Data collection and analysis are vital for organizations. Although, they are becoming more costly than ever. Does your organization know how to minimize data analysis costs?
Depending on the size of your enterprise, you can spend hundreds of thousands to hundreds of millions of dollars managing your essential data. It’s important to reduce these data costs while retaining full use of that data and the insights it provides.
Fortunately, you can minimize your data collection and analysis costs by better organizing that data, rethinking your data access and governance, and improving data quality. Read on to learn more.
- Data collection and analysis can be expensive
- To minimize data analysis costs, start by optimizing data collection
- Streamlining reporting and analysis can also reduce costs – as can instituting internal billing for data use
- Deploying a hype converged infrastructure is a more efficient way to manage data
- Significant cost reductions can come from improving data quality
Collecting and Analyzing Data Can Be Costly
Collecting and analyzing isn’t cheap – and it’s getting more expensive as more data is collected.
According to IDC, spending on big data and analytics (BDA) reached $215.7 billion in 2021. That’s an increase of 10% over prior-year levels. McKinsey estimates that an enterprise with $5 billion in operating costs spends more than $250 million on data sourcing, architecture, governance, and consumption – anywhere from 2.5% to 25% of the firm’s total IT budget.
Data management costs vary by industry and by company size, of course. In all environments, managing data is a significant and necessary expense – yet one that can be minimized with the right management.
5 Effective Ways to Minimize Data Analysis Costs
How can your company minimize the expense of big data collection and analysis? Shaving even a few points off your data management budget has a significant impact on your bottom line.
It is possible to retain all the benefits of data analysis while reducing the cost of managing that data. Here are five proven ways to keep your data management costs from getting out of control.
Optimize Data Collection
How much does your company spend on data collection? You may find that you’re spending much more than you need to.
Conduct a thorough analysis of where and how essential data is being collected. You’re likely collecting similar data in multiple departments, and this redundancy can be eliminated. You may also discover that you’re paying outside sources for data that you already are or could easily be collecting internally. The goal is to collect the right data – no more, no less – at the lowest possible cost.
Streamline Reporting and Analysis
Unfortunately, many of the reports created in a typical enterprise are of little to no value. All they do is waste valuable computing resources and add to your data management costs.
To reduce unnecessary reporting and analysis, do the following:
- Survey key stakeholders in relevant departments as to which reports they receive each week, and which reports they use
- Eliminate reports that are of little to no value
- Identify and eliminate duplicative reports
- Combine similar reports from different departments
You may also want to encourage some employees to use real-time dashboards instead of generating custom reports. Creating a custom report can be costly, especially when the same data may be available at a glance on an existing dashboard.
Bill for Internal Data Use
One way to streamline data usage is to make key stakeholders aware of how much data they’re using. When department heads realize the cost of their data usage, they’re apt to be more judicious in their use.
The best way to do this is to internally bill each department for the data analysis services they use. Employing an internal pay-per-use model will increase awareness of how data resources are being used and encourage resource efficiencies, reducing your overall costs.
Move to a Hyperconverged Infrastructure
Switching to a hyperconverged infrastructure (HCI) is another way to reduce data management costs. HCI combines all the elements of a traditional data center (storage, computing, networking, and management) in virtualized fashion into a single unified system. The virtualization enables the pooling of all available resources and allocates them dynamically to applications running virtual machines, thus more efficiently using existing resources.
Improve Data Quality
Perhaps the most significant way you can minimize the cost of data analysis is to improve the quality of the data you collect. Poor data quality (DQ) can dramatically impact your bottom line – IBM estimates that bad data costs U.S. businesses more than $3 trillion every year. The typical enterprise loses more than 30% of its revenue due to bad data.
To stem these losses and reduce overall data collection and analysis costs, you need to improve your company’s data quality. You can do this by using data validation software, such as DataBuck, to identify bad data and either fix it or remove it. Constant and autonomous DQ monitoring can improve the accuracy and usability of the data you collect and reduce the costs associated with missing, incomplete, or incorrect data.
For example, one major U.S.-based bank used DataBuck to validate its data and realized cost savings of more than 50%. The following video further describes how HCI works and why it’s important.
Let DataBuck’s Autonomous DQ Monitoring Minimize Your Company’s Cost of Data Collection and Analysis
DataBuck from FirstEigen is an autonomous data quality monitoring solution powered by AI/ML technology. It automates more than 70% of the laborious work of data monitoring and dramatically lowers the cost of data management.
DataBuck works by creating and enforcing a variety of DQ validation rules. It automates data monitoring processes and improves and updates them over time—autonomously validating thousands of data sets in just a few clicks. It also provides your company with dependable reports, analytics, and models.
Contact FirstEigen today to learn how DataBuck can help your company minimize data analysis costs!