Business Technology Management Technology is the business Database Management Database infrastructures are increasingly complex, progressively heterogeneous and more integral to providing business services and delivering long-term business value. Your IT organization needs a database management solution that can:  Efficiently design high-quality databases that support business initiatives  Manage highly complex database environments that span operating systems and database platforms  Achieve high service levels with limited budgets BTM delivers powerful Database Management solutions:  Streamline database management across heterogeneous and distributed environments  Break down the silos in IT, improving responsiveness, reduce costs and increase productivity  Simplify the complex tasks of analyzing, designing and implementing database applications and business processes  Implement best practices like ITIL, SOA and delivering IT as a service Maintaining a competitive edge requires faster, better decisions based on accurate datawhile facing increasingly complex challenges, such as mergers and acquisitions, budget reductions and evolving technologies. In order to build high-performance business applications, data marts and data warehouses (DWs) or Master Data Management (MDM) initiatives — and to be able to get the information you need out of those systems — BTM helps you:  Ensure consistent understanding of your existing and legacy database environments  Build a solid database foundation for the future  Control the total cost of ownership (TCO) of the entire data lifecycle A robust data infrastructure consists of both accurate data and a strong architectural foundation for storing and retrieving this information. Accurate data helps ensure that strategic decisions are made on valid information, while a strong architectural foundation helps ensure that the data is delivered quickly and efficiently. Data profiling helps you assess the quality of your information and generate metrics and statistics to quantify the results. If an organization is making decisions based on certain information, it is important that this data is reliable, complete and free from error. Data modeling helps you understand both the structure and meaning of your information. Business definitions can be maintained in a data model and structural information can be defined to help ensure that data is stored efficiently. For legacy systems (where this structural information is not well defined), data profiling can help assess the structure based on statistical analysis of data values. Thus, the combination of data profiling and data modeling is crucial for maintaining the quality of the information that drives your business decisions. Reducing the Cost and Risk of Data Integration In the age of information, organizations depend on their data assets for mission-critical decision making of all kinds — from assessing the position of a competitor, to forecasting future sales, to determining inventory levels for future purchases. Business intelligence (BI) applications make it easier for business and IT alike to gain access to that strategic data in the form of customized reports. To be effective, front-end BI applications must be supported by a robust data architecture that ensures that the information populating the back-end reports is accurate, timely and high quality. And in addition to demanding information that is correct, most users want it at the speed of business. But, again, a fast response time for reports requires the right underlying architecture — one that ensures that data is structured and stored in a way that decreases retrieval time, reduces redundancy and minimizes storage volumes. In other words, you need to be able to quickly understand and cross-reference your data sources and generate accurate, reusable data models. The combination of data profiling and data modeling can assist with creating valuable, well-managed information that helps drive revenue and decrease costs. Improving Data Quality and Analyzing Cross-system Data The costs associated with poorly documented, or completely undocumented, data sources often represent a large portion of a project’s overall budget — putting the success of any DW, BI, MDM or integration-oriented data management project at risk. Data profiling helps increase the integrity of your critical data assets by performing cross-system analysis, generating robust data quality metrics and statistics and validating live instance data with database design and architecture. The task of improving data quality through profiling, however, is compounded when there are multiple, disparate data sources to evaluate. As a result of mergers, acquisitions or geographically and organizationally disparate business units, information is often lacking in centralization and reliability. Thus, it is important to identify overlapping data and combine it with data quality statistics in order to create a single source of record. For example, if two banking organizations merge, they are likely to be interested in ascertaining how many total customers they have, how many might have accounts with both institutions and whether the information stored for each customer is the same (account number, address, age, gender and so on.) Matching Design with Reality Most data systems make use of a data model to design the structure and meaning of information. Take, as an example of a simple data structure, the column headings of a spreadsheet. These headings define what type of data should be stored in each column, which, in other words, is, the actual and intended meaning and context of the information assets, or metadata. But in too many organizations, the best intentions of the data architecture team are not followed, and columns are used for an unfortunate and all too vague something else. Rather than add a new column to the data source to store the information they need, rogue data entry personnel simply appropriate an “empty” column for an entirely different purpose. In the example below, for instance, the “Year Purchased” field was used for a promotion code instead. Imagine the difficulties that would result from running or using a report on this data with the goal of determining how long specific customers have been with the company. Understanding Legacy Data While the problem of database design failing to align with data values can cause reporting errors, it is compounded for many legacy systems because they have no structure defined at all. Imagine trying to understand the meaning of a spreadsheet with no column headers. For the many legacy sources with millions of rows of data, this task becomes even more difficult without the help of an automated system. To be more specific, the primary and foreign keys that are defined in many relational database systems are often not defined at all in legacy data sources.