Three Steps to Trusting Your Data in 2011

Arvind Krishna
In the business world, data quality problems can be the difference between success and failure.

As retail and manufacturing businesses expand their channels for reaching consumers and sourcing products, global economic complexity is making the maintenance of high levels of data accuracy a growing challenge. Data problems can lead to lost revenues and market share, reduced profits and customer dissatisfaction. For professionals responsible for the management of product data, maintaining accurate data is an imperative.

In health care, poor data quality can also create confusion by duplicating patient entries in a hospital IT system. Incorrect or mixed medical records can result in inefficiencies, duplicate testing or even more dangerous results such as drug interactions.

As more focus is placed on Big Data for 2011, problems with the quality of that data pose an increasing threat to organizations concerning their ability to support business processes, comply with regulations and make accurate and informed decisions. In fact, bad data can spread quickly like a virus from master data management (MDM) systems to databases, applications and beyond through the supply chain of information.

Over the years, more companies and government organizations have learned that data quality is key, but they are often not sure how to achieve it and have not evolved their processes, policies and infrastructure to be able to ensure high data quality levels.

Yes, battling data quality issues can seem like a daunting task, but there is a solution. Detailed below are three steps executives should take in 2011 to ensure that high levels of data quality are achieved and so that their data can be trusted: practicing effective information governance, establishing a unified management foundation and implementing proven technologies.  

Practice Effective Information Governance: Effective information governance can enhance the quality, availability and integrity of a company's critical data. Organizations are beginning to adopt information governance-a quality-control discipline for managing, using, improving and protecting information. It fosters cross-organizational collaboration and structured policy-making and balances factional silos directly impacting the four factors that an organization cares about most: increasing revenue, lowering costs, reducing risks and increasing data confidence.

Information governance is a holistic approach to managing and leveraging information for business benefits and encompasses information quality, information protection and information life cycle management. With information governance, organizations achieve many goals, from improving decision making to simplifying and strengthening regulatory compliance.

The success of an information governance program and supporting data quality initiatives hinges upon a robust data integration technology infrastructure. The platform needs to leverage information across all of its sources and deliver the functions required to integrate, validate and deliver trusted information for key business initiatives. It should enable organizations to:

Define a common business language for the information.

Understand all sources of information within the business, analyzing its usage, quality and relationships.

Cleanse and standardize and continuously monitor information to assure its quality and consistency.

Transform information to provide enriched and tailored information.

Federate and deliver information to make it transparently accessible to people, processes and applications.

Establish a Unified Management Foundation: Once an information governance program has been implemented, a unified MDM foundation is required. MDM enables the seamless sharing of knowledge throughout the organization, and a detailed understanding of what the information means, where it comes from and how it relates to information in other systems.

Organizations such as a hospital, bank or manufacturer can easily centralize and manage multiple data domains across a wide set of business requirements, wherever trusted data is required delivering a single version of truth of an organization's critical data entities-patient, customer, product, supplier and more-helping them make better decisions and achieve better business outcomes.

For example, in a health care ecosystem, the ability to accurately identify individual patients, clinicians and facilities is critical to enabling accurate and appropriate data exchange. With MDM, key medical information such as patient demographics, allergies, clinical diagnoses, medication history, radiology reports, laboratory investigations and discharge summaries can be exchangeable among health care providers. Patients benefit from proper, right-sited disease management and cost savings, as duplicate or unnecessary tests are eliminated and medication errors are reduced.

Implement Proven Technologies: Before you can implement an information governance program or information-centric project, you must know what data you have, where it is located and how it relates between systems. The entire data discovery process and analysis can be automated so it is less time-consuming and error-prone. This establishes an understanding of your data sources and how they relate to each other, generating actionable output that can be immediately consumed by a wide range of information projects including archiving, test data management, data privacy, data integration, MDM and data consolidation.

A comprehensive process to maintain data quality will include these capabilities:

Investigation: Understand the nature and extent of data anomalies and enable more effective cleansing and matching.

Standardization: Create a standardized view of data, whether it's customer, partner or product information.

Matching: A matching engine helps ensure the best match results possible, usually built on a platform enabled for high connectivity and scalability.

Survivorship: Helps ensure the ideal consolidated view of record information-accurate outlook of customers, partners, products and more.

Companies have learned that the smallest data-related mistake can have a negative impact on decision making. The effects of poor data quality are detrimental to a business's operations resulting in lower productivity and wasted materials, and lost, inaccurate or incomplete information can generate higher costs and extra work, such as hunting down information or additional reconciliation. For these reasons, most organizations today recognize the value of their data, the importance of having data they can trust and the need to develop a systematic approach to managing its use.  

By implementing information governance policies around data quality with the proper infrastructure in 2011, greater insight for businesses can be gained without adding any real operational costs. It helps organizations derive more value from high-quality and trusted data that's spread across networks and systems, and allows key decision makers to access and use information in new ways essentially driving innovation, helping to increase operational efficiency and helping to lower risk.

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Jun 7, 2011 6:06 AM Office 2007 Office 2007  says:
It helps organizations derive more value from high-quality and trusted data that�s spread across networks and systems, and allows key decision makers to access and use information in new ways essentially driving innovation, helping to increase operational efficiency and helping to lower risk. Reply

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