What is data efficiency, and how to master it? 

Many organizations struggle to manage their growing data corpus. Learn the processes and configuration changes organizations can make to improve their data efficiency.

What is data efficiency?

Data efficiency is the configuration of information storage systems and the application of various processes so businesses can put their data to use optimally.

Strong data efficiency makes information easier to locate by those who need to retrieve it at the speed required for their particular use case. 

In today’s data-driven world, organizations collect and generate increasingly large volumes of information from a disparate variety of sources, in various formats, and in real-time. It is forecasted that by the end of 2025, 181 zettabytes of global data will be produced by 2025. That's a lot of consumable data.

For larger organizations, the challenge is categorizing the information more efficiently to manage and access across internal data centers, virtual environments, and cloud repositories. Let’s explore the critical role of data efficiency, ways of improving it, and how to meet your information management challenges with more confidence.  

Benefits of data efficiency

Data efficiency is the configuration of information storage systems and the application of various processes so businesses can put their data to use optimally. Strong data efficiency makes information easier to locate by those who need to retrieve it, and at the speed required for their particular use case. The top benefits of data efficiency for organizations include:

1. Better quality analytics

When analysts want to extract insights from the available data in an organization, the quality of their insights depends on the ability to locate and retrieve all relevant and useful sources of information. Efficient data processes contribute to improved analytics outcomes by making data easier to locate.  

2. Improved productivity

An inefficient approach to information management invariably hampers productivity. Users can be left frustrated waiting to pull data from outdated, slow systems that fail to properly strike a balance between storage cost and effectiveness. Having to manually comb through systems for hours just to collect data for a specific purpose also drains productivity. According to Gartner, poor data quality can cost organizations an average of $12.9 million.

3. Lower costs

A pivotal element in data efficiency is choosing an optimal storage medium for data, given how frequently (or infrequently) it’s retrieved and used. Organizations benefit from lower costs when they opt for suitable storage media depending on the frequency of access required for certain categories of information. Data efficiency also involves decreasing file sizes, which further reduces costs by getting more from your available storage capacity.

How to master data efficiency in 6 simple steps

Here are some effective strategies for improving data efficiency at your organization.

Step 1: Choose storage media based on the frequency of access

Research shows that only 32% of data available to enterprises is put to work. That leaves 68% of data unleveraged. Having infrequently accessed archive data sitting around on high-performance and costly solid-state hard drives (SSD) is a significant waste of resources and a drain on data center efficiency. Similarly, it’s detrimental to an organization’s end users' productivity when they need to retrieve frequently accessed vital data from lower-performance storage media. 

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‍The conceptual model of hot (“high performance”) and cold (“cheap and deep”) storage tiers provides a foundational guide when deciding where data should live. Tiering also moves data objects and files between hot and cold tiers over their lifecycle, depending on access frequency. 

Any effort to improve data efficiency must account for the need to match storage media with the frequency of data access. Even if your data center location expands from on-premise to cloud-based storage in Google Data Centers (or similar), you’re still faced with the task of efficiently using your available resources. From this starting point, you already ensure that the location of data storage is fully optimized, given what resources are available for different categories of information.

Step 2: Rethink geo-redundancy

A related consideration for data efficiency is geo-redundancy. Many businesses or cloud vendors replicate the same data between different data center locations across multiple regions if the primary system fails. While useful for resilience and business continuity, geo-redundancy may reduce data efficiency by introducing challenges related to increased data storage. Organizations must find a balance between redundancy and data efficiency by rethinking how necessary geo-redundancy is and carefully selecting which data to replicate across different regions.

Step 3: Data compression

Data compression involves minimizing the size of data files without substantial loss of information. Text and multimedia files are particularly suited to compression because you can represent these files with fewer bits without noticeably degrading the quality of the data. Given that 80 percent of enterprise data comes from unstructured sources such as text files, PDF documents, social media posts, and audio/video files, compression can free up a lot of storage space that would otherwise be unnecessarily filed. 

Data compression is a central step to improving data efficiency because it helps make more efficient use of the storage capacity while driving costs down. With high volumes of data inundating an organization’s systems each day, storage costs quickly skyrocket. 

Compressed records and files also transfer faster over the network or through a data pipeline so that analysts and other business users aren’t sitting idle to get the data needed. Compression also makes more efficient use of network bandwidth and reduces network latency, which are crucial aspects of enhancing data transmission and improving overall system performance.

Step 4: Deduplication

Deduplication is a process that helps to reduce storage space requirements, although it achieves this storage efficiency in a different way than compression. Geo-redundancy focuses on maintaining multiple copies of data across different geographic locations to ensure availability and resilience. 

Deduplication results in cost savings by optimizing storage space utilization within a single storage environment, while geo-redundancy generally involves additional costs related to maintaining redundant infrastructure in multiple geographic locations. 

Another efficiency benefit of deduplication is how it allows organizations to recover their data from backups much faster if needed, as there is no duplicate information so the recovery process is quicker. Deduplication ties into a wider approach to data minimization, which improves efficiency by enabling organizations to retrieve their data more swiftly when needed.

Redundant, Obsolete, or Trivial data (ROT) is something else to take into consideration for data recovery. 

Redundant, Obsolete, or Trivial data (ROT) is comprised of classifications for data that organizations retain despite not being necessary. It aligns with both deduplication and geo-redundancy strategies. ROT becomes a critical consideration as it underscores the importance of identifying and eliminating unnecessary copies or outdated information within the data storage system. ROT also introduces the concept of unintentional redundancies or obsolete data that may persist despite these efforts.

Step 5: Thin provisioning

With many organizations today using a storage area network (SAN) to facilitate virtualized environments and Virtual Desktop Infrastructures (VDIs), storage allocation in a SAN is an integral component of modern data efficiency. 

Thin provisioning is a method of dynamically allocating storage based on current user requirements, ensuring that a virtual disk utilizes only the space necessary at any given time. This method contrasts with thick provisioning, which allocates storage space in anticipation of future needs. Thick provisioning is a less efficient and more costly way to use virtual storage than thin provisioning.

Step 6: Improve big data pipeline efficiency

Organizations regularly deal with massive volumes of data. Research reveals that data professionals see data volume grow by an average of 63% every month in their companies.

Big data pipelines help enterprises ingest, process, and move large volumes of fast-moving data from source to (post-process) destination. Central to this processing is transformation, which prepares data for realizing its value by converting it into another format, standard, or structure. 

With large volumes of unstructured data, the efficiency of a data pipeline depends partly on the queries you write. Efficient queries help cut processing times and remove the performance footprint on the underlying infrastructure that powers the pipeline. 

What are the key data efficiency obstacles? 

1. Poor data quality

Poor data quality refers to inaccurate or incomplete information, but it also includes ROT data. In fact, only 3% of the data in a business enterprise meets quality standards. 

When data is wrong, out-of-date, no longer relevant, or incomplete, the entire organization is impacted by the time spent manually fixing quality issues, which drains productivity and team resources.

2. Data silos

Data silos are detrimental to efficiency because they create isolated pockets of information within an organization. These silos are datasets that only one department, team, or app has access to. Aside from specific cases where special categories of information need extra protection (e.g., for compliance, privacy, or security), data silos can negatively impact an organization’s decision-making abilities. 

Silos make it difficult for different personnel to extract the full value of the data at their organization. Furthermore, constantly shifting between different sources of information wastes a lot of time compared to having a single source of truth for all data. 

3. Data governance 

Without data governance, it becomes unclear as to who has accountability over specific data assets. This can create errors and misdirection in terms of decision-making. Compliance also becomes a major concern as the absence of a designated owner leaves regulatory responsibilities unfulfilled. 

Organizations will need to rethink their existing data strategies to overcome these hurdles and improve data efficiency. 

Are you looking for a full lifecycle solution for data governance? Explore RecordPoint’s AI and ML-powered data solutions today.

What are some key metrics to monitor data efficiency?

To demonstrate that your data efficiency strategies are working, you need to track metrics and link them to your broader objectives. The following metrics can be used to track the quality and performance of your data infrastructure.

1. Storage utilization rate

Measure how efficiently you are using storage space. This helps to identify systems that are underutilized or overburdened so you can reorganize storage layouts and repurpose capacity as you see fit.

2. Data processing time

Track how quickly data operations such as ETL (Extract, Transform, Load) workflows are executed to evaluate your ability to handle high-volume data sets and support fast decision-making.

3. Data access latency

Measure how long it takes for users and systems to retrieve important data. This metric will flag any bottlenecks during the retrieval process so you can prioritize solutions such as caching or load balancing.

4. Data accuracy

Monitor how accurate your data is by setting benchmarks, using automated validation tools, and conducting data audits (cross-referencing, comparisons, etc.). This ensures the integrity of data for critical decisions and supporting core processes.

5. Data completeness

Evaluate how complete data is by tracking completeness metrics (e.g., field, record, dataset) to ensure data is always reliable and comprehensive.

6. Data consistency

Monitor how data is aligned across datasets by tracking consistency metrics (I.e., field, dataset, reference) and using automated tools to flag irregularities.

What are some examples of data efficiency?

Data efficiency is actionable and achievable, as the following case studies attest. A selection of the biggest corporations worldwide are changing how they store and use data to drive gains across the business. 

Procter & Gamble

Consumer goods giant Procter & Gamble (P&G) was unhappy at losing 70% of the value of the manufacturing data it collects. It finally decided to act by greenlighting a large-scale cloud data transformation. 

P&G recently finished building a customized cloud infrastructure to overhaul how it ingests and catalogs data ready for use across the business. Rather than managing three very different data sets (ERP and manufacturing separately), the new system consolidates them in one place. It does this automatically, without human intervention. 

The migration has been transformative for data efficiency. P&G has cut on-premise storage and maintenance costs, while the cloud makes light work of heavy compute loads and empowers decision-makers to leverage data for critical insights, which has unlocked greater efficiencies and performance.

Netflix 

Data efficiency has been a constant challenge for Netflix. The streaming giant has to juggle dozens of data platforms and hundreds of third-party data sources. Netflix found that a traditional approach to data storage was impossible due to the highly distributed nature of its infrastructure.

To deliver the data efficiency and transparency its decision-makers and stakeholders need, Netflix created an all-in-one custom dashboard. This facilitates data “feedback loops”. Netflix revealed that its unique solution, broadly defined as a Data Mesh, has reduced its data warehouse storage footprint by 10%.

Future trends in data efficiency

Data efficiency is in an exciting place: Generative AI models and machine learning methods promise to drive further innovation during the next decade. This new tech is shaping trends like data mesh and data fabric, two frameworks that will address challenges in scalability and complexity while powering productivity gains.

Data mesh

As Netflix has shown, a data mesh architecture has incredible potential. This decentralized approach makes it possible through recent advances in neural information processing systems, allowing each data domain to optimize and scale independently. 

Integrating machine learning models, such as convolution neural networks featuring convolutional layers, enables domains to process data more efficiently and improve model accuracy.

The benefits of data mesh architecture have been well documented at the International Conference on Machine Learning (ICML). Now, organizations like Netflix are integrating these distributed systems seamlessly to scale and leverage vital data without sacrificing performance. 

Data fabric 

Data fabric is another emerging data integration design concept. Again, it aims to untangle the web of disparate data platforms and sources organizations manage. Data fabric is a unified architecture. It ensures data is integrated and accessible, even across hybrid and multi-cloud environments and edge technology.

At the heart of the data fabric are efficient deep-learning technologies. By tapping into deep neural networks and other AI tools, a data fabric architecture increases the quality and accessibility of data. This is great for data analytics teams leveraging deep network models within the architecture to tease out predictive insights and empower leaders to make accurate predictions and decisions.

Deep learning models also optimize workflows, increase data consistency and completeness, and reduce latency. It’s easy to see how deep data-efficient techniques will transform data efficiency, especially for processes such as data security and data governance. 

Streamline data efficiency with RecordPoint 

The RecordPoint platform is composed of records management and data lineage tools that simplify data and records management. The platform improves data efficiency by breaking down silos, automatically improving data quality while allowing businesses to deeply understand the data they have and remove what they don't need. Here’s how: 

• Data inventory: Our platform finds and classifies all your data so that you can manage and use it more effectively. This comprehensive data inventory provides ample opportunity to eliminate storage inefficiencies by only keeping the data you need for reduced storage costs. Furthermore, as data passes through an intelligent pipeline before being inventoried and classified, we apply deduplication, detect and eliminate ROT, and improve data quality. 
• Federated data management: A centralized, user-friendly dashboard provides you with a single source of data truth. Having the full context of your organization’s data in a single place brings powerful data efficiency benefits because your users can much more easily find whatever they’re looking for without wasting time or being constrained by data silos.   
• Connectors: Our unique connector framework helps to uncover all your data (structured or unstructured) from any source, no matter where it lives. Connect to all of the vital software systems that your organization depends on. Enhance data efficiency by conducting a comprehensive data inventory across all content sources, ensuring that analysts have a holistic view for making more informed decisions.

FAQs

What are some common data efficiency challenges that organizations face?

Maximizing value from data is more challenging when organizations wrestle with fragmented data silos, slow processing speeds, and poor data quality. Overhauling data storage so it’s clean, accurate, and accessible is key to addressing these issues.

How does data efficiency contribute to sustainability efforts?

Efficient data practices reduce energy consumption and minimize waste by getting rid of redundant storage and optimizing computational cost resources. The lower footprint supports greener IT operations.

What are some sustainable data practices that organizations can adopt?

Sustainable data practices start with responsible data management. Effective strategies include limiting the amount of data collected, a process known as data minimization to reduce waste and enhance privacy, migrating to energy-efficient cloud services, and deduplicating redundant data.

How do you measure data efficiency?

You can measure data efficiency by monitoring key performance indicators (KPIs) such as storage utilization, access latency, processing times, and accuracy rates and linking these to your business goals and objectives.

What is efficient data access?

Efficient data access is the process of retrieving accurate and relevant data quickly while minimizing IT and energy resources to enhance productivity and performance at a lower cost.