Fundamentals

Consider this ● for many small business owners, the digital age feels less like progress and more like an avalanche of numbers, a daily deluge of data threatening to bury them under spreadsheets and analytics dashboards they barely understand.

Understanding Data Overload in Small Business

Small and medium-sized businesses (SMBs) often operate on tight margins, where every dollar counts and every minute is precious. The promise of data-driven decisions is alluring, yet the reality frequently involves drowning in information, struggling to discern actionable insights from noise. This isn’t a theoretical problem; it’s a daily grind for entrepreneurs juggling sales, marketing, operations, and customer service, often with limited staff and even tighter budgets. Data reduction, in this context, is not some abstract IT concept; it’s a lifeline, a way to regain control and make data work for, not against, the business.

Why Data Reduction Matters for SMBs

Imagine a local bakery diligently tracking customer orders, inventory, and social media engagement. Without a strategy to manage this data, they quickly face escalating storage costs, sluggish systems, and the daunting task of finding meaningful patterns within the chaos. Data reduction Meaning ● Data reduction, crucial for SMB growth, automation, and implementation, involves streamlining data to reduce storage needs and enhance analytical speed. strategies offer a practical solution, allowing SMBs Meaning ● SMBs are dynamic businesses, vital to economies, characterized by agility, customer focus, and innovation. to streamline their operations, improve efficiency, and unlock valuable insights without breaking the bank. It’s about making data manageable and affordable, transforming a potential burden into a genuine asset.

Core Data Reduction Techniques Explained Simply

For an SMB owner just starting to grapple with data management, the technical jargon can be intimidating. Let’s break down some fundamental data reduction strategies into plain English.

Data Compression ● Shrinking the Digital Footprint

Think of data compression as zipping up your files on your computer. It reduces the size of your data, making it easier to store and faster to transfer. For SMBs, this translates directly into lower storage costs, quicker backups, and improved website loading times. There are two main types ● lossy and lossless.

Lossless compression is like zipping a document; you lose no information. Lossy compression, often used for images and videos, sacrifices some less critical data to achieve even greater size reduction. Choosing the right type depends on the kind of data and the business needs.

Data Deduplication ● Eliminating Redundancy

Data deduplication is about finding and removing duplicate copies of data. Imagine repeatedly photocopying the same document and storing each copy. Deduplication identifies these identical copies and stores only one, replacing the rest with pointers back to the original.

For SMBs, this is particularly effective in backup systems, where redundant data is common. By eliminating duplicates, businesses can significantly reduce storage space and backup times, saving both money and operational headaches.

Data Archiving ● Moving Less-Used Data to Cheaper Storage

Not all data is created equal. Some data is accessed frequently, while other data becomes less relevant over time but still needs to be retained for compliance or historical purposes. Data archiving involves moving this less-used data to cheaper, slower storage.

Think of it as moving old tax returns from your filing cabinet to a storage box in the attic. For SMBs, archiving frees up expensive primary storage for active data, reducing costs and improving the performance of systems that handle day-to-day operations.

Data Thin Provisioning ● Allocating Storage on Demand

Data thin provisioning is a more advanced technique, but its concept is straightforward. Instead of allocating a fixed amount of storage upfront, thin provisioning allocates storage space only as it’s actually needed. Imagine ordering a large pizza for a party, but only paying for the slices that are actually eaten.

For SMBs, this means avoiding wasted storage capacity and the associated costs. It requires careful monitoring to ensure storage doesn’t run out unexpectedly, but when managed correctly, it can be a very efficient way to use storage resources.

Practical Benefits for the SMB Bottom Line

Beyond the technical explanations, the real question for any SMB owner is ● “How does this help my business?” The benefits of data reduction are tangible and directly impact the bottom line.

Reduced Storage Costs

Less data means less storage space required. Whether using cloud storage or on-premises servers, reduced storage needs translate directly into lower monthly bills or capital expenditures. For SMBs operating on tight budgets, these savings can be significant, freeing up resources for other critical areas like marketing or product development.

Improved System Performance

Smaller datasets are faster to process. Data reduction can lead to quicker backups, faster data retrieval, and improved application performance. For SMBs, this means less downtime, increased employee productivity, and a smoother overall operation. Imagine customer service representatives accessing information instantly, or marketing reports generating in minutes instead of hours ● these are real-world improvements driven by efficient data management.

Enhanced Data Backup and Recovery

Smaller backups take less time and storage space. Data reduction makes the entire backup and recovery process more efficient and reliable. For SMBs, this is crucial for business continuity. In the event of a system failure or data loss, faster recovery times minimize disruption and ensure the business can get back on its feet quickly.

Better Data Analysis and Insights

When data is streamlined and well-organized, it becomes easier to analyze and extract meaningful insights. Data reduction can help SMBs focus on the data that truly matters, filtering out noise and redundancy. This leads to better-informed decisions, improved marketing strategies, and a deeper understanding of customer behavior. It’s about turning raw data into actionable intelligence that drives business growth.

Data reduction isn’t a luxury for large corporations; it’s a practical necessity for SMBs seeking to thrive in the data-driven economy. By understanding and implementing these fundamental strategies, small businesses can unlock the power of their data without being overwhelmed by its volume and complexity.

Data reduction strategies empower SMBs to transform data from a potential burden into a valuable asset, driving efficiency and informed decision-making.

Getting Started with Data Reduction ● First Steps for SMBs

Implementing data reduction doesn’t require a massive overhaul or a team of IT experts. SMBs can take simple, incremental steps to start benefiting immediately.

Assess Your Current Data Landscape

The first step is understanding the current situation. What types of data are being collected? How much data is being stored? Where is it stored?

A simple data audit can reveal areas where reduction strategies can be most effective. For example, a bakery might realize they are storing years of customer order data that is rarely accessed, making it prime for archiving.

Prioritize Quick Wins

Start with the easiest and most impactful strategies. Data compression is often a straightforward first step, easily implemented with readily available tools. Similarly, identifying and deleting truly unnecessary data, like old temporary files or outdated marketing materials, can yield immediate storage savings.

Leverage Cloud-Based Solutions

Cloud storage providers often offer built-in data reduction features like compression and deduplication. For SMBs already using cloud services, leveraging these features is a simple way to gain benefits without significant technical expertise or investment. Cloud platforms also handle the complexities of storage management, freeing up SMB owners to focus on their core business.

Educate Your Team

Data reduction is not just an IT issue; it’s a business-wide practice. Educating employees about data management Meaning ● Data Management for SMBs is the strategic orchestration of data to drive informed decisions, automate processes, and unlock sustainable growth and competitive advantage. best practices, such as avoiding duplicate file creation and regularly cleaning up unnecessary data, can contribute significantly to overall data reduction efforts. A simple training session can empower employees to become data stewards, contributing to a more efficient and cost-effective data environment.

Embarking on a data reduction journey is about taking control, simplifying operations, and making smarter use of resources. For SMBs, it’s a path toward sustainable growth Meaning ● Growth for SMBs is the sustainable amplification of value through strategic adaptation and capability enhancement in a dynamic market. and a more data-savvy future.

Strategic Data Reduction For Business Advantage

Beyond the basic mechanics of shrinking file sizes and eliminating duplicates, data reduction emerges as a strategic lever for SMBs aiming to not just survive, but to actively compete and expand in increasingly data-saturated markets.

Data Reduction as a Strategic Imperative

The modern SMB operates within an ecosystem where data volume expands exponentially, driven by everything from customer interactions across multiple channels to the proliferation of IoT devices and increasingly sophisticated operational software. Ignoring data reduction in this environment is akin to ignoring rising rent costs or escalating supply chain expenses; it’s a drain on resources that, if unaddressed, can stifle growth and erode profitability. Strategic data Meaning ● Strategic Data, for Small and Medium-sized Businesses (SMBs), refers to the carefully selected and managed data assets that directly inform key strategic decisions related to growth, automation, and efficient implementation of business initiatives. reduction moves beyond tactical cost-cutting; it’s about proactively shaping data infrastructure to fuel business objectives.

Aligning Data Reduction with Business Goals

Effective data reduction strategies are not implemented in a vacuum. They must be intrinsically linked to overarching business goals. For an e-commerce SMB focused on rapid scalability, data reduction might prioritize optimizing database performance for faster transaction processing and improved customer experience.

For a healthcare SMB operating under stringent regulatory compliance, data reduction strategies must incorporate robust archiving and data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. policies to ensure long-term data integrity and accessibility for audits. The key is to identify how data reduction can directly support key performance indicators (KPIs) and strategic initiatives.

Advanced Data Reduction Techniques for SMBs

As SMBs mature and their data needs become more complex, moving beyond basic compression and deduplication becomes necessary. Advanced techniques offer more granular control and greater efficiency, albeit often requiring a deeper understanding of data infrastructure and management.

Data Virtualization ● Abstraction for Efficiency

Data virtualization presents a transformative approach by creating an abstract data layer that sits between applications and disparate data sources. Instead of physically moving or consolidating data, virtualization allows applications to access and manipulate data regardless of its location or format. For SMBs dealing with data silos across various departments or cloud services, virtualization simplifies data access, reduces data duplication, and enhances data agility. It’s about creating a unified data view without the complexity and cost of traditional data integration projects.

Storage Tiering ● Optimizing Data Placement

Storage tiering involves automatically migrating data to different storage tiers based on its access frequency and business value. Frequently accessed “hot” data resides on high-performance, expensive storage, while less frequently accessed “cold” data is moved to lower-cost, slower storage. For SMBs, tiering optimizes storage costs by ensuring that expensive storage resources are reserved for critical, performance-sensitive data, while less demanding data is efficiently stored on more economical tiers. This approach requires intelligent storage management software that can analyze data access patterns and automate tiering decisions.

Data Lifecycle Management (DLM) ● Governing Data from Creation to Disposal

Data Lifecycle Management (DLM) is a holistic approach to managing data throughout its entire lifecycle, from creation and storage to archiving and eventual disposal. DLM policies define how data is handled at each stage, ensuring compliance, optimizing storage, and mitigating risks associated with data sprawl. For SMBs, implementing DLM involves establishing clear data retention policies, automating data archiving and deletion processes, and implementing data governance frameworks to ensure data integrity and security. DLM is not a one-time project; it’s an ongoing practice that requires continuous monitoring and refinement.

Data Subsetting and Masking ● Reducing Data Footprint for Development and Testing

In software development and testing environments, using full production datasets can be inefficient and pose security risks, especially when dealing with sensitive customer data. Data subsetting involves creating smaller, representative subsets of production data for development and testing purposes. Data masking further enhances security by replacing sensitive data elements with fictitious but realistic values. For SMBs developing in-house applications or customizing off-the-shelf software, subsetting and masking reduce storage requirements in non-production environments, accelerate testing cycles, and minimize the risk of exposing sensitive data.

Quantifiable Business Benefits ● ROI of Strategic Data Reduction

Moving beyond cost savings, strategic data reduction Meaning ● Strategic Data Reduction within an SMB context represents a deliberate process of minimizing data volume to streamline operations and enhance business intelligence. delivers a range of quantifiable business benefits that contribute directly to improved profitability and competitive advantage.

Enhanced Operational Efficiency

Streamlined data infrastructure translates to faster data access, improved application performance, and reduced IT management overhead. Strategic data reduction frees up IT resources to focus on more strategic initiatives, such as innovation and business expansion, rather than being bogged down by data management complexities. Improved operational efficiency directly impacts productivity across all departments, from sales and marketing to operations and customer service.

Improved Data Analytics and Business Intelligence

Reduced and well-managed datasets are easier to analyze, leading to faster insights and more accurate business intelligence. Data virtualization and data quality initiatives, often integral to strategic data reduction, improve data accessibility and reliability, empowering SMBs to leverage data analytics for better decision-making, targeted marketing campaigns, and proactive identification of market opportunities and threats.

Strengthened Data Governance and Compliance

Strategic data reduction, particularly when incorporating DLM and data archiving, strengthens data governance and compliance posture. Clearly defined data retention policies, automated archiving processes, and robust data security measures ensure that SMBs meet regulatory requirements, mitigate legal risks associated with data breaches or non-compliance, and build customer trust by demonstrating responsible data handling practices.

Increased Business Agility and Scalability

A well-optimized data infrastructure built on strategic data reduction principles enhances business agility and scalability. Virtualization and storage tiering provide the flexibility to adapt to changing data needs and business demands without costly and disruptive infrastructure upgrades. SMBs with agile data infrastructure can respond more quickly to market changes, scale operations efficiently, and innovate more rapidly, gaining a significant competitive edge.

Strategic data reduction transforms data management from a cost center into a strategic asset, driving operational efficiency, enhancing analytics capabilities, and fostering business agility.

Implementing Strategic Data Reduction ● A Phased Approach for SMBs

Implementing strategic data reduction requires a structured, phased approach, starting with a comprehensive assessment and progressing through implementation Meaning ● Implementation in SMBs is the dynamic process of turning strategic plans into action, crucial for growth and requiring adaptability and strategic alignment. and ongoing optimization.

Phase 1 ● Data Assessment and Strategy Formulation

This initial phase involves a thorough assessment of the current data landscape, including data volume, data types, data sources, data usage patterns, and existing data management practices. The assessment should identify pain points, inefficiencies, and areas where data reduction can deliver the greatest impact. Based on the assessment, a strategic data reduction plan is formulated, outlining specific objectives, target techniques, implementation timelines, and key performance indicators (KPIs) to measure success.

Phase 2 ● Technology Selection and Pilot Implementation

Phase two focuses on selecting appropriate data reduction technologies and conducting pilot implementations to validate their effectiveness and refine implementation strategies. SMBs may choose to pilot data virtualization in a specific department or implement storage tiering for a subset of their data. Pilot projects provide valuable insights into technology performance, integration challenges, and user adoption, allowing for adjustments before full-scale deployment.

Phase 3 ● Full-Scale Implementation and Integration

Following successful pilot projects, phase three involves full-scale implementation of chosen data reduction technologies across the organization. This phase requires careful planning, data migration, system integration, and user training. Change management is crucial to ensure smooth adoption and minimize disruption to business operations. Implementation should be phased, prioritizing critical systems and data first and gradually expanding to other areas.

Phase 4 ● Monitoring, Optimization, and Continuous Improvement

Data reduction is not a one-time project; it’s an ongoing process. Phase four focuses on continuous monitoring of data infrastructure performance, tracking KPIs, and identifying areas for further optimization. Regular data audits, performance analysis, and feedback from users inform ongoing refinement of data reduction strategies and ensure that they continue to align with evolving business needs and technological advancements. This iterative approach ensures that data reduction remains a strategic asset, delivering sustained benefits over time.

By adopting a strategic and phased approach to data reduction, SMBs can transform their data management practices, unlock significant business value, and position themselves for sustained growth and competitive success in the data-driven era.

Transformative Data Reduction ● Automation, Growth, and Future-Proofing SMBs

The discourse surrounding data reduction for SMBs often fixates on immediate cost savings and operational efficiencies. A more profound perspective recognizes data reduction as a catalyst for transformative growth, a cornerstone of automation Meaning ● Automation for SMBs: Strategically using technology to streamline tasks, boost efficiency, and drive growth. strategies, and a critical element in future-proofing the SMB enterprise against the relentless tide of data expansion.

Data Reduction as an Enabler of SMB Automation

Automation, frequently touted as the next frontier for SMB competitiveness, hinges critically on efficient data management. Automated processes, whether in customer relationship management (CRM), marketing automation, or robotic process automation (RPA), are inherently data-intensive. Uncontrolled data growth can quickly negate the benefits of automation, leading to sluggish performance, increased error rates, and escalating infrastructure costs.

Data reduction, therefore, is not merely a complementary strategy to automation; it’s a prerequisite for realizing its full potential. It provides the lean, agile data foundation upon which robust and scalable automation frameworks can be built.

Integrating Data Reduction into SMB Growth Strategies

Growth-oriented SMBs understand that data is not just a byproduct of operations; it’s a strategic asset that, when properly harnessed, can fuel expansion and innovation. Data reduction plays a pivotal role in this growth trajectory by ensuring that data infrastructure scales efficiently alongside business expansion. As SMBs grow, data volumes inevitably increase, potentially straining existing systems and hindering performance.

Proactive data reduction strategies, integrated into growth plans, prevent data bottlenecks, maintain system responsiveness, and ensure that data remains a valuable resource rather than a liability. This forward-thinking approach allows SMBs to leverage data to identify new market opportunities, personalize customer experiences, and optimize business processes, driving sustainable and data-informed growth.

Future-Proofing SMBs with Intelligent Data Reduction

The data landscape is in constant flux, characterized by ever-increasing volumes, evolving data types, and emerging technologies. SMBs that fail to adapt to this dynamic environment risk being overwhelmed by data complexity and falling behind competitors. Intelligent data reduction strategies, incorporating artificial intelligence (AI) and machine learning (ML), offer a pathway to future-proof SMB data infrastructure.

AI-powered data reduction can automate data classification, identify and eliminate redundant data with greater precision, and dynamically optimize storage tiering based on real-time data access patterns. This proactive and adaptive approach ensures that SMBs can not only manage current data volumes effectively but also scale and adapt to future data challenges, maintaining a competitive edge in an increasingly data-centric world.

Advanced Data Reduction Techniques Leveraging AI and ML

The evolution of data reduction is being significantly influenced by advancements in AI and ML. These technologies are enabling more sophisticated and automated data reduction techniques that go beyond traditional methods.

AI-Powered Deduplication and Compression

Traditional deduplication and compression algorithms often rely on fixed rules and patterns. AI and ML enhance these techniques by enabling intelligent data analysis to identify more nuanced redundancies and optimize compression ratios dynamically. For example, AI algorithms can analyze data content to identify semantic similarities beyond simple byte-level duplicates, leading to more effective deduplication.

Similarly, ML models can learn data characteristics and adjust compression algorithms on the fly to achieve optimal size reduction without compromising data integrity. This intelligent approach maximizes storage efficiency and minimizes processing overhead.

Predictive Data Archiving and Tiering

Traditional data archiving and tiering often rely on static rules based on data age or access frequency. AI and ML enable predictive data management by analyzing historical data access patterns, user behavior, and business trends to forecast future data needs. Predictive archiving can automatically identify data that is likely to become inactive and proactively migrate it to cheaper storage tiers before it consumes valuable primary storage.

Predictive tiering can dynamically adjust data placement across storage tiers based on real-time access predictions, ensuring optimal performance and cost efficiency. This proactive and data-driven approach optimizes storage resource utilization and minimizes manual intervention.

Intelligent Data Lifecycle Management with Automation

DLM policies are often implemented manually, requiring significant administrative overhead. AI and ML can automate and optimize DLM processes by analyzing data characteristics, usage patterns, and compliance requirements to dynamically enforce data retention policies, automate archiving and deletion workflows, and ensure data governance. AI-powered DLM can also detect anomalies and potential compliance violations, proactively alerting administrators to take corrective actions. This intelligent automation streamlines DLM operations, reduces administrative burden, and enhances data governance and compliance posture.

Semantic Data Reduction and Data Minimization

Beyond technical data reduction techniques, semantic data reduction focuses on reducing the amount of data collected and stored by identifying and eliminating data that is not truly necessary for business purposes. AI and ML can analyze data value and relevance, identifying redundant or low-value data that can be safely discarded without impacting business operations or analytical capabilities. Data minimization, a core principle of data privacy regulations like GDPR, aligns with semantic data reduction by advocating for collecting and storing only the data that is strictly necessary for specific purposes. AI-powered data analysis can help SMBs implement data minimization strategies effectively, reducing data footprint, minimizing privacy risks, and optimizing data governance.

Strategic Implications for SMB Competitive Advantage

Transformative data reduction, leveraging automation and AI, offers SMBs a strategic pathway to achieve significant competitive advantages in the data-driven economy.

Enhanced Innovation and Faster Time-To-Market

Efficient data management, enabled by intelligent data reduction, accelerates innovation cycles and reduces time-to-market for new products and services. Streamlined data access, improved data analytics capabilities, and agile data infrastructure empower SMBs to experiment more rapidly, iterate on product development cycles faster, and respond quickly to market demands. Data reduction frees up resources and reduces complexities associated with data management, allowing SMBs to focus on innovation and value creation.

Superior Customer Experience and Personalization

Data reduction, coupled with advanced analytics, enables SMBs to gain deeper insights into customer behavior, preferences, and needs. This enhanced customer understanding allows for more personalized marketing campaigns, tailored product offerings, and proactive customer service, leading to improved customer satisfaction, loyalty, and advocacy. Efficient data management ensures that customer data is readily accessible and actionable, empowering SMBs to deliver superior customer experiences and build stronger customer relationships.

Optimized Resource Allocation and Cost Leadership

Transformative data reduction optimizes resource allocation across the SMB enterprise. Reduced storage costs, improved system performance, and streamlined IT operations free up financial and human resources that can be reinvested in strategic initiatives, such as marketing, sales, and product development. Efficient data management contributes to overall cost leadership by minimizing operational expenses and maximizing resource utilization, enabling SMBs to compete more effectively on price and value.

Resilience and Business Continuity in the Face of Data Growth

Future-proof data infrastructure, built on intelligent data reduction principles, enhances SMB resilience and business continuity. Scalable data management systems, automated data protection mechanisms, and robust data governance frameworks ensure that SMBs can withstand data growth challenges, mitigate risks associated with data breaches or system failures, and maintain business operations even in the face of unforeseen disruptions. Data reduction contributes to a more agile and resilient SMB enterprise, capable of adapting to changing market conditions and emerging threats.

Transformative data reduction is not merely about managing data volume; it’s about strategically leveraging data efficiency to drive automation, fuel growth, and future-proof SMBs in an increasingly data-centric world.

Implementing Transformative Data Reduction ● A Roadmap for SMB Leaders

Adopting transformative data reduction requires a strategic roadmap that aligns with SMB business objectives and technological capabilities. This roadmap should encompass key steps, from strategic planning to technology adoption and ongoing optimization.

Step 1 ● Define Strategic Data Reduction Objectives

The initial step involves clearly defining strategic objectives for data reduction that are directly linked to SMB business goals. Objectives might include improving automation efficiency, enhancing data analytics capabilities, reducing storage costs, strengthening data governance, or future-proofing data infrastructure. Specific, measurable, achievable, relevant, and time-bound (SMART) objectives provide a clear direction for data reduction initiatives and enable effective progress tracking.

Step 2 ● Assess AI and ML Readiness and Capabilities

Transformative data reduction leverages AI and ML technologies. SMBs need to assess their current AI and ML readiness, including in-house expertise, data infrastructure capabilities, and technological resources. This assessment informs decisions about technology adoption strategies, whether to build in-house AI capabilities, partner with technology providers, or leverage cloud-based AI services. A realistic assessment of AI readiness ensures that data reduction initiatives are aligned with SMB capabilities and resources.

Step 3 ● Select AI-Powered Data Reduction Technologies and Solutions

Based on strategic objectives and AI readiness assessment, SMBs select appropriate AI-powered data reduction Meaning ● AI-Powered Data Reduction, crucial for SMB growth, strategically employs artificial intelligence to minimize the volume of data needing storage and processing. technologies and solutions. This selection process should consider factors such as technology effectiveness, scalability, integration capabilities, cost, and vendor support. Pilot projects and proof-of-concept deployments can help validate technology choices and refine implementation strategies before full-scale adoption. Choosing the right technologies is crucial for achieving desired data reduction outcomes and maximizing ROI.

Step 4 ● Implement Automation and AI-Driven Data Reduction Workflows

Step four involves implementing automated and AI-driven data reduction workflows. This includes integrating AI-powered tools into existing data management systems, automating data classification, deduplication, compression, archiving, and DLM processes, and establishing monitoring and alerting mechanisms to ensure workflow effectiveness. Implementation should be phased, starting with pilot deployments and gradually expanding to broader data environments. Careful planning, testing, and user training are essential for successful implementation and user adoption.

Step 5 ● Continuously Monitor, Optimize, and Innovate Data Reduction Strategies

Transformative data reduction is an ongoing journey. SMBs need to establish continuous monitoring mechanisms to track data reduction performance, measure progress against strategic objectives, and identify areas for further optimization. Regular data audits, performance analysis, and feedback from users inform ongoing refinement of data reduction strategies and technology adoption. Embracing a culture of continuous innovation ensures that SMBs stay ahead of the curve in data management, leveraging emerging technologies and best practices to maximize data efficiency and drive sustained business value.

1. Strategic Alignment ● Data reduction must directly support SMB business goals and KPIs.
2. AI and Automation ● Leverage AI and ML to automate and optimize data reduction processes.
3. Future-Proofing ● Design data reduction strategies to adapt to future data growth and technological changes.
4. Continuous Optimization ● Regularly monitor, analyze, and refine data reduction strategies for sustained effectiveness.
5. Holistic Approach ● Integrate data reduction into broader data management and business strategies.

References

• Stonebraker, Michael; Brown, Peter. Readings in Database Systems. 5th ed., Morgan Kaufmann, 2015.
• Laney, Douglas B. “3D Data Management ● Controlling Data Volume, Velocity, and Variety.” META Group Research Note, 6 Feb. 2001.
• Zikopoulos, Paul; Eaton, Chris. Understanding Big Data ● Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, 2011.