Master Data Management tools
Masters of the Data
CIOs Tune in to the Importance of Data Quality, Data Governance, and Master Data Management (MDM) Modern business applications produce ever more relevant and actionable information for decision makers, but in many cases the data sources are fragmented and inconsistent. Despite tremendous advancements at the application layer, nearly all IT initiatives succeed or fail based on the quality and consistency of the underlying data.
Why this glaring oversight? According to Mark A. Smith, CEO and Chief Research Officer at Ventana Research, CIOs are responsible for making information available to their businesses in a consistent and timely basis, but in most organizations, information management is seen as a delegated set of tasks and is not the CIO’s top priority.
“Key initiatives such as master data management, data virtualization, data quality, data integration and data governance are employed by just a fraction of organizations that should be mastering the science of information management, ” Smith said, citing a Ventana Business Analytics Benchmark study of more than 2, 800 organizations.
While CIOs are aware that effective information management results in faster decision-making, according to the Ventana study, only 43% of organizations have undertaken information management initiatives in data governance, data integration, data quality, master data management and data virtualization during the last two years, and less than one fifth have completed those projects. The largest obstacles to completing information management projects are insufficient staffing (68%), inadequate budget (63%) and insufficient training and skills (59%).
Smith believes that overcoming these obstacles and rectifying these deficiencies should be a top agenda item for CIOs. Consolidating and propagating accurate master data can reduce operational costs, increase supply chain and selling efficiencies, improve customer loyalty, and support sound corporate governance. “Other priorities, including business analytics, business applications, and Big Data, will not reach their full potential without top-notch information management that integrates business and IT efforts, ” he notes.
Analyzing the Problem
Data quality problems are caused by a variety of factors, beginning with error-prone manual data entry by employees across multiple departments, each with its own rules and methods. For example, the sales department’s requirements for entering customer data into a sales automation application are quite different from the accounting department’s need for customer data in an Accounts Receivable application.
Other inbound information channels include data entry from self-service Web portals, supply-chain interactions, and a steady influx of automated data entry from click streams, equipment sensors, and systems integration efforts.
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