The 4 Keys to a Sound Enterprise Information Solution Strategy
For years, businesses have been establishing Business Intelligence (BI) as a means of delivering insight, facilitating better decision making while providing enterprise performance. In the beginning, BI systems demanded certain information access and end users were happy accessing locked data in transactional systems. While some organizations used BI through the integration of multiple data sources into a single data source, others offered direct access to data with little quality assurance.
Data is disparate and dynamic, and traditional warehousing of data cannot address the diverse forms of accessing information and analysis requirements. Enterprise information solutions or Enterprise Information Management (EIM) systems are required to address this challenge. EIM provides businesses and technology frameworks with the support necessary for a unified and consistent view, improving operational efficiency and performance.
Achieving a single view of the entire enterprise is a long-standing goal that few organizations have achieved. An EIM strategy ought to address business-critical core issues that include the following key areas.
For a BI to be successful, data integration is a crucial component. Gaining a single view of the truth of your business will be a difficult task without a comprehensive strategy that unifies disparate data. Data integration involves the use of different technologies that offer a distinct advantage that helps you meet your business’ requirements.
Extract, Transform, and Load (ETL) technology is capable of building a single data warehouse from disparate system sources. Enterprise Integration Information (EII), on the other hand, provides organizations with the ability to achieve real-time information requirements. Lastly, Enterprise Application Integration (EAI) allows the application systems to exchange data.
This is a critical part of the BI and data warehousing implementation project. Data quality controls ensure that the information you receive is accurate and trustworthy. However, achieving credible information requires you to carry out a few critical steps.
The first step involves data profiling to understand the source data, then data cleansing to identify, correct, and consolidate data. The third step is a validation of the data followed by auditing to verify that expected data is processed and loaded successfully.
At the base of every business application and domain is the possibility of inconsistent semantics. This is because every department in your organization is offering a conflicting view of the business, and this can strangle agility and efficiency. To attain semantic reconciliation, you need to develop a standard definition, while managing the semantic via a master reference.
One way of achieving consistency across the organization is by having a data steward who’s responsible for promoting inter-departmental standards that describe your employees, products, and customers. For instance, if you have a metric called “cost per employee,” do two part-time staff members working the same hours a day count as a single employee or two. Each department will likely have a different answer unless you have an organization-wide definition.
Deploying BI involves using several tools, each with metadata and a fair amount of overlap. While databases generate metadata for data definitions, ETL tools create metadata for transformation and data quality. At the same time, BI systems offer metadata for reports, goals, and metrics while modeling tools have metadata on logical mappings.
Metadata management allows BI-related metadata consolidation and integration into a single component. This allows your IT staff to view, explore, and analyze generated metadata from disparate systems.
To achieve timely and consistent access to trustworthy information, your business has to implement an EIM strategy that combines the four keys above. This will help you benefit from a deeper level of visibility and insight of the organization.
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