Data Warehousing Solution for Greater Return on Investment
Building a data warehouse is a time-consuming, expensive, and complex task. Consequently, depending on the complexity of a warehouse, Data Warehousing solutions may require a big initial investment and time. But with in-depth insights and knowledge about the business, one can reach the expected results.
Many data warehousing initiatives go over budget, are late, or fall short of expectations. Hence, justifying and evaluating data warehousing investments is of major interest to people. Companies consider this essential to implement cloud data warehouse solutions.
Data warehouses require a significant investment of time and money from the organization. As a result, there’s a lot of curiosity about how they’re justified at first and then evaluated. The expenses of data warehousing are straightforward to calculate, but the benefits are more complex to assess.
ROI Analysis of A Data Warehousing Solution
ROI analysis is a useful decision-making tool in big data analytics. It ensures investments are in line with the company’s strategy and efficiently use resources. Though calculating ROI for a cloud data warehouse is a difficult undertaking, tools have made it easier to predict the cost. But some of the potential advantages are more difficult to quantify.
While net present value and internal rate of return are commonly linked with ROI, it encompasses a broader set of indicators, such as payback period and returns on investment over a specified period. As a result financial measures aren’t always employed with data storage.
Cost of Data Warehousing
The cost of data warehousing is relatively easy to estimate. Like most IT projects, it includes the associated hardware, software, and personnel costs. It is also based on the amount of query data produced in the organization from multiple data sources and how it will be used.
Benefits of Data Warehousing For Greater Return on Investment
Many potential benefits are resulting in a higher return on investments from data warehousing, such as –
Datamart consolidation
- Reduction of multiple decision support platforms
- Hardware and software cost savings
- Operational efficiencies
Time savings
- IT professionals download less data for users
- IT staff writes fewer queries for users
- Less time spent looking for information
- Analysts respond less to information requests
Better and More Information
- Having access to information that was previously unavailable
- The ability of users to perform data analysis in novel ways
- The ability to think about the company in fresh ways
Personal savings
- Redeployment of IT personnel
- Faster company growth without adding personnel
- Redeployment of operational personnel to higher-value-producing activities
Improved decision making
- Rather than relying on intuition, make decisions based on facts
- Ability to make faster decisions and better examine options
- Better ability to recognize and respond to difficulties
Business process improvement
- Redesign of jobs
- Procurement savings
- Shorter business cycles
- Ability to recognize and resolve issues with business processes
Support for strategic business
- Faster response to changing market conditions
- Increased market share
- Improved speed to market with new products
- Supply chain integration
How Much ROI can Businesses obtain from Data Warehousing?
There is no standardized method for calculating data warehousing ROI. The following factors can be included in its calculation –
- Net present value
- Internal rate of return
- An average rate of return (over a specific time horizon)
- Payback period
The International Data Corporation (IDC) undertook the first and the most well-known research on data warehousing. IDC discovered a 401 percent average ROI over three years based on 62 enterprises (IDC Report, 1996).
The fact that the warehouse is not immediately linked to the results complicates calculating data warehousing ROI. Rather, it is a financial investment in decision-making infrastructure. Consequently, based on the information collected from the warehouse, someone or some process must take action.
As a result, warehousing has secondary or tertiary effects—it leads to processes that result in lower costs or higher revenues. As a result, valuing the warehouse in isolation from the value provided by other people and processes is difficult.
Using ROI has its critics as well. We can often not possibly know the returns from data warehousing until the warehouse is completed and in use. Data miners may or may not find relationships and insights that are valuable to the organization. Sometimes it is also difficult to make data-driven decisions in real-time.
Case Study – Cornell University
Cornell University was founded in 1865 as a privately sponsored research university. Ranked in the top one percent of universities globally, Cornell comprises 14 colleges and schools serving roughly 22,000 students.
The Primary Issue
Cornell was transforming and merging data into an Oracle Data Warehouse with Cognos Data Manager. However, IBM bought Data Manager and discontinued support for it.
The University requires a replacement for Data Manager because it contains millions of lines of code. In addition, the director of Cornell University’s IT department saw it as an opportunity to add additional capabilities to their data warehouse, making it run more efficiently.
The Assessment
Batch processing from financial or student records couldn’t start warehouse processing until the conclusion of normal operations, therefore the IT team had to limit processing to hours when the University was closed.
All these also had to be finished timely. Outdated documentation was a problem. Because it is an academic institution, licensing and staffing costs were essential factors. Although it is an essential setback in government and higher education organizations where the administration has increasing data needs, the pool of people is small.
Finding a Solution
The head of the IT department was hunting for ETL tools intending to make some modifications. When assessing vendors, he looked for documentation, license fees, improved performance, and the ability to operate within existing workforce levels. CPU-based licensing costs would be significant.
As a result, they tried to figure out how to re-architect the entire system. They did it to reduce the CPU footprint enough so that the licensing could work. But it is a process that would create other limitations. It was a clear advantage to integrate and use the product without having to hire more developers. Therefore it has been a major motivator for companies considering automation for their teams.
The Final Solution
Cornell University’s business intelligence projects and decision-making were aided by a data warehouse. This adaptable framework has collected and analyzed a large amount of data from a variety of sources. Hence, they adopted a better way of database management.
Conclusion
Overall, we can say that data warehousing is an excellent option for generating a higher return on investment. If you, too, want to earn more returns and grow your business through business intelligence, then Thinklayer will help you fulfill this dream by providing the best Data Warehouse Solutions.
References –
