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Data Management | Data Integrity | Data Quality

Data Integrity | Data Quality Services

The importance of data management is often overlooked by many companies that 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 inaccurate information exposes companies to many business risks that lead to increased costs, customer dissatisfaction, poorer decision making and lost business. 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 data cleansing, data integration, data enrichment and data mining services in support of accurate reporting, analysis and business decisions; consequentially, they can 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.

Fig 1. A graphical example of data parsing:

Full names are separated into Title, First, Middle, Last, and suffix columns using pattern recognition rules. Some complex parsing projects may require many different rules to get a presentable percentage of successful rate.

Fig 2. A graphical example of data standardization:

Various versions of "New York" are standadized into one unique name.

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.

Fig 3. A graphical example of data integration:

The two tables are consolidated to form a third table by linking the source tables with the first and last names.

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.

Fig 4. A graphical example of data enrichment:

For customers targeting purpose, income classification is used to assign the income level to the customers.

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.

Fig 5. A graphical example of crosstab reporting:

The row sale dat is converted into a useful monthly sale report by country. More Info

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Data Mining and Forecasting

Using data mining, companies and organizations can increase the profitability of their businesses by uncovering opportunities and detecting potential risks. Our data mining and analysis consulting services can help you to extract valuable information out of your data by utilizing forecasting modeling (regression and time series analysis). We can analyze your data and provide you with forecasting reports that will suite your need
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