Data FAQ - Frequently Asked Questions

Just like all industries there are a myriad of terms and phrases. You will come across many in literature, articles and websites. This data FAQ is designed to help you understand what they mean.

Terms and phrases are listed alphabetically. Please scroll down to find the term you are seeking.

Business Intelligence

Business Intelligence provides business users with information about their organisations. Information is typically subject orientated e.g. financials, sales and marketing. The software allows users to view business from many different perspectives e.g. customer segments and product categories.

Terms that essentially cover the same domain: Executive Information System (EIS), Decision Support System (DSS) or Management Information System (MIS).

Find out more about business intelligence.

CRM - Customer Relationship Management

CRM is a system that provides a customer orientated view of business. It includes contact management, sales management and customer service. The goal of CRM is to better serve customers and promote relevant products and services.

The term CRM is widely used to identify systems that manage customer centric data. Some just focus on contact management others may have marketing functions.

Cubes

A cube is subject orientated set of data e.g.: sales or finance. Data is loaded into a compressed form for efficient information retrieval and navigation. It's rather like a fast pivot table within a spreadsheet. Like a Rubik cube, it allows you to view (information from) different perspectives.

Similar terms: Business Intelligence, OLAP

Customer Lifetime Value - CLV

Customer lifetime value is calculated to determine the potential future value of customers. The calculation is based on:

• Gross margin of purchases
• Depreciation percentage
• Cost of acquisition and marketing communications

Customer Scoring

Customer scoring is used to identify key behavioural traits.

By applying a scoring method such as recency, frequency and value a score can be applied that takes into consideration all these attributes and thus provides a more complete view of the customer.

Depending on the business, other attributes can be assessed e.g. level of returns, customer service calls, etc.

Find out how customer scoring is applied to customer segmentation.

Customer Segmentation

Customer segmentation is the defining of groups using customer characteristics and or behavioural attributes e.g.:

• Demographic or business classification
• Propensity to purchase a given product or combination of products
• Customer performance scores

Customer segments are then used to drive targeted marketing communications.

Find out more about customer segmentation.

Data Exploitation

Data exploitation is "gaining commercial and competitive advantage through the application of data".

Data is a fantastic resource, volumes of data are exploding and with it, there is huge potential for tapping into the valuable insights it contains.

Find out more about data exploitation.

Data Analysis or Data Analytics

Data analysis is the process of determining why things happen. It is breaking data into constituent parts, examining and determining their relationship and value. Data analytics is similar, but normally includes data modelling to support the analysis. The results of analysis and analytics are illustrated by a series of findings.

Neither should be confused with reporting which simply conveys a piece information.

Data analysis and analytics are key to a Business Opportunity Assessment™.

Data Audit

A data audit is a process of highlighting issues concerning data quality or integrity.

It is essential to conduct either a data audit or appraisal prior to data analysis. The quality of the results are directly affect by data quality.

Typically a data audit would:

• Analyse data values, frequency, patterns and format
• Discover important data statistics (Min, Max, Nulls etc.)
• Identify redundant or duplicate data
• Review referential integrity (analysing joins between tables)
• Provide considerations for data cleansing and or enhancement

To understand more about data audit request our free guide to generating revenue from your corporate data (top right of this page).

Data Appraisal

A data appraisal is similar to an audit. It differs only by being limited to data which will be analysed. It's aim is to ensure that the results of analysis are robust.

Find out more about appraising data in the Business Opportunity Assessment™.

Data Enhancement

Data enhancement typically involves the appending of new information to existing data records. This can reveal greater insights when analysing your corporate data.

Consumer data

• Demographics: age, income, disabilities, mobility, educational attainment, home ownership, employment status, ethnic group
• Economic situation,  buying power, social classification

Business data

• Industry classification (SIC code), number of offices, employees, etc and turnover
• Names of directors
• Business performance
• Industry specific data: Education (head teacher, number on role, specialism), health (type of service e.g. care home: number of beds), media monitoring, FMCG, pharmaceutical prescriptions, etc, etc.

Data Mart

A data mart is a subject orientated database e.g. customer sales, finance, marketing. It provides all the information a user needs to satisfy their specific needs. A data mart is usually part of a wider data warehouse strategy.

Find out more about data mart and data warehouse.

Data Mining

Data mining is uncovering patterns within data. Typically sophisticated software algorithms are used to detect these patterns.  The end-user view is software that enables users to analysis and discover patterns. The later is the common understanding, the former an important distinction when selecting technology.

Examples of data mining:

• Managing customer retention / customer churn
• Identifying purchasing characteristics

Data Warehouse

A data warehouse is a database. It contains a copy of transaction data, probably sourced from multiple systems. The data is integrated and structured for query, reporting and analysis.

A data warehouse should allow users to ask any questions about their business. A data warehouse typically comprises a number of data marts e.g.: customer sales, finance, marketing. The data marts are integrated, allowing navigation from one mart to another.

Find out more about data warehouses

Dimensions

A dimension is a technical implementation of a business structure e.g.: customer, product, etc. A dimension is used to navigate and summarise data. It is a term typically used in connection with data warehouses, data marts and business intelligence.

Conformed dimensions are dimensions which are common to a number of data marts. They contain the same information e.g.: customer name and address, product code and category.

Exploration Warehouse

Typically a database that combines a myriad of information used to analyse or solve a problem. Usually user led rather than IT, data is loaded on as need be basis. It could be considered a data warehouse prototype, not optimised for query performance but focused on business issues.

Similar term: Project Warehouse

We use this approach in the Business Opportunity Assessment™.

Facts or Measures

Facts or measures are the names given to the numeric values being analysed e.g.: sales, quantity, cost, etc.

Each fact relates to one or more dimensions e.g. sales dimensioned by customer, product.

It is a term typically used in connection with data warehouses, data marts and business intelligence.

Federated Data Warehouse

A federated data warehouse is a series of integrated data marts. The user navigates the data warehouse via conformed dimensions. This is probably one of the best ways to structure a data warehouse. It provides users with a clear view of available information and IT with a detailed definition of business needs.

Market Basket Analysis

Significant increases in cross-sell revenues can be achieved by using knowledge of customer purchasing habits such as:

• Which product combinations are bought
• When they are purchased; and in
• What sequence

Market basket analysis delivers this understanding, enabling business to promote their products.

Find out more about market basket analysis.

OLAP

On-Line Analytical Processing is another term given to Business Intelligence. OLAP provides business users with information about their organisations. Information is typically subject orientated e.g. financials, sales and marketing. The software allows users to view business from many different perspectives e.g. customer segments and product categories.

Find out more about business intelligence and OLAP.

RFV - Recency, Frequency & Value

A method for scoring customers based on their purchasing behaviour. It is used by retailers but is just as applicable to other markets where there is high order volumes.

RFV quotients:

• Recency - time since last order
• Frequency - regularity of purchasing
• Value e.g. sales value, gross margin

Find out how recency, frequency and value is used in customer segmentation

XYZ - Help what does this term mean?

Haven't found that term or phrase or perhaps you need more specific help? Just ask your question in The Data Surgery.