Excel Consulting Services - Data Management - Data Mining

The importance of data management is often overlooked by many companies. They frequently underestimate the important contribution data management makes to the success or failure of their operations.

Data quality is vital to business intelligence. Companies typically spend thousands and even millions of dollars setting up business intelligence systems to improve their operations, but the results generated by these efforts are only as good as the data that is fed into them. Many fall short of their expectations because of poor data quality issues. Contradictory, inconsistent or

Clean, high quality data helps company decision makers to accurately and correctly assess their business activities and avoid potential pitfalls that can significantly impair a company’s profitability.

At Excel Business Solutions we offer companies with data cleansing, data integration, data enrichment and data mining services in support of accurate reporting, analysis and business decisions; and consequenentially, minimize risk and cost, enhance business opportunity and increase returns.

Data Cleansing
| Data De-Duping | Data Standardization | Data Parsing |

The terms “data cleansing” and “data scrubbing” are interchangeable; both involve detecting and correcting (or removing) corrupt or inaccurate records from a database. Data cleansing services can transform and combine different data, remove inaccuracies, standardize common values, remove redundancy, parse values and cleanse corrupt data to create consistent, reliable information.

A graphical example of data parse and data standardization:

Data integration

Data integration is the process of combining data from different sources and providing the user with a unified view of the data. Data cleansing supplements this process.

During the process of data integration, data from multiple sources are combined into a single data set. Redundant data entries are identified for consolidation or elimination.

Data integration is essential to business intelligence because it connects together information needed to make strategic decisions across asset types, provides quick and convenient access to data, improves quality and comprehensiveness of data, promotes consistency and reduces the cost of data collection, storage and processing. An organization will benefit most from enterprise business intelligence when it helps users generate concise information from multiple data sources.

A graphical example of data integration:

Consider a web application where a user can query a variety of information about cities such as crime statistics, weather, hotels, demographics, etc. Traditionally, the information must exist in a single database with a single schema. Information of this breadth, however, is difficult and expensive for a single enterprise to collect. Even if the resources exist to gather the data, it would likely duplicate data in existing crime databases, weather websites, and census data.

A data integration solution addresses this problem by considering these external resources as materialized views over a virtual mediated schema. This means application developers construct a schema to best model the kinds of answers their users want. This virtual schema is called the mediated schema. Next, they design "wrappers" or adapters for each data source, such as the crime database and weather website. These adapters simply transform the local query results (those returned by the respective websites or databases) into an easily processed form for the data integration solution. When an application-user queries the mediated schema, the data integration solution transforms this query into appropriate queries over the respective data sources. Finally, the results of these queries are combined into the answer to the user's query.

A convenience of this solution is that new sources can be added by simply constructing an adapter for them. This contrasts with ETL systems or a single database solution where the entire new dataset must be manually integrated into the system.

Data Enrichment
Data enrichment or data enhancement adds more info from other internal or external data sources to information already used in the organization. This process increases the analytic value to the existing information. One example of the data enrichment process is to associate the current customer records in the current database with buying behaviors and demographical information from other sources.

A graphical example of data enrichment:

Data Mining and Reporting

Data mining uncovers patterns in data. This process can be effected by descriptive statistics, data summary, and/or predictive techniques. These patterns play a critical role in decision making. Using data mining, companies and organizations can increase the profitability of their businesses by uncovering opportunities and detecting potential risks. It lies at the core of business intelligence.

A graphical example of acrosstab reporting process: