“Pattern” is a key term that every business should be aware of, if they want to generate more revenue from their customers. Yes, every business wants to know their customers and store their spending pattern over a period of time. Finally they aggregate this information in their Data Warehouse environment. Pattern generally refers to purchasing behavior, online browsing habits, social network sharing etc.
To monitor the spending pattern of a customer is very essential, because an enterprise generates more revenue only from the information that has been gathered and aggregated.
Let’s see how a business collects your information.
Here are some of the examples.
- When you browse for products and shop online, advertisers and publishers might collect and share your pattern of searching, websites you visited and in fact the content you read.
- When you engage in social networking sites, applications or tools are likely to have access to the information about you and your network.
- When using mobile phones with location services enabled, several applications may have a detailed access to your location. Advantages are many for e.g. mobile device applications give consumers location specific search results, access to information about the local events, and more timely delivery of sales offers.
- When you use your reward card at a grocery store or at a pharmacy, your profile information, household details and the information about your purchase may be shared with other market research companies or data brokers.
Advantages of a Data Warehouse
- Access to a wide variety of data within the organization. This data includes company’s total revenue by the individuals, city, state and/or country etc.,
- Increased data consistency and efficiency.
- Potentially lower computing costs and increased productivity.
- Empowering the business to perform any level of random reporting without impacting the performance of the daily operation.
- Highly paid salaries with an average of more than 100,000 US Dollars per year. In the midst of recession, average salaries remained unchanged for the last couple of years.
- It helps to forecast the product on demand.
- Market Basket Analysis.
- Determining the selling price for products.
- Data Warehouse is a foundation for an effective decision support system.
Evolution of Data Warehouse
Here are the different stages of data warehouse.
Level 1 – Report Generation: How will you find out the total earnings for a specific department in an organization during the last quarter? Generating such a report is pretty simple only if you have the data available from different source channels in an unified format. To achieve tremendous value for the data warehouse, you must integrate the data from different channels into one. This is a first step for an evolving data warehouse.
Level II – Data Analysis –As a department head if you see a dip in your revenue report when compared to the last year’s revenue and would like to know the cause, you should analyze the data stored in a data warehouse. As a part of data analysis, a data warehouse should be capable of delivering detailed transactions from the aggregated data. e.g., Performance tuning is a key factor, as the data delivery will be very slow when a user has to dive into the details of every transaction for data analysis.
Level III Forecasting: When your data warehouse stores several years of aggregated data and able to deliver detailed information, it can be easy enhanced to deliver a predictive analysis report. for. e.g. you can predict how well a specific department will function in future.
Level IV Online Data Warehousing: Both aggregated and granular data in a data warehouse should be up to date. For an instance if there is bulk order dispatched from the supply chain department then data warehouse should immediately update the data marts with available stocks on hand. Given these details on hand helps logistics, supply chain, inventory management and accounting will function efficiently. In order to achieve online data warehousing, every data acquisition process in the data warehouse executes continuously.
Level V Automation: This is an extremely complex and intensive stage to any successful organization. Decision should be made within a fraction of second and completely automated. When users visit the Amazon store, the inventory list changes automatically based on the users browsing pattern or shopping history. In order to deliver such a sophisticated user experience, the data warehouse should be built with extreme engineering.
By considering all the above factors, Data Warehousing is surely an evolving technology monster in any business.
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