still an under appreciated topic. Are you seeing a rising appreciation of the strategic benefits of data ownership and new consumption scenarios like network APIs?