C2-Data Storage Issues
Content
Data Backup
Data Backup is the physical copying of data files to a removable storage device that allows the data
to be stored in another location. When needed, an individual data file or an entire set of data files, can be restored to a computer system. The term data backup refers to either the activity of copying computer files for purposes of retention or disaster recovery or to the actual repository of computer files which have been made for those purposes. Data backup (the activity) is usually accomplished through the process of copying files to tape, disk or other medium. Two key concepts are important relating to data backup. First, deciding between local versus offsite storage of the data is always a consideration. Having an offsite backup provides protection from a disaster which affects your entire place of business such as a fire, flood, tornado, hurricane, earthquake, etc. Offsite storage is generally now being accomplished through online storage. Please see our definition of Online Storage. Second, data backup generally includes a retention policy. A retention policy defines the period of time for which the additional copy will be kept. An example of a typical retention policy is a policy used for Global Data Vault’s typical Advanced Data Protection service, and is shown below: For 7 days, users can go back to any individual point when a data backup was taken. Typically this is hourly for most customers. Customers actually set the policy, but we recommend hourly from 8am to 6pm, Monday to Friday which is the default. Then, for 14 days, users can recover any individual day, of which the most recent 7 allow access to any hour as above. Then for 30 days, users can choose a particular week. After that, users can choose a specific month To copy files to a second medium (a disk or tape) as a precaution in case the first medium fails. One of the cardinal rules in using computers is back up your files regularly.
Data Mining
What is Data Mining? Generally, data mining (sometimes called data or knowledge discovery process) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures. Wal-Mart is pioneering massive data mining to transform its supplier relationships. Wal-Mart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. Wal-Mart allows more than 3,500 suppliers, to access data on their products and performs data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, Wal-Mart computers processed over 1 million complex data queries. The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game. By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot. How does data mining work? While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical
software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes. Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table.
Data processing
data processing may refer to a discrete step in the information processing cycle in which data is acquired, entered, validated, processed, stored, and output, either in response to queries or in the form of routine reports; the processing is the step that organizes the information in order to form the desired output. Used in a more general sense, data processing may also refer to the act of recording or otherwise handling one or more sets of data, and is often performed with the use of computers. There are three types of data processing. 1) Manual data processing 2) Mechanical data processing 3) Electronic data processing
Data Warehousing
Data warehousing is combining data from multiple and usually varied sources into one comprehensive and easily manipulated database. Common accessing systems of data warehousing include queries, analysis and reporting. Because data warehousing creates one database in the end, the number of sources can be anything you want it to be, provided that the system can handle the volume, of course. The final result, however, is homogeneous data, which can be more easily manipulated.
Data warehousing is commonly used by companies to analyze trends over time. In other words, companies may very well use data warehousing to view day-to-day operations, but its primary function is facilitating strategic planning resulting from long-term data overviews. From such overviews, business models, forecasts, and other reports and projections can be made. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only. If you want to update the data stored via data warehousing, you'll need to build a new query when you're done. This is not to say that data warehousing involves data that is never updated. On the contrary, the data stored in data warehouses is updated all the time. It's the reporting and the analysis that take more of a long-term view. Data warehousing is typically used by larger companies analyzing larger sets of data for enterprise purposes. Smaller companies wishing to analyze just one subject, for example, usually access data marts, which are much more specific and targeted in their storage and reporting. Data warehousing often includes smaller amounts of data grouped into data marts. In this way, a larger company might have at its disposal both data warehousing and data marts, allowing users to choose the source and functionality depending on current needs.
Data Backup is the physical copying of data files to a removable storage device that allows the data
to be stored in another location. When needed, an individual data file or an entire set of data files, can be restored to a computer system. The term data backup refers to either the activity of copying computer files for purposes of retention or disaster recovery or to the actual repository of computer files which have been made for those purposes. Data backup (the activity) is usually accomplished through the process of copying files to tape, disk or other medium. Two key concepts are important relating to data backup. First, deciding between local versus offsite storage of the data is always a consideration. Having an offsite backup provides protection from a disaster which affects your entire place of business such as a fire, flood, tornado, hurricane, earthquake, etc. Offsite storage is generally now being accomplished through online storage. Please see our definition of Online Storage. Second, data backup generally includes a retention policy. A retention policy defines the period of time for which the additional copy will be kept. An example of a typical retention policy is a policy used for Global Data Vault’s typical Advanced Data Protection service, and is shown below: For 7 days, users can go back to any individual point when a data backup was taken. Typically this is hourly for most customers. Customers actually set the policy, but we recommend hourly from 8am to 6pm, Monday to Friday which is the default. Then, for 14 days, users can recover any individual day, of which the most recent 7 allow access to any hour as above. Then for 30 days, users can choose a particular week. After that, users can choose a specific month To copy files to a second medium (a disk or tape) as a precaution in case the first medium fails. One of the cardinal rules in using computers is back up your files regularly.
Data Mining
What is Data Mining? Generally, data mining (sometimes called data or knowledge discovery process) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among "internal" factors such as price, product positioning, or staff skills, and "external" factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to "drill down" into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual's purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures. Wal-Mart is pioneering massive data mining to transform its supplier relationships. Wal-Mart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. Wal-Mart allows more than 3,500 suppliers, to access data on their products and performs data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, Wal-Mart computers processed over 1 million complex data queries. The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game. By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick's defense and then finds Williams for an open jump shot. How does data mining work? While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical
software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes. Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyze the data by application software. Present the data in a useful format, such as a graph or table.
Data processing
data processing may refer to a discrete step in the information processing cycle in which data is acquired, entered, validated, processed, stored, and output, either in response to queries or in the form of routine reports; the processing is the step that organizes the information in order to form the desired output. Used in a more general sense, data processing may also refer to the act of recording or otherwise handling one or more sets of data, and is often performed with the use of computers. There are three types of data processing. 1) Manual data processing 2) Mechanical data processing 3) Electronic data processing
Data Warehousing
Data warehousing is combining data from multiple and usually varied sources into one comprehensive and easily manipulated database. Common accessing systems of data warehousing include queries, analysis and reporting. Because data warehousing creates one database in the end, the number of sources can be anything you want it to be, provided that the system can handle the volume, of course. The final result, however, is homogeneous data, which can be more easily manipulated.
Data warehousing is commonly used by companies to analyze trends over time. In other words, companies may very well use data warehousing to view day-to-day operations, but its primary function is facilitating strategic planning resulting from long-term data overviews. From such overviews, business models, forecasts, and other reports and projections can be made. Routinely, because the data stored in data warehouses is intended to provide more overview-like reporting, the data is read-only. If you want to update the data stored via data warehousing, you'll need to build a new query when you're done. This is not to say that data warehousing involves data that is never updated. On the contrary, the data stored in data warehouses is updated all the time. It's the reporting and the analysis that take more of a long-term view. Data warehousing is typically used by larger companies analyzing larger sets of data for enterprise purposes. Smaller companies wishing to analyze just one subject, for example, usually access data marts, which are much more specific and targeted in their storage and reporting. Data warehousing often includes smaller amounts of data grouped into data marts. In this way, a larger company might have at its disposal both data warehousing and data marts, allowing users to choose the source and functionality depending on current needs.
