Using Data Warehouse to support building strategy

Today I want to talk about a very important subject, especially for large companies that are facing the problem of transforming the way of storage and analyzing data.

First of all, we should clarify why the data warehousing (DW) is becoming increasingly important. Well if we consider in terms of strategic decision, DW making through their capacity to integrate heterogeneous data from multiple information sources in a common storage space, for querying and analysis. So it can evolve into a multi-tier structure where parts of the organization take information from the main data warehouse into their own systems. These may include analysis databases or dependent data marts. As the data warehouse evolves and the organization gets better at capturing information on all interactions with the customer: indeed, DW can track customer interactions over the whole of the customer’s lifetime.

Is easy to understand that Data warehouse is a heart of Business Intelligence which is essential for any effective application. In other words, data warehouse is a consolidated view of your enterprise data, optimized for reporting and analysis. Hence, data warehouse can greatly enhance abilities of decision making. In terms of CRM, data warehouse and data mining are utilized to provide valuable reports about actual business as well as to forecast about needs of customer (this is very important point). We will discuss more about characteristics of data warehouse and how it is reflected in the project.

Nowadays problem can easily presented as follow: many companies do not have anything using data warehouse to support building strategy or forecast business tend. All the jobs of data collection and consolidation have been done manually (excel file reports….)

Clearly, in order to improve the performance of the tasks, the company should own a methodology and data warehouse infrastructure: is important to design a DW lifecycle that we must pay more attention at Design and Prototype steps before implementing to ensure that result will match with user desire. This is very important to lead to success of a data warehouse project. Designing a right DW architecture is crucial for any organization: DWA shall include types of data like Meta data, raw data and Summary data. From the DW, meaningful data with new view will be generated to Data Marts for Purchasing, Sales & Marketing and Inventory. Decision makers will access the data marts to forecast and to give right decision. It also allows generating meaningful reports instead of doing manually as before. This saves time a lot and everybody agrees that “saving time is equal to save money”.

There is another important point: as DW has become the most significant repository for business intelligence and decision-making support. Thus, the system security is set at a crucial priority. Each companies shall implement highest level of security by establishing user profiles (user groups) with different of authority concerning to the accessible information, performed operation, etc… and requiring regularly updated password.

In terms of DBMS, shall put attention on MS SQL Server to deploy the project because it provides benefits like:

• Well-supported if bugs are reported
• Provide ability of data encryption on DB server itself
• It is also Microsoft technology so we can apply security policies across systems in the domain
• Provide a strong framework with powerful components for data warehouse.

Data warehouse is designed to centralize different data sources. It creates a transparent data environment for user, helping user do data mining, report and analyse data efficiently. The DW would be applied in nearest future, at the higher level which already planned as mentioned. So which are the main benefit of creating a DWA? Companies are requiring more data and greater integration of data. I am now thinking about call centre, customer communication, general ledger, project, human resources and operations. The volume of data expands and the complexity increases, this may result in many databases and data marts. Therefore, it is much more logical and beneficial to have one repository for data. The data warehouse can evolve into a multi-tier structure where parts of the organization take information from the main data warehouse into their own systems. These may include analysis databases or dependent data marts. As the data warehouse evolves and the organization gets better at capturing information on all interactions with the customer. Data warehouse can track customer interactions over the whole of the customer’s lifetime.

Again I would like to stress the point that 'information' is a company’s best asset and today’s leading businesses are harnessing it to make real-time critical business decisions that will impact the company’s bottom line, meet regulatory deadlines and address customer needs. But, how can companies be assured that the information is always accurate and up to date with data being integrated from disparate sources and with different standards?

IT organizations are required to tackle these data management issues head on and solve the complex issues around designing the infrastructure, ensuring data quality, understanding the source of data and business rules, managing the metadata and extracting and transforming it to standardized formats for a data warehouse. Yet, solving these issues can take many weeks and months while your business users wait impatiently for this critical information.

As today's decisions in the business world become more real-time, the systems that support those decisions need to keep up. It is only natural that Data Warehouse, Business Intelligence, Decision Support, and OLAP systems quickly begin to incorporate real-time data. Data warehouses and business intelligence applications are designed to answer exactly the types of questions that users would like to pose against real-time data. They are able to analyse vast quantities of data over time, to determine what is the best offer to make to a customer, or to identify potentially fraudulent, illegal, or suspicious activity. Ad-hoc reporting is made easy using today's advanced OLAP tools. All that needs to be done is to make these existing systems and applications work off real-time data.