Predictive analytics is about finding and quantifying hidden patterns in the data using complex mathematical models that can be used to predict future outcomes. Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and is reactive in nature, while predictive analysis focuses on the future. These systems are also used for customer relationship management CRM. The concept of data warehousing dates back to the late s  when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse".
In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it.
In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations, it was typical for multiple decision support environments to operate independently.
Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems usually referred to as legacy systems , was typically in part replicated for each environment.
Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from " data marts " that was tailored for ready access by users. Facts, as reported by the reporting entity, are said to be at raw level. Facts at the raw level are further aggregated to higher levels in various dimensions to extract more service or business-relevant information from it. These are called aggregates or summaries or aggregated facts.
For instance, if there are three BTS in a city, then the facts above can be aggregated from the BTS to the city level in the network dimension. In a dimensional approach , transaction data are partitioned into "facts", which are generally numeric transaction data, and " dimensions ", which are the reference information that gives context to the facts.
For example, a sales transaction can be broken up into facts such as the number of products ordered and the total price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order. A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly.
Facts are related to the organization's business processes and operational system whereas the dimensions surrounding them contain context about the measurement Kimball, Ralph Another advantage offered by dimensional model is that it does not involve a relational database every time. Thus, this type of modeling technique is very useful for end-user queries in data warehouse.
The model of facts and dimensions can also be understood as data cube. Where the dimensions are the categorical coordinates in a multi-dimensional cube, while the fact is a value corresponding to the coordinates. In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories e.
The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented Kimball, Ralph Some disadvantages of this approach are that, because of the number of tables involved, it can be difficult for users to join data from different sources into meaningful information and to access the information without a precise understanding of the sources of data and of the data structure of the data warehouse.
Both normalized and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the two models is the degree of normalization also known as Normal Forms. These approaches are not mutually exclusive, and there are other approaches.
Dimensional approaches can involve normalizing data to a degree Kimball, Ralph In Information-Driven Business ,  Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents even when the same fields are used in both models but this extra information comes at the cost of usability.
The technique measures information quantity in terms of information entropy and usability in terms of the Small Worlds data transformation measure. In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. These data marts can then be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts , which are dimensions that are shared in a specific way between facts in two or more data marts.
The top-down approach is designed using a normalized enterprise data model. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. Data warehouses DW often resemble the hub and spokes architecture.
Legacy systems feeding the warehouse often include customer relationship management and enterprise resource planning , generating large amounts of data.
To consolidate these various data models, and facilitate the extract transform load process, data warehouses often make use of an operational data store , the information from which is parsed into the actual DW. To reduce data redundancy, larger systems often store the data in a normalized way. Data marts for specific reports can then be built on top of the data warehouse. A hybrid DW database is kept on third normal form to eliminate data redundancy.
A difficult task is correlating information between the in-house CRM and time-reporting databases. The systems don't share information such as employee numbers, customer numbers, or project numbers. In this phase of the design, you need to plan how to reconcile data in the separate databases so that information can be correlated as it is copied into the data warehouse tables. You'll also need to scrub the data. In online transaction processing OLTP systems, data-entry personnel often leave fields blank. The information missing from these fields, however, is often crucial for providing an accurate data analysis.
Make sure the source data is complete before you use it. You can sometimes complete the information programmatically at the source. You can extract ZIP codes from city and state data, or get special pricing considerations from another data source. Sometimes, though, completion requires pulling files and entering missing data by hand. The cost of fixing bad data can make the system cost-prohibitive, so you need to determine the most cost-effective means of correcting the data and then forecast those costs as part of the system cost.
Make corrections to the data at the source so that reports generated from the data warehouse agree with any corresponding reports generated at the source. You'll need to transform the data as you move it from one data structure to another. Some transformations are simple mappings to database columns with different names.
Some might involve converting the data storage type. Some transformations are unit-of-measure conversions pounds to kilograms, centimeters to inches , and some are summarizations of data e. And some transformations require complex programs that apply sophisticated algorithms to determine the values. So you need to select the right tools e. Base your decision mainly on cost, including the cost of training or hiring people to use the tools, and the cost of maintaining the tools.
You also need to plan when data movement will occur. While the system is accessing the data sources, the performance of those databases will decline precipitously. Schedule the data extraction to minimize its impact on system users e. Data warehouse structures consume a large amount of storage space, so you need to determine how to archive the data as time goes on. But because data warehouses track performance over time, the data should be available virtually forever.
So, how do you reconcile these goals? The data warehouse is set to retain data at various levels of detail, or granularity. This granularity must be consistent throughout one data structure, but different data structures with different grains can be related through shared dimensions.
As data ages, you can summarize and store it with less detail in another structure. You could store the data at the day grain for the first 2 years, then move it to another structure. The second structure might use a week grain to save space. Data might stay there for another 3 to 5 years, then move to a third structure where the grain is monthly.
By planning these stages in advance, you can design analysis tools to work with the changing grains based on the age of the data. Then if older historical data is imported, it can be transformed directly into the proper format. After you've developed the plan, it provides a viable basis for estimating work and scheduling the project.
The scope of data warehouse projects is large, so phased delivery schedules are important for keeping the project on track.
Even with a beautiful design model in your mind's eye, the question of how to build the data warehouse raises its ugly head! Ugly because no. About this course: The capstone course, Design and Build a Data Warehouse for Business Intelligence Implementation, features a real-world case study that.
We've found that an effective strategy is to plan the entire warehouse, then implement a part as a data mart to demonstrate what the system is capable of doing. As you complete the parts, they fit together like pieces of a jigsaw puzzle. Each new set of data structures adds to the capabilities of the previous structures, bringing value to the system. Data warehouse systems provide decision-makers consolidated, consistent historical data about their organization's activities. With careful planning, the system can provide vital information on how factors interrelate to help or harm the organization.
A solid plan can contain costs and make this powerful tool a reality. More information about text formats. Text format Comments Plain text. Web page addresses and e-mail addresses turn into links automatically.
Lines and paragraphs break automatically. Leave this field blank. Sponsored Content Zero To Hero: Azure Cosmos DB vs. Our data model was also always evolving, which meant that our data over time could be inconsistent, which made it hard to clean up and ensure uniformity. While these were all points that could have been improved, the most critical downside was that we had Looker directly hooked into our production instance instead of a slave replica, for a variety of reasons.
Under a heavy load, long queries would have had a negative impact on the performance of the order checkout page. At the end of the day, we knew we needed a different approach. The most obvious solution was to build our own data warehouse. As I mentioned above, originally we had our Looker instance directly associated with our production database.
The hybrid approach recommends spending about two weeks developing an enterprise model in third normal form before developing the first data mart. Each store could have several departments. The company's market is rapidly changing, and its leaders need to know what adjustments in their business model and sales practices will help the company continue to grow. When I started at Glossier in , I became part of a small team working on building a data warehouse prototype. And of course, maintenance, maintenance, maintenance. External market forces are changing the balance between a national and regional focus, and the leaders need to understand this change's effects on the business. Business intelligence Data management Data warehousing Information technology management.
While we were aware that this could have been improved, we were constrained by elements that were beyond our control at the time. A couple of weeks later, we got the go-ahead to build the data warehouse and started the process of moving all of our sources to this database. One of the initial sources that we set-up in the data warehouse was to have the read-only replica of the production database saved under a schema named prod more on our naming convention later.
Since Amazon offers a database syncing service that allows us to output data from a schema to another schema of a different database, this was a simple change for us. The fact that our data analyst could now run long queries without impacting customer experience on Glossier. Beyond running long queries, there were other advantages that we were seeking with our data warehouse, such as the ability to scale. When we were at the point of choosing a data warehouse platform, after investigating multiple solutions, we decided to opt-in with Redshift for our first iteration.
We chose it for several reasons, primarily because it was very similar to what we were used to with PostgreSQL, and the fact that it was a product offered by Amazon, which is the main service we use for our whole tech infrastructure. Other benefits of Redshift include easy scalability and its columnar storage , making it performant for our type of queries. It requires time and dedication to get your table efficient to query.
You can tune the compression encoding for each individual columns, you can set sort keys , and a distribution style per table. For more on this, check out this tutorial on tuning table design and this article on optimizing Redshift performance with dynamic schemas. Another benefit of using Redshift is that it allows us to easily export data unload to Amazon S3 and import data copy from S3, giving us the possibility of increasing the efficiency of our processes.
We now have our data residing in our data warehouse without too much effort. With more polishing, this also allows us to save a historical version of the raw CSV file so that we can rollback to that version at any time. Something like Athena seems fun to use. Previously, we had all of our data residing in multiple places. Some parts were in Excel spreadsheets, while others were in our transactional databases, on third-party APIs, or even in a totally different database.
This would get very complicated if we wanted to retrieve some pieces of data. You would have to either remember how to fetch it, or read documentation to refresh your memory. This single space ended up being, as you have likely guessed, our data warehouse. Since we have a lot of data coming from different sources, we had to create set of rules in order to define how this data would reside in the warehouse. Based on experiences of different people within the team, we quickly decided that all of our different sources of data should reside in their own schemas.
In this way, we could easily connect to the database with whatever tool we felt like using and easily access different types of data. In order to save the different data sources into our warehouse, we had to combine multiple tools together. All in all, we have about 19 different sources of data flowing into our data warehouse, which segues nicely into our next benefit. Before having a database, it was impossible for us to access different parts of the data coming from other parties.