Support Get in Touch
Managed Services
Managed Services
Managed Services
Enterprise-level processes, technology and strategy for small and medium businesses. Outsourced services, all supported by members of the Impact team.
See Our Approach
20 Years in the Making
Learn more about Impact Networking, our team and history.
Learn More About Impact
Resources Support Inquiries Get in Touch

Infographic: Business Intelligence and Data Warehousing Explained

Business intelligence and data warehousing are two aspects of digital transformation that are closely related when it comes to how information is stored, secured, and utilized.

In short, data warehousing refers to the methods organizations use to collect and store their information, assembling them in data “warehouses”.

Business intelligence refers to the methods used to analyze this information in order to provide executives with actionable data for decision making.

Both of these are absolutely crucial to a modern business, for which the effective leveraging of data is an important part of operations and a key competitive differentiator across all industries today.

Take a look at the infographic:

Business Intelligence and Data Warehousing Explained

How Data Warehousing and Business Intelligence Come Together

Data warehousing and business intelligence, when used effectively, can function as the information backbone of an organization, helping them align every line of business to facilitate a truly data-driven operation.

What do we mean by this?

Data silos, which occur when departments in a company become detached from one another in terms of their information sharing, are much more common than you might imagine in businesses.

It’s especially common in organizations where different departments operate on legacy software that are not integrated which each other through enterprise resource planning.

This leads to data siloing—and while departments may have access to business intelligence solutions, the data is mostly restricted to these silos and is inaccessible to anybody else within the organization.

The “State of the Customer Journey 2019” report showed that silos, in particular, were hurting marketers looking to leverage data—47% of marketers said that their information is siloed and difficult to access.

To counteract this, the concept of a data warehouse was conceived, whereby data streams from all sources within a business would be directed to a central repository and can then be accessed by those who need it with ease.

Outline of How Data Warehousing and Business Intelligence Work

Data Source

The first part of data warehousing that needs to be addressed are the sources from which data will need to be retrieved and uploaded to the warehouse (or its sub-categories, “data marts”, which house data for specific business functions of departments).

This will typically involve determining who the key stakeholders are and the reporting they do that’s necessary to funnel into the data warehouse.

Much of this will be self-explanatory. For example, marketing reports from the CRM or accounting reports from the ERP. Some will be less easy to identify, and might involve more overlooked aspects of data that may be necessary to report, like customer telephone calls or email records.

Data Warehouse

Once the data that is needed has been identified, it’s time to extract and load it into the data warehouse.

This process is what’s referred to as “extract, transform, load” (ETL) and is a crucial component of loading data from multiple sources into one unified data repository.

ETL is very important because not only are you extracting the necessary information to the data warehouse, but also cleansing it to ensure quality of data and consistency across all databases—regardless of where or which system the information came from.

The basic premise of ETL is that data is extracting to what’s called a “staging area”, which will comprise the data in raw form.

Unstructured data collectively accounts for 80-90% or more of all data and is continuing to grow.

Then it is transformed and undergoes data processing.

Data processing means taking the raw data and ensuring that it is ready to be used for analytical purposes by end users.

Data processing involves filtering good data from bad (unusable) data, filtering it, removing duplications, validating it, and making adjustments for consistency (common in spreadsheets, for example).

Finally comes the load step, where the newly transformed data is sent from the staging area to its correct repository within the data warehouse.

When data is loaded, it is typically a fully-automated process that is done in batches on a continuous basis.

Business Intelligence

Once the data is in the data warehouse and has been processed correctly, it is ready to be analyzed by business intelligence (BI) programs.

BI software will take the data from warehouses and parse it for insights, further transforming the information into data that is actionable and easy for decision makers to understand.

In short, business intelligence acts as the bridge between the data warehouse and the end user.

Through automation, machine learning, and the ability to analyze in seconds what would take a human employee weeks, BI tools are able to query data and generate reports, charts, and other actionable data sets.

While over half of all enterprises consider cloud BI to be either “critical” or “very important” to their ongoing and future initiatives, Gartner found that 87% of businesses are considered to have a low level of analytics maturity.

End User Access

Once the business intelligence solution has used the data to generate the desired reports for end users, the system has to deliver this information to them in a way that is actionable.

The first three steps of this process as a whole are all focused in ensuring that the data is stored and prepared properly for use—these are backend processes.

The final step is a front end process—the way the information is actually used by stakeholders.

Most market-leading business intelligence tools, like Microsoft’s PowerBI, have great visualization so users who are not technical can begin applying the data in their decision making without difficulty.

Ensuring that end users are getting the information they need in a way that is digestible is an important aspect of data warehousing and business intelligence.

The purpose of this entire process is to get valuable information into the hands of those that need it but aren’t necessary predisposed to being comfortable working with complex data sets, so end user access is one of the key considerations that should be made when deciding on a BI solution.

Bottom Line

Business intelligence and data warehousing are important for modern organizations.

This is because companies today compete far more on the basis of their ability to leverage data than they ever have done.

48% of organizations consider cloud BI to be “critical” or “very important” to their future business productivity plans.

As a result, the need for businesses to invest in ways that unify their data and offer opportunities to utilize it for their initiatives is an important consideration to make.

Impact Networking offers business intelligence solutions to clients from all across the country. To learn more about how you can kickstart your strategy for digitization, take a look at our business intelligence consulting services.