Data Warehouse Consulting 101: The Ultimate Guide

Data warehouse consulting

Data warehouse consulting transforms your data warehouse into the engine room of your entire BI strategy. Data goes in, and is processed then refined into powerful, real-time reports that drive key strategy decisions.

This basic process is important to keep in mind, because where data engineering is concerned, things can get super technical super quickly. Figuring out what a data warehouse is, whether you need one and what implementation options you have can feel like falling into an unfamiliar black hole of jargon and new concepts.

If you’re tech-minded, you’ll likely have a great time in there. If you’re a non-technical founder or organizational leader looking into options for better business analytics, not so much.

That’s why we’ve put together this 101 guide. Below, we’ll be covering data warehouse basics, decoding key jargon and running through everything you need to know about potential outsourcing options.

What is a Data Warehouse?

Right now, your business is probably collecting lots of data.

Much of that data will be stored in separate systems. So, your sales teams’ databases might hold customer transaction history, whilst your marketing systems record social media interactions and your CX team track feedback, reviews and complaints.

All of this data would be hugely powerful if used together. Imagine the opportunities for personalization, or more accurate targeting, or enhanced buyer personas!

Unfortunately, many businesses can’t currently unlock all that potential because:

  • Data is siloed into separate, non-integrated systems
  • There is simply too much data to process manually

This makes advanced data processing impossible – and that’s where a data warehouse comes in.

A data warehouse is a big, virtual repository for all your data. A data warehouse takes current and historical data from disparate sources across your business and transforms it into usable, informative analytics for BI purposes.

In the example above, that would mean massively enhanced marketing and sales capability, for example.

How does this work? At the top level, we can break it down into three steps:

  1. Data from integrated systems enters the data warehouse via an automated data pipeline (Extracting)
  2. The data is transformed from its original schema to its destination schema, so that it is usable for analytics purposes (Transforming)
  3. The data is processed and directed to integrated BI and analytics tools across the business (Loading)

As part of step two, you may also want to include:

  • Validation: ensuring that data is valid within its own constraints (for example, country matching address or dates being in valid format)
  • Cleansing: removal of corrupt, duplicate or defunct data
  • Harmonization: making sure data is in a consistent format, such as making sure all units are metric rather than imperial
  • Enrichment: combining data from different sources to improve quality

This process is known as Extract Transform Load (ETL). And that, in a nutshell, is exactly what a data warehouse does. No more. No less.

Data Lakes vs Data Warehouses: What’s the Difference?

As soon as you start reading around data warehouses, you’ll find that other, similar terms start popping up – just as you thought you were starting to get a handle on everything.

So, data lakes vs data warehouses…what’s the deal?

A data lake is a large virtual pool of unprocessed data. They offer a larger capacity and ability to hold all types of data without any preprocessing.

The main difference here is that data lakes store raw data, whilst data warehouses store processed data ready for reporting. This makes them useful in different circumstances.

If you’re big into data science or have extensive machine learning needs, a data lake can be a better option because unprocessed data is more malleable. If you want a data store that’s more suited to creating enhanced reports for strategic decision-making, a data warehouse is the way to go.

When Should We Consider Data Warehouse Consulting?

Designing, building and implementing a data warehouse is a hugely involved, time-consuming process which requires a lot of expertise to get right.

If you have that expertise on hand internally…great! An internal team with the right budget, data engineering expertise and enough time can be a real boost, given they start with an intimate knowledge of your existing data structures.

Equally, many businesses don’t have the expertise in house to run such a massive project – and all but the largest IT teams will struggle with their existing duties when landed with a data warehouse implementation.

That’s when outsourcing to external data warehouse consultants can be a good idea.

What Do We Mean By Data Warehouse Consulting?

Data warehouse consulting is the use of outsourced data professionals to create, implement and maintain your internal data warehouse.

You might want to build a completely customized data warehouse from scratch, or simply help with the implementation of off-the-shelf data warehouse software. Whichever option you’re going for, there are consultancy options out there to suit you.

Data warehouse consultants help with:

  • Data warehouse modeling and database design
  • Data warehouse construction and maintenance
  • Data integration and migration
  • Building data pipelines
  • Developing ETL tools for a smoother migration

In some cases, data warehouse consulting will require more specialist help, such as the services of a data engineer to build data pipelines. Depending on what you need, you may find yourself outsourcing to a team of specialists, rather than one generalist consultant.

Will Data Warehouse Consulting Pay Off in the Long Run?

The real value in data warehouse consulting relies on three things:

  • Faster data warehouse implementation, meaning reduced time to value
  • It’s cheaper than maintaining a full-time team for your data warehouse
  • Reduced efficiency losses as internal IT teams can carry on with everyday duties

A data warehouse offers you huge number of benefits, including:

  • Enhanced historical insight
  • Better data quality
  • Better data governance
  • More powerful data analytics
  • High scalability
  • High query performance and insight

In short, a data warehouse offers better data and faster, more accurate analytics than relying on source systems. This translates directly into more revenue as your workforce uses this to make key strategic decisions, generate leads and improve products and services.

An outsourced team of data warehouse professionals will realize these benefits more quickly than internal teams because they don’t have to juggle data warehouse implementation with other day-to-day tasks. You’ll avoid major productivity drops, and won’t have the time and cost barriers of scaling a new team internally.

What is the Data Warehouse Implementation Process?

So, let’s say you want to onboard an external data warehouse team. What does the process look like from there?

Here’s a top-level look at the process data warehouse consulting teams follow:

1. Requirements Gathering

Like any project, thorough requirements gathering at the start of the process is essential for success. Data warehouse consultants will have thorough discussions with key stakeholder groups to establish your organizational needs and desired outcomes.

Stakeholder groups may vary from organization to organization, but will usually include key decision makers, internal IT teams, analysts and security professionals.

2. Creation of Warehouse Environments

As part of your requirements gathering process, one of the key decisions you’ll have to make is where to host your data warehouse. Your options here are:

  • Public cloud, via a hosted solution like AWS
  • Private cloud, hosted on your own hardware or by a private third party
  • Hybrid cloud, using a mixture of cloud and on-premise storage or processing power
  • On-premise, storing data on your own servers

The first practical step of the implementation is the creation of this environment. Your data warehouse consultants will take you through the pros and cons of each, and offer their recommendations before starting the build.

3. Creation of Development, Testing and Production Environments

Development teams create three separate warehouse environments – one for the build (development), one for testing out updates before going live (testing) and one live warehouse which your wider business uses (production).

This is so that developers, engineers and consultants can build and test their work without corrupting live production data or disrupting business use. Any changes to your data warehouse will be set live in the development and testing environments before going live in your production environment.

4. Data Model Creation

The database powering your data warehouse is central to its success, so it’s vital to design it well. Use data models to build databases from scratch and design a schema that works for your needs.

Data modeling is a huge and often complex topic and we can’t cover it adequately here (without an extra thousand words or so!). Read our recent guide to data modeling for a more in-depth understanding of what data models are and what they do.

5. ETL Setup and Automation

It’s now time to set up those ETL processes we discussed above.

Data engineers create automated pipelines to extract data from source systems before transformation. There are also a number of options for automated data transformation, which can be customized to your business’s needs.

The final step here is to integrate your chosen BI and analytics tools with the data warehouse. Most modern systems will be able to integrate relatively easily, but if you’re using a legacy solution it might require a little more thought.

6. Data Mart Creation

Not all of your users will need access to all of the data in your warehouse. Having one, massive data warehouse that everyone accesses can create inefficiencies, and also increases security risk.

A good option here is to create distributed data marts, which give access to analytics data on a smaller (often departmental) scale. This ensures that everyone has access to the reports they need whilst keeping access to data on a need-to-know basis.

7. Audit and Review

Congrats – your data warehouse is up and running! Once live, however, it’s essential that you and your data warehouse consultants put some ongoing measures in place to maintain data quality.

You can use automated tools to measure the quality of your warehouse contents. It’s also worthwhile performing the occasional ‘sense check’ for discrepancies between data in source systems and data stored in your warehouse.

Sound Intimidating? It Doesn’t Have to Be…

You got it. Data warehouse implementation is a lot of work.

But that doesn’t mean it has to be a major source of stress for your organization. With the right experts and the right approach, that hard work will pay off with faster, enhanced and more accurate analytics.

At Tivix, we have over a decade of data warehouse consulting experience under our belts, with plenty of satisfied clients (the UN, Tesla and Zoetis to coyly name drop a couple). We use our global network of data professionals to scale the team your project needs as soon as we’re onboard, and work until you’re getting the results you want.

Get in touch today for a quick chat

Data Warehouse Consulting 101: The Ultimate Guide

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