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The true cost of your data, beyond server costs

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As businesses try to get a handle on their swelling stacks of data, they’re finding budgets strained by hidden costs.

The Big Data era has given businesses unprecedented insight into their operations and customers.

But it’s come with a mountain of bills.

A startling report from IDC highlights this growing challenge: “spending on compute and storage infrastructure products for cloud deployments…increased 18.5% year over year in the fourth quarter of 2023…to $31.8 billion.” They project these increases will continue in 2024 and beyond.

Direct costs like cloud storage are just the tip of the iceberg — as businesses try to get a handle on their swelling stacks of data, they’re finding their budgets strained by hidden costs. Lucas Pinto, a senior tech lead at Lumenalta, expects “a significant increase in the total cost of ownership (TCO) of data over the next several years due to higher data volume intake, increased data complexity, and enhanced security and compliance requirements.”

It doesn’t have to be this way. Tailored data management solutions are a proven way for businesses to optimize their data-related spend.

But before you can unlock savings, you need to understand the all-in, true cost of your data.

Uncover the true cost of your data

Direct costs like servers are just one piece of the total cost. To get the whole picture, you also need to take indirect and opportunity expenses into account.

Direct data costs: What’s on the surface

Direct data management and sharing costs tend to be highly visible and meticulously budgeted. They lay the groundwork for an organization’s data infrastructure and usually include investments in physical and virtual resources necessary for storing, processing, and analyzing data.

Data infrastructure expenses

Infrastructure is the backbone of data management. These expenses usually involve the acquisition and upkeep of servers and storage solutions. Whether you opt for physical servers housed on-premise or cloud-based storage services, these costs are pivotal.

Along with the purchase price or subscription fees, infrastructure expenses include fees related to power, cooling, and space utilization for on-premises solutions.

Businesses are increasingly opting for the hassle-free nature of cloud-based storage solutions, which are projected to grow significantly in the coming years. In contrast, on-premises storage is expected to remain flat.

Related: Data lake vs data lakehouse

Software licenses

The cost of software licenses, whether for database management systems, analytics platforms, or data security tools, represents a significant portion of direct expenses.

The cost of software licenses, whether for database management systems, analytics platforms, or data security tools, represents a significant portion of direct expenses. These license costs can vary widely, depending on the provider, the scale of use, and the specific features required.

Data processing fees

The manipulation and analysis of data, especially in large volumes, incurs processing fees. These costs are particularly prevalent in cloud-based solutions, where organizations pay for both storage and computational power used to query, analyze, and transform data.

While it can lead to variability in monthly expenses, consumption-based pricing is quickly becoming the industry norm. Bain & Company reports that “80% of customers [on a consumption-based pricing plan] report better alignment with the value they receive.”

Indirect costs of data management: The hidden costs of data

Indirect costs of data management are easy to overlook but can add up quickly. These tend to include expenses related to maintaining, securing, and ensuring the accessibility of data.

Data governance expenses

Implementing policies, standards, and procedures to ensure data accuracy, availability, and security incurs costs that aren’t always straightforward to quantify.

However, the absence of effective data governance can lead to inefficiencies and increased risk of non-compliance with regulations, thereby escalating costs in the long run.

Data security costs

The importance of safeguarding data against breaches and unauthorized access cannot be overstated. While investments in encryption, intrusion detection systems, and secure data storage solutions are essential, they add to the indirect cost burden.

Yet, these investments pale in comparison to the potential costs of a data breach, which can include regulatory fines, litigation costs, and reputational damage. IBM found that the average cost of a data breach in 2023 reached $4.45 million.

Personnel training

The complexity of modern data environments necessitates ongoing training for IT staff. This training ensures that staff are aware of the latest data management practices and security protocols, reducing the risk of data loss or breaches due to human error.

But this training should extend beyond IT staff. As more and more companies are empowering business users to leverage data for strategic decision-making, it’s critical that end users also have a firm grasp on data security.

For example, during the pandemic, Microsoft introduced a Cybersecurity Awareness Kit to its remote workforce. The kit featured a mix of videos and interactive courses covering topics like phishing. By widening the scope of cybersecurity initiatives beyond IT staff, Microsoft reduced the chance of breaches across all levels of the organization.

Data quality management expenses

Maintaining high data quality is an ongoing effort that requires sophisticated tools and processes. The costs associated with data cleansing, deduplication, and validation processes are indirect but critical for ensuring the reliability of data analytics and business intelligence tools.

Learn about our data engineering services.

Data compliance costs

Adhering to data protection regulations such as GDPR, HIPAA, and CCPA requires an upfront investment in compliance initiatives. These include conducting regular audits, updating privacy policies, and ensuring data handling practices meet legal standards. These investments are imperative for avoiding hefty fines and sanctions.

Opportunity costs: More than you might think

A data management ROI analysis isn’t complete without considering opportunity costs. Often ignored, the opportunity costs of poor data management tend to be substantial.

Missed revenue opportunities

Inadequate data management can prevent organizations from recognizing data-driven opportunities, such as personalized marketing campaigns, dynamic pricing strategies, and new product development insights.

Inadequate decision-making

Gartner found that poor data quality costs organizations $12.9 million per year. And that’s just immediate impacts — over the long run, suboptimal business decisions based on shoddy data can lead to much larger losses.

Diminished competitive edge

Being able to swiftly adapt and innovate based on actionable data-derived insights can give businesses a material competitive advantage. Firms that neglect to invest in robust data management practices risk falling behind competitors who utilize their data assets more effectively.

Recommendations to minimize expenses and optimize the data process

Navigating the complexities of data management and its associated costs requires a multifaceted data strategy. Below are detailed recommendations for organizations aiming to optimize their data management processes and minimize associated risks and expenses.

Read also: 6 considerations that strengthen your data strategy

1. Invest in data governance

A robust data governance framework is foundational to maintaining the quality and integrity of your data assets. This framework should define clear policies and procedures for data access, quality control, and compliance with relevant regulations.

This isn’t just about enforcing rules; effective data governance facilitates easier access to high-quality data for all stakeholders, ensuring that it’s available, accurate, and secure.

Doing so can bring significant financial benefits. McKinsey estimates that by “Applying greater management discipline to what can often be sprawling data-architecture, -sourcing, and -use practices…companies can recover and redeploy as much as 35 percent of their current data spend.”

2. Leverage data automation and analytics

From data collection and cleaning to analysis and reporting, data automation technologies and analytics platforms play a critical role in streamlining data management tasks.

By automating routine data processes, organizations can reduce the time and labor costs associated with manual data handling by as much as 25%.

Furthermore, advanced analytics tools can uncover valuable insights hidden within data, driving more informed decision-making across the organization. The key is to select tools that integrate well with existing systems and are scalable to meet future data needs.

3. Implement robust security measures

Data breaches not only have a direct financial impact in terms of regulatory fines and remediation costs but also cause long-term reputational harm.

Implementing comprehensive security measures, including encryption, access controls, and regular security audits, is essential to protect sensitive data from unauthorized access and breaches.

Additionally, employee training on data security best practices can help minimize the risk of data leaks due to human error.

4. Foster a data-driven culture

Cultivating an organizational culture that values data-driven decision-making is key to maximizing data value. This involves promoting data literacy across all levels of the organization, from the C-suite to frontline employees.

Encouraging the use of data ensures that decisions are based on evidence rather than intuition, leading to better outcomes.

An IDC survey found that “83% of CEOs want their organization to be more data driven.” Those who achieve this goal take bold, decisive steps toward establishing a data-driven culture within their teams.

Embracing the complete cost of data

Understanding and managing the full spectrum of data management costs — from direct and indirect expenses to opportunity costs — is essential for businesses aiming to unlock the full value of their data assets.

Organizations can minimize data costs and bolster their competitive position in an increasingly data-centric world through strategic investments in data management and closely adhering to best practices.

Begin your data optimization journey by engaging with our team of specialists. Reach out today and let us help you understand your true cost of data, then design a strategy to optimize how it’s managed.

Read next: How to avoid SaaS data fragmentation

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