Fundamentals

Consider the local bakery, thriving for decades on word-of-mouth and community goodwill. Suddenly, they adopt a customer loyalty app, collecting data on purchasing habits, birthdays, and even locations. Initially, it seems harmless, perhaps even beneficial for targeted promotions.

However, if this data is used to subtly price-gouge regular customers, or worse, sold to third parties without consent, the aroma of fresh bread quickly turns sour. This scenario, though simplified, illustrates the core issue ● unethical data use, irrespective of scale, poses tangible business challenges, especially for small and medium-sized businesses (SMBs).

Erosion of Customer Trust

Trust functions as the bedrock of any successful business, particularly for SMBs where personal relationships often outweigh transactional interactions. Unethical data practices act as a corrosive agent, weakening this foundation. When customers discover their data is mishandled, misused, or exploited, the resulting breach of trust can be devastating. This isn’t merely about losing a single sale; it’s about the potential exodus of loyal patrons and the chilling effect on new customer acquisition.

In an era where data breaches and privacy violations dominate headlines, consumers are increasingly wary, possessing a heightened sensitivity to data ethics. A single misstep can trigger a disproportionate backlash, amplified by social media and online review platforms, where negative experiences spread rapidly and broadly.

Unethical data use fundamentally undermines customer trust, a critical asset for SMB longevity and growth.

Imagine a scenario where a local gym, aiming to personalize workout plans, collects fitness data through wearable devices. If this data is then shared with insurance companies without explicit consent, leading to increased premiums for some customers, the gym faces not only legal repercussions but also a mass cancellation of memberships. The perception of betrayal, the feeling of being manipulated, outweighs any perceived benefit of data-driven personalization.

This loss of trust extends beyond immediate customers; it permeates the community, damaging the SMB’s reputation and long-term viability. Word travels fast in smaller communities, and a reputation for unethical practices, even if unintentional, is remarkably difficult to repair.

Legal and Regulatory Repercussions

The legal landscape surrounding data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. is becoming increasingly complex and stringent. Regulations like GDPR in Europe and CCPA in California, while primarily targeting larger corporations, set precedents and raise expectations for all businesses, including SMBs. Ignorance of these regulations, or a deliberate disregard for 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. handling, can lead to significant financial penalties, legal battles, and operational disruptions.

For SMBs operating on tighter margins, even seemingly minor fines can be crippling. Beyond financial penalties, legal challenges consume valuable time and resources, diverting attention from core business activities and hindering growth initiatives.

Consider a small e-commerce business that collects customer emails for marketing purposes. If they fail to comply with CAN-SPAM Act regulations, sending unsolicited emails or lacking proper opt-out mechanisms, they risk substantial fines per violation. Similarly, if an SMB collects and stores sensitive 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. without adequate security measures, a data breach can trigger legal action, regulatory investigations, and mandatory notifications, all of which are costly and time-consuming.

Navigating the intricacies of data privacy law requires expertise and diligence, resources often scarce within SMBs. This necessitates proactive measures, including legal consultation, employee training, and robust 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. protocols, to mitigate legal risks associated with unethical data use.

Damage to Brand Reputation

In today’s interconnected world, brand reputation Meaning ● Brand reputation, for a Small or Medium-sized Business (SMB), represents the aggregate perception stakeholders hold regarding its reliability, quality, and values. is paramount. It’s the currency of customer loyalty, investor confidence, and talent acquisition. Unethical data use acts as a powerful reputational toxin, capable of inflicting long-lasting damage.

News of data breaches, privacy violations, or manipulative data practices spreads like wildfire, amplified by social media and online news outlets. For SMBs, who often rely on positive word-of-mouth and community standing, a tarnished reputation can be particularly detrimental, leading to customer attrition, difficulty attracting talent, and reduced business opportunities.

Imagine a local restaurant chain that uses facial recognition technology to track customer preferences without explicit consent. If this practice is exposed, even if intended to improve service, the public outcry can be swift and severe. Social media campaigns, negative reviews, and boycotts can erupt, significantly impacting customer traffic and revenue.

The brand image, once associated with quality food and community engagement, becomes tainted by accusations of privacy invasion and unethical surveillance. Rebuilding a damaged brand reputation requires significant investment in public relations, ethical reforms, and demonstrable commitment to data privacy, a costly and protracted process for any SMB.

Operational Inefficiencies and Increased Costs

Unethical data use often leads to operational inefficiencies and hidden costs, despite the initial allure of quick gains or cost savings. Practices like purchasing low-quality data lists, engaging in deceptive data collection, or neglecting data security ultimately create more problems than they solve. Poor 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. resulting from unethical sourcing leads to inaccurate insights, flawed decision-making, and wasted marketing efforts.

Data breaches, a frequent consequence of inadequate security, incur significant costs associated with incident response, data recovery, legal fees, and customer compensation. Furthermore, the need to rectify unethical data practices, whether due to regulatory pressure or reputational damage, necessitates costly system overhauls, data audits, and employee retraining.

Consider an SMB that purchases a cheap email list to boost marketing campaigns. This list, likely compiled through unethical means, contains outdated and inaccurate information, resulting in low engagement rates, wasted marketing spend, and potential spam complaints that damage sender reputation. Alternatively, an SMB that cuts corners on data security to save costs becomes vulnerable to cyberattacks and data breaches.

The ensuing operational disruptions, data recovery efforts, and regulatory fines far outweigh any initial cost savings. Ethical data practices, while requiring upfront investment, ultimately contribute to long-term operational efficiency, data quality, and cost-effectiveness, preventing the hidden expenses associated with unethical shortcuts.

Hindered Innovation and Growth

Innovation thrives on trust and ethical foundations. When businesses engage in unethical data practices, they create a climate of fear and distrust that stifles innovation and sustainable growth. Customers become hesitant to share data, limiting the potential for personalized services and data-driven improvements.

Employees, aware of unethical practices, may become disengaged or even whistleblowers, disrupting operations and damaging morale. Furthermore, a focus on unethical data exploitation often distracts from genuine innovation, diverting resources and talent away from developing ethical and sustainable business models.

Imagine an SMB developing a groundbreaking AI-powered customer service chatbot. If the training data for this chatbot is collected unethically, potentially biased or discriminatory, the resulting AI system will perpetuate and amplify these biases, leading to unfair or discriminatory customer interactions. This not only damages brand reputation but also hinders the very innovation the SMB sought to achieve.

Ethical data practices, conversely, foster a culture of trust and transparency, encouraging customers to share data willingly, enabling the development of truly innovative and beneficial products and services. Sustainable growth is built upon ethical foundations, where data is treated as a valuable asset to be handled responsibly and respectfully.

Table ● Business Challenges of Unethical Data Use for SMBs

List ● Immediate Actions for SMBs to Address Ethical Data Use

1. Conduct a Data Audit ● Understand what data you collect, how you collect it, where it’s stored, and how it’s used.
2. Review Privacy Policies ● Ensure your privacy policies are clear, concise, and easily accessible to customers.
3. Implement Data Security Measures ● Protect customer data from unauthorized access, breaches, and cyberattacks.
4. Train Employees on Data Ethics ● Educate your team on ethical data handling Meaning ● Ethical Data Handling for SMBs: Respectful, responsible, and transparent data practices that build trust and drive sustainable growth. practices and regulatory compliance.
5. Seek Legal Counsel ● Consult with legal professionals to ensure compliance with data privacy regulations.

Ignoring ethical data considerations is akin to building a house on sand. The immediate convenience may seem appealing, but the long-term instability is inevitable. For SMBs, embracing ethical data practices Meaning ● Ethical Data Practices: Responsible and respectful data handling for SMB growth and trust. is not merely a matter of compliance; it’s a strategic imperative for building sustainable customer relationships, fostering brand trust, and ensuring long-term business success. The future of SMB growth hinges on responsible data stewardship, recognizing that ethical data use Meaning ● Ethical Data Use, in the SMB context of growth, automation, and implementation, refers to the responsible and principled collection, storage, processing, analysis, and application of data to achieve business objectives. is not a constraint, but a catalyst for enduring prosperity.

Intermediate

The narrative surrounding unethical data use frequently casts large corporations as the primary offenders, overlooking the subtle yet significant challenges faced by SMBs. While corporate giants grapple with headline-grabbing data breaches, SMBs often navigate a more insidious landscape of ethical compromises driven by resource constraints and competitive pressures. Consider a burgeoning SaaS startup, eager to demonstrate rapid growth to secure funding.

The temptation to acquire user data through less-than-transparent methods, or to employ aggressive tracking mechanisms, can be considerable. This pressure-cooker environment highlights that unethical data use for SMBs isn’t always a matter of malicious intent, but rather a consequence of systemic pressures and a potentially skewed perception of what constitutes “necessary” for survival and expansion.

Systemic Market Distortions

Unethical data practices, when pervasive across a market, create systemic distortions that disproportionately disadvantage ethical SMBs. Consider the realm of online advertising, where data-driven targeting reigns supreme. SMBs operating ethically, adhering to stringent privacy standards, may find themselves at a competitive disadvantage Meaning ● In the realm of SMB operations, a Competitive Disadvantage signifies a characteristic or deficiency that positions a business unfavorably relative to its rivals, hindering its capacity for growth, successful automation implementation, and efficient business process deployment. against rivals who aggressively harvest and exploit user data, even if through questionable means.

This creates an uneven playing field where ethical conduct is not necessarily rewarded, and unethical practices can seemingly confer a competitive edge, at least in the short term. Such distortions can stifle innovation among ethical SMBs, as they struggle to compete against those who prioritize data acquisition over ethical considerations.

Systemic unethical data use in markets can create unfair competitive landscapes, penalizing ethical SMBs and rewarding dubious practices.

Imagine two competing online retailers, both selling similar products. Retailer A adheres strictly to data privacy regulations, obtaining explicit consent for data collection and limiting data usage to essential functions. Retailer B, conversely, employs aggressive tracking technologies, purchases third-party data lists of questionable origin, and engages in opaque data sharing practices.

Retailer B, with its richer data trove, can execute more targeted advertising campaigns, potentially undercutting Retailer A’s marketing efforts. This scenario illustrates how systemic unethical data use can create a market distortion, where ethical SMBs are forced to compete against rivals operating with fewer ethical constraints, potentially hindering their growth and market share.

Automation and Algorithmic Bias Amplification

Automation, often touted as a solution for SMB efficiency and scalability, introduces a new dimension to the challenges of unethical data use. Algorithms, the engines of automation, are trained on data. If this training data is tainted by unethical collection practices, or reflects existing societal biases, the resulting automated systems will inevitably amplify these ethical flaws and biases.

For SMBs increasingly reliant on AI-powered tools for tasks ranging from customer service to hiring, this algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. can have profound and often unseen consequences. Unethical data inputs translate into unethical algorithmic outputs, perpetuating unfair or discriminatory outcomes, even when unintentional.

Consider an SMB using an AI-powered hiring platform to streamline recruitment. If the historical hiring data used to train this AI system reflects past biases against certain demographic groups, the automated system will likely perpetuate these biases, inadvertently screening out qualified candidates from underrepresented groups. This not only raises ethical concerns but also limits the SMB’s access to a diverse talent pool, hindering innovation and long-term competitiveness.

Similarly, an SMB using an AI-driven marketing automation system trained on biased customer data may inadvertently target certain customer segments with predatory or discriminatory offers. Automation, without rigorous ethical data governance, can become a powerful tool for amplifying and scaling unethical practices, creating significant business challenges.

Supply Chain and Third-Party Data Risks

SMBs rarely operate in isolation. They are embedded within complex supply chains and rely heavily on third-party data providers for various business functions. This interconnectedness introduces significant risks related to unethical data use, even if an SMB itself adheres to the highest ethical standards.

If a supplier or data partner engages in unethical data practices, the repercussions can cascade down the supply chain, impacting the SMB’s reputation, legal standing, and customer trust. Due diligence in vetting suppliers and data partners for ethical data practices becomes paramount, yet often overlooked by resource-constrained SMBs.

Imagine an SMB fashion retailer sourcing fabrics from a supplier who uses forced labor in their production processes. Even if the retailer is unaware of these unethical labor practices, and focuses solely on data ethics Meaning ● Data Ethics for SMBs: Strategic integration of moral principles for trust, innovation, and sustainable growth in the data-driven age. within their own operations, the discovery of unethical practices within their supply chain can trigger a reputational crisis. Similarly, an SMB relying on a third-party marketing data provider who obtains data through deceptive or unethical means exposes themselves to legal and reputational risks.

The ethical responsibility extends beyond direct data handling; it encompasses the entire data ecosystem within which an SMB operates. Managing supply chain and third-party data risks requires proactive vetting, contractual safeguards, and ongoing monitoring to ensure ethical data integrity across the extended business network.

Evolving Consumer Expectations and Ethical Brand Loyalty

Consumer expectations regarding data privacy and ethical business conduct are rapidly evolving. Customers are not merely passive recipients of products and services; they are increasingly conscious of the ethical footprint of the businesses they patronize. This heightened ethical awareness is particularly pronounced among younger demographics, who prioritize ethical brand values and are willing to boycott businesses perceived as unethical.

For SMBs, building ethical brand loyalty Meaning ● Brand Loyalty, in the SMB sphere, represents the inclination of customers to repeatedly purchase from a specific brand over alternatives. in this evolving landscape requires demonstrable commitment to data privacy, transparency, and responsible data stewardship. Ethical data use is no longer a niche differentiator; it’s becoming a core expectation, shaping consumer purchasing decisions and brand preferences.

Consider two coffee shops in the same neighborhood. Coffee Shop A collects minimal customer data, clearly communicates its privacy practices, and emphasizes ethical sourcing of coffee beans. Coffee Shop B aggressively collects customer data through loyalty programs and online tracking, with opaque privacy policies, and focuses primarily on price competitiveness.

In an environment of heightened ethical consumerism, Coffee Shop A, despite potentially higher prices, may attract a more loyal and growing customer base due to its perceived ethical stance. Ethical brand loyalty, built upon trust and transparency, becomes a significant competitive advantage, particularly for SMBs seeking to cultivate long-term customer relationships Meaning ● Customer Relationships, within the framework of SMB expansion, automation processes, and strategic execution, defines the methodologies and technologies SMBs use to manage and analyze customer interactions throughout the customer lifecycle. in an ethically conscious marketplace.

Table ● Intermediate Business Challenges of Unethical Data Use for SMBs

List ● Intermediate Strategies for Ethical Data Use in SMBs

1. Develop a Data Ethics Framework ● Establish clear principles and guidelines for ethical data collection, use, and storage.
2. Implement Privacy-Enhancing Technologies ● Utilize tools and techniques that minimize data collection and maximize privacy.
3. Conduct Regular Ethical Audits ● Periodically review data practices to ensure adherence to ethical standards and identify areas for improvement.
4. Engage in Industry Collaboration ● Participate in industry initiatives to promote ethical data practices and level the playing field.
5. Communicate Ethical Commitments ● Transparently communicate data privacy policies and ethical values to customers and stakeholders.

Navigating the intermediate challenges of unethical data use requires a shift from reactive compliance to proactive ethical leadership. SMBs must recognize that ethical data practices are not merely risk mitigation measures, but strategic investments in long-term sustainability and competitive advantage. By embracing ethical data governance, fostering transparency, and prioritizing customer trust, SMBs can not only mitigate the risks of unethical data use but also position themselves as ethical leaders in an increasingly data-driven and ethically conscious business environment. The intermediate stage demands a strategic integration of ethical data considerations into the very fabric of SMB operations and growth strategies.

Advanced

The discourse surrounding unethical data use often fixates on immediate harms ● privacy violations, data breaches, regulatory fines. However, a deeper, more advanced analysis reveals that the most profound business challenges stem from the insidious erosion of societal trust and the distortion of market dynamics at a systemic level. Consider the burgeoning field of personalized medicine, reliant on vast datasets of patient information to drive breakthroughs.

If public trust in data privacy falters due to widespread unethical data practices, individuals may become reluctant to share sensitive health data, thereby stifling medical innovation and hindering societal progress. This macro-level perspective underscores that unethical data use transcends individual business challenges; it poses an existential threat to the very fabric of a data-driven economy and society, demanding a more sophisticated and ethically grounded approach from all businesses, including SMBs.

Data Colonialism and Asymmetric Power Dynamics

Advanced analysis of unethical data use reveals patterns of data colonialism, where powerful entities, often large corporations, extract and exploit data from individuals and SMBs with limited agency or control. This data colonialism Meaning ● Data Colonialism, in the context of SMB growth, automation, and implementation, describes the exploitation of SMB-generated data by larger entities, often tech corporations or global conglomerates, for their economic gain. manifests in various forms, from opaque data collection practices to exploitative data sharing agreements, creating asymmetric power dynamics that disadvantage SMBs. Smaller businesses, lacking the legal and technical resources of larger counterparts, are often compelled to accept unfavorable data terms, effectively surrendering valuable data assets in exchange for access to essential platforms or services. This data asymmetry not only hinders SMB competitiveness but also perpetuates a cycle of data dependency and vulnerability.

Data colonialism, characterized by asymmetric power dynamics, allows larger entities to extract and exploit data from SMBs, hindering their competitiveness and perpetuating dependency.

Imagine an SMB reliant on a dominant social media platform for marketing and customer engagement. The platform, while providing valuable reach, often dictates opaque data terms, granting itself broad rights to collect and utilize SMB customer data, while offering limited transparency or control to the SMB itself. This power imbalance allows the platform to monetize SMB data assets, potentially even competing against the SMB using its own data insights.

This scenario exemplifies data colonialism, where SMBs, in their reliance on dominant platforms, inadvertently contribute to their own data exploitation and competitive disadvantage. Addressing data colonialism requires advocating for greater data transparency, fairer data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. frameworks, and empowering SMBs with greater data agency and control.

Algorithmic Governance and Ethical Accountability Gaps

The increasing reliance on algorithms for business decision-making introduces complex ethical accountability gaps. Algorithms, often perceived as objective and neutral, are in reality reflections of the data they are trained on and the biases embedded within their design. When algorithms are used for critical business functions, such as loan approvals, pricing strategies, or risk assessments, unethical data inputs or biased algorithmic designs can lead to discriminatory or unfair outcomes, with limited avenues for accountability or redress. For SMBs adopting AI-powered tools, understanding and mitigating these algorithmic governance Meaning ● Automated rule-based systems guiding SMB operations for efficiency and data-driven decisions. risks becomes crucial, demanding a proactive approach to ethical algorithm design, data provenance tracking, and transparent algorithmic auditing.

Consider an SMB fintech company utilizing an AI-powered loan application system. If the algorithm is trained on historical loan data that reflects past discriminatory lending practices, the automated system may perpetuate these biases, unfairly denying loans to qualified applicants from certain demographic groups. Furthermore, the opacity of complex algorithms often makes it difficult to identify and rectify these biases, creating an ethical accountability gap. Who is responsible when an algorithm makes an unethical decision?

The data scientists who designed the algorithm? The SMB executives who deployed it? The regulatory bodies who oversee financial institutions? Addressing algorithmic governance gaps requires establishing clear lines of accountability, promoting algorithmic transparency, and developing ethical auditing frameworks to ensure AI systems are fair, unbiased, and aligned with ethical business principles.

Data Fiduciaries and the Trust Economy Paradigm Shift

A potential paradigm shift in addressing unethical data use lies in the concept of data fiduciaries. This framework proposes that certain entities, particularly those handling sensitive personal data, should be legally obligated to act as fiduciaries, prioritizing the data subject’s interests above their own profit maximization. For SMBs, particularly those in sectors handling sensitive customer data like healthcare or finance, embracing a data fiduciary mindset can be a powerful differentiator, fostering customer trust Meaning ● Customer trust for SMBs is the confident reliance customers have in your business to consistently deliver value, act ethically, and responsibly use technology. and building a competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. in an increasingly trust-centric economy. Adopting data fiduciary principles requires a fundamental shift in data governance, moving beyond mere compliance to a proactive commitment to ethical data stewardship Meaning ● Responsible data management for SMB growth and automation. and prioritizing data subject well-being.

Imagine an SMB healthcare provider adopting a data fiduciary Meaning ● A Data Fiduciary, within the SMB landscape, assumes a legally defined duty of care when handling data, similar to a financial fiduciary. model. This would entail not only complying with HIPAA regulations but also proactively implementing data privacy measures that go beyond legal requirements, transparently communicating data usage policies to patients, and empowering patients with greater control over their health data. This commitment to data fiduciary principles can build deep patient trust, attracting and retaining patients who value data privacy and ethical healthcare practices. In a trust economy, where consumer trust is paramount, SMBs that embrace data fiduciary principles can cultivate stronger customer relationships, enhance brand reputation, and achieve sustainable competitive advantage by prioritizing ethical data stewardship Meaning ● Ethical Data Stewardship for SMBs: Responsible data handling to build trust, ensure compliance, and drive sustainable growth in the digital age. as a core business value.

The Tragedy of the Data Commons and Sustainable Data Ecosystems

Unethical data use, particularly the unchecked exploitation of personal data, can be viewed as a “tragedy of the data commons.” Just as the overexploitation of shared natural resources leads to environmental degradation, the unsustainable extraction and misuse of personal data can deplete societal trust, erode privacy norms, and ultimately undermine the very foundations of a data-driven economy. Building sustainable data ecosystems Meaning ● A Data Ecosystem, in the SMB landscape, is the interconnected network of people, processes, technology, and data sources employed to drive business value. requires a collective shift towards ethical data practices, recognizing data as a shared resource to be managed responsibly and equitably. For SMBs, this entails advocating for industry-wide ethical data standards, participating in data collaboratives that prioritize ethical data sharing, and adopting business models that are inherently sustainable and respectful of data privacy.

Consider the challenge of combating online misinformation. The unchecked spread of fake news and disinformation, often facilitated by unethical data practices and algorithmic amplification, erodes public trust in information sources and undermines democratic processes. Addressing this “tragedy of the data commons” requires a collective effort, involving governments, technology platforms, and businesses, including SMBs.

SMBs can contribute to sustainable data ecosystems Meaning ● A Sustainable Data Ecosystem for SMBs refers to a strategic framework that ensures the responsible and efficient management of data assets, supporting long-term business growth and automation. by promoting ethical content moderation practices, supporting fact-checking initiatives, and advocating for greater transparency and accountability in online information dissemination. Building sustainable data ecosystems requires a shared commitment to ethical data governance, recognizing that the long-term health of the data economy depends on responsible data stewardship and collective action.

Table ● Advanced Business Challenges of Unethical Data Use for SMBs

List ● Advanced Strategies for Ethical Data Leadership in SMBs

1. Champion Data Ethics Advocacy ● Become vocal advocates for ethical data practices within your industry and community.
2. Invest in Ethical AI Development ● Prioritize ethical considerations in the design, development, and deployment of AI systems.
3. Explore Data Fiduciary Models ● Consider adopting data fiduciary principles to build stronger customer trust and ethical differentiation.
4. Participate in Data Collaboratives ● Engage in industry-wide data sharing initiatives that prioritize ethical data governance Meaning ● Ethical Data Governance for SMBs: Managing data responsibly for trust, growth, and sustainable automation. and societal benefit.
5. Promote Sustainable Data Literacy ● Educate employees, customers, and stakeholders on the importance of ethical data practices and sustainable data ecosystems.

References

• Zuboff, Shoshana. The Age of Surveillance Capitalism ● The Fight for a Human Future at the New Frontier of Power. PublicAffairs, 2019.
• O’Neil, Cathy. Weapons of Math Destruction ● How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.
• Mayer-Schönberger, Viktor, and Kenneth Cukier. Big Data ● A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt, 2013.