What is a Data Warehouse?

As marketers, we regularly come into contact with new and emerging acronyms, industry-related jargon and technical definitions, all of which can be tough to keep up with.

It’s common to feel out of your depth in this space and so, as marketers working in the tech industry, we like to keep things simple. Have you found yourself asking what is a data warehouse? In this blog, you’re going to learn what a data warehouse is and the benefits and drawbacks of data warehousing.

Definition of data warehousing

A data warehouse is a place where companies store their valuable data assets including customer data, sales data, employee data and so on.

It’s a repository (a central place where a collection of data is kept and maintained in an organised way) for data generated and gathered by an enterprise’s various operational systems. For example, a data warehouse would be used by retailers for distribution and marketing processes; helping to track items, customer buying patterns and promotions. Or consider the investment and insurance sector — data warehousing is utilised here in order to analyse data patterns, customer trends and to track market movements, and so on.

Think of a data warehouse as any other warehouse. In this functional storage space, businesses can retrieve their informational inventory whenever necessary for the purpose of business intelligence (BI).

What is business intelligence (BI)?

Business intelligence (BI) refers to technologies, applications and practices that collect, integrate, and organise business information which enables enterprises to gain a comprehensive understanding of business operations and customers. This technology-driven process allows companies to take data and transform it into actionable insights that are then utilised for informed operational decision-making and strategic planning.

BI strategies help executives, analysts and managers to support business endeavours, improve the decision-making process and gain a competitive advantage. It

BI does not instruct users directly, instead, it offers a way for people to analyse and explore data, understand trends and draw useful insights and conclusions by streamlining information into a usable format.

Related reading: Big Data for Beginners: Improve Your Marketing Strategy

Take Coca-Cola Bottling Company (CCBC) — CCBC is Coca-Cola’s largest independent bottling partner and so, as you can imagine, they work with a lot of data. Their issue, put across in this example by Tableau, was that their manual reporting process restricted access to real-time sales and operations data. This process would not only have eaten up a lot of time but manual reporting processes also increase the risk of human error and restrict the ability to gain real-time insights. CCBC’s solution was to utilise business intelligence by implementing a platform that automates the reporting process. Automation and data integration systems save the CCBC team over 260 hours per year — more than six 40-hour working weeks.

Okay, so that was a rather lengthy explanation of business intelligence. But as BI systems provide historical, current and predictive views of business operations most often using data that’s been gathered into a data warehouse, it seemed only logical.

We have established that a data warehouse is the electronic storage of a large collection of business data. This data comes from different places such as internal applications, the likes of marketing, sales, finance, external partner systems, customer-facing apps, and so on. And it’s used for business intelligence, the process wherein actionable insights are gathered in order to answer complex questions and make more well informed data-driven decisions.

Now that we understand what a data warehouse is, let’s quickly cover how a data warehouse works (simplified).

How does a data warehouse work (simplified)?

Side note — ETL (super simplified):

first of all, data is extracted and moved from the original database into the ‘staging area’. When it reaches the staging area, it is then transformed in order to make it useful (made into the same format — names, currencies, etc.). It is then loaded into the data warehouse.

Organisations typically have a number of functional departments — marketing, sales, finance, etc. And each of these departments will likely have its own database . As information is coming from different databases, even though it’s within the same company, it needs to be transformed (often referred to as cleansing) into the same format before it enters the warehouse.

For example, in the marketing department, the names of customers might be all in one field i.e. “FullName” and the sales team might have multiple fields such as “FirstName”, “LastName” and “Title”. You might also have data in pounds in one table and euros in another. So this information needs to be transformed into a logical framework. This is often done using the ETL (Extraction, Transformation and Loading) process.

Once the data has been transformed into a logical format with accurate relationships and enters the data warehouse, it can then be utilised for business intelligence.

Benefits of data warehousing

Having the ability to analyse large amounts of variant data and extract significant value from this information provides organisations with an array of valuable advantages:

Source:

Global Market Insights, Inc

Of course, all of these benefits sound great to any company that has a lot of data and is keen to gain a competitive market advantage. But as with many business processes; where there are advantages, there are a few disadvantages lurking alongside.

Data warehousing is a big IT project, therefore weighing up the pros and cons, or the cost/benefit ratio is a must for any business:

  • Inflexibility: standardisation of data and the inability to alter information or make adjustments. Whilst this can be an advantage, having data that’s homogenised can, for some businesses, be a disadvantage.
  • Potential incompatibility: data warehousing technology may require a company to modify the systems that they already have in place and that function well.
  • Not always cost-effective: data warehousing requires regular maintenance which can be costly. As well as having consistently available updates that don’t come cheap. It can also require the use of technical knowledge that not all businesses have which incurs training costs.
  • Limited use: if a company works with sensitive information, the use of data warehousing will be limited to a group of people and precautions may have to be put in place. This restriction of access and usability of the storage system limits business intelligence, business strategy and decreases the overall value of your data warehouse.

Conclusion

Businesses can get more from their information and optimise their analytics efforts when they move beyond simple databases and utilise data warehousing. Finding the right data warehousing solution to fit business needs can make a huge difference to the decision-making process and improve how effectively a company serves its customers and grows its operations.

But it’s important to remember that, as with many technical processes, data warehousing has both benefits and drawbacks. This means it’s vital for businesses to consider both sides of the coin, accurately research and assess the company needs before committing to the implementation of a data warehouse system.

Are you struggling to integrate data from your marketing tools? Book a free demo here to see howHurree’s data unification and segmentation platform can help your business. Feel free to get in touch viastevie-rose@hurree.co with any questions or comments you may have💌 It’s time toget your tools talkingand we can help you do it!

Originally published at https://blog.hurree.co.

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