Fundamentals

Thirty-six percent of small businesses do not use data analytics, a figure that highlights a significant untapped potential within the SMB landscape. Predictive data, often perceived as a complex tool reserved for large corporations, actually holds surprisingly accessible and potent benefits for small and medium-sized businesses.

Demystifying Predictive Data For Small Business Owners

Predictive data, at its core, is about looking ahead, not just behind. It uses historical information to forecast future trends and outcomes. Think of it like this ● a seasoned farmer doesn’t just look at today’s weather; they consider past seasons, soil conditions, and planting cycles to predict the best time to sow seeds for a bountiful harvest. Predictive data works similarly, but with business information.

For SMBs, predictive data is not about complex algorithms or massive datasets; it is about smart, informed decision-making.

Why Should Smbs Care About Predictions?

Small businesses often operate with limited resources and tight margins. Every decision, from inventory management Meaning ● Inventory management, within the context of SMB operations, denotes the systematic approach to sourcing, storing, and selling inventory, both raw materials (if applicable) and finished goods. to marketing campaigns, carries significant weight. Predictive data offers a way to make these crucial decisions with greater confidence and less guesswork. It helps SMBs anticipate market changes, understand customer behavior, and optimize operations in ways that were previously out of reach.

Simple Starting Points For Predictive Data

The idea of implementing predictive data might sound daunting, but it does not require a massive overhaul of existing systems. SMBs can begin with tools they likely already use, such as spreadsheet software or basic customer relationship management (CRM) systems. The key is to start small and focus on specific, manageable areas where predictions can make a tangible difference.

Leveraging Existing Sales Data

Sales data is a goldmine of predictive insights. By analyzing past sales trends, SMBs can forecast future demand for products or services. This allows for better inventory management, ensuring businesses are neither overstocked nor understocked. Consider a local bakery ● by tracking daily sales of different types of bread over several weeks, they can predict how much of each type to bake each day, minimizing waste and maximizing customer satisfaction.

Customer Behavior Analysis

Understanding customer purchasing patterns is crucial. Predictive data can help SMBs identify when customers are most likely to buy, what products they tend to purchase together, and even when they might be at risk of churning or stopping their patronage. A small online retailer, for example, can analyze past purchase history to predict which customers are likely to be interested in a new product line, allowing for targeted marketing efforts that are more effective and less costly than broad, untargeted campaigns.

Operational Efficiency Improvements

Predictive data extends beyond sales and marketing; it can also enhance operational efficiency. For service-based SMBs, like plumbing or electrical companies, analyzing historical service request data can predict peak demand times. This enables better scheduling of staff and resources, reducing wait times for customers and improving overall service delivery. Imagine a plumbing company using predictive data to anticipate higher call volumes during cold snaps, allowing them to proactively increase staffing levels to handle the expected surge in burst pipe incidents.

Practical Tools And Techniques

SMBs do not need to invest in expensive, complex software to start using predictive data. Several affordable and user-friendly tools are available. Spreadsheet programs like Microsoft Excel or Google Sheets, with their built-in charting and analysis features, can be surprisingly powerful for basic predictive analysis.

Cloud-based CRM systems often include reporting and analytics dashboards that provide predictive insights Meaning ● Predictive Insights within the SMB realm represent the actionable intelligence derived from data analysis to forecast future business outcomes. into sales pipelines and customer behavior. Even simple data visualization tools can help SMB owners spot trends and patterns that might otherwise be missed in raw data.

To illustrate, consider a small coffee shop aiming to optimize its staffing levels. By using a simple spreadsheet, they can track customer foot traffic at different times of day and days of the week. Over time, patterns will emerge, showing peak hours and slower periods. This data can then be used to predict staffing needs, ensuring they have enough baristas during busy times and avoid overstaffing during quiet periods, leading to significant cost savings and improved customer service.

Here is a table showcasing basic predictive data applications for SMBs:

Avoiding Common Pitfalls

When starting with predictive data, SMBs should be mindful of common mistakes. One frequent error is focusing on too much data too soon. It is better to start with a small, manageable dataset and gradually expand as comfort and expertise grow. Another pitfall is relying solely on predictions without considering external factors or qualitative insights.

Predictive data is a tool to inform decisions, not dictate them. Human judgment and business acumen remain essential.

For instance, a clothing boutique might use predictive data to forecast sales based on past trends. However, if a major local event is announced that will significantly increase foot traffic in their area, they need to adjust their predictions accordingly. Ignoring such external factors can lead to inaccurate forecasts and suboptimal decisions.

The Human Element Remains Key

Predictive data, for all its power, is not a replacement for human intuition and experience. It is a tool that augments human capabilities, providing insights that might be difficult to discern otherwise. SMB owners and employees bring valuable contextual knowledge and understanding to the table. The most effective use of predictive data involves combining these quantitative insights with qualitative understanding and human judgment.

Consider a restaurant using predictive data to optimize its menu based on customer preferences. While data might suggest certain dishes are less popular, a chef’s intuition might recognize that these dishes are essential for catering to specific dietary needs or offering a balanced menu. The data informs the decision, but the human element ensures it aligns with broader business goals and customer needs.

Predictive data empowers SMBs to move beyond reactive decision-making and embrace a more proactive, forward-thinking approach. It is about using readily available information to make smarter choices, optimize operations, and ultimately, achieve sustainable growth. The journey begins with simple steps, a willingness to learn, and a focus on practical applications that deliver tangible results.

Intermediate

Despite the increasing accessibility of data analytics Meaning ● Data Analytics, in the realm of SMB growth, represents the strategic practice of examining raw business information to discover trends, patterns, and valuable insights. tools, a mere twenty-three percent of SMBs actively employ data-driven strategies, indicating a considerable gap between potential and practical application. Moving beyond basic spreadsheets, SMBs ready to deepen their engagement with predictive data can unlock sophisticated strategies that drive competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. and sustainable growth.

Strategic Integration Of Predictive Data

At the intermediate level, predictive data ceases to be a supplementary tool and becomes an integral component of strategic decision-making. It is about embedding predictive analytics Meaning ● Strategic foresight through data for SMB success. into core business processes, from marketing and sales to operations and customer service. This requires a more structured approach, involving data infrastructure development, skill enhancement, and a commitment to data-informed culture.

Strategic use of predictive data is not about isolated projects; it is about creating a data-fluent organization where insights guide every level of operation.

Building A Robust Data Foundation

Effective predictive analytics relies on a solid data foundation. This involves ensuring data quality, accessibility, and integration across different business systems. SMBs at this stage should invest in consolidating data from various sources ● CRM, point-of-sale (POS), marketing automation Meaning ● Marketing Automation for SMBs: Strategically automating marketing tasks to enhance efficiency, personalize customer experiences, and drive sustainable business growth. platforms, and even social media ● into a centralized repository. Cloud-based data warehouses offer scalable and cost-effective solutions for SMBs to manage growing data volumes.

Data Quality And Governance

High-quality data is paramount. Inaccurate or incomplete data can lead to flawed predictions and misguided decisions. SMBs need to implement data governance practices to ensure data accuracy, consistency, and reliability.

This includes establishing data entry standards, regular data cleansing processes, and protocols for data validation. Investing in data quality Meaning ● Data Quality, within the realm of SMB operations, fundamentally addresses the fitness of data for its intended uses in business decision-making, automation initiatives, and successful project implementations. upfront pays dividends in the accuracy and effectiveness of predictive models.

Data Integration And Accessibility

Siloed data limits predictive capabilities. Integrating data from different systems provides a holistic view of the business and unlocks richer insights. Modern integration platforms can automate data flow between applications, making data readily accessible for analysis.

This eliminates manual data manipulation and ensures that predictive models Meaning ● Predictive Models, in the context of SMB growth, refer to analytical tools that forecast future outcomes based on historical data, enabling informed decision-making. are based on the most comprehensive and up-to-date information. For example, integrating CRM data with marketing automation data allows for a deeper understanding of customer journeys and marketing campaign effectiveness.

Advanced Predictive Modeling Techniques

While basic spreadsheet analysis is a starting point, intermediate SMBs can leverage more advanced predictive modeling techniques for deeper insights and more accurate forecasts. These techniques, often accessible through user-friendly analytics platforms, include regression analysis, time series forecasting, and machine learning Meaning ● Machine Learning (ML), in the context of Small and Medium-sized Businesses (SMBs), represents a suite of algorithms that enable computer systems to learn from data without explicit programming, driving automation and enhancing decision-making. algorithms. The choice of technique depends on the specific business problem and the nature of the data.

Regression Analysis For Demand Forecasting

Regression analysis is a statistical technique used to model the relationship between a dependent variable (e.g., sales demand) and one or more independent variables (e.g., marketing spend, seasonality, economic indicators). SMBs can use regression models to forecast future demand based on historical data and relevant external factors. This allows for more precise inventory planning, production scheduling, and resource allocation. A retail business, for instance, can use regression analysis Meaning ● Regression Analysis, a statistical methodology vital for SMBs, facilitates the understanding of relationships between variables to predict outcomes. to predict sales of winter coats based on historical sales data, temperature forecasts, and promotional activities.

Time Series Forecasting For Trend Analysis

Time series forecasting focuses on analyzing data points collected over time to identify patterns and trends. Techniques like ARIMA (Autoregressive Integrated Moving Average) and exponential smoothing can be used to forecast future values based on past trends. SMBs can apply time series forecasting to predict website traffic, customer churn rates, or sales revenue. A subscription-based service, for example, can use time series analysis to predict subscriber churn and proactively implement retention strategies.

Introduction To Machine Learning

Machine learning (ML) algorithms are becoming increasingly accessible to SMBs through cloud-based platforms. ML techniques, such as classification and clustering, can uncover complex patterns and relationships in data that might be missed by traditional statistical methods. For predictive purposes, ML can be used for tasks like customer segmentation, fraud detection, and personalized recommendations. An e-commerce business can use ML to segment customers based on their purchasing behavior and tailor marketing messages accordingly, or to detect potentially fraudulent transactions in real-time.

Implementing Predictive Analytics In Key Business Functions

The true power of predictive data emerges when it is strategically applied across various business functions. This section explores how intermediate SMBs can leverage predictive analytics in marketing, sales, operations, and customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. to drive tangible improvements.

Predictive Marketing For Enhanced Campaigns

Predictive marketing goes beyond basic customer segmentation. It uses predictive models to anticipate customer needs, personalize marketing messages, and optimize campaign timing and channels. This results in higher engagement rates, improved conversion rates, and a more efficient marketing spend.

For example, predictive analytics can identify customers who are most likely to respond to a specific promotion, allowing marketers to focus their efforts on the most receptive audience. Furthermore, it can predict the optimal time to send marketing emails or run social media ads to maximize open and click-through rates.

Sales Forecasting And Pipeline Management

Accurate sales forecasting Meaning ● Sales Forecasting, within the SMB landscape, is the art and science of predicting future sales revenue, essential for informed decision-making and strategic planning. is crucial for business planning and resource allocation. Predictive analytics enhances sales forecasting by incorporating historical sales data, market trends, and lead scoring models. This provides a more realistic view of future sales performance and allows for proactive adjustments to sales strategies. Predictive models can also be used to score leads based on their likelihood to convert, enabling sales teams to prioritize their efforts on the most promising prospects and optimize sales pipeline management.

Operational Optimization Through Predictive Maintenance

Predictive maintenance leverages sensor data and predictive algorithms to anticipate equipment failures and schedule maintenance proactively. This reduces downtime, minimizes repair costs, and extends the lifespan of assets. For SMBs in manufacturing, logistics, or any industry reliant on equipment, predictive maintenance Meaning ● Predictive Maintenance for SMBs: Proactive asset management using data to foresee failures, optimize operations, and enhance business resilience. can lead to significant operational efficiencies and cost savings. Imagine a delivery company using predictive maintenance to monitor vehicle performance and schedule maintenance before breakdowns occur, ensuring timely deliveries and minimizing disruptions.

Customer Service Enhancement With Predictive Support

Predictive support anticipates customer service needs and proactively addresses potential issues before they escalate. By analyzing customer interaction data, predictive models can identify customers who are likely to require support or are at risk of churn. This allows customer service teams to proactively reach out, offer assistance, and personalize support interactions, leading to improved customer satisfaction Meaning ● Customer Satisfaction: Ensuring customer delight by consistently meeting and exceeding expectations, fostering loyalty and advocacy. and loyalty. A software-as-a-service (SaaS) company, for instance, can use predictive support Meaning ● Predictive Support, within the SMB landscape, signifies the strategic application of data analytics and machine learning to anticipate and address customer needs proactively. to identify users who are struggling with a particular feature and offer proactive tutorials or assistance.

Here is a list of intermediate predictive data tools for SMBs:

• Advanced Spreadsheet Software ● Microsoft Excel with Power Query and Power Pivot, Google Sheets with Apps Script.
• Cloud-Based CRM with Analytics ● Salesforce Essentials, HubSpot CRM, Zoho CRM.
• Business Intelligence (BI) Platforms ● Tableau Public, Power BI Desktop, Google Data Studio.
• Marketing Automation Platforms with Predictive Features ● Marketo Engage, Pardot, ActiveCampaign.
• Cloud-Based Machine Learning Platforms (for Accessible ML) ● Google Cloud AI Platform, Amazon SageMaker, Microsoft Azure Machine Learning.

Navigating Data Privacy And Ethical Considerations

As SMBs delve deeper into predictive data, data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. and ethical considerations become increasingly important. Collecting and using customer data responsibly is not only a legal requirement but also crucial for maintaining customer trust and brand reputation. SMBs must comply with data privacy regulations Meaning ● Data Privacy Regulations for SMBs are strategic imperatives, not just compliance, driving growth, trust, and competitive edge in the digital age. like GDPR and CCPA and adopt ethical data Meaning ● Ethical Data, within the scope of SMB growth, automation, and implementation, centers on the responsible collection, storage, and utilization of data in alignment with legal and moral business principles. practices.

Data Security And Compliance

Protecting customer data from unauthorized access and breaches is paramount. SMBs need to implement robust data security measures, including encryption, access controls, and regular security audits. Compliance with data privacy regulations requires transparency in data collection practices, obtaining consent for data usage, and providing customers with control over their data. Data breaches can have severe consequences, including financial penalties, reputational damage, and loss of customer trust.

Ethical Use Of Predictive Data

Beyond legal compliance, ethical considerations guide the responsible use of predictive data. This includes avoiding biased algorithms, ensuring fairness and transparency in predictive models, and using predictions in a way that benefits customers. For example, using predictive data to personalize product recommendations is generally considered ethical, while using it to price discriminate or unfairly target vulnerable customer segments is not. SMBs should establish ethical guidelines for data usage and regularly review their predictive analytics practices to ensure they align with ethical principles.

Moving to an intermediate level of predictive data utilization empowers SMBs to make more informed, strategic decisions across their operations. It requires investment in data infrastructure, skill development, and a commitment to ethical data practices. However, the rewards ● improved efficiency, enhanced customer engagement, and a stronger competitive position ● are substantial and pave the way for advanced predictive capabilities.

Advanced

Despite the recognized potential of data analytics, a mere fraction, approximately ten percent, of SMBs have reached a level of advanced data maturity, signaling a significant opportunity for those willing to embrace sophisticated strategies. For SMBs aspiring to data leadership, advanced predictive analytics represents a paradigm shift, transforming data from a reactive reporting tool into a proactive strategic asset.

Transformative Predictive Strategies For Smbs

At the advanced stage, predictive data transcends functional applications and becomes a catalyst for business model innovation Meaning ● Strategic reconfiguration of how SMBs create, deliver, and capture value to achieve sustainable growth and competitive advantage. and competitive disruption. It is about leveraging cutting-edge techniques, integrating predictive insights into the very fabric of the organization, and fostering a culture of continuous data-driven optimization. This necessitates a deep understanding of advanced analytics Meaning ● Advanced Analytics, in the realm of Small and Medium-sized Businesses (SMBs), signifies the utilization of sophisticated data analysis techniques beyond traditional Business Intelligence (BI). methodologies, a robust data science capability, and a strategic vision to leverage predictions for transformative outcomes.

Advanced predictive data utilization is not about incremental improvements; it is about fundamentally reshaping business operations and creating new sources of value.

Building An Advanced Data Science Capability

Reaching advanced predictive capabilities requires building or accessing a sophisticated data science capability. This involves acquiring talent with expertise in statistical modeling, machine learning, data engineering, and business domain knowledge. For SMBs, this might involve hiring data scientists, partnering with analytics consulting firms, or leveraging cloud-based data science platforms that offer advanced tools and resources.

In-House Data Science Teams Vs. Outsourcing

SMBs face a strategic choice ● build an in-house data science team or outsource their advanced analytics needs. Building an in-house team provides greater control and domain expertise but can be costly and challenging to recruit and retain talent. Outsourcing offers access to specialized skills and scalable resources but requires careful vendor selection and management to ensure alignment with business objectives. A hybrid approach, combining a small in-house team with external expertise, can be a pragmatic solution for many SMBs.

Leveraging Cloud-Based Data Science Platforms

Cloud-based data science platforms democratize access to advanced analytics tools and infrastructure. Platforms like Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning provide SMBs with scalable computing power, pre-built machine learning algorithms, and collaborative development environments. These platforms significantly reduce the barriers to entry for advanced predictive analytics, enabling SMBs to develop and deploy sophisticated models without massive upfront investments in infrastructure and software.

Cutting-Edge Predictive Analytics Methodologies

Advanced SMBs leverage a range of cutting-edge predictive analytics methodologies to address complex business challenges and unlock deeper insights. These methodologies extend beyond traditional statistical techniques and incorporate advanced machine learning, deep learning, and artificial intelligence (AI) approaches.

Deep Learning For Complex Pattern Recognition

Deep learning, a subset of machine learning, utilizes artificial neural networks with multiple layers to analyze complex data patterns. Deep learning excels in areas like image recognition, natural language processing, and time series forecasting with intricate dependencies. SMBs can apply deep learning to analyze customer sentiment from social media data, predict equipment failures based on complex sensor readings, or personalize customer experiences with highly granular insights. For instance, a fashion retailer could use deep learning to analyze images of clothing styles and predict emerging fashion trends, informing product development and merchandising decisions.

AI-Powered Predictive Decision Systems

Artificial intelligence (AI) integrates predictive analytics with decision-making processes to create intelligent systems that automate and optimize business operations. AI-powered predictive decision systems can make real-time recommendations, automate complex tasks, and adapt to changing conditions. SMBs can implement AI-driven systems for dynamic pricing optimization, automated inventory replenishment, or personalized customer engagement. Consider an online travel agency using AI to dynamically adjust flight prices based on real-time demand predictions and competitor pricing, maximizing revenue and competitiveness.

Causal Inference For Strategic Insights

While traditional predictive analytics focuses on correlation, causal inference Meaning ● Causal Inference, within the context of SMB growth strategies, signifies determining the real cause-and-effect relationships behind business outcomes, rather than mere correlations. aims to understand cause-and-effect relationships. Advanced techniques like Bayesian networks and causal forests enable SMBs to go beyond prediction and gain deeper strategic insights into the drivers of business outcomes. Understanding causality allows for more effective interventions and strategic decision-making. For example, a marketing agency could use causal inference to determine the true impact of different marketing channels on customer acquisition, enabling them to optimize marketing spend and channel allocation more effectively.

Transformative Applications Of Predictive Data In Smbs

At the advanced level, predictive data applications extend beyond functional improvements and drive transformative changes across the business. This section explores how SMBs can leverage predictive analytics for business model innovation, competitive advantage, and sustainable growth.

Predictive Business Model Innovation

Predictive data can be a catalyst for business model innovation, enabling SMBs to create new products, services, and revenue streams. By leveraging predictive insights, SMBs can anticipate future customer needs, identify unmet market demands, and develop innovative offerings. For example, a traditional manufacturer could use predictive analytics to transition to a service-based business model, offering predictive maintenance and performance optimization services to their customers, creating recurring revenue streams and stronger customer relationships. Research by Porter and Heppelmann (2014) in the Harvard Business Review highlights how smart, connected products, powered by predictive analytics, are transforming competition and creating new business models.

Competitive Differentiation Through Predictive Personalization

In increasingly competitive markets, predictive personalization Meaning ● Predictive Personalization for SMBs: Anticipating customer needs to deliver tailored experiences, driving growth and loyalty. becomes a key differentiator. Advanced SMBs leverage predictive analytics to deliver highly personalized experiences to customers across all touchpoints, from marketing and sales to customer service and product recommendations. This level of personalization fosters customer loyalty, enhances brand perception, and drives competitive advantage. A personalized learning platform, for instance, could use predictive analytics to tailor learning paths and content recommendations to individual student needs and learning styles, providing a superior educational experience compared to generic offerings.

Predictive Supply Chain Optimization For Resilience

Advanced predictive analytics plays a crucial role in building resilient and agile supply chains. By predicting demand fluctuations, supply chain disruptions, and potential risks, SMBs can optimize inventory levels, production schedules, and logistics operations. This enhances supply chain efficiency, reduces costs, and improves responsiveness to market changes.

In today’s volatile global environment, supply chain resilience Meaning ● Supply Chain Resilience for SMBs: Building adaptive capabilities to withstand disruptions and ensure business continuity. is a critical competitive advantage. Sodhi and Tang (2021) in Supply Chain Management Meaning ● Supply Chain Management, crucial for SMB growth, refers to the strategic coordination of activities from sourcing raw materials to delivering finished goods to customers, streamlining operations and boosting profitability. Review emphasize the importance of predictive analytics for building resilient supply chains Meaning ● Dynamic SMB networks adapting to disruptions, ensuring business continuity and growth. in the face of uncertainty.

Here is a table showcasing advanced predictive data methodologies and applications for SMBs:

Ethical Ai And Responsible Predictive Data Practices

At the advanced level, ethical considerations surrounding AI and predictive data become paramount. SMBs leveraging cutting-edge technologies must prioritize responsible AI practices, ensuring fairness, transparency, and accountability in their predictive systems. This includes addressing algorithmic bias, ensuring data privacy, and maintaining human oversight Meaning ● Human Oversight, in the context of SMB automation and growth, constitutes the strategic integration of human judgment and intervention into automated systems and processes. of AI-driven decisions. O’Neil’s (2016) Weapons of Math Destruction serves as a critical reminder of the potential harms of unchecked algorithms and the importance of ethical AI Meaning ● Ethical AI for SMBs means using AI responsibly to build trust, ensure fairness, and drive sustainable growth, not just for profit but for societal benefit. development.

Algorithmic Bias Mitigation

Algorithmic bias can creep into predictive models if training data reflects existing societal biases or if model design inadvertently perpetuates unfair outcomes. Advanced SMBs must actively mitigate algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. through careful data preprocessing, algorithm selection, and ongoing model monitoring. This includes auditing models for fairness across different demographic groups and implementing techniques to debias algorithms and ensure equitable outcomes.

Transparency And Explainability In Ai Systems

Transparency and explainability are crucial for building trust in AI-driven predictive systems. Advanced SMBs should strive for interpretable models, or at least provide clear explanations of how AI systems arrive at their predictions and decisions. This is particularly important in sensitive areas like credit scoring, hiring, or customer service interactions. Explainable AI (XAI) techniques are emerging to address the “black box” nature of some advanced AI models, providing insights into model reasoning and enhancing transparency.

Human Oversight And Accountability

Even with advanced AI systems, human oversight and accountability remain essential. AI should augment human decision-making, not replace it entirely. SMBs need to establish clear lines of responsibility for AI-driven decisions and ensure that humans retain the ability to override or intervene when necessary. Ethical AI governance frameworks and responsible AI policies are crucial for guiding the development and deployment of advanced predictive analytics in a responsible and ethical manner.

Reaching an advanced level of predictive data utilization transforms SMBs into data-driven powerhouses, capable of innovating business models, differentiating themselves through personalization, and building resilient operations. It requires a significant investment in data science capabilities, cutting-edge methodologies, and ethical AI practices. However, for SMBs with the vision and commitment, advanced predictive analytics unlocks a new era of strategic advantage and sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. in the data-driven economy.

References

• Porter, Michael E., and James E. Heppelmann. “How Smart, Connected Products Are Transforming Competition.” Harvard Business Review, vol. 92, no. 11, 2014, pp. 64-88.
• O’Neil, Cathy. Weapons of Math Destruction ● How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
• Sodhi, ManMohan S., and Christopher S. Tang. “Supply Chain Resilience for Covid-19 and Beyond.” Supply Chain Management Review, vol. 25, no. 1, 2021, pp. 16-23.