Data Profit vs. Data Waste
Real World Information Optimization

Imagine a factory where large amounts of raw materials lie unused and strewn about at various points along the assembly line. Imagine, too, that the factory’s manager allowed large numbers of finished goods to sit on the loading dock indefinitely without ever being shipped to customers.

It’s hard to imagine such a situation being tolerated in any modern production facility. Yet very similar conditions exist in most corporate data environments. Massive amounts of potentially useful data remain unused and strewn about the enterprise.  And information that could be potentially quite useful to end-users across the company never arrives on their desktops.

This is a fairly recent phenomenon, and four things have occurred over the past 10 to 15 years to create it:

1. There are more disparate systems in place now than ever before: System “sprawl” has fragmented the enterprise environment, making it increasingly difficult for users to access the data they need at any given moment, and more challenging to successfully aggregate related, relevant data from all systems;

2. Systems are being used in richer, more diverse ways: CRM systems, for example, may now include customer feedback on product quality, as well as unstructured “notes” fields that describe events of some importance. The increasing diversity of this data further fuels data waste, in part because the host systems were not designed to extract value from unstructured data;

3. Total data volume has grown exponentially: The sheer volume of data scattered across the enterprise has reached massive proportions. This has dramatically driven up the scale of the data waste problem; and

4. The volume of unstructured data has grown especially fast: Unstructured data—in documents, in emails, Web pages and audio/video files—is growing faster than structured data. IDC predicts a compound annual growth rate of 61.7% for unstructured data—far outpacing the 21.8% growth projected for transactional data. This growing volume of unstructured data holds tremendous value to business, yet it is much more prone to waste than structured data residing in database-driven applications.

The Effects of Data Waste
Businesses depend more and more on information to succeed. So the more data they waste, the more they compromise their performance. Key areas of concern include:

• Missed opportunities—Companies discover opportunities through exposure to diverse types of data: industry news, market research, feedback from customers and salespeople, sales trends, etc. If this information doesn’t get to the right people at the right time, companies can miss these opportunities and cede them to better-informed competitors.
• Weaker customer relationships—Salespeople, account managers and other customer-facing staff can more effectively serve customers when they have access to all relevant data—whether that data is a bit of corporate intelligence someone picked up via email or a major article in last week’s Wall Street Journal. Poor use of this data can therefore have an ongoing, adverse impact on a company’s current and long-term relationships with its customers.
• Poor decisions—Executives are constantly called upon to make decisions about markets, personnel, resource allocation, technologies and partnerships. They don’t always need more data to make better decisions—but they always need the right data. If they can’t find that data or don’t even know it exists, their decision-making will suffer.
• Legal and regulatory risk—The ability to mitigate exposure to legal and regulatory risks is largely contingent upon ready access to all data relevant to those risks. Both the courts and regulatory agencies have a very high expectation that companies will have, use and be able to produce data about all types of business processes and events. Companies that suffer from data waste do not adequately use and cannot readily provide the data that they actually have.
• Higher operating costs—Companies that waste data spend money they don’t need to spend. They buy reports that have already been purchased by someone in another division or location. Their employees make 20 phone calls looking for the answer to a question that could have been found with a few keystrokes.

Data Profit
The opposite of data waste is data profit. Data profit occurs when a company starts to consistently extract the full potential business value from the data assets dispersed across the organization—and when it begins to successfully minimize instances of data waste. Data profit is not an abstract or esoteric concept. It is an extremely pragmatic principle that has substantial impact on day-to-day business performance. These real-world examples illustrate how companies benefit from data profit:

A leading healthcare provider: A leading healthcare provider substantially enhanced its compliance position by automating the capture of new compliance-related data from all sources—including internal memos, Web page updates and regulatory bulletins—as well as the selective distribution of that data to targeted staff members. This significantly reduced the company’s exposure to fines, injunctions and other compliance-related risks, and allowed the company to reduce its knowledge worker headcount by 140 FTEs, resulting in labor savings of $11.2 million annually.

A leading aircraft manufacturer: A leading aircraft manufacturer implemented a portal that gave its employees vastly improved visibility into all unstructured data relating to the supply chain for a major new model. Supply-chain managers were able to retrieve critical data from spec sheets, bills of lading and other documents rapidly, allowing them to avoid delivery delays and saving the company millions. The company also estimated that it was able to reduce knowledge worker headcount by 9% and realize another $1 million savings on the printing, distribution and storage of paper documents alone.

A leading market analysis firm: A leading market analysis firm was able to cut its R&D spending 22% annually by making better use of existing research. The firm was also able to cut the cost of training new knowledge workers in half by creating complete, concise packages of all unstructured data assets on the topics relevant to their specific roles and responsibilities.

A leading supplier of electrical power management solutions: A leading supplier of electrical power management solutions faced a particularly challenging data waste problem: its unstructured data included both collaboration/brainstorming documents and authoritative content on products, business controls and operational standards. By segmenting the two types of data and making them accessible in the proper context, the company was able to accelerate time-to-market, avoid repeatedly “reinventing the wheel” and better maintain consistent standards across business units.

Information Optimization
Companies minimize data waste and achieve data profit through information optimization—a set of technology-enabled disciplines that enable users across the organization to make optimum use of all structured and unstructured data assets that are relevant to whatever business challenge is at hand. Information optimization can be generally understood in terms of three primary disciplines:

1. Access to structured and unstructured data. To use data effectively, people obviously have to be able to get access to it. So any successful information optimization solution has to include a robust set of data access mechanisms. Search is a core element of this essential data access. Users must be able to easily find relevant data regardless of where it resides and what form it is in.