A Marketer’s Guide to Data Warehousing and Business Intelligence

In times gone by, marketing would have entailed representing a product and encouraging customers and prospects to purchase that product by shouting ‘buy here! Buy now!’, or something of the sort. Of course, we’re talking decades ago, centuries, or even millennia. Despite the overall objectives remaining the same, the way that businesses carry out their marketing today is much more advanced. 

Data enables organisations to look internally and analyse each and every aspect – its customers, what it’s doing right, where it could improve, and its progression over time. But let’s get down to the brass tacks – how do businesses attain, organise and practically utilise this data? The answer is through data warehousing and business intelligence

What is a data warehouse?

A data warehouse is a central repository of integrated data. Data warehouses store current and historical data, gathered from an organisation’s diverse source systems, and consolidate it all in one place. They are specifically structured to facilitate querying and analysis. Data warehouses often form the basis of a business intelligence platform. Though they are different, data warehouses and business intelligence work in tandem. 

A standard data warehouse will include a relational database and an ETL (Extraction, Transformation, Loading) solution that enables BI capabilities. 

A relational database is a type of database that stores and provides access to data in relation to other data. The information is organised into tables that can be linked based on data common to each. This makes it easier for organisations and/or analysts to better understand relationships amongst available customer data and gain new insights. 

An ETL solution is a common way that organisations combine data from multiple, disparate sources into a data warehouse where it is then used for business intelligence. 

Side note: How ETL works:

  • Extraction: data is retrieved from one or more sources (online, on-premises, legacy, SaaS, etc.) after this, data is put into a staging area.
  • Transformation: data is taken, cleaned, and put into a common format. Cleaning typically involves taking out duplicate, incomplete or inaccurate data.
  • Loading: inserting the formatted data into the target database, data store, data warehouse or data lake. 

What is business intelligence?

Business intelligence (BI) is a set of technology-driven processes that take data and transform it into actionable insights that inform and influence an organisation’s strategic and tactical business decisions. BI seeks to address the known unknowns of a business. To empower companies, and the decision-makers within, to discover things about their business that they had often never even considered. 

Research shows that 74% of top performers find that siloed or fragmented data is one of the most challenging aspects of business intelligence. This is where data warehousing and business intelligence combine to make the perfect team. The purpose of a data warehouse is to perform queries and analysis of large amounts of historical data. Data that resides in a warehouse is defined as being in-use, as it has been processed for a particular reason and into a specific format, namely business intelligence. 

Some business intelligence programs pull information directly from source applications and others require the use of a data storage system to aggregate diverse data sets. Business intelligence typically combines disparate data sources via a data warehouse, which acts as a central area where BI applications can query and analyse. Analytics and reporting tools can technically function without data warehousing, however, doing so can be limiting; data stored within different systems often exist in different formats, making it harder to draw connections and identify patterns. Data warehouses cleanse and standardise data making it more consistent, accurate and of better quality. This, in turn, streamlines business intelligence processes.

Business intelligence for marketing

Business intelligence software takes data analytics beyond the basic realms of organisational analysis. Business intelligence expert, Cindi Howson, differentiates two different types of BI: traditional (or classic) business intelligence and modern business intelligence.

  1. Traditional BI: IT professionals use in-house transactional data to generate reports. 
  2. Modern BI: business users interact with agile, intuitive systems to analyse data more quickly. 

For certain types of reporting that require rigid accuracy, like financial reporting, organisations will opt for traditional BI. However, for marketers who operate in a fast-paced environment, modern business intelligence is preferred as it allows users to gain insight into dynamics, behaviours and information that are rapidly changing, such as marketing events or campaign performance.

When it comes to marketers utilising business intelligence, the common BI features we think of are dashboards and reports.

Dashboards are hosted software applications that automatically pull together available information into charts and graphs that give a view of the immediate state of the company. They allow people to examine information, understand trends and derive insights into current aspects of an organisation e.g. where are sales prospects sitting in the pipeline right now? How many marketing qualified leads have we gained this month? 

They’re single-page visualisations that include the likes of colourful graphs and charts, making it easy for decision-makers to analyse the aspect of the company or campaign that they’re focusing on. Dashboards can be personalised and offer consolidated information in real-time (seeing what’s happening now). They offer easy-to-understand KPIs, summaries of information that enable agile decision-making and eliminate guesswork. Additionally, dashboards are a lot more versatile than reports in that they can be used and understood by a lot more people within a company.