Today, organisations generate huge amounts of data on a daily basis. Aware of the importance of data, companies are storing data from different departments to gather insights, helping the organisation make better decisions. This data can amount to petabytes (1,000,000 gigabytes) as it is collected from multiple sources and generally stored in an unstructured format. Sometimes organisations rely on external data in addition to internal data to learn more about their competition. A data warehouse allows data from heterogeneous sources to be aggregated and stored in one place for the purpose of analysis, reporting and business intelligence rather than transaction processing. It is the merging of components and technologies that helps create a strategic use of data.

The vast amount of data stored in a data warehouse comes from different departments, such as marketing, sales, finance, customer-facing apps, third-party partner systems and so on. This data is then transformed and loaded to match the data already available in the data warehouse. This data is then made accessible to users.

Concept of data warehousing

Data warehousing is the process of systematising data from multiple sources in an organisation and storing it in one place for further analysis, reporting and decision-making. Generally, organisations have a transactional database that contains information about all daily activities. Organisations also have other data sources – related to external data or internal activities. Data from all these sources are merged and stored in a data warehouse through an ELT or ETL process. The data model of this data warehouse is designed so that it is feasible to collect data from all these sources and make decisions based on them.

Functioning of a data warehouse

A data warehouse is designed as a central repository to consolidate business data from different sources. The data flowing into it consists of all types and sizes – structured, semi-structured and unstructured data. Moreover, it can come from different sources, such as internal applications, customer applications and external systems.

Once the data enters the data warehouse, it is further loaded, transformed and processed and other predefined steps are performed to turn it into information that can be quickly accessed and used for decision making. Consolidating large amounts of information in the data warehouse can help an organisation make a more holistic analysis, to ensure that all available information has already been considered before a decision is made.

Recognising the need for a Data Warehouse

  • Organisations usually fail to implement a Data Warehouse simply because they have not been able to identify a clear use case for it. Organisations that recognise a business problem for their data can stay focused on finding a solution.

    Here are a few key reasons why a Data Warehouse offers benefits:

    Improved decision-making: Data Warehousing improves the efficiency and speed of access to data, allowing users to create data-driven strategies to outperform the competition.

  • Standardisation of data: Data Warehouses store data in a standard format, making it easier for users to analyse it and extract applicable insights. Standardisation of data collected from different heterogeneous sources reduces the risk of errors and increases overall accuracy.
  • Lower costs: Data Warehouses enable users to dive deeper into historical data and ensure the success of previous initiatives. They can also visualise course corrections to minimise costs, drive growth and increase operational efficiency.

Data warehouses serve as flexible and scalable data repositories to centralise an enterprise’s data and facilitate information analysis. Companies continue to embrace data warehouses at a rapid pace.

Below is a list of six data warehouse best practices that can help companies with their requirements to meet time-to-value for data analytics and business intelligence.

  1. Engage stakeholders early and regularly
    A data warehouse must meet the requirements of various stakeholders, such as department managers, business analysts and data scientists, as they all need access to the information in the warehouse to perform analyses and generate reports. By including feedback from these stakeholders, you increase the likelihood that an organisation’s decision-makers will have the necessary information at their disposal to make informed choices and reduce the likelihood of having to make a major reform later. Without the support of management, a data warehouse project may never get off the ground or even be abandoned halfway through.
  2. Integrating data governance
    If poor-quality data is fed into a warehouse, centralising it for analysis is fruitless – the results of analyses will be inaccurate and misleading – ‘garbage in, garbage out’. To avoid this, organisations need to implement a robust data governance process where all departments work together to define policies for security, collaboration and retention of their data based on business and legal requirements.
  3. Defining user roles
    Defining user roles to determine who can read, write and update data within a data warehouse is critical. Without proper processes and access controls, users will over time undermine the data pipeline by not coordinating their work, especially in large organisations where many analysts work with the data warehouse. However, companies also need to ensure that their user controls are not too restrictive, as overly bureaucratic systems inhibit productivity by blocking experimentation and iterations.Organisations need to strike the right balance between security and the operational flexibility needed for analysts to work effectively. As part of this balance, an enterprise could have analysts set up sandboxes for testing changes to data structures and require that prior approval be obtained before merging modified data.
  4. Understanding data warehouse schema design
    An organisation must design its schemas to match business requirements with the data warehouse technology being implemented. Today, the scalability of data warehouses in the cloud makes it possible to denormalise data to increase search speed without resource constraints.
  5. Frequent iteration and testing
    An agile approach to data warehouse development and maintenance can improve repository performance and ensure that it can adapt to an organisation’s ever-changing requirements.Applying short development paths with small, well-defined tasks and test plans can ensure that development teams get feedback from relevant stakeholders faster.They can then continuously iterate to upgrade their systems and processes.This provides a fast feedback loop for product development and enables the identification and resolution of warehouse problems before they affect user services.Effective use of ELT and data warehouses in the cloudETL (extract, transform, load) and ELT (extract, load, transform) are two processes used for pulling data in from the source, transforming it as necessary and storing it in the data warehouse.By moving the transformation step to the end of the process, ELT makes it possible to ingest data and quickly start a data analysis.
  6. Effective use of ELT and data warehouses in the cloud
    ETL (extract, transform, load) and ELT (extract, load, transform) are two processes used to ingest data from the source, transform it as necessary and store it in the data warehouse.By moving the transformation step to the end of the process, ELT makes it possible to ingest data and start data analysis quickly.Cloud data warehouses are suitable for use with ELT because their scalable CPU capacity can handle data transformation after loading.Cloud data warehouses have several other advantages over their local counterparts.Because ELT loads data into the target system before it is transformed, an enterprise must have a data modelling tool to prepare data for use in analytics. Data engineers who design and maintain data warehouses should be guided by subject matter experts to provide end users with data in a format that meets their requirements.

Other data warehouse best practices

Besides the main practices mentioned above, there are several other factors that determine the success of a data warehouse implementation. Some of the most important are the following.

  • Metadata management – Documentation of metadata related to all source tables, staging tables and derived tables is extremely important for gaining actionable insights from the data.The ELT tool can be designed to capture even the data lineage of the data.Some of the widely used ELT tools also perform well in tracking data lineage.
  • Logging – Logging is often an overlooked aspect. Having a centralised repository where logs can be inspected and analysed contributes greatly to quick debugging and creating a robust ELT process.
  • Data ‘concatenation’ – Most ELT tools have the ability to concatenate data in the extraction and transformation phases. It is advantageous to investigate whether expensive joins are needed in the ELT tool or whether the database handles that.In most cases, databases are better optimised for handling joins.
  • Compartmentalisation of transaction databases – Transaction databases should be kept separate from extraction jobs and it is always best to run them on a staging or a replication table so that the performance of the primary operational database remains fault-free.
  • Status monitoring and alerts – Continuous monitoring of the ETL/ELT process and building in the provision for alerts is important to ensure system reliability.
  • Timely recovery – Even with the best monitoring, logging and fault tolerance, these complex systems can have failures. The ability to recover such faults in to previous stages should also be considered while designing the data warehouse process.

Summary

The success of a project like data warehouse implementation depends heavily on understanding the business, its IT needs and its pain points. Understanding all these aspects helps the organisation put in place the structure needed to be successful in data warehousing. Before you start developing a data warehouse, it is advisable to review the above data warehouse best practices to save time in project development and achieve cost optimisation.