What Is the Basis of Data Value?
Organizations can apply innovative data refinement to derive value from useful data that is used
Valuing data as an asset the same way experienced employees, real estate, or plants and equipment are valued as assets is old news. But what is the basis of data value? Is a high volume of data inherently more valuable than data available in small quantities? Is fast-moving data more valuable than data at rest? Is XML data better than data in a NoSQL database? Is data in the cloud worth more than data in an on-premises application? The answer to all of these questions is a resounding “no.”
Organizations are becoming more effective at handling increasing amounts of data, in different locations, in different forms, and at varying speeds. While obviously a variety of factors can affect the ease with which data can be accessed and put to work, the real value of data lies in its use.
As a result, the same information may have high value to one organization but low value to another organization in the same industry. The data can have a lot of value for a company poised to use it to make smarter business decisions or drive business operations. However, it can have reduced value to a company that takes note of it without actually making any strategic decisions or adjusting any business practices. If data use determines its value, then considering what can be done to make raw data in any form more usable and useful than ever before is worthwhile.
Tapping into useful data
Consider the data problem for business analysts today.1 They are trying to leverage data to understand their markets better than has previously been possible, to understand customer behavior, to improve operational efficiency, or to build enhanced products. But gaining access to data that already exists within an organization—not to mention the vast world of data that lives outside corporate walls—can be a formidable task. Information is tightly controlled by an IT group that typically has a long backlog of its own projects. Despite good intentions, IT simply cannot keep up with the demand for data.
Meanwhile, application developers face challenges similar to those of line-of-business users.2 Within the organizational structure, they may be closer to IT than business users are, but that position typically doesn’t give them a big advantage when it comes to data access. Application developers rarely have tools or services that enable easy, fast access to relevant data, so the applications they build may not properly integrate and govern the data on which the applications rely. Unfortunately, new applications that are not data aware require IT to intervene with integration and governance after the fact, which can add to the challenges of an already overworked IT group.
Within IT departments, there can be frustration when demands for data from across the organization exceed their capacity to respond.3 The emergence of shadow IT in functional areas around the organization to work around IT delays only compounds the problem. Providing self-service to business users and application developers may sound like a good idea to help relieve the backlog, but IT lacks easy ways to enable self-service approaches while also maintaining control over data access, quality, and security.
Working with self-service IT
Fortunately, meeting today’s data demands in a fresh, highly efficient way is now possible. The key is cloud-based data access and refinement services that make data available anytime, anywhere whether it is needed for vital business analysis or for day-to-day business operations. The availability of these services removes data access as a roadblock to projects across the enterprise.
Data refinement is the transformation of raw data into relevant and actionable information.4 In short, it makes data useful—thus increasing its potential value to the organization. To move data from useful to used—rendering it valuable—the organization needs to put the data to work, and business users in organizations across multiple industries are beginning to do exactly that.
A 2014 IBM Center for Applied Insights study found that Generation D organizations—an emerging class that is “data-rich and analytically driven”—are twice as likely as other enterprises to automate processes and decisions based on insights from analytics. In other words, they make data work, and using the data does make a difference. In the study, Generation D organizations report stronger results than other organizations across a range of key performance indicators (KPIs), including operating efficiently, managing risk, and developing new revenue streams.5
Accessing cloud-based data refinement services
The new IBM® DataWorks™ data refinery offers cloud-based data access and refinement services that help developers build data awareness into applications without slowing down delivery cycles.6 These readily available services can be built into any cloud computing application. The services are available as application programming interfaces (APIs) that developers can use to enrich their applications and improve the quality and usability of data that businesspeople receive.
Business users can benefit from applications that incorporate services from IBM DataWorks so that access to clean, appropriate data is built right into the applications. For example, the IBM Watson™ Analytics platform embeds services that enable end users to access data for analysis from within their analytics applications.7 In the process, IBM DataWorks selects the appropriate data and resolves anomalies. The cleaned-up data is immediately presented to end users, who do not need to know the details of how their application works.
Developers of other applications can take advantage of the same toolset on the IBM Bluemix™ platform, which offers IBM DataWorks in a platform-as-a-service (PaaS) implementation with functions that can be called up as part of any process.8 This advanced approach not only empowers business users and application developers, but also helps free IT from an endless series of routine data access requests from line-of-business users. IT can focus on high-value activities such as solving complex problems, creating self-service environments in which appropriate governance is automatic, and undertaking new initiatives that help grow the business.
Using useful data
Refinement services can provide easy access to data, bring data into alignment with standards, filter master data from inconsistent or overlapping alternatives, and protect personally identifiable information. What other similar services may deliver high value to an organization by making data more useful to business users than was possible previously?
Please share any thoughts about this or other questions in the comments.
1 “Data Refinement: Invisible to Business Analysts,” Data Refinement website at ibm.com.
2 “Data Refinement for Applications in the Cloud,” Data Refinement website at ibm.com.
3 “Cloud-Based Data Access and Refinement,” Data Refinement website at ibm.com.
4 “What Is Data Refinement?” Data Refinement website at ibm.com.
5 “Meet Generation D: Data-Rich, Analytically Driven Enterprises,” IBM Center for Applied Insights report, October 2014.
6 “Data Refinement Products: Introducing IBM DataWorks,” Data Refinement website at ibm.com.
7 IBM Watson Analytics website at ibm.com.
8 IBM Bluemix website at ibm.com.
- “Declaring Data Independence,” by Paula Wiles Sigmon, Big Data & Analytics Hub blog, September 2014.
- “It’s Time to Evolve the Way the World Works with Data,” Big Data & Analytics Hub data refinement infographic.
- “From IT Drudgery to IT Freedom,” by Paula Wiles Sigmon, Big Data & Analytics Hub blog, December 2014.
- “Revolutionizing the Business Analyst Experience,” IBM Software solution brief, October 2014.
- “Revolutionizing the Data Experience for IT,” IBM Software solution brief, October 2014.
- “Revolutionizing the Developer Experience,” IBM Software solution brief, October 2014.
- “Taking a More Refined Approach to Big Data,” by David Corrigan, Big Data & Analytics Hub blog, November 2014.