The transformation of your data warehouse into the heart of your overall BI strategy occurs through data warehouse consulting. Data enters, gets analysed, and then is refined into potent, real-time reports that are the foundation of important strategic choices.
Because data engineering is a field that can quickly become extremely complex, it’s crucial to keep this fundamental procedure in mind.
Finding out what a data warehouse is, if you need one, and your alternatives for implementation can feel like falling into a strange, foreign black hole of jargon and new ideas.
Data Warehouse: What Is It?
Your company is likely now gathering a lot of data. There will be several systems where that data will be kept.
As a result, the databases of your sales teams may have information about past customer transactions, while the social media interactions are captured by your marketing systems and the comments, reviews, and complaints are monitored by your CX team.
The combined potency of all of the information would be enormous. Sadly, a lot of firms are unable to fully realise that potential at the moment because:
- Data is compartmentalised into distinct, non-integrated systems;
- There is simply too much data to process manually.
You can store all of your data in a large, virtual data warehouse. It converts current and historical data from various sources across your company into useful, educational analytics for BI.
Read: ETL Data Warehousing: A Core Component For Reporting
How does that function?
Divide it into three steps at the highest level:
- By an automated data pipeline, data from integrated systems is brought into the data warehouse (Extracting)
- The data is changed from its source schema to the desired schema so that it may be used for analytics (Transforming)
- The data is prepared and sent to BI and analytics tools that are integrated throughout the organisation (Loading)
You might want to incorporate step two as part of:
- Validation: confirming the accuracy of data within its own limitations (for example, country matching address or dates being in valid format)
- Harmonization: ensuring that data is in a consistent format, such as making sure all units are metric rather than imperial;
- Cleaning: removing corrupt, duplicate, or obsolete data;
- Enrichment: combining data from different sources to improve quality
The name of this procedure is Extract Transform Load (ETL). And that, in a nutshell, is exactly what a data warehouse does. No more. No less.
As soon as you start reading about data warehouses, you’ll find that new, related terms start appearing just as you start to understand everything.
Read: Data Warehousing: The Concepts, Methods, and Structures
Major difference between data lakes and data warehouses
A data lake is an enormous virtual collection of raw data. They can store any type of data without any pre-processing and have a larger storage capacity.
The main difference is that data lakes contain raw data, and data warehouses store processed data prepared for reporting. This makes them useful in a variety of circumstances.
If you’re really into data science or have extensive machine learning requirements, a data lake can be a better option because raw data is more malleable.
If you want a data store that is better suited for providing enhanced reports for strategic decision-making, a data warehouse is your best bet.
Read: How To Use Data Warehousing Solutions To Generate A High ROI
When Should We Consider Data Warehouse Consultancy
The design, development, and implementation of a data warehouse are incredibly difficult, time-consuming tasks that demand in-depth expertise to properly perform.
It’s wonderful If you already have staff members with this knowledge. An internal team with the right resources, including funds, time, and data engineering skills, can be of great assistance, provided that they start off with a full grasp of your current data architecture.
Most businesses, however, lack the internal capabilities required to oversee a project of this size, and all but the largest IT teams will find it challenging to juggle their current duties with a data warehouse installation.
At that point, hiring outside data warehouse consultants as an outsourcing option may make sense.
What Exactly Does Data Warehouse Consultancy Mean
Using external data experts to design, develop, and maintain your own data warehouse is known as data warehouse consultancy.
You might choose to start from scratch and create a completely unique data warehouse, or you might only assist with the deployment of pre-made data warehouse software. There are consulting choices to fit you, regardless of the choice you make.
Consultants for data warehouses assist with:
- Creating ETL tools for a more seamless transfer;
- Data warehouse modelling and database design;
- Data warehouse building and management;
- Data integration and migration;
- Designing data pipelines;
In some circumstances, data warehouse consulting will need more specialised assistance, such as a data engineer’s skills to construct data pipelines. You might outsource to a group of specialists rather than a single generalist consultant, depending on what you need.
8 Three factors are necessary for data warehouse consulting to be truly valuable:
Reduced time to value due to faster data warehouse implementation. Less expensive than keeping a full-time team for your data warehouse. Less efficiency loss because internal IT teams can continue with their regular tasks.
A data warehouse provides you with a vast array of advantages, including:
Improved data quality, better data governance, more potent data analytics, high scalability, improved historical insight, better query performance, and better query insight.
A data warehouse, in comparison to relying on source systems, provides better data and faster, more accurate analytics. 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.
To evaluate the calibre of the items in your warehouse, you can utilise automated technologies. Also, it’s beneficial to periodically “sense check” your warehouse’s data for conflicts between the data there and the data in your source systems.Implementing a data warehouse requires a lot of labour.
Yet, it doesn’t follow that it must be a significant source of worry for your company. That hard effort will pay off with quicker, better, and more accurate analytics with the appropriate specialists and the correct methodology.
