From insight into action: data warehousing, business intelligence and analyticsPosted on: August 11, 2023
Our world revolves around data – its collection and analysis, as well as the opportunities it presents.
In today’s competitive, fast-paced and often-saturated marketplaces, organisations having comprehensive, holistic oversight of their own data is business-critical. Failure to do so can result in poor business decisions, lost market share, an inability to adapt to supply or market fluctuations, process inefficiencies, missed opportunities, and challenges in driving meaningful, profitable change.
Businesses across all sectors and industries understand the traction data insights can give them. As a result, the growth of data warehousing – and the scale of its potential impact on business and the wider world we live in – shows no sign of slowing.
- Data warehousing, as a service, is projected to reach $7.69 billion by 2028.
- 85% of businesses believe big data will revolutionise the way they do business, with 79% stating that not using big data will end in their bankruptcy.
- Poor data quality – which warehousing addresses – costs the US economy approximately $3.1 trillion per year.
- Up to 2.5 quintillion bytes of raw data are created every day.
As effective data management is so valuable to organisations, there are always businesses proactively seeking professionals with specialist skills across areas such as data science, data analytics and data modelling to maximise insights and apply them to growth and development.
What is data warehousing?
Oracle defines a data warehouse as ‘a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics’. The purpose of a data warehouse is performing queries and analyses involving vast amounts of historical data derived from numerous data sources, for example from transactions and log files.
The historical records contained within a data warehouse – and the ability to analyse them in great detail – enable businesses of all types to improve their decision-making and set relevant KPIs and other metrics to boost their bottom-line. In its simplest terms, a data warehouse stores data and provides a clear, real-time picture of a business at a given point in time. Many call them ‘single sources of truth’. It plays an integral role in automation; as such, many businesses now rely on easy-to-use dashboards, ERPs and CRMs to support employees with their business activities.
There are different architectures involved in data warehousing, such as simple, simple with a staging area, hub and spoke, and sandboxes. Depending on organisational needs, data warehouses can be hosted on-premise, multi-cloud or hybrid.
How can a data warehouse help with BI?
BI refers to the use of specialist software that combines everything data-related – data analytics, data tools, data mining, data visualisation and data infrastructure – to inform and make better, data-driven decisions.
BI relies heavily on data storage, which is where data warehouses come in: they serve as the digital ‘backbone’ of data storage solutions. To facilitate BI’s requirement for multiple, vast and varied data sets and the ability to perform complex queries, three activities must be undertaken.
- Data wrangling, generally via extract, transform, load (ETL) technologies.
- Data storage, which uses online analytical processing (OLAP) to integrate, summarise and transform data from different sources and different formats so it can be analysed.
- Data analysis, using business intelligence tools and software such as Tableau, Microsoft Power BI, Qlik, Domo, Amazon Redshift, Snowflake and Sisense.
Data warehousing enhances BI performance, and its capabilities, as it draws on multiple sources. Its other business benefits include:
- creating a centralised, stable repository for vast amounts of historical data
- improving the overall quality of business data and data forecasting
- providing organisation-wide access to data
- connecting various groups of stakeholders
- enabling data wrangling, analytics, machine learning and artificial intelligence (AI)
- offering cost efficiency over the long-term, as intelligence systems help to reduce inefficiencies and support resource allocation and management.
An organisation may not automatically need to use a data warehouse. Most move through a data storage hierarchy – for example, using data lakes before progressing to a data warehouse or data mart – using technologies best suited to their needs at the time and in line with their growth.
What are the business intelligence and data-led trends for 2023 and beyond?
Leading technology consultants, Gartner, highlight three key business intelligence solutions and themes they believe are important for organisations who rely on BI software, data and business analytics.
- Theme 1: Think like a business – trends that are proactive in delivery value and accepting responsibility for implications of activities, such as value optimisation, managing AI risk, data sharing, data and analytics sustainability, and observability.
- Theme 2: Move from platforms to ecosystems – trends that enable data integration and agility across the wider organisation, such as practical data fabric, emergent AI, and converged and composable ecosystems.
- Theme 3: Don’t forget the humans – trends that urge organisations to remain employee-centric (rather than data-centric) and remain aware of broader responsibilities, such as humans as decision-makers and consumers as creators.
As our world continues to evolve, so will our data – and trends indicate demand and impact will only increase. As data engineers and other professionals with the ability to manage and manipulate data are highly sought-after to help address critical business needs, now is the ideal time to upskill.
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