Datenkontrolle - unerlässliche „Check ups“ für das Data Governance im Unternehmen

A DataFlux White Paper Prepared by: DataFlux Corporation Data Monitoring: Add Controls to Your Data Governance Program Leader in Data Quality www.dataflux.com International and Data Integration 877–846–FLUX +44 (0) 1753 272 020 Many companies today have come to realize the substantial impact that its underlying data can have on every aspect of a company’s life. From corporate strategy to customer service to supply chain management, the information that you gather on customers, prospects, products, inventory or employees can be a major factor in your organization’s ability to properly understand its current corporate landscape. In fact, good data holds the key to better decision-making, as it gives you the background to assess your business situation and locate trends that can give you a competitive edge. Good data helps you respond quickly to shifts in customer demands or to changes in the supply chain. And due to the rise of data governance – the overall management of the accuracy and security of corporate information – the quest for good data is now a vital component to any corporate IT initiative. But an organization cannot simply cleanse and improve the quality of its data, then expect this data to be a static resource over time. Data is a fluid, dynamic and ever- changing resource, so data quality is not a once-and-done activity. The need for data quality never goes away. It requires continual oversight. Data, in essence, reflects the changing world around you. Customer records become obsolete as people move or switch jobs. Catalogs for products and supplies become outdated. Without a commitment to ongoing data quality, the integrity of an organization’s data quickly becomes incorrect or invalid as it reaches core applications. Data monitoring has become a key component of a complete data quality integration practice, giving you the tools you need to understand how and when your data strays from its intended purpose. Monitoring also helps you identify and correct these inefficiencies through the automated, ongoing enforcement of customizable business rules. Data monitoring ensures that once your data becomes consistent, accurate and reliable, it remains that way over time, giving you confidence when making information-based decisions for your organization. The Role of Data Monitoring Building better information resources is an arduous task for any organization. Improvement typically happens gradually and requires difficult and time-consuming Quality processes to understand specific data quality issues – and correct these issues. data To start any data improvement initiative, you have to identify and fix the problems takes that created bad data if you are to reap the rewards of high-quality data. For most constant organizations, these objectives require a cross-section of resources from multiple vigilance. departments, often taking months, if not years, to correct. Unfortunately, many organizations believe that once they have improved existing data sources, their data quality problems are cured. And indeed, by improving data quality in a one-time project, they have improved the quality of their decision-making. However, as companies accumulate new data from a wide variety of sources over time, the overall quality of data most likely will suffer. Quality data takes constant vigilance. Data monitoring may include simple data profiling trend analysis over time, or it may include specific analysis of complex business rules. By configuring rules that define acceptable data quality values, monitoring can be used to automatically identify records that violate quality standards and alert users of the violations. 1 Monitoring supports ongoing data governance efforts to ensure that all incoming and existing data meets pre-set business rules. If you have set up a data improvement or data governance program already, you are ahead of the game, and your organization is headed in the right direction. You are already able to recognize data problems, inspect data sources and data processes, and implement the data and process corrections needed to get the problems fixed. By adding a data monitoring component to that program, you greatly enhance your data quality efforts and make your data much more reliable. By continually checking and re-checking the integrity and viability of your corporate data, you can make sure that high-quality data remains consistent, accurate and reliable. Data monitoring builds on your initial data quality initiatives by providing the technology needed to make sure your data continues to provide the most value for your organization. Set Controls on Your Data Traditionally, data quality meant implementing a narrow set of data cleansing and matching procedures such as finding and eliminating duplicate records or correcting customer names and addresses. Data quality was a tactical effort, typically employed Data within a department or business unit – and it rarely, if ever, received executive support or acknowledgement. monitoring asks a Today, the need for validating and correcting this data – and the detailed rules that simple oversee this process – is more important than ever. The amount of new data pouring question. Is in from multiple, disparate data sources is growing exponentially as organizations, your data along with their data, merge and consolidate in the new global economy. doing what With these and other factors directly affecting your own information, you need to you expect understand if the data that you have in your applications and databases is still doing it to do? what you expect it to do. Is your data still valid? Is it still meeting its intended use? For almost all businesses today, the answer to both of these questions is no. And given the growing number of compliance initiatives you now face – from internal data governance programs to government-driven regulations – you need greater control over your data, now more than ever. A data monitoring solution can help you maintain consistent, accurate and reliable data by helping you: • Identify trends in data quality metrics and data values • Provide instant alerts for violations of pre-established business rules • Quantify the costs associated with data quality and business rule violations • Detect variances from cyclical runs • Recognize when data exceeds pre-set limits, allowing you to immediately update this data by addressing the problems upfront before the quality of your data declines Data monitoring gives you the assurance to know that once you fix your data problems, they will remain within tight limits. When data does get out of control, you know immediately – and can address problems before the quality of your data declines. Data monitoring allows you to confidently reclaim control of your company’s data, not let it control you. 2 Enforcing Business Rules for Data Monitoring The business reasons for implementing a data quality program are changing. Once, companies used data quality to cleanse customer data that could help drive sales and marketing efforts. Although, this is a worthy goal, companies have begun to ask their data quality tools to perform more in-depth examinations of data. The result is the adoption of business rules monitoring capabilities, which allows users to establish and enforce complex, configurable standards for data. By monitoring business rules, you can: • Enforce data governance standards • Audit and validate the quality of your company’s data on an ongoing basis • Create customized rules to streamline your operational processes • Identify and fix operational inefficiencies with a continual, automated enforcement of pre-set rules • Improve the accuracy of your business processes • Understand and refine those processes by logging exceptions and violations to business rules in a repository, allowing you to see and address trends Both IT and business users can create and enforce customized rules to monitor any data-driven, enterprise wide initiative. These business rules include detecting unexpected variances, validating numeric calculations, testing domain values against a list of acceptable values, or validating data before allowing a transaction to continue. Once you discover violations, the software automatically notifies other designated users of rule violations and corrects the data.