Business and IT Collaboration: Essential for Big Data Information Governance
Well-informed business decisions depend on trustworthy information obtained from agile, automated integration and governance
Business leaders are eager to harness the power of big data. However, as the opportunity increases, it becomes exponentially more difficult for IT teams to ensure that source information is trustworthy and protected. If this trustworthiness issue is not addressed directly, business users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats.
Take for example the case of a large multinational corporation whose CFO asked a very simple question every month: How did our sales this month compare to the same month last year? Sales were calculated and reported in various systems: material planning, financial, rent and royalty, real estate, and so forth. In each case, a different number was reported. The reason is that each system defined a sale slightly differently, and therefore extracted different data from the millions of daily transactions generated around the world. So, what was the truth? No one knew.
The sheer volume and complexity of big data means that the traditional method of discovering, governing, and correcting information using manual stewardship may not apply. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost their confidence in big data.
A solid information integration and governance program should include automated discovery, profiling, and understanding of diverse data sets to provide context and enable employees to make informed decisions. The program must be agile to accommodate a wide variety of data, and it should seamlessly integrate with diverse technologies, from data marts to Apache Hadoop systems. Plus, it must automatically discover, protect, and monitor sensitive information as part of big data applications.
Big data, big opportunities
Big data presents significant opportunities for increased growth and profitability—and forward-thinking organizations are already realizing some of these benefits:
- A retailer actively uses social media data to analyze customer sentiment and improve loyalty, helping to drive revenue growth.
- A transportation company uses machine data to optimize logistics, thereby reducing shipping costs.
- A telecommunications company slashes developer costs by over 50 percent by articulating clear terms and policies for the data it needed.
- A manufacturing company enhances the trustworthiness of its reports after discovering and eliminating 37 unique definitions of customer across its enterprise and agrees to a single, standard definition.
Capturing these sorts of benefits from big data requires knowing what the business needs and being able to find key items within the larger mass of big data. The first step is to articulate the goals of the business; then, determine the analytics and reports necessary to support those business objectives. Finally, decisions can be made about which data is needed to inform the reports.
Standard terminology is a critical piece of this process. For example, there should be no misunderstanding about what constitutes a sale or what customer means. From standard terms, organizations can create standard policies such as defining the data that composes a complete customer record.
Armed with objectives and standard terms and policies, teams can create data processing flows that automate the extraction of relevant data from the mass of big data. Importantly, this understanding also includes knowing at all times where data is coming from, how it is being manipulated, and where it is going. This chain of understanding builds trust into information—and promotes confident decision making.
Collaboration, understanding lead to trustworthy information
Successful businesses depend on trusted information. IBM® InfoSphere® Business Information Exchange helps organizations create an understanding of big data that enables it to be converted into trusted information. It empowers business users to play an active role in information-centric projects and to collaborate with technical teams—all without the need for technical training. Decisions are enhanced by more accurate information, and business opportunities can be readily captured. The result is an organization with a consistent understanding of information: what it means, how it is used, and why it can be trusted.
To help individuals across the organization reach a shared understanding of key terms, InfoSphere Business Information Exchange includes a business glossary that enables business and IT to create and agree on definitions, rules, and policies. InfoSphere Business Information Exchange also includes data modeling capabilities that allow data architects to determine where each piece of data will come from and where it will go. These capabilities help ensure that everyone involved in the big data project knows exactly what key metrics mean and where the data should originate. In this way, organizations can establish the truth as it relates to their business data.
The data quality and data governance capabilities of InfoSphere Business Information Exchange help companies know for certain that their data is good, trustworthy, and true. That knowledge enables them to be increasingly confident in the results of their analytics activities and to take action quickly and assertively. When they have a solid foundation of good data, business leaders can build an organization that is highly flexible, agile, and well positioned to identify and capitalize on business opportunities.
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