Do you know that 2.5 quintillion bytes of data are generated each day? It has been found that less than 0.5% of this data is used and the rest of it is just there, scattered around in enterprises. Even with more and more enterprises adopting data-driven technology, not all of them can make most of the data they have.
A primary reason for this is the lack of proper data storage arrangements. Huge volumes of data have to be stored, cleaned, processed and analyzed to derive insights that help SMEs make correct decisions. But where and how should you store such vast amounts of data? Ordinary storage systems are no longer effective.
That’s where data warehousing has begun. It is hardly a new concept but is gaining more popularity as enterprises are moving towards streamlined business systems.
Overview of Topics Covered
What is Data Warehouse?
Simply put, a data warehouse is a place to store historical and real-time data, which is processed and analyzed to help the sales, marketing, customer service teams, and other departments make better decisions. The data warehouse is not the same as an operational database. It is more expansive and is not updated as frequently as the operational database.
A data warehouse provides a long-range view of data from the past and present, and hence the analytics run on this data delivers more insights. It can be either an in-house storage system or a cloud storage system.
So how do we pick the right data warehouse for the business? We’ll evaluate all the necessary factors in this post. But before we see more about these factors, let us read a little more about data warehouses.
Reasons to Choose Data Warehouse
What makes a data warehouse a necessary service for today’s enterprises? How does data warehousing help streamline business operations?
- Data warehouses are programmed to format and structure raw data to make data analytics easier. The raw data collected from multiple sources is converted into a single format. This brings consistency and allows employees to analyze and share insights with everyone in the enterprise.
- By ensuring consistency in data structuring and format, enterprises can reduce the risk of human error and arrive at accurate insights.
- With data analytics becoming a part of the business systems, you can make faster decisions and implement them in less time. There is no need to take days and weeks to process data, manually compare the figures, and then arrive at a decision.
- Since the data warehouse houses historical data, it provides a comprehensive view of the business from the day it began. You can hire a data warehousing company to help you collect, clean, and analyze vast amounts of data. You can check which of the previous decisions were successful and which ones led to unfavorable results.
- End-users can use the information in the data warehouses to make various changes and decisions in different departments.
- The sales team can monitor and modify marketing campaigns
- Helps improve customer satisfaction and enhance customer relationship
- Predict the future opportunities, growth areas, hurdles, and challenges
- Improve the overall performance and productivity of the enterprise
- Consolidate and store data collected from numerous sources in a central database that can be accessed by employees
- A cloud data warehouse is economical and helps enterprises save money by allowing you to pay only for the server space and services you utilize
- A cloud data warehouse can be accessed by any number of users. Employees from various departments can simultaneously access the data they require
Steps to Choose a Data Warehouse
Investing in a data warehouse doesn’t directly guarantee results unless you choose the right data warehouse for your business requirements. Whether it is choosing between the types of data warehouses or the service providers, you will first need to understand the business requirements. Hiring offshore data warehousing services from data analytics companies will help you get a complete picture of how to plan, adapt, and implement data warehousing in your organization.
The first step is to understand your business systems. If you have a specific data administrator, you will need to choose a data warehouse that is compatible and can be integrated with it. Read the use cases shared by other companies. Ask the consulting agencies to analyze your business system and suggest the best suitable data warehouse.
Data warehouses are usually designed to suit the varying needs of different SMEs across industries. However, you still need to ensure that the data retrieval speed, data storage speed, and flexibility you require can be provided in a data warehouse.
Billing Structure and Resources
This point is important when you opt for cloud data warehousing. Each cloud provider follows a different billing structure. The cost of investment in both the short and long terms must be considered.
While all data warehouses promise data security, the actual security levels and encryption methods depend on the individual service providers. Does what they offer to match your security requirements?
Once you are fully aware of your business systems and what you need from the data warehouse, it’s time to consider the different factors that help you choose the right data warehouse for your enterprise.
1. Cloud vs. On-Premises
We have been talking cloud data warehouse for a while now. It has been more popular in recent times when compared to on-premises data warehousing. However, that doesn’t mean cloud services are suitable for every business.
For example, if majority of your data is stored in on-premises systems that are not fully compatible with cloud platforms, you will find it easier to invest in an in-house data warehouse. Of course, you can still migrate the entire business system to the cloud and upgrade your IT infrastructure. Companies like Oracle, Microsoft, and IBM offer on-premises data warehousing services. Microsoft has both on-premises and cloud data warehouses.
2. Type of Data
What type of data do you plan to store in the data warehouse? Will it be structured or unstructured? Based on the type of data, you can choose between a relational database and a non-relational database.
A relational database is suitable for structured data arranged neatly in the rows and columns of a spreadsheet.
A non-relational database is ideal for large semi-structured data. Semi-structured data consists of emails, social media posts, demographic and geographical data, audios, videos, etc.
What if you have unstructured data? In that case, a data lake might be an effective choice as it has been designed for the same. A data lake is a relatively new concept that promises to offer much more than a data warehouse. An in-depth comparison between a data warehouse and a data lake will give you a better idea about which one is the best for your organization.
3. Cost and Time Factors
It can be quite a task to compare the costs of data warehousing services offered by different companies. The calculations are unique to each service provider, and unless you make a detailed comparison of what they offer and what they don’t, it can be hard to decide just by looking at the numbers.
Remember that the cost here should also include the cost of implementation. If you hire data analytics companies to assist, you will need to pay them as well.
Generally speaking, the cost of data warehousing depends on the storage, size of the warehouse, the resources required to run and maintain it, and the number of queries you run. If more than one team will access the data warehouse, the query count will be high. In such instances, you might find it cost-effective to invest in a cloud data warehouse.
However, consider the time taken to run the query and deliver the results. A low-cost data warehouse that takes more time isn’t really a good choice. You need to find a balance between the cost and time.
4. Performance and Efficiency
As we mentioned in the previous point, the efficiency of the data warehouse depends on how fast it can produce the results for a query. The data warehouse consultant will train your employees and assist the data analytics team to effectively use the system by understanding how data warehousing works.
Not all data analytics require real-time data. But depending on what percentage of your decisions depend on immediate results, you will need to choose a service provider who offers that level of efficiency. Otherwise, a bit of lag in results shouldn’t be a problem.
Do you know that the performance and scalability of a data warehouse are sort of connected? As your requirements increase, the data warehouse will deliver better performance.
5. Flexibility and Scalability
In today’s world, flexibility and scalability are two features every software or storage system should offer to enterprises. SMEs are growing faster, and they need a service provider who can keep up with the changing requirements. Cloud data warehousing offers better scalability compared to on-premises systems. It’s easier to buy more server space on the cloud. Snowflake has an auto-scale feature that ups and downs the cluster as and when necessary. Nevertheless, you need to make calculated decisions about how much you want to scale and when.
6. Ecosystem for Data Tools
Did you already invest in a data tool ecosystem? You will most probably be using what’s available. If not, you will need to choose a data warehousing service provider who has a good collection of data tools suitable for your business needs. This just makes things easier and faster.
7. Community and Support
Though this might not seem like a big deal compared to technical specifications, it is actually required to opt for a service provider who offers continuous support. You don’t want to wait for the support staff to respond when they are free, right? It’s your business at stake. Also, enquire what kind of support services are offered for the price package you’ve chosen.
Many companies create an online community support group where data warehouse consulting companies, professionals, clients, etc., participate in discussions and help each other. An active group denotes that you are more likely to get proper help from someone who knows how to do the job.
Big Names in Data Warehouse Services
On-Premises Data Warehousing
Oracle Database: Oracle is a well-known database service provider since the 70s’. This on-premises model is a traditional database setup usually used by large enterprises.
Microsoft SQL Server: The SQL Server is an on-premises version, a traditional model that has been designed to support BI, data analytics, and much more.
IBM DB2: This is a Relational Database Management System (RDBMS) to store, analyze, and retrieve data. It supports non-relational structures with XML and object-oriented features.
MySQL: It is a Relational Database Management System used not only for data warehousing but also for eCommerce and logging applications.
PostgreSQL: It is a highly stable primary data warehouse used for web, mobile, analytical, and geospatial applications.
Cloud Data Warehousing
Amazon Redshift: It is an efficient, fast, and scalable data warehousing service that’s a part of the AWS ecosystem. It supports big data, is cost-efficient, and optimizes processing speed.
Microsoft Azure SQL Data Warehouse: Azure is a cost-effective cloud-based data warehouse with better control over-indexing and is suitable for teams with strong SQL skills.
Google BigQuery: It is a highly scalable cloud-based data warehouse that can process large datasets. It is also user-friendly and offers several complex backend operations.
Snowflake Computing: This data warehouse is available as a plug-and-play application and can be hosted on multiple cloud platforms like Azure, AWS, or Google Cloud. It can automate recurring tasks and has a flexible pricing policy.
Choosing data warehousing services that meet your budget, business model, and existing systems will deliver you the kind of results you expect. We can summarize that on-premises data warehouses are suitable for those who want speed, control, and higher security. If you need a cost-effective option and more scalability, a cloud-based data warehouse would be a better choice. Contact a data analytics company for more information.
Kavika is Head of Information Management at DataToBiz. She is responsible for the identification, acquisition, distribution & organization of technical oversight. Her strong attention to detail lets her deliver precise information regarding functional aspects to the right audience.