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A CRM allows staff to manage data pertaining to customers, prospects, contacts, tasks, sales opportunities and marketing campaigns in a dynamic environment. Using a centralized, hosted platform, data can be accessed from wherever you can connect to the internet, which can mean more productivity amongst decision makers using these tools. An important aim of a CRM is to integrate different types of data to ensure that data and effort is not duplicated. An effective CRM system not only collects data but also provides organisation and interpretation of that data.
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Customer Relationship Management
CRM covers practices used by companies to manage and enhance relationships with customers, including the capture, storage and analysis of customer, vendor and sales information, the distribution of specified information to related departments or tools, and then the deployment of marketing ideas and campaigns. The three main parts to CRM, which may be implemented separately, are;
Operational - the automation of customer processes, including sales and service functions
Collaborative - direct communication with customers, mail outs, sales focus
Analytical - analysis of customer data
Business Intelligence
Business Intelligence is the process of analyzing large amounts of data, usually stored in large databases such as a Data Warehouse, tracking business performance, detecting patterns and trends, and
helping enterprise business users make better decisions. Metadata describes how data is queried, filtered, analyzed, and displayed by software, including reporting tools, migration tools, OLAP tools and Data Mining systems.
OLAP metadata - descriptions and structures
of Dimensions, Cubes, Measures or Metrics, Hierarchies and levels
Report metadata - descriptions and structures of Reports, Charts, Queries, DataSets, Filters and Variables
Data Mining metadata - descriptions and
structures of DataSets, Algorithms and Queries
Business Intelligence metadata can be used to understand how corporate financial reports reported are calculated, how revenues, expenses and profits are collected and
with varying detail levels from individual sales transactions to some demographic range. |
Data Warehouses became a distinct type of computer database after it was realized that traditional storage mechanisms were often insufficient to reporting and other types of retrieval. They were developed to meet the demand for management information and analysis that could not be handled by systems in operation
or where the size of data was prohibitive to timely reporting.
Processing load of reporting systems can reduce the response time of an operational system.
The database design of operational systems is optimized for operations, and not reporting.
Organizations with more than one operational system require company-wide reporting, which typically cannot be supported by a single system
Development of reports in operational systems requiring ad hoc software each time is too expensive.
As a result, separate computer databases began to be built that were specifically designed to support reporting and analysis. These data warehouses were then able to retrieve data from a range of different sources, including mainframes, minicomputers, PCs and the internet, and integrate this information to a single location (the new source). This capability, while reducing or eliminating operational load, has led to the demand for data warehouse systems.
Data Warehouse Models
Off-line Operational Databases - the Data Warehouse is simply mirrored from the operational database. This could be an off-line server where the processing load of reporting does not impact on the operational system performance
Off-line Data Warehouse - data is updated at a regular schedule, daily, weekly or monthly, from the operational systems and the data is stored in a report-centric, optimized structure
Real Time - data is updated on a transaction basis with no delay
Integrated Data Warehouse - the data warehouse receives, modifies and reissues data to the operational system
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Using various software tools, the business may generate their own ad hoc queries from any enterprise point, or even using a web browser.
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Data Analysis ToolsData analysis tools and software are typically used to sort through,
categorize and organize, enterprise wide data in order to identify patterns and establish relationships between sets of data. Data analysis techniques are useful for virtually any business to gain greater insight into trends within their business.
With access to analytical tools, business analysts can perform tasks with the data, hypothesize new processes in advance, forecast trends. On-line analytical processing (OLAP) allows analysts to sample data quickly and in
multiple ways, allowing the analyst to dynamically interact with data (information)
in a report.
Data Mining
Traditionally, analysts have performed the task of extracting useful information from recorded data. However, the increasing volume of data requires other methods. As data sets have grown in size and complexity, there has been a
move away from direct hands-on data analysis
towards automatic data analysis using more sophisticated tools.
Advances in technology, network speed, and the robustness of hosting servers have made
data collection and the storage of that data easier. However, data
still needs to be converted into organized information to become
useful.
Data mining is the process of applying computer-based methodology, including new techniques for
data discovery.
Data mining identifies trends within data that go beyond what analysis may
reveal. Through the use of sophisticated algorithms, users have the ability to identify key
information within business processes and even create new opportunities.
Data Acquisition
This is the capture of data via some mechanism whereby it can be stored for later retrieval and analysis. Data acquisition can involve some software or hardware
device; sometimes it involves the acquisition of signals and waveforms by remote sensors and cameras, and then
there is the processing in order to store rational, usable data. |
Data Modeling
A data model describes the structure of data within a domain and, by implication, the structure of that domain
itself. This means that a data model specifies a dedicated grammar for the dedicated artificial language. The data model describes the organization of the data, represents classes of entities
about which the business wishes to maintain information. The attributes that the data carries or is assigned and the relationships amongst the entities.
Metadata
This is the categorization of the data, which in turn creates more data called again Metadata. An item of metadata may describe an individual item or the content of an item, or a collection of data items.
Data Integrity and Security
While Data Integrity and Security has wider
implications for the business, more generally, Data Integrity and Corruption refers to errors in computer data that occur during transmission, retrieval
or storage, introducing unintended changes to the original data and results. Computer storage and transmission systems use a number of measures to provide data integrity.
Data loss or corruption can be detected by the use of checksums and can often be corrected by the use of error
correcting software. In some events, were data or system corruption / failure is detected, automatic retransmission or restoration from backups can be applied. RAID disk arrays test parity bits for data across a set of hard disks and can reconstruct data. Data corruption during transmission has a variety of causes. Interruption of data transmission causes information loss
while environmental conditions may interfere with data transmission, especially with wireless where networks are susceptible to interference from other devices. |
