Isuaja (2007), Enterprise Database management is a strategy to manage growth in any organization. Data is categorized as per its value to the organization. Underutilized, less valuable data is archived and stored. As a result of this, there is improvement in application functioning.

Organizations maintain access through the native enterprise application layer to ensure seamless data access for near and long term reporting requirements. Data growth is exponential in any organization and enterprise database management gives organizations the ability to stay ahead of data growth and achieve higher application performance (Isuaja, 2007). Below are some of the dominant strategies used by enterprises in managing their enterprise:

Upstream data growth
Duzuli (2010), “escalation of data being collated by organizations about their business, employees, customers and suppliers, sometimes requires coordination between multiple sectors such as IT, Retail, Banking, Healthcare etc”. Enterprises need to identify whether or not the database management system is adequate for handling transactions. More and more content is being created and all that data need to be managed. 80% of the data is unstructured, in the form of documents, Images, and audio visual files. One needs to look for
• Atomicity or Consistency in software, in a database transaction, either all or none of the update becomes available to anyone beyond the user or the application performing the update.
• Isolation- The results of a transaction are invisible to other users until the transaction is complete.
• Durability – Once done the results of transaction are permanent and survive future system failures.

Data replication
Enterprises must focus on formalizing database management through planning, standardization, and best practice adoption to improve operational efficiency and to lower costs. To increase efficiency, control where you can share info faster and compliance with security practices is a must. This requires making multiple copies of database located on different servers. The use of database in conjunction with various types of networks is increasing with the growth of networks and network technology. In many situations, it is desirable to manage database in a distributed manner i.e. multiple copies of the same database is stored in different servers, which in turn is connected to a network. Thus instead of a single server being accessed by all users, several different servers are provided thereby distributing the load. Distributed Database provides verbosity, such that when one of the servers fails data can be accessed through other servers which contain identical data. One has to make sure that all databases are in synchronization with others (Miguel, 2011).

Data warehousing
Miguel (2011), large databases require routine backup and maintenance with regular replication of data needed for tests and development processes. Managing such large databases, add risk and cost to organizations. Corporate policies and Government regulatory bodies make sure that data is retained by IT companies for a minimum of 5 years and by financial sectors for 10 years and so on.Enterprise data management helps reduce the burden on the organization to not only retain data but maintain accessibility.

PROBLEMS CAUSED BY NOT HAVING AN INTEGRATED SYSTEM

Innocent (2005) when high-growth companies have several soloed applications, there are many business challenges that arise. These challenges can become so severe, that they can cripple growth. Here is a summary of the five main issues that can hinder your growth if you run a business with disparate business software systems:

• Wasted Employee Productivity
When your company is in growth mode, every employee must be operating at optimal productivity. If your employees are bogged down with inefficient and disjointed processes, it increases errors and takes time away from their more important core duties. Important processes such as order processing, invoicing, expense approvals, and fulfillment, to name a few, can take a lot longer to get completed, and are often erroneous. For instance, your employees may be spending hours existingly re-entering order information into the accounting and invoicing system, while other employees pull that same information from your CRM system for their order fulfillment processes and to calculate sales commissions. If any orders are canceled in the meantime, your employees have to sift through mounds of data to reconcile this information again. Such labor-intensive and existing tasks reduce the agility that your company needs to grow.

• Lack of Real-time Visibility
When software systems are un-integrated, you have multiple overlapping databases, and cannot easily get a view of business performance in a timely fashion. Reports showing performance across your finance, sales, marketing, service, and fulfillment departments are crucial to giving you an integrated view of your company’s operations. Most companies simply give up on acquiring this information on a regular basis because of the amount of time it takes to source, extract and analyze this data. For those that do, countless hours are wasted trying to tie unrelated, error-prone, and out of date information together. Consequently, businesses either end up making critical decisions slowly, based on inaccurate information, or they make hasty and risky decisions off of gut instinct.

• Integration Complexity and Cost
With so many disparate applications, IT wastes an enormous amount of time and money on integrating, maintaining, and acquiring new versions of these applications. Often times, once new versions are purchased, even more integration and maintenance needs to be performed for all the different versions of software to work together. Consequently, valuable IT time that could be used to make the business more productive is wasted, while maintenance costs skyrocket.

• Increased Customer Churn:
Customer acquisition and revenue growth are key pillars to your company’s continued success. With fierce competition, it is essential that your company provide an exceptional customer experience or risk having customers take their business elsewhere. When customers are unable to quickly get information on their order status, can’t get issues resolved in a timely manner, or have to frequently deal with products being out of stock, they will be less satisfied and less likely to continue purchasing from you. An integrated software system ensures that customers have the right information and customer experience and that your employees have the instantaneous access to all the customer information they need to service and sell to your customers.

Solutions to the Identified Problems on Web Based Management System for Integrated Multi-located Supermarket

Ibegbu (2013), an enterprise database management system (DBMS) is a system used by companies to manage databases. It helps a company increase efficiency and is useful for companies with a large number of computer users needing access to information. Enterprises use DBMSs to plan and standardize their practices to increase overall efficiency in the company. They also help enterprises lower their costs. Databases must be managed efficiently and thoroughly to promote effectiveness in an organization. In general, the proposed system will provide solutions to the identified problems in the following ways.
• Storage
The new system will use a centralized database to tackle the storage problem. The sales data will be well stored and protected for future. The computer system memory will be large. There will also be extended and expanded memory facilities. In the event of increase in the volume of information, the system can always be upgraded to take care of the in.

• Information Referencing
This system presents an overall reference model for data management. This includes the identification of supporting layers, strategies for migration, transport, and reference data, and the four data standards. There are finally three dimensions for data to wisdom, data interoperability maturity, and interlocking data communities.
• Support
This project work sets out the requirements for data management program components, costing, and schedule. It lists the requirements for the various components, describes the operational environment, and sets out the needs for software, evolution,maintenance, technical support, and training.
• Overall Efficiency
The new system will be more efficient in terms of inputs, process and output of information.Information will be accurate, complete, clear concise and legible. There will be cost effective production. The integrated database system will operate at optimal efficiency.

PREPARING FOR INTEGRATION
According to Bruce (2009) generally, the databases to be integrated have to be developed independently and are heterogeneous in several respects. A worthwhile first step is therefore to attempt to reduce or eliminate such discrepancies. The path from heterogeneity to homogeneity may take three complementary routes:
• Syntactic Rewriting
The most visible heterogeneity is when existing databases have been installed on DBMSs based on different data models (relational, CODASYL, object-oriented). Efficient interoperation calls for the adoption of a common data model serving as information exchange standard among participating locations. Dedicated wrappers have to be developed to enforce data model transformations between the local model and the common model (Hammer 1997).
• Semantic Enrichment
Data model heterogeneity also induces semantic heterogeneity, in the sense that constructs in one model may provide a more accurate description of data than constructs in another model. For instance, an entity-relationship schema has different constructs for entities and associations, while some equivalent relational schema may describe the same data without making an explicit distinction between entities and associations. To compare the two schemas, one should be able to identify, in the relational schema, which relations describe entities and which relations describe associations. This is a veryprimitive form of semantic enrichment, i.e. the process that aims at augmenting theknowledge about the semantics of data.
• Representational Normalization
One more cause of heterogeneity is the non-determinism of the modeling process. Two designers representing the same real world situation with the same data model will inevitably end up with two different schemas. Enforcing modeling rules will reduce the heterogeneity of representations. This is referred to as representational normalization.

HISTORY OF DATA INTEGRATION
Wallace (1999) issues with combining heterogeneous data sources under a single query interface have existed for some time. The rapid adoption of databases after the 1960s naturally led to the need to share or to merge existing repositories. This merging can take place at several levels in the database architecture.One popular solution is implemented based on data warehousing. The warehouse system extracts, transforms, and loads data from heterogeneous sources into a single view schema so data becomes compatible with each other. This approach offers a tightly coupled architecture because the data are already physically reconciled in a single queriable repository, so it usually takes little time to resolve queries. However, problems lie in the data freshness, that is, information in warehouse is not always up-to-date. Thus updating an original data source may outdate the warehouse, accordingly, the ETL process needs re-execution for synchronization. Difficulties also arise in constructing data warehouses when one has only a query interface to summary data sources and no access to the full data. This problem frequently emerges when integrating several commercial query services like travel or classified advertisement web applications.
Bruno (2012) as of 2009 the trend in data integration has favored loosening the coupling between data and providing a unified query-interface to access real time data over a mediated schema, which allows information to be retrieved directly from original databases. This approach relies on a mappings between the mediated schema and the schema of original sources, and transform a query into specialized queries to match the schema of the original databases.

COMPONENTS OF A DBMS
Christabel (2007) DBMSs are the technology tools that directly support managing organizational data. With a DBMS you can create a database including its logical structure and constraints, you can manipulate the data and information it contains, or you can directly create a simple database application or reporting tool. Human administrators, through a user interface, perform certain tasks with the tool such as creating a database, converting an existing database, or archiving a large and growing database. Business applications, which perform the higher level tasks of managing business processes, interact with end users and other applications and, to store and manage data, rely on and directly operate their own underlying database through a standard programming interface like ODBC.

• Database Engine:
The Database Engine is the core service for storing, processing, and securing data. The Database Engine provides controlled access and rapid transaction processing to meet the requirements of the most demanding data consuming applications within your enterprise. Use the Database Engine to create relational databases for online transaction processing or online analytical processing data. This includes creating tables for storing data, and database objects such as indexes, views, and stored procedures for viewing, managing, and securing data. You can use SQL Server Management Studio to manage the database objects, and SQL Server Profiler for capturing server events (Christabel, 2007).

• Data dictionary
A data dictionary is a reserved space within a database which is used to store information about the database itself. A data dictionary is a set of table and views which can only be read and never altered. Most data dictionaries contain different information about the data used in the enterprise. In terms of the database representation of the data, the data table defines all schema objects including views, tables, clusters, indexes, sequences, synonyms, procedures, packages, functions, triggers and many more. This will ensure that all these things follow one standard defined in the dictionary. The data dictionary also defines how much space has been allocated for and / or currently in used by all the schema objects.A data dictionary is used when finding information about users, objects, schema and storage structures. Every time a data definition language (DDL) statement is issued, the data dictionary becomes modified (Christabel, 2007).

• Query Processor
A relational database consists of many parts, but at its heart are two major components: the storage engine and the query processor. The storage engine writes data to and reads data from the disk. It manages records,
controls concurrency, and maintains log files.The query processor accepts SQL syntax, selects a plan for executing the syntax, and then executes the chosen plan. The user or program interacts with the query processor, and the query processor in turn interacts with the storage engine. The query processor isolates the user from the details of execution: The user specifies the result, and the query processor determines how this result is obtained.

• Report writer
Also called a report generator, a program, usually part of a database management system, that extracts information from one or more files and presents the information in a specified format. Most report writers allow you to select records that meet certain conditions and to display selected fields in rows and columns. You can also format data into pie charts, bar charts, and other diagrams. Once you have created a format for a report, you can save the format specifications in a file and continue reusing it for new data.

DATABASE USER
According to Anthony (2010) there are four different types of database users.

• Application programmers
A person who prepares application program are called application programmer. Application programs operates on the data in all the usual ways: retrieving information, creating new information, deleting or changing existing information.

• Sophisticated users
Sophisticated users interact with the system without writing programs. Instead, they form their requests in a database query language. Each such query is submitted to a query processor whose function is to take a DML statement and break it down into instructions that the database manager understands.

• Specialized users
Some sophisticated users write specialized database application that do not fit into the traditional data processing framework. Among these application are computer-aided design systems, knowledgebase and expert systems, systems that store data with complex data types eg:-For Graphics and Audio data.
• End users
Unsophisticated users interact with the system by invoking one of the permanent application programs that have been written previously. Thus they are persons who uses the information generated by a computer based system. Retrival is the most common function for this class of user.
• Naive users
They are unsophisticated users who interact with the system by using permanent application programs (e.g. automated teller machine).

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