Enterprise data warehouses (EDW) have existed for thirty years already. EDW is an essential business element since it allows for enhancing analytics, boosting business processes, creating more efficient promotion companies, personalizing the user experience, etc. This article will tell you what an enterprise data warehouse is and how it functions. Also, we will provide examples of why implementing EDW will help your business.
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What is EDW?
Enterprise Data Warehouse (EDW) is a centralized repository that consolidates and stores all enterprise business data from various sources. If necessary, one can extract the data from these warehouses to physical devices like HDD, SDD, CD, flash-cards, etc., or via instruments like client relationship management (CRM) and Enterprise resource planning (ERP). Therefore, companies can swiftly process massive data sets within one storage structure and single unification instead of searching for and combining them from various dissimilar databases.
An analogy is searching for information in books. Earlier, there were only paper books in various libraries; hence, searching, comparing, and analyzing data from books was time-consuming and expensive. Furthermore, the problem worsened if the books were from different epochs or countries. It causes difficulties with terms, proper names, and contextualization. Almost any reader can find a digital copy, and many (at least those in English) are on Google Books Ngram Viewer. Therefore, people can easily access, compare, and analyze them.
It is likewise with business data. They were formerly stored on paper in different storages; hence, they were harder to use. Then they became digital, yet held in other places, namely disks, servers, and databases. Currently, business data are easy to keep in one place; thus, all parties can access them if necessary, from the seller to the company director or an audit. These systems are enterprise data warehouses.
How EDWs function?
Numerous architecture approaches provide particular features, benefits, and pros and cons for enterprise data warehouses. Yet, let’s focus on the most significant ones.
One-tier architecture. One of the most straightforward architectures for EDW. Here, reporting means connecting to the data storage linked to various analytical instruments. This EDW architecture is easy and cost-efficient to implement and adjust, yet storing more than 100 gigabytes of information will cause some issues: low speed, a necessity of thorough inquiry, confusing results, and limited flexibility. Hence, one-tier architecture for enterprise data warehouses is suitable only for small data volumes.
Two-tier architecture. This type of storage implements a ‘data mart layer’ between the reporting layer and EDW. Data marts are like small databases that store information about a specific element (sales, income, marketing, staff, etc.). Namely, this two-tier architecture distributes the information within EDW to various components depending on the type of information. At the same time, the reporting instruments link to the particular marts instead of the entire data storage.
This approach boosts inquiries' processing speed and makes them less demanding to input accuracy. Furthermore, data marts can limit access for end-users; thus, EDW becomes more secure. A two-tier enterprise data warehouse is better for real employment scenarios in business processes.
An example of a one-tier architecture for EDW. Source
Three-tier architecture. This EDW implements an additional layer for online analytical processing (OLAP) between the mart layer and reporting layer. OLAP cube is a database that provides data from several dimensions (usual relation databases work with one spreadsheet, while OLAP allows comparing data from two or more spreadsheets). Therefore, OLAP allows compiling in several sizes (subdivisions, regions, channels, etc.) that help to get advanced analytics on various parameters.
Hence, three-tier EDW perfectly functions for large companies, those with a branched business structure, and companies working in various directions.
EDW's two-tier architecture has data marts containing information about a different subject area.Source
A three-tier architecture for EDW adds an OLAP cube layer that can accept information from distributed marts or directly from EDW.Source
The benefits EDW brings to the business
Permanent access to business data. The prime benefit of an enterprise data warehouse for business is that the parties involved have constant access to business data. It is much better than separate data storage for each major affiliate or organization subdivision, leading to a complex data extraction process. Moreover, it causes more issues in output data and is not secure for client privacy and company protection.
Fast and easy access to business data. Being able to make the right decision rapidly is one of those factors that provide critical advantages for business. When business owners have instant access to structured and accurate data, even if they have enough time, employing EDW will benefit them since it allows for more time on data analysis rather than collecting it.
The reaction of shops to the Covid-19 pandemic is a shining example. The companies with EDWs reacted to changing the demand structure since they had data in real time. Thus, they ordered the resupply for high-demand products faster than competitors — for example, masks, sanitizers, tablets, and laptops.
Higher quality of business analytics. Enterprise data warehouses save CEOs and directors the trouble of making decisions on limited data or instincts. All critical decisions that impact the strategy and company rely on precise facts supported by information from data warehouses. Furthermore, these can be original solutions, like collecting data in one place and finding unexpected patterns after the analysis. For instance, before the hurricanes in the USA, the sales of flashlights grew, and what is less obvious, the strawberry PopTarts cookies and dry lunch sales, grew too.
More straightforward and significant integrations. Modern software solutions for data storage can integrate with numerous instruments for collecting and analyzing data. We shall consider not only Excel or CRM/ERP solutions but IBM Cognos Analytics, SAP Business Objects, Microsoft SQL Server, etc. Yet, all these instruments are efficient only if one can unite the data from alien systems in a single structured database. It will eliminate the chance of duplicated data and helps to extract them easily and fast. Furthermore, integrating these instruments into an enterprise warehouse is much faster and cost-efficient than incorporating them into several storages.
Data quality and consistency. Since enterprise data warehouses collect information from various sources and transfer it into a unified format, they offer more accurate data for decision-making company processes. Financial, marketing, and sales offices within a company can employ the data for their needs, and they will be sure that it is accurate and true. Hence, each office will provide consistent results which motivate teamwork.
High investment return. A business's gain in income after an investment is called return on investment (ROI). EDWs can reduce expenses and increase revenue while also boosting business analytics quality, significantly increasing the return on investments and preventing the business from going bankrupt.
Historical intelligence. Many enterprises employ EDWs for history reports that helps to conduct advanced business analytics involving trend analysis, deep pattern search, and forecasting business development in the long term.
How to implement EDW into business.
Enterprise data warehouse is a complex software solution that must be secure and reliable yet easy to use and appealing. Developing an EDW from scratch is the only solution to achieving it. Employing ready solutions involves risks in security and compromising what you require and what the supplier offers. Furthermore, trust the development of your EDW only to experienced teams like Merehead. Since 2015 we have been creating enterprise solutions of various complexity, from websites to highly-secure crypto exchanges and NFT platforms. Please, visit our website to study the portfolio and contact our consultants.
Step 1. Determine your business needs
Business needs will impact almost any solution in the development process of enterprise data warehouses, starting with what user role (seller, marketer, or director) will access certain information to how frequently they will use it. Therefore, it is reasonable to begin by questioning your business users before developing the EDW. The survey will provide you with the following:
Also, it is worth asking the IT specialists (in-house developers, experts on operating source systems, database administrators, etc.) about whether the present data will suffice for such business requirements as:
- General business targets and tasks of your company and its targets within individual business units, departments, branches, production lines, etc.
- Methods and indicators for estimating the success and completing the business targets and aims of the company. These methods and indicators will be standard for the entire company or vary depending on the department.
- Critical issues that the business faces. How will the enterprise data warehouse solve or reduce them?
- The types of routine data analysis the company currently performs, including the data used to do it, how often the research is done, what potential improvements it has brought, etc.?
- Operational systems used by your company;
- The frequency of updating data entirely and within units;
- The availability of historical data. Period. The level of standardization;
- The instruments for accessing business data;
- The tools for analyzing business data;
- Which types of analytical data are generated regularly;
- Whether the unique queries are processed well;
Step 2. EDW conceptualization and selecting the technology
Based on the information collected during step 1, you can define the scale of the project, its requirements, and the expectations of your employees and business. It requires thorough analysis and categorization to create an optimized set of functions and specifications for the upcoming EDW. In particular, it will help you to select the architecture of the enterprise data warehouse, its type, and optimal technology for each architecture component. While listing the stack of technologies, consider the following factors:
It is also at this stage to decide whether to deploy EDW on-premises, in the cloud or in a hybrid deployment. The choice of deployment option is determined by numerous factors, such as budget, security requirements, the volume of data, nature of data, number of users, and location.
Here is an example of a possible technology stack for EDW:
- Your current technical environment;
- Planned strategical technological vectors;
- The technical expertise of the development team members;
- Special requirements for data security;
- Other essentials.
- Receiving data: Fivetran, Airbyte, Meltano, Estuary Flow.
- Data storage: Redshift, BigQuery, Snowflake, Databricks, any relation database (Oracle, Teradata, Vertica, Greenplum, DB2, MySQL, SQL Server, etc.), platform Hadoop (Apache Hadoop, MapR, Hortonworks, Cloudera), NoSQL database. — MongoDB, Cassandra, MapR DB, and others.
- Business analytics and data visualization: Looker, Mode, Tableau, Preset, Superset, Thoughtspot, Chartio, Orange, Opentext Content Analytics, OpenRefine.
- Operationalization or «reverse ETL»: Census, Hightouch, Rudderstack.
- Survey and monitoring: Monte Carlo, Observe.ai, Splunk, Datadog, Datakin.
- Metadata management: OpenMetadata, Informatica, MANTA.
- Orchestration: Airflow, Prefect, Dagster, Astronomer.
Step 3. Developing the data warehouse environment design
Not only that, but during the development of your enterprise data warehouse, you will have to determine the data sources and analyze the information within them, namely what data type they offer, the recording structure, generation time and speed, data quality, accuracy, and privacy concerns.
The following step is the logical modeling of data or organizing company data into logical relations called creatures (real-world objects) and attributes (the characteristics that define these objects). Then these logical data models become structures within the database; for instance, the creatures become spreadsheets, attributes become columns, relations transfer to limitations for the external key, etc.
When the modeling is ready, the next step will be designing the intermediate data environment to provide the warehouse with high-quality aggregate data and send the data stream from the source to the target object during all subsequent data uploading.
An example of structuring data in a logical relation model. Source