Today, less than 0.5% of data is actually being used, and businesses lose over $600 billion a year because of bad data. That means that, by and large, businesses are not properly storing, using and analyzing the data that they have coming in each day.
This is a huge problem, especially in today’s data-driven culture. Your data is only powerful if you can use it - and it is no good to you if you can’t properly organize and analyze it.
For many, this bad data problem comes from choosing the wrong type of data storage and running ineffective analytics as a result. Here, we break down the basics of data warehouses and discuss the important criteria that companies must keep in mind when considering a data warehouse for their business.
What Is a Data Warehouse?
In broad terms, a data warehouse (DWH) is a place that stores current and historical data - from marketing, sales and other departments - in one central repository so that it can be analyzed for market research, analytical reports and decision support.
While traditional operational databases are updated in real-time to maintain accurate, up-to-the-minute data, data warehouses are updated less frequently and are designed to give a historical, long-range view of data. The analytics that are run in a data warehouse, then, are usually more of a comprehensive view of your company’s history rather than a snapshot of the current state of your business.
Further, since the data in a data warehouse is already integrated and transformed, it allows you to easily compare older, historical data and track marketing and sales trends. These historical comparisons can be used to track successes and failures and predict how to best proceed with your business ventures in order to increase profit and long-term ROI.
Specifically, end users can use the information in their data warehouses to:
- Monitor or modify marketing campaigns
- Manage and improve customer relationships
- Clean and organize company data
- Predict future growth, needs and pain points
- Track, understand and improve company performance
- Consolidate data from multiple sources, etc.
In short, data warehouses are great for long-range, comprehensive analytics without negatively impacting daily operations, and this kind of analytics is always necessary if businesses expect to improve their strategies and make long-term progress.
The Data Warehouse Options
There are four primary types of data warehouse platforms, and many options within each of these categories. These are:
- Traditional Database Management Systems (DBMS)
These are usually row-based relational platforms. Dominant brands of this software include IBM DB2, Microsoft SQL Server, Oracle and SAP
- Specialized Analytics DBMS
These are usually columnar data stores designed specifically for managing and running analytics. Dominant brands of this software include Teradata, HPE Vertica and Greenplum
- Out-of-the-box data warehouse appliances
These typically combine software and hardware with a DBMS pre-installed. In theory, all the customer needs to do is plug it in and turn it on. Dominant brands of this solution include IBM Netezza, Oracle Exadata and SAP Hana
- Cloud-hosted data warehouse tools
Here, the DBMS is not installed - it is accessed over the internet. This essentially offers data warehouses as a service without requiring any hardware. Dominant brands here include Amazon Redshift, Google BigQuery and Microsoft Azure SQL Data Warehouse
Each of these are good options - making a choice for your business is all about figuring out what will work best for your company, your budget, your employees and your overall structure. Let’s get into what you should look out for and how to choose.
Criteria to Keep in Mind When Choosing a Data Warehouse Platform
1. First thing’s first: cloud VS on-premise (usually DBMS or specialized DBMS)
The first decision that one has to make when choosing a data warehouse is between cloud and on-premise data warehouse systems, and there are certainly advantages and disadvantages to both.
In a nutshell, cloud services offer:
- Scalability : with no on-premise software or hardware, it’s easy, cost-effective, simple and flexible to scale with cloud services
- Low entry cost: with no servers, hardware, IT work or operational costs, cloud services cost substantially less up-front.
- Connectivity: With cloud services, it is much easier to connect to other cloud services, which means that it’s easier to digest, store and file data.
- Security: Typical cloud providers stay hyper up-to-date with security patches and protocols to keep their host of customers safe and happy
- Availability/Reliability: If you have a strong provider, cloud solutions can have a very high up-time, which makes them reliable no matter what.
Conversely, on- premise solutions offer:
- Speed: Cloud solutions usually rely on far-away servers, and the time that it takes for data to bounce through those servers and reach the end user from these servers can be unacceptable for many businesses. On-premise solutions don’t have to deal with this problem - with local servers, speed and latency can be better managed, at least for businesses based in one geographical location.
- Security: on-premise warehouses allow organizations to have full control over their security and access, which is important for businesses in which this is a big priority.
- Availability/Reliability: If you have a good staff and exceptional hardware, on-premise solutions can be highly available and very reliable.
2. Vendor selection
Once you choose between cloud and on-premise systems, there are various vendors to choose from. For on-premise systems, some options include:
- Oracle Database
- Microsoft SQL Server
- IBM DB2
While there are many fantastic options here, we recommend PostgreSQL for its proven track record of reliability, compatibility and open source roots. Of course, in any case, it is important to consider your company’s data types and your existing ecosystem, since you’ll have to choose a data warehouse that’s compatible.
For cloud systems, top solutions include:
- Amazon Redshift
- Microsoft Azure SQL Data Warehouse
- Google BigQuery
Here, Amazon Redshift and Google BigQuery are certainly the biggest players. That said, most recent data shows that Amazon Redshift is the highest-performing cloud solution overall.
In the big picture, it’s important to choose a data warehouse solution that will fit in with your business model, your budget and your existing systems. If you’re looking for speed, security and control, then an on-premise solution may be the best choice for you. Conversely, if you’re looking for low entry cost and easily scalability, you may want to go with a cloud service. Either way, making the right decision for your company will help you run the effective analytics and BI that you will need for long-term stability and success.