Excel Business Solutions
Excel Programming and Automation
Data Mining and Forecasting
Data Integration and Enrichment
Data Reporting and Analysis
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Need to create an Excel macro to do a major task you have?
Doing a repetitious job in Excel that takes hours or days to do manually?
Been asked to create an Excel macro to solve a major need?
Need to convert raw data into useful Excel format?
Need to create an Excel macro to be used by many people in your company?
Out of time or on a tight timeline?
Doing a repetitious job in Excel that takes hours or days to do manually?
Been asked to create an Excel macro to solve a major need?
Need to convert raw data into useful Excel format?
Need to create an Excel macro to be used by many people in your company?
Out of time or on a tight timeline?
Are you faced with any of these challenges?
Need Excel Consulting And Data Mining Services?
Database Programming
Report Automation | Reporting
Data Manipulation and Conversion
Database Marketing
Data Audit
Data Cleansing
Data Mining and Forecasting
Data Integration
Financial Modeling
Query Calculator and Tools
Customized Excel Functions
Spreadsheet and Database Design
Report Automation | Reporting
Data Manipulation and Conversion
Database Marketing
Data Audit
Data Cleansing
Data Mining and Forecasting
Data Integration
Financial Modeling
Query Calculator and Tools
Customized Excel Functions
Spreadsheet and Database Design
Our services include, but not limited to:
At Excel Business Solutions, we provide Excel consulting services through database programming, Excel VBA (macro) automation, data mining, time series forecasting, financial and business modeling. Most of our Excel consultants have more than 10 years of professional experience in data analysis with advanced business degree.
Excel Business Solutions has served many clients from diverse business and public sectors providing Excel and database consulting services and solutions. Excel Business Solutions is headquartered in New York City and maintains offices in California and New Delhi (India).
Excel Business Solutions has served many clients from diverse business and public sectors providing Excel and database consulting services and solutions. Excel Business Solutions is headquartered in New York City and maintains offices in California and New Delhi (India).
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'Excel Business Solutions provided reliable and professional support when it came to developing a tool for project managers in our
company. The fact that Anthony and his team managed to provide solutions for complex tasks make him an invaluable IT expert. I highly
recommend Excel Business Solutions! '
- Batoor Khan, ATB Austria -
'Excel Business Solutions have risen to all the challenges I have posed to them, tailoring solutions to our unique requirements in making EXCEL more functional to our analysts.'
- Dr Stephen St.George-Smith, PICR, United Kingdom -
'Anthony and his team were able to create very powerful yet easy to use pivot tables that have helped in developing our business analytics.'
- Joe C., Stone Oak Underwriters, LLC. USA. -
'Excel Business Solutions saved me TIME, money and frustration. Smart people surround themselves with competent people.'
- Jim H. , Whitman County Assessor's Office. USA -
'Thanks to Anthony for his expert Excel skills and amazing turnaround time, I've come to call him my "IT Genius" and given him multiple projects that consistently yield outstanding results. Finding him through an Internet search was like finding gold in a haystack. Thanks again.'
- Lee., Altschuler Law. USA -
'One of our high-tech Fortune 100 clients handed us a very complicated report in order to make it more user-friendly. Excel Business Solutions provided an elegant solution to a very challenging assignment. They surpassed our expectations and our client was blown away!'
- Sparky Witte, Kolar Advertising & Marketing. USA -
- Batoor Khan, ATB Austria -
'Excel Business Solutions have risen to all the challenges I have posed to them, tailoring solutions to our unique requirements in making EXCEL more functional to our analysts.'
- Dr Stephen St.George-Smith, PICR, United Kingdom -
'Anthony and his team were able to create very powerful yet easy to use pivot tables that have helped in developing our business analytics.'
- Joe C., Stone Oak Underwriters, LLC. USA. -
'Excel Business Solutions saved me TIME, money and frustration. Smart people surround themselves with competent people.'
- Jim H. , Whitman County Assessor's Office. USA -
'Thanks to Anthony for his expert Excel skills and amazing turnaround time, I've come to call him my "IT Genius" and given him multiple projects that consistently yield outstanding results. Finding him through an Internet search was like finding gold in a haystack. Thanks again.'
- Lee., Altschuler Law. USA -
'One of our high-tech Fortune 100 clients handed us a very complicated report in order to make it more user-friendly. Excel Business Solutions provided an elegant solution to a very challenging assignment. They surpassed our expectations and our client was blown away!'
- Sparky Witte, Kolar Advertising & Marketing. USA -
Excel is a powerful spreadsheet allows you to store, manipulate, analyze, and visualize data. It features an intuitive interface and capable calculation and graphing tools which, has made Excel one of the most popular microcomputer applications to date. It is overwhelmingly the dominant spreadsheet application available for these platforms and has been so since version 5 in 1993 and its bundling as part of Microsoft Office.
Excel has included Visual Basic for Applications (VBA), a programming language based on Visual Basic which adds the ability to automate tasks in Excel and to provide user defined functions (UDF) for use in worksheets. VBA is a powerful addition to the application which, in later versions, includes a fully featured integrated development environment (IDE). Macro recording can produce VBA code replicating user actions, thus allowing simple automation of regular tasks. VBA allows the creation of forms and in-worksheet controls to communicate with the user. The language supports use (but not creation) of ActiveX (COM) DLL's; later versions add support for class modules allowing the use of basic object-oriented programming (OOP) techniques.
Excel has included Visual Basic for Applications (VBA), a programming language based on Visual Basic which adds the ability to automate tasks in Excel and to provide user defined functions (UDF) for use in worksheets. VBA is a powerful addition to the application which, in later versions, includes a fully featured integrated development environment (IDE). Macro recording can produce VBA code replicating user actions, thus allowing simple automation of regular tasks. VBA allows the creation of forms and in-worksheet controls to communicate with the user. The language supports use (but not creation) of ActiveX (COM) DLL's; later versions add support for class modules allowing the use of basic object-oriented programming (OOP) techniques.
Data mining is the process of automatically searching large volumes of data for patterns. It is usually used by businesses and other organizations, but is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimentation.
Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data, and produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.
Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.
The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction rather than knowledge discovery.
Although data mining is a relatively new term, the technology is not. Companies for a long time have used powerful computers to sift through volumes of data such as supermarket scanner data, and produce market research reports. Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of analysis.
Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, users have the ability to identify key attributes of business processes and target opportunities.
The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user. Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g. rule based systems) and opaque in others such as neural networks. Moreover, some data mining systems such as neural networks are inherently geared towards prediction rather than knowledge discovery.
Forecasting is the process of estimation in unknown situations. Prediction is a similar, but more general term, and usually refers to estimation of time series, cross-sectional or longitudinal data. Forecasting is commonly used in discussion of time-series data.
Time series methods use historical data as the basis for estimating future outcomes.
• Moving average
• Exponential smoothing
• Extrapolation
• Linear prediction
• Trend estimation
• Growth curve
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecasted. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.
• Regression analysis using linear regression or non-linear regression
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA) e.g. Box-Jenkins
• Econometrics
In statistics, regression analysis is the process used to estimate the parameter values of a function, in which the function predicts the value of a response variable in terms of the values of other variables. There are many methods developed to fit functions and these methods typically depend on the type of function being used.
An autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average or (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series. The model is generally referred to as an ARIMA(p,d,q) model where p, d, and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.
Time series methods use historical data as the basis for estimating future outcomes.
• Moving average
• Exponential smoothing
• Extrapolation
• Linear prediction
• Trend estimation
• Growth curve
Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecasted. For example, sales of umbrellas might be associated with weather conditions. If the causes are understood, projections of the influencing variables can be made and used in the forecast.
• Regression analysis using linear regression or non-linear regression
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA) e.g. Box-Jenkins
• Econometrics
In statistics, regression analysis is the process used to estimate the parameter values of a function, in which the function predicts the value of a response variable in terms of the values of other variables. There are many methods developed to fit functions and these methods typically depend on the type of function being used.
An autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving average or (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series. The model is generally referred to as an ARIMA(p,d,q) model where p, d, and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated, and moving average parts of the model respectively.
Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing.
The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built.
The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built.
Reference *
Microsoft Excel and VBA
Database Marketing
Data Mining
Data Integration
Forecasting Analysis
Data Cleansing
Data cleansing is the act of detecting and correcting (or removing) corrupt or inaccurate records from a record set.
After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by different data dictionary definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage.
Preprocessing the data will also guarantee that it is unambiguous, correct, and complete.
The actual process of data cleansing may involve removing typos or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid ZIP code) or fuzzy (such as correcting records that partially match existing, known records).
Data cleansing is synonymous with the less frequently-used term data scrubbing. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.
After cleansing, a data set will be consistent with other similar data sets in the system. The inconsistencies detected or removed may have been originally caused by different data dictionary definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage.
Preprocessing the data will also guarantee that it is unambiguous, correct, and complete.
The actual process of data cleansing may involve removing typos or validating and correcting values against a known list of entities. The validation may be strict (such as rejecting any address that does not have a valid ZIP code) or fuzzy (such as correcting records that partially match existing, known records).
Data cleansing is synonymous with the less frequently-used term data scrubbing. Data cleansing differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at entry time, rather than on batches of data.
Data integration is the problem of combining data residing at different sources and providing the user with a unified view of these data. This important problem emerges in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories). Data integration appears with increasing frequency as the volume and the need to share existing data explodes. It has been the focus of extensive theoretical work and numerous open problems remain to be solved. In practice, data integration is frequently called Enterprise Information Integration.
* Information obtained from en.wikipedia.org.
* Information obtained from en.wikipedia.org.
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Do so at a reasonable cost.
Deliver the macro quickly and on time.
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Database marketing emphasizes the use of statistical techniques and data analyses to develop models of customer behavior, which are then used to select customers for communications. The benefit of database marketing is the ability to target your marketing efforts. Companies can concentrate their marketing effort on customers that are most likely to buy.
Example: An Internet marketing company is sponsoring a trade show in Houston. Instead of sending invitations to thousands of their customers around the United States, the company runs a query in its marketing database and extracts a list of customers located in the Houston metro area. The company then sends out its invitation package to this targeted list.
Example: An Internet marketing company is sponsoring a trade show in Houston. Instead of sending invitations to thousands of their customers around the United States, the company runs a query in its marketing database and extracts a list of customers located in the Houston metro area. The company then sends out its invitation package to this targeted list.
Why Database Marketing?
Since 1993, Excel has included Visual Basic for Applications (VBA), a programming language based on Visual Basic which adds the ability to automate tasks in Excel and to provide user defined functions (UDF) for use in worksheets. VBA is a powerful addition to Excel. Excel macros are programs that store a series of commands that you can “play back” the actions. They can reduce the number of steps required to complete tasks and significantly reduce the time users spend creating, formatting, modifying, and printing their spreadsheets. A macro can be as simple as replicating some formatting tasks or as complex as querying information from various data sources through database programming.
Example: A financial analyst downloads financial tables from the Internet each day for analyses and report generating. She covers 30 stocks for the computer industry. Each day, she picks up the numbers from the tables, performs some financial ratio analyses, and enters the ratios into different reports. She has to do these repetitive tasks for each of the 30 companies that she covers. These tasks take up most of her time in the office. With the help of a consulting service, she is now able to have all of the reports completed in minutes, giving her more time to write her reports and to relax.
Example: A financial analyst downloads financial tables from the Internet each day for analyses and report generating. She covers 30 stocks for the computer industry. Each day, she picks up the numbers from the tables, performs some financial ratio analyses, and enters the ratios into different reports. She has to do these repetitive tasks for each of the 30 companies that she covers. These tasks take up most of her time in the office. With the help of a consulting service, she is now able to have all of the reports completed in minutes, giving her more time to write her reports and to relax.
How can Excel programming work for you?
How important is data quality?
Does data mining really help?
Data mining uncovers patterns in data using 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. Forecasting is a component of data mining. It is the process of estimation in unknown situations. Prediction is a similar, but more general term, and usually refers to estimation of time series, cross-sectional or longitudinal data. Forecasting is commonly used in discussion of time-series data.
Example: One Midwest grocery chain used data mining method to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Example: One Midwest grocery chain used data mining method to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Data Quality refers to the quality of data. Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J.M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. One industry study estimated the total cost to the US economy of data quality problems at over US$600 billion per annum (Eckerson, 2002). In fact, the problem is such a concern that companies are beginning to set up a data governance team whose sole role in the corporation is to be responsible for data quality. Although most companies tend to focus their quality efforts on name and address information, data quality is recognized as an important property of all types of data.
Data quality process may include some or all of the following:
Data profiling - initially assessing the data to understand its quality challenges. Data standardization - a business rules engine that ensures that data conforms to quality rules. Matching or Linking - a way to compare data so that similar but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often recognizes that “Bob”, “Bobby”, “Rob” and 'Robert' may be the same individual. Monitoring - keeping track of data quality over time and reporting variations in the quality of data.
Data quality process may include some or all of the following:
Data profiling - initially assessing the data to understand its quality challenges. Data standardization - a business rules engine that ensures that data conforms to quality rules. Matching or Linking - a way to compare data so that similar but slightly different records can be aligned. Matching may use "fuzzy logic" to find duplicates in the data. It often recognizes that “Bob”, “Bobby”, “Rob” and 'Robert' may be the same individual. Monitoring - keeping track of data quality over time and reporting variations in the quality of data.
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