Fundamentals

Forty-three percent of small businesses do not track any metrics at all, a figure that should alarm anyone concerned with growth in a competitive landscape. Ignoring data in today’s market is akin to navigating a ship without a compass, especially for small to medium-sized businesses (SMBs) striving for stability and expansion. For many SMB owners, data strategy Meaning ● Data Strategy for SMBs: A roadmap to leverage data for informed decisions, growth, and competitive advantage. sounds like corporate jargon, something reserved for sprawling enterprises with dedicated analytics departments. However, the truth is that data, in its most basic form, is simply organized information, and refining how SMBs use this information can be a game-changer, not a budget-breaker.

Understanding Data Basics

Before diving into sophisticated strategies, SMBs must grasp the fundamental concept ● data is everywhere. It resides in sales figures, customer interactions, website traffic, social media engagement, and even employee feedback. The initial step involves recognizing these disparate pieces of information as valuable assets, not just byproducts of daily operations.

Many SMBs already collect data, perhaps unknowingly, through basic tools like spreadsheets, accounting software, or customer relationship management Meaning ● CRM for SMBs is about building strong customer relationships through data-driven personalization and a balance of automation with human touch. (CRM) systems. The refinement process begins with intentional data collection, moving from haphazard accumulation to structured organization.

Consider a small bakery. Transaction data from a point-of-sale (POS) system reveals popular items and peak hours. Customer feedback, whether through comment cards or online reviews, offers insights into product satisfaction and service quality. Website analytics Meaning ● Website Analytics, in the realm of Small and Medium-sized Businesses (SMBs), signifies the systematic collection, analysis, and reporting of website data to inform business decisions aimed at growth. track which pastries attract online interest and which pages deter visitors.

Individually, these data points might seem insignificant. Collectively, they paint a picture of customer preferences, operational efficiency, and marketing effectiveness. For this bakery, refining its data strategy starts with centralizing these data streams and asking simple questions.

For SMBs, data strategy refinement begins not with complex algorithms, but with recognizing the data already at their fingertips.

Simple Tools for Data Collection

SMBs do not require expensive enterprise-level software to begin refining their data strategies. Numerous affordable and user-friendly tools are available. Spreadsheets, like Microsoft Excel or Google Sheets, remain powerful for basic data organization and analysis. Free or low-cost CRM systems, such as HubSpot CRM or Zoho CRM, can manage customer interactions and track sales pipelines.

Website analytics platforms, like Google Analytics, provide invaluable insights into online behavior. Social media analytics Meaning ● Strategic use of social data to understand markets, predict trends, and enhance SMB business outcomes. dashboards, available on platforms like Facebook and Instagram, offer data on audience engagement and content performance. The key is to select tools that align with specific business needs and are easy to implement and use without requiring extensive technical expertise.

For a retail boutique, a simple POS system integrated with 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. software can track sales trends and stock levels. A basic email marketing platform, like Mailchimp, can collect data on customer preferences through surveys and track the effectiveness of promotional campaigns. A free survey tool, such as SurveyMonkey, can gather customer feedback Meaning ● Customer Feedback, within the landscape of SMBs, represents the vital information conduit channeling insights, opinions, and reactions from customers pertaining to products, services, or the overall brand experience; it is strategically used to inform and refine business decisions related to growth, automation initiatives, and operational implementations. on product satisfaction and in-store experience. These tools, when used strategically, transform raw data into actionable insights, guiding decision-making in areas like inventory management, marketing campaigns, 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. improvements.

Defining Key Performance Indicators (KPIs)

Data collection without purpose is merely data hoarding. Refining a data strategy necessitates identifying Key Performance Indicators Meaning ● Key Performance Indicators (KPIs) represent measurable values that demonstrate how effectively a small or medium-sized business (SMB) is achieving key business objectives. (KPIs) that directly reflect business goals. KPIs are measurable values that demonstrate how effectively a company is achieving key business objectives. For SMBs, KPIs should be simple, relevant, and actionable.

Instead of tracking vanity metrics like social media followers, focus on metrics that impact the bottom line, such as customer acquisition cost, customer retention rate, average order value, or sales conversion rate. The selection of KPIs should be directly tied to the SMB’s strategic objectives, whether it’s increasing sales, improving customer satisfaction, or streamlining operations.

A small e-commerce store aiming to increase online sales might focus on KPIs like website conversion rate (percentage of website visitors who make a purchase), average order value (average amount spent per transaction), and customer lifetime value (total revenue a customer generates over their relationship with the business). A service-based SMB, such as a cleaning company, might prioritize KPIs like customer satisfaction Meaning ● Customer Satisfaction: Ensuring customer delight by consistently meeting and exceeding expectations, fostering loyalty and advocacy. score (measured through surveys), customer churn Meaning ● Customer Churn, also known as attrition, represents the proportion of customers that cease doing business with a company over a specified period. rate (percentage of customers lost over a period), and service delivery time (time taken to complete a service). By focusing on these specific KPIs, SMBs can use data to monitor progress, identify areas for improvement, and make data-driven decisions to achieve their goals.

Data-Driven Decision Making ● Starting Small

The shift to a data-driven culture within an SMB does not require an overnight transformation. It begins with small, incremental changes. Start by using collected data to inform everyday decisions. For example, a restaurant owner can analyze POS data to optimize menu offerings, removing less popular items and highlighting bestsellers.

A marketing agency can track campaign performance data to refine ad spending and target audiences more effectively. A consulting firm can use client feedback data to improve service delivery and tailor solutions to specific client needs. These small wins build confidence in the value of data and pave the way for more sophisticated data strategies in the future.

Consider a local coffee shop. Analyzing sales data reveals that iced lattes are significantly more popular during the afternoon hours. Based on this data, the coffee shop can adjust staffing levels to handle the afternoon rush and optimize inventory of latte ingredients. Tracking customer feedback indicates that customers appreciate faster service during peak hours.

The coffee shop can implement a mobile ordering system to streamline the ordering process and reduce wait times. These data-informed adjustments, while seemingly minor, contribute to improved customer satisfaction and operational efficiency, demonstrating the practical benefits of a basic data strategy.

Table 1 ● Simple Data Tools for SMBs

Building a Data-Aware Culture

Refining data strategies within SMBs is not solely about tools and technology; it’s about fostering a data-aware culture. This involves educating employees about the importance of data, encouraging data-driven thinking at all levels, and making data accessible to relevant team members. Start with training sessions to explain basic data concepts and the tools being used. Encourage employees to identify data relevant to their roles and to suggest ways data can improve their work.

Share data insights openly and celebrate data-driven successes to reinforce the value of this approach. Creating a culture where data is valued and utilized is crucial for long-term success in refining data strategies.

In a small team, regular team meetings can incorporate data discussions. Sales teams can review sales performance data, marketing teams can analyze campaign results, and operations teams can examine efficiency metrics. Sharing dashboards or reports that visualize key data points can make data more accessible and understandable for everyone.

Recognizing and rewarding employees who use data effectively in their roles further reinforces a data-centric culture. This cultural shift, starting from the fundamentals, lays the groundwork for more advanced data strategies as the SMB grows and evolves.

Data refinement for SMBs is not a destination, but a continuous journey of learning, adapting, and improving.

Intermediate

Beyond the rudimentary tracking of basic metrics, a significant competitive advantage awaits SMBs willing to delve into intermediate data strategies. While initial data efforts might focus on descriptive analytics ● understanding what happened ● intermediate strategies shift towards diagnostic and predictive analytics Meaning ● Strategic foresight through data for SMB success. ● exploring why it happened and what might happen next. This transition requires a more sophisticated approach to data collection, analysis, and interpretation, moving beyond simple spreadsheets and basic reports to leverage more advanced techniques and tools. For SMBs aiming for sustainable growth and operational efficiency, mastering these intermediate strategies becomes increasingly crucial.

Moving Beyond Descriptive Analytics

Descriptive analytics, while foundational, only scratches the surface of data’s potential. Intermediate data refinement involves progressing to diagnostic analytics, which aims to understand the reasons behind observed trends. For instance, knowing that sales declined last month (descriptive) is less valuable than understanding why sales declined (diagnostic). Was it due to seasonal factors, a competitor’s promotion, or internal operational issues?

Diagnostic analytics requires deeper data exploration, often involving data segmentation, correlation analysis, and root cause analysis. By understanding the ‘why’ behind the ‘what,’ SMBs can move from reactive responses to proactive strategies.

Consider an online clothing retailer that notices a drop in website conversion rates. Descriptive analytics reveals the decline. Diagnostic analytics investigates potential causes. By segmenting website traffic data, they might discover that conversion rates decreased primarily among mobile users.

Further analysis reveals slow page loading times on mobile devices. The root cause is identified ● poor mobile website performance. This diagnostic insight leads to a targeted solution ● optimizing the mobile website for speed and user experience. This example illustrates how moving beyond descriptive analytics enables SMBs to identify specific problems and implement effective solutions.

Implementing Customer Segmentation

Customer segmentation is a cornerstone of intermediate data strategies. It involves dividing customers into distinct groups based on shared characteristics, allowing for more targeted marketing, personalized customer experiences, and optimized product development. Segmentation can be based on various factors, including demographics, purchase history, website behavior, psychographics, or geographic location.

Effective segmentation requires collecting and analyzing customer data Meaning ● Customer Data, in the sphere of SMB growth, automation, and implementation, represents the total collection of information pertaining to a business's customers; it is gathered, structured, and leveraged to gain deeper insights into customer behavior, preferences, and needs to inform strategic business decisions. from multiple sources, such as CRM systems, sales records, and marketing platforms. By understanding different customer segments, SMBs can tailor their offerings and communications to maximize impact and resource allocation.

A local fitness studio can segment its customer base based on membership type (e.g., yoga, HIIT, personal training), attendance frequency, and age group. Analyzing data within each segment reveals distinct preferences and needs. The studio might discover that younger members prefer high-intensity classes and social media engagement, while older members favor yoga and personalized attention. This segmentation allows the studio to create targeted marketing Meaning ● Targeted marketing for small and medium-sized businesses involves precisely identifying and reaching specific customer segments with tailored messaging to maximize marketing ROI. campaigns, offer tailored class schedules, and personalize communication strategies for each segment, leading to increased customer engagement and retention.

List 1 ● Customer Segmentation Meaning ● Customer segmentation for SMBs is strategically dividing customers into groups to personalize experiences, optimize resources, and drive sustainable growth. Variables for SMBs

• Demographics ● Age, gender, income, education, occupation, family status
• Geographics ● Location, region, urban/rural, climate
• Psychographics ● Lifestyle, values, interests, personality, attitudes
• Behavioral ● Purchase history, frequency of purchase, spending habits, website activity, product usage
• Technographics ● Technology adoption, device usage, online behavior

Predictive Analytics for Forecasting and Planning

Predictive analytics takes data strategy to the next level by using historical data to forecast future trends and outcomes. This involves employing statistical techniques 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 to identify patterns and predict future events. For SMBs, predictive analytics can be applied to various areas, including sales forecasting, demand planning, inventory management, customer churn prediction, and risk assessment.

While sophisticated, predictive analytics tools are becoming increasingly accessible and affordable for SMBs. By anticipating future trends, SMBs can make proactive decisions, optimize resource allocation, and gain a competitive edge.

An online bookstore can use predictive analytics to forecast book demand during the holiday season. By analyzing historical sales data, seasonal trends, and marketing campaign data, they can predict which book categories and titles are likely to be most popular. This allows them to optimize inventory levels, ensuring they have sufficient stock of high-demand items while minimizing overstocking of less popular titles.

Predictive analytics can also help personalize product recommendations for customers, increasing sales and customer satisfaction. Furthermore, predictive models can identify customers at high risk of churn, enabling proactive retention efforts.

Intermediate data strategies empower SMBs to move from understanding the past to anticipating the future.

Data Visualization and Dashboards

As data becomes more complex, effective data visualization Meaning ● Data Visualization, within the ambit of Small and Medium-sized Businesses, represents the graphical depiction of data and information, translating complex datasets into easily digestible visual formats such as charts, graphs, and dashboards. becomes essential. Data visualization transforms raw data into graphical representations, making it easier to understand patterns, trends, and insights. Dashboards consolidate key data visualizations into a single, easily accessible interface, providing a real-time overview of business performance.

For SMBs, data visualization tools and dashboards simplify data interpretation, facilitate communication of insights, and enable faster, data-driven decision-making. User-friendly platforms like Tableau Public, Google Data Studio, and Power BI offer accessible options for creating compelling visualizations and interactive dashboards.

A restaurant chain with multiple locations can use data visualization to monitor performance across different branches. Dashboards can display KPIs such as sales revenue, customer foot traffic, average order value, and customer satisfaction scores for each location. Visualizations like bar charts, line graphs, and heat maps can quickly highlight top-performing and underperforming locations, enabling management to identify best practices and address operational issues promptly. Interactive dashboards allow users to drill down into specific data points for deeper analysis, facilitating a more nuanced understanding of performance drivers.

Integrating Data Across Departments

Data refinement at the intermediate level involves breaking down data silos and integrating data across different departments. Often, SMBs have valuable data scattered across sales, marketing, operations, and customer service departments, but this data remains isolated and underutilized. Integrating data from these disparate sources provides a holistic view of the business, enabling more comprehensive analysis and strategic decision-making.

Data integration can be achieved through various methods, including data warehousing, data lakes, and application programming interfaces (APIs). While full-scale data warehousing might be complex for smaller SMBs, starting with API integrations between key systems and exploring cloud-based data lake solutions can be practical steps.

Consider a manufacturing SMB. Sales data resides in the CRM system, production data in the manufacturing execution system (MES), and inventory data in the warehouse management system (WMS). Integrating these data sources allows for a comprehensive analysis of the entire supply chain.

By combining sales forecasts with production capacity and inventory levels, the SMB can optimize production planning, minimize inventory holding costs, and ensure timely order fulfillment. Data integration Meaning ● Data Integration, a vital undertaking for Small and Medium-sized Businesses (SMBs), refers to the process of combining data from disparate sources into a unified view. also facilitates better communication and collaboration between departments, fostering a more data-driven and efficient organization.

Table 2 ● Intermediate Data Strategy Tools and Techniques

Developing Data Governance Basics

As SMBs advance their data strategies, establishing basic data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. practices becomes increasingly important. Data governance refers to the framework of rules, policies, and processes that ensure data quality, security, and compliance. For intermediate-level SMBs, data governance does not need to be overly complex, but it should address key areas such as 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. management, data security protocols, and data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. compliance (e.g., GDPR, CCPA).

Implementing basic data governance practices builds trust in data, reduces errors, and mitigates risks associated with data breaches or non-compliance. Starting with simple data quality checks, access controls, and data privacy policies is a practical approach for SMBs.

A healthcare clinic SMB, even at an intermediate stage, must prioritize data governance, particularly data privacy. Implementing access controls to patient data, establishing protocols for data encryption, and ensuring compliance with HIPAA regulations are crucial. Regular data quality audits to verify the accuracy and completeness of patient records are also essential.

Developing a simple data privacy policy and training staff on data security Meaning ● Data Security, in the context of SMB growth, automation, and implementation, represents the policies, practices, and technologies deployed to safeguard digital assets from unauthorized access, use, disclosure, disruption, modification, or destruction. best practices are fundamental steps in establishing data governance. These measures not only protect patient data but also build trust and credibility with patients and regulatory bodies.

Moving to intermediate data strategies requires not only advanced techniques but also a commitment to data quality and responsible data handling.

Advanced

For SMBs poised for exponential growth and market leadership, refining data strategies transcends basic analytics and delves into the realm of advanced, multi-dimensional data ecosystems. At this stage, data is not merely a tool for operational improvement; it becomes a strategic asset, driving innovation, creating new revenue streams, and fostering a culture of continuous learning and adaptation. Advanced data strategies for SMBs involve leveraging sophisticated technologies like artificial intelligence Meaning ● AI empowers SMBs to augment capabilities, automate operations, and gain strategic foresight for sustainable growth. (AI), machine learning (ML), and big 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. to unlock deep insights, automate complex processes, and personalize customer experiences at scale. This requires a significant investment in data infrastructure, talent, and a fundamental shift in organizational mindset, viewing data as the lifeblood of the business.

Leveraging Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) represent the pinnacle of advanced data strategies. AI refers to the broad concept of machines performing tasks that typically require human intelligence, while ML is a subset of AI that enables systems to learn from data without explicit programming. For SMBs, AI and ML offer transformative potential across various functions, from automating customer service with chatbots to optimizing pricing strategies with dynamic pricing algorithms and personalizing marketing campaigns Meaning ● Marketing campaigns, in the context of SMB growth, represent structured sets of business activities designed to achieve specific marketing objectives, frequently leveraged to increase brand awareness, drive lead generation, or boost sales. with AI-powered recommendation engines. Implementing AI and ML requires access to large datasets, specialized expertise, and robust computing infrastructure, but cloud-based AI/ML platforms are making these technologies increasingly accessible to SMBs.

A subscription box SMB can utilize ML algorithms to predict customer preferences and personalize box contents. By analyzing historical subscription data, customer feedback, and product attributes, ML models can learn individual customer tastes and curate boxes tailored to their specific needs and desires. AI-powered chatbots can handle routine customer service inquiries, freeing up human agents to focus on complex issues.

Dynamic pricing algorithms can optimize subscription pricing based on demand, competition, and customer behavior. These AI/ML applications enhance customer satisfaction, improve operational efficiency, and drive revenue growth, transforming the SMB into a data-driven, intelligent organization.

Building a Data Lake for Big Data Analytics

Advanced data strategies often necessitate the creation of a data lake, a centralized repository that stores vast amounts of raw, unstructured, and semi-structured data from diverse sources. Unlike traditional data warehouses, which require structured data and predefined schemas, data lakes offer flexibility and scalability for handling big data. For SMBs generating large volumes of data from various sources (e.g., IoT devices, social media streams, customer interaction logs), a data lake provides a platform for advanced analytics, including real-time processing, complex event processing, and exploratory data science. Cloud-based data lake solutions, such as AWS S3, Azure Data Lake Storage, and Google Cloud Storage, offer cost-effective and scalable options for SMBs to manage big data.

A logistics SMB utilizing a fleet of vehicles equipped with GPS trackers and IoT sensors generates massive amounts of real-time data on vehicle location, speed, fuel consumption, and engine performance. A data lake can ingest and store this data, along with data from other sources like weather forecasts, traffic conditions, and delivery schedules. Big data analytics techniques can then be applied to optimize route planning in real-time, predict vehicle maintenance needs proactively, and improve overall fleet efficiency. Real-time dashboards can visualize fleet performance and identify potential issues immediately, enabling proactive intervention and optimization.

List 2 ● Advanced Data Strategy Meaning ● Advanced Data Strategy, within the SMB context, involves a comprehensive and forward-looking plan for leveraging data assets to drive business growth, enhance automation, and optimize implementation processes. Technologies for SMBs

• Artificial Intelligence (AI) ● Machines performing tasks requiring human intelligence
• Machine Learning (ML) ● AI subset enabling systems to learn from data
• Big Data Analytics ● Processing and analyzing extremely large datasets
• Data Lake ● Centralized repository for raw, unstructured, and semi-structured data
• Cloud Computing ● On-demand access to computing resources over the internet
• Edge Computing ● Processing data closer to the source, reducing latency
• Real-Time Analytics ● Analyzing data as it is generated, providing immediate insights

Personalization at Scale with Customer Data Platforms (CDPs)

Customer Data Platforms (CDPs) are a critical component of advanced data strategies focused on personalized customer experiences. A CDP unifies customer data from all sources into a single, persistent customer profile, creating a 360-degree view of each customer. For SMBs aiming to deliver highly personalized marketing, sales, and service interactions, a CDP provides the foundation for understanding individual customer needs, preferences, and behaviors across all touchpoints. CDPs enable advanced segmentation, personalized content delivery, omnichannel marketing automation, and real-time customer journey orchestration, driving customer engagement and loyalty.

A multi-channel retailer SMB can leverage a CDP to personalize the entire customer journey. The CDP integrates data from online stores, physical stores, mobile apps, email marketing, social media, and customer service interactions. This unified customer profile enables personalized product recommendations on the website, targeted email campaigns based on past purchases, personalized in-store offers via mobile app notifications, and proactive customer service interventions based on real-time behavior. Personalization at scale, powered by a CDP, transforms the customer experience from generic to highly relevant and engaging, fostering stronger customer relationships and driving revenue growth.

Advanced data strategies are about transforming data from a supporting function to a core driver of business innovation and competitive advantage.

Implementing Edge Computing for Real-Time Insights

Edge computing is an emerging paradigm that brings computation and data storage closer to the location where data is generated. This is particularly relevant for SMBs dealing with real-time data streams from IoT devices, sensors, or mobile applications. Edge computing Meaning ● Edge computing, in the context of SMB operations, represents a distributed computing paradigm bringing data processing closer to the source, such as sensors or local devices. reduces latency, bandwidth consumption, and reliance on centralized cloud infrastructure, enabling faster processing and immediate insights.

For SMBs in industries like manufacturing, logistics, and retail, edge computing can power real-time analytics, predictive maintenance, autonomous systems, and enhanced customer experiences. Combining edge computing with cloud computing creates a hybrid architecture that leverages the strengths of both approaches.

A smart agriculture SMB deploying IoT sensors in fields to monitor soil conditions, weather patterns, and crop health can benefit significantly from edge computing. Sensors generate vast amounts of data in real-time. Processing this data at the edge, closer to the sensors, reduces latency and bandwidth requirements. Edge devices can perform initial data analysis, identify anomalies, and trigger immediate actions like adjusting irrigation systems or pest control measures.

Only aggregated and processed data is sent to the cloud for long-term storage, trend analysis, and strategic decision-making. Edge computing enables real-time responsiveness and optimized resource utilization in dynamic environments.

Ethical Considerations and Data Responsibility

As SMBs embrace advanced data strategies, ethical considerations and data responsibility become paramount. Collecting and utilizing vast amounts of customer data raises ethical concerns related to data privacy, security, bias, and transparency. SMBs must 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, ensuring data is collected and used responsibly, transparently, and with respect for customer privacy.

This includes implementing robust data security measures, obtaining informed consent for data collection, anonymizing data where appropriate, and avoiding biased algorithms that perpetuate unfair or discriminatory outcomes. Building customer trust and maintaining ethical data practices Meaning ● Ethical Data Practices: Responsible and respectful data handling for SMB growth and trust. are essential for long-term sustainability and reputation in the age of data-driven business.

An online education platform SMB using AI-powered learning analytics to personalize education must be acutely aware of ethical implications. Algorithms should be designed to avoid bias and ensure equitable learning opportunities for all students, regardless of background. Data privacy must be rigorously protected, and student data should not be used for purposes beyond educational improvement without explicit consent.

Transparency about data collection and usage practices is crucial for building trust with students and parents. Ethical data governance, embedded in the design and implementation of advanced data strategies, is not merely a compliance requirement but a fundamental aspect of responsible business practice.

Table 3 ● Advanced Data Strategy Implementation Considerations

The future of SMB competitiveness hinges on the ability to not just collect data, but to ethically and strategically transform it into actionable intelligence and innovative solutions.

References

• Provost, Foster, and Tom Fawcett. Data Science for Business ● What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media, 2013.
• Manyika, James, et al. Big Data ● The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, 2011.
• Davenport, Thomas H., and Jeanne G. Harris. Competing on Analytics ● The New Science of Winning. Harvard Business Review Press, 2007.
• O’Neil, Cathy. Weapons of Math Destruction ● How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
• Marr, Bernard. Big Data in Practice ● How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley, 2016.