Forrester’s Business Technographics Global Data and Analytics Survey shows that the data storage capacity of 59% of companies exceeded 100TB in 2017. This percentage is twice to that of 2016. Companies are looking for advanced data management solutions to manage the increasing amount of data. And data warehousing is at the top of the list. It is 1 of the 4 high priority investment areas for businesses that have increased their budgets in 2018.
Data warehousing solutions are constantly undergoing improvements to meet the changing demands of the industry. Where the traditional data warehouses were rigid and incapable of storing large data amounts, the modern ones are flexible and agile. New trends, geared towards reducing the system inefficiencies, are making data warehouses more efficient.
Let’s look at some of these trends and how they are changing the data warehousing landscape:
Looking Ahead – Date Warehousing Trends to Watch Out For
We have listed down a few data warehousing trends that are expected to make waves in the industry:
1. Complex Data Marts Will Define the Future Business Models
Data marts surfaced as a subset of data warehouses, designed to address the requirements of a specific business function. However, the ability of large and complex data marts to pull data from disparate sources and make it accessible to business users is making it a rising trend in data warehousing.
The recent developments in the construction of data marts allow integration of web and enterprise data. This enables evaluation of the transformation process from source to data mart. Also, it extends the benefits of analytics throughout the organization.
Another feature that is expected to enhance the functionality of data marts is speed. Modern data marts will be designed to offer cloud-scale speed with 24/7 functional processing power, network, and disk. The result will be efficient, cost-effective, and resilient data marts.
2. Column-based Storage is on the Rise
When it comes to retrieving analytical queries, the efficiency of column-based storage is higher than its row-based alternative. This is one of the reasons this trend is gradually gaining popularity.
The primary goal of data warehousing is to store data in a way that speeds up the query response time, consequently enabling efficient data evaluation and analyzation. And column-based storage can make that happen. It significantly compresses the data because columns store similar values.
Here’s an example of column-based data storage:
Storing data in a column-oriented data warehouse can help businesses conduct advanced business analytics. In addition, this storage system allows tight integrations and easy data warehouse setup due to enhanced disk performance. It cuts down the system’s I/O requirements and ensures minimum data is uploaded from the disk. Also, column-based DBMSs make for a good data mart platform.
3. Mixed Workloads Are Becoming Common
A data warehouse platform delivers six types of workloads:
- Basic reporting
- Continuous/real-time load
- Batch/bulk load
- Operational BI
- Online analytical processing (OLAP)
- Data mining
To ensure optimum performance of a data warehouse that delivers all these workloads, it’s essential to plan and assess the output predictability. Inability to do so may cause three major problems:
- Sustainability issues
- Increased administration costs (due to added volume and workload)
- Low performance
4. Data Warehouse Automation (DWA)
Data warehouse implementations are generally dependent on IT personnel. It can take years to build a data warehouse, making the whole process time-intensive, expensive, and slow. Adding the automation factor to the equation makes it easier for organizations to navigate the complexities of data warehousing and eliminates the repetitive, time-consuming tasks from the process cycle. This consequently results in low project costs and high productivity.
Moreover, DWA significantly reduces the dependency on the IT staff. It eliminates the need for hand coding, empowering business users with less technical knowledge to take the lead, simultaneously making the process cycle faster.
The recent developments in DWA have enabled enterprises to accelerate BI project implementation, making up for the agility of the traditional ETL tools. The capability of the system to counter the changing business conditions and market dynamics in less time makes DWA a valuable business tool. It enables business users to extract the latest data from their BI tools and take accurate and timely decisions.
Another area that is gaining popularity in this domain is data analysis speed. DWA’s ability to acquire data from different sources, run it through the BI development cycle, and get fast results enables businesses to modify their BI strategy promptly for favorable outcomes.
DWA solutions are accredited with the best practices of standardization that are otherwise difficult to accomplish with systems that are continually changing.
5. Data Warehouses are Becoming Cloud-centric
The cloud is fast becoming a preferred choice for users looking to acquire data warehousing capabilities. Why? Because in addition to supporting all the functions like that of a traditional data warehouse, cloud data warehouses optimize deployments like data-governance hubs, BI backends, analytic data marts, etc.
With the load-and-go feature, cloud data warehouses eliminate the need for businesses to invest in hardware and IT staff. The feasibility to deliver dynamic workflow management and high performance, without any manual training, makes this solution a cost-effective option. In addition, the efficient compression and built-in technology enable scalability when data varieties and volumes grow.
To sum it up, data warehousing is gradually extending beyond simple data storage to new ways of ingesting, extracting, and analyzing data. The advancements drive the increased need in technology and cloud computing. Accepting and implementing these industry trends can enable organizations to make better business decisions and improve productivity.