Archive for September 2011
Effective Data Management
September 25, 2011 | 
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Mining data is one of the keys to running an effective business. Here’s a primer on effectively managing your business data to maximize the efficiency of your business.
Effective data management plays an essential role for any growing business. Information technology has generated advanced tools for analyzing and managing data. Use of these tools can improve the performance of almost any operation. Steps made in capturing mass data electronically have developed the need for effective management strategies. Getting more and more data and transforming it into usable information is a major concern of today’s services and industries.
New technologies require new expertise, internal procedures and decision-making methods. Earlier companies were creating electronic databases, which were non-relational and difficult to use. Now with the use of highly sophisticated software and high-speed computers, businesses are reaping huge benefits from the computer/information revolution. Businesses are continuously making steps in managing data by using various tools to optimize information for sorting, searching and presentation in meaningful formats.
Many software programs and database applications are available on the market that enable companies to manipulate data in real time, capture knowledge for future use, ease the progress of operations to save time and costs and also to coordinate operations with partners.
The amount of data storage necessary and the duration it is kept online is growing swiftly, yet resources to manage data are limited. Data storage is a test to those companies wishing to maximize the value of their available data and also a huge task for storage professionals to manage and protect this data. Enterprises are struggling to bring together highly reliable platforms that can recognize where data is located in a company and whether it is utilized efficiently. Data management solutions must track, monitor and be vigilant of the conditions of your company data. It should also manage and distribute data efficiently. It should unify and simplify the administration of storage infrastructure.
Data is growing exponentially. Companies need maximum scalability, performance and production for data rigorous applications. They also need an easy to use, backup tool that provides transparency to where and how data and storage is utilized. Before choosing such and important process as data management, be sure to research your options and go with a solution that is flexible and scalable.
Posted in Data Management | 
Tags: Business Information Technology, Operation Steps, Platforms | 
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Relational Data Modeling
Relational Data Model is a data management model devised by Edgar F. Codd in the year 1970. It is considered as one of the most beautifully designed and widely used data models in recent times. Based on the predicate logic and set of theory of mathematics, relational data models help in managing the data efficiently.
A relational data model is implemented in a database where a relation is represented by a table, a tuple is represented by a row, an attribute is represented by a column of the table, attribute name is the name of the column such as ‘identifier’, ‘name’, ‘city’ etc., attribute value contains the value for column in the row, constraints are applied to the table and form a logical schema. Mostly, relational data modeling is used in OLTP systems, which are transaction, oriented, while dimensional data modeling is used in OLAP systems that are analytical based. Relational data modeling is closely related to data warehousing as in a data warehouse environment, the staging area is designed on OLTP concepts. Therefore, the data requires be normalizing, cleansing and profiling before it is loaded into a data warehouse.
Relational algebra operations such as Select, Intersection, Product, Union, Difference, Project, Join and Division, Merge can be performed on a relational data model. Below are the fundamental concepts in relational data models:
– Domain: A domain “D” is the original sets of atomic values used to model data. Atomic here refers to each value in the domain that is indivisible as far as the relational model is concerned.
– Relation (Relation state): A relation is a subset of the Cartesian product of a list of domains characterized by a name. Relation can be viewed as a “table”. In that table, each row represents a tuple of data values and each column represents an attribute.
– Attribute: A column of a relation designated by name and the name associated should be meaningful. Further, each attribute associates with a domain.
– Relation schema: Denoted by “R”, relation schema is a list of attributes. The degree of the relation is the number of attributes of its relation schema. The cardinality of the relation is the number of tuples in the relation.
Following are the terms used in relational data model:
– Candidate Key: Candidate key refers to any field or a combination of fields that identifies a record uniquely. The Candidate Key cannot contain NULL value and should always contain a unique value.
– Primary Key: Primary key is a candidate key that identifies a record uniquely.
– Foreign Key: A Foreign key is a primary key for other table, in which it uniquely identifies a record. Such a key defines relation between two (or more) tables. It can contain NULL value.
– Constraints: Constraints are logic rules used to ensure data consistency or avoid certain unacceptable operations on the data.
A relational data model provides basis for:
– Research on theory of data/relationship/constraint.
– Numerous database design methodologies.
– The standard database access language SQL.
– Several modern commercial database management systems.
Posted in Data Modeling | Tags: Cartesian Product, Relational Data Models, Staging Area | No Comments »
Six Sigma Data Collection Tools
For collecting and analyzing data, Six Sigma makes use of different types of data collection tools, as it would be quite impossible to carry out the task manually. Six Sigma data collection tools are employed in all processes where data is generated. Most of these tools are available in the form of excel sheets, excluding a few tools that are standalone applications. Given below are some of the data collection tools and their uses.
Commonly Utilized Raw Data Collection Tools
1. Operational Definitions Sheet- by defining the metrics, this tool helps in maintaining the consistency of the data collection process.
2. Voice Of The Customer (VOC) Data Collection Tool- this tool is used for collecting data from organizational databases, surveys, interviews, listening posts and observations related to the VOC and for organizing the collected data in a systematic manner.
3. Worksheet For Customer Segmentation- this tool helps in identifying the requirements of the main and minor segments and classifying them into finer details.
4. Check Sheets- this tool is very handy and is used for collecting smaller sample data. It helps in defining problem areas and checking the validity of planned outcomes.
5. Data Sheets- this tool is similar to Check Sheets and is more or less used for the same purpose, i.e. defining problems and substantiating outcomes.
Commonly Utilized Data Assessment Tools
Although these tools are not classified as decision-making tools such as the Thought Map Relation Diagram or the Ishikawa Fishbone Chart, they do however aid the decision makers. Given below is the list of some of the most commonly utilized data assessment tools:
1. Customer Requirement Translation And Analysis Tool- this tool helps in converting vague customer requirements such as ‘better quality’ or ‘low price’ into more measurable terms. This helps the change agents to design and develop new products and services that are in line with customer requirements and needs.
2. Sipoc Diagram- this tool is utilized both as a presentation tool and as an analytical tool. It is very effective in providing an overview of the inputs and outputs of the various business processes related to the vendors and the customers as well.
3. Pareto Chart- this tool helps in identifying small problems related to the implementation that are often held responsible for causing around eighty percent of the troubles. Six Sigma teams utilize this tool for reducing or even eliminating both the wastage of efforts and scarce resources. This helps teams to allocate resources in the right manner for enabling continuous quality improvements.
4. Production Scheduling And Actual Scorecard- this tool is utilized for comparing the actual results with the planned results related to operations and sales. This helps Six Sigma teams in evaluating the status of the project and in defining the direction in which the project is currently headed.
5. QFD (Quality Function deployment) House Of Quality Chart- this tool is quite popular as it is an all in one tool. It performs varied functions such as identifying customer requirements in relation to products or services, developing effective blueprints, and formulating newer techniques for eliminating the faults inherent in a business process.
Proper knowledge about the correct use of all the above stated tools is necessary for ensuring the successful and timely implementation of any Six Sigma project.
Posted in Data Tools | Tags: Customer Segmentation, Decision Making Tools, Ishikawa Fishbone | No Comments »