Improving data efficiency to produce better quality insights and improved productivity

In today’s data-driven world, companies collect and generate increasingly large volumes of information from a disparate variety of sources, in various formats, and in real-time. From 2020 to 2022 alone, the volume of data in enterprises worldwide more than doubled. For these larger organizations, the challenge is arranging information so that it’s easier to manage and access across internal data centers, virtual environments, and cloud repositories. This article describes data efficiency, how to improve it, and how it’ll meet your information management challenges. 

Benefits of data efficiency

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

• Better quality analytics: When analysts want to extract insights from the available data in an organization, the quality of their insights depends on being able to find and retrieve all relevant and useful sources of information. Analytics outcomes improve with data efficiency because the processes applied to data make it easier to find.   
• 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 Users can be left frustrated waiting to pull data from outdated, slow systems that fail to properly strike a balance between storage cost and effectiveness.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 trawl through systems for hours just to collect data for a specific purpose also drains productivity.
• 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. Businesses benefit from lower costs when they opt for suitable storage media given 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 improve data efficiency 

Here are some pointers for improving data efficiency at your organization. 

Choose storage media based on the frequency of access

It is a huge waste of resources and a drain on data center efficiency to have infrequently accessed archive data sitting around on high-performance and costly solid-state hard drives (SSD). Similarly, it’s detrimental to business users’ productivity when they need to retrieve frequently accessed vital data from lower-performance storage media. 

The cloud model of hot and cold storage tiers provides a useful foundation when deciding where data should live. The basic idea is that rarely accessed data is stored on cheaper object or tape storage while data that’s frequently accessed lives on strong performance media like SSDs. Tiering also moves data objects/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 optimized given what resources are available for different categories of information.

Rethink geo-redundancy

A related consideration for data efficiency in today’s cloud-driven world is geo-redundancy. Many businesses or cloud vendors replicate the same data between different data center locations across multiple regions.  While it’s useful for resilience and business continuity, geo-redundancy compounds the problem of data inefficiency. It’s worth rethinking how necessary geo-redundancy is and carefully choosing exactly which data to replicate across different regions.

Data compression

Data compression is the application of an algorithm to files so that you can remove unnecessary or repetitive bits to make those files smaller in size. 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, audio, and video files, compression can free up a lot of storage space that would otherwise be unnecessarily filed. 

Data compression is central to data efficiency because it helps make better use of the effective storage capacity and bring costs down. With high volumes of data inundating their systems each day, storage costs can quickly add up. 

Another benefit of compression is how compressed records and files transfer faster over the network or through a data pipeline so that analysts and other business users aren’t twiddling their thumbs waiting to get the data they need. Compression makes more efficient use of network bandwidth and reduces network latency. 

Deduplication

Deduplication is another process that helps to reduce storage space requirements, although it achieves this storage efficiency in a different way than compression. Data deduplication gets rid of redundant copies of data by identifying unique patterns of data and removing any matches found with that same pattern. 

Deduplication lowers storage costs and ensures the available storage space is more efficiently used. Another benefit of deduplication that ties into efficiency is how it allows businesses to recover their data from backups much faster if needed because there is no duplicate information so the recovery process is quicker. Deduplication ties into a wider approach of data minimization, which improves efficiency by ditching the data you don’t need and discovering the data that matters faster.   

Thin provisioning

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

Thin provisioning is a way to allocate storage in an on-demand way based on user requirements at the time so that a virtual disk only consumes the space that it needs. 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. 

Improve Big Data pipeline efficiency

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 pipeline depends partly on the queries you write. Efficient queries help to cut processing times and remove the performance footprint on the underlying infrastructure that powers the pipeline. 

Data efficiency obstacles 

Poor data quality

Poor data quality is a problem for efficiency because low-quality data is not fit for its intended usage. Often, poor data quality refers to inaccurate or incomplete information but it also includes redundant, obsolete, or trivial (ROT) data. When data is wrong, out-of-date, no longer relevant, or incomplete, efficiency is impacted by time spent manually fixing quality issues. 

Data silos

Aside from specific cases where special categories of information need extra protection (e.g. for compliance), data silos are usually not good for efficiency. These silos are datasets that only one department, team, or app has access to. 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. 

The cost vs benefit trade-off

The energy consumption and storage costs of certain data storage media can lead IT decision-makers to disregard their benefits. This cost versus benefit trade-off lies at the heart of data efficiency and data center operations, but a myopic focus on the costs ultimately reduces efficiency because the most regularly accessed information ends up suboptimally stored on older, slower, or outdated hardware. 

How RecordPoint helps

RecordPoint is a customizable data trust platform composed of records management and data lineage tools that simplify data and records management. In particular, our platform improves data efficiency by breaking down silos, improving data quality automatically, and allowing businesses to deeply understand the data they have and dispose of 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 one 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 within your complex IT ecosystem. Connect to all of the vital software systems that your organization depends on, including custom in-house apps. Drive increased data efficiency by leaving no stone unturned with a true data inventory from all of your content sources so that analysts can make better decisions.   

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