Fundamentals

Consider the small bakery, lovingly crafting artisanal breads and pastries; they meticulously track ingredient costs, oven temperatures, and staff schedules, yet often overlook the scattered customer feedback forms, the haphazardly stored online orders, and the inconsistent records of daily sales. This seemingly minor disarray is not simply a matter of untidiness; it represents a silent drain on potential profit, a missed opportunity for deeper customer understanding, and a vulnerability to operational inefficiencies. For small and medium-sized businesses (SMBs), the absence of strong data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. is akin to navigating without a compass in an increasingly data-driven marketplace.

The Unseen Cost of Data Chaos

Many SMB owners, particularly those focused on immediate operational demands, might view data governance as a concept reserved for large corporations with sprawling IT departments. They might think, “We’re too small for all that.” However, this perspective overlooks a fundamental truth ● data is the lifeblood of any modern business, regardless of size. Without a clear system to manage, protect, and utilize this data effectively, SMBs are unknowingly incurring significant costs. These costs are not always immediately apparent, manifesting instead as wasted marketing spend, lost sales opportunities, and increased operational overhead.

Imagine the bakery again. Without proper data governance, they might send out promotional emails to customers who have already opted out, leading to wasted marketing dollars and potentially damaging their brand reputation. They might struggle to identify their most popular products or peak sales hours, hindering their ability to optimize inventory and staffing.

Customer feedback, if not properly collected and analyzed, becomes a collection of random opinions rather than a valuable source of insights for product improvement and service enhancement. These seemingly small inefficiencies accumulate, eroding profitability and hindering sustainable growth.

Data Governance Demystified For SMBs

Data governance, at its core, is not about complex IT infrastructure or bureaucratic processes. For SMBs, it’s about establishing simple, practical guidelines and procedures to ensure data is accurate, consistent, secure, and readily available for those who need it. Think of it as creating a well-organized kitchen in the bakery ● ensuring ingredients are properly labeled and stored, recipes are clearly documented, and everyone knows where to find what they need. This organizational clarity translates directly into efficiency and reduces the chances of errors.

Implementing data governance in an SMB doesn’t require a massive overhaul. It can start with small, manageable steps. For example, a small retail store could begin by standardizing 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. entry at the point of sale, ensuring consistent spelling of names and addresses.

They could implement a simple system for backing up sales data regularly to prevent loss. These initial steps, while seemingly basic, lay the foundation for a more robust data governance framework Meaning ● A structured system for SMBs to manage data ethically, efficiently, and securely, driving informed decisions and sustainable growth. as the business grows.

Immediate Benefits ● Clarity and Efficiency

The most immediate business benefits of strong data governance for SMBs are increased clarity and efficiency. When data is well-managed, employees spend less time searching for information, correcting errors, and resolving data inconsistencies. This saved time translates directly into increased productivity and allows employees to focus on more strategic tasks. Consider a small e-commerce business.

With effective data governance, their customer service team can quickly access accurate order information, resolve customer inquiries efficiently, and provide a better overall customer experience. Marketing teams can segment customer lists accurately for targeted campaigns, improving campaign effectiveness and reducing wasted ad spend.

Furthermore, data governance enhances decision-making at all levels of the SMB. With reliable data at their fingertips, business owners and managers can make informed decisions about pricing, inventory, marketing strategies, and operational improvements. They can identify trends, spot potential problems early, and react quickly to changing market conditions. This data-driven approach replaces guesswork with informed action, significantly increasing the likelihood of positive business outcomes.

Strong data governance empowers SMBs to move from reactive problem-solving to proactive opportunity creation, using data as a strategic asset rather than a source of headaches.

Building a Foundation for Future Growth

Beyond immediate efficiency gains, strong data governance provides SMBs with a solid foundation for future growth and scalability. As businesses expand, the volume and complexity of data inevitably increase. Without a well-established data governance framework, managing this growing data becomes increasingly challenging, leading to data silos, inconsistencies, and inefficiencies that can stifle growth. SMBs that invest in data governance early are better positioned to handle this data growth and leverage it for strategic advantage.

Automation, a key driver of SMB growth, relies heavily on high-quality, well-governed data. To automate processes effectively, businesses need to trust the data feeding into their automated systems. Inaccurate or inconsistent data can lead to automation errors, inefficiencies, and even costly mistakes. Strong data governance ensures the reliability of data, making automation initiatives more successful and delivering greater returns.

For instance, a small manufacturing company looking to automate its inventory management system needs accurate and consistent data on stock levels, production schedules, and sales forecasts. Data governance ensures this data is reliable, enabling the automation system to function effectively and optimize inventory levels.

Practical Steps for SMB Data Governance Implementation

Implementing data governance in an SMB need not be daunting. It’s about taking a phased approach, starting with the most critical data areas and gradually expanding the scope. Here are some practical first steps:

Identify Key Data Assets

Begin by identifying the most important data assets for your SMB. This might include customer data, sales data, financial data, inventory data, or operational data, depending on the nature of your business. Focus on the data that is most critical for decision-making and day-to-day operations.

Establish Basic Data Quality Standards

Define basic 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. standards for your key data assets. This includes ensuring data accuracy, completeness, consistency, and timeliness. Implement simple data validation rules at the point of data entry to minimize errors. For example, ensure that email addresses are in the correct format and phone numbers have the right number of digits.

Assign Data Responsibilities

Clearly assign data responsibilities within your team. Identify individuals who are responsible for data quality, data security, and data access for specific data assets. This doesn’t necessarily require hiring new roles; it can be integrated into existing job responsibilities. For example, the sales manager could be responsible for the accuracy of sales data, while the marketing manager could be responsible for customer data quality.

Implement Basic Data Security Measures

Take basic steps to secure your data from unauthorized access and cyber threats. This includes using strong passwords, implementing access controls, and regularly backing up your data. Consider using cloud-based data storage solutions that offer built-in security features. For example, ensure that customer data is encrypted and access is restricted to authorized personnel only.

Regularly Review and Improve

Data governance is not a one-time project; it’s an ongoing process. Regularly review your data governance practices, identify areas for improvement, and adapt your approach as your business evolves. Encourage feedback from your team on data-related challenges and opportunities. For example, conduct quarterly reviews of data quality metrics Meaning ● Data Quality Metrics for SMBs: Quantifiable measures ensuring data is fit for purpose, driving informed decisions and sustainable growth. and identify areas where data accuracy can be improved.

By taking these practical steps, SMBs can begin to reap the significant business benefits of strong data governance, transforming data from a potential liability into a powerful asset for growth and success. The journey starts not with grand pronouncements, but with the quiet, persistent work of organizing the kitchen, one ingredient at a time.

Intermediate

The narrative often painted for small to medium-sized businesses is one of scrappy resilience, of nimble adaptation in the face of market goliaths. This image, while romantic, can sometimes obscure a critical vulnerability ● a laissez-faire attitude toward data. While enterprise-level corporations grapple with petabytes and regulatory thickets, the SMB might operate under the assumption that “good enough” data management suffices.

This assumption, however, is akin to navigating a competitive race with a slightly deflated tire ● seemingly functional, yet subtly undermining performance and long-term viability. The intermediate stage of understanding data governance for SMBs moves beyond basic hygiene to strategic advantage.

Beyond the Basics ● Data Governance as a Strategic Lever

At the fundamental level, data governance addresses immediate operational needs ● accuracy, efficiency, and basic security. However, for SMBs aiming for sustained growth and competitive differentiation, data governance becomes a strategic lever. It transitions from a reactive measure to a proactive enabler, allowing businesses to not only avoid data-related pitfalls but also to actively leverage data for innovation, market expansion, and enhanced customer engagement.

Consider a regional chain of fitness studios. At a basic level, data governance might ensure accurate membership records and class schedules. At an intermediate level, strong data governance allows them to analyze member attendance patterns across locations, identify peak demand times for specific classes, and personalize marketing offers based on individual workout preferences. This strategic use of data transforms their operations from simply offering fitness classes to delivering a data-informed, personalized fitness experience, fostering greater customer loyalty and attracting new members.

Data Quality ● The Bedrock of Strategic Advantage

Data quality moves beyond mere accuracy to encompass broader dimensions like completeness, validity, timeliness, and consistency. For SMBs seeking strategic advantage, ensuring high data quality is paramount. Poor data quality not only leads to operational inefficiencies but also undermines the reliability of data-driven insights, rendering strategic initiatives ineffective. A marketing campaign based on inaccurate customer demographics, for example, is not simply inefficient; it can actively damage brand reputation and alienate potential customers.

Improving data quality requires a systematic approach. This involves establishing data quality metrics, implementing data validation rules, and regularly monitoring and cleansing data. SMBs can leverage data quality tools to automate data profiling, identify data quality issues, and streamline data cleansing processes.

For instance, a small online retailer can use data quality tools to validate customer addresses during checkout, reducing shipping errors and improving customer satisfaction. They can also monitor product data for completeness and consistency, ensuring accurate product listings and enhancing the online shopping experience.

Data Security and Compliance ● Mitigating Risks, Building Trust

Data security and compliance are no longer optional considerations for SMBs; they are critical business imperatives. Data breaches and regulatory non-compliance can result in significant financial losses, reputational damage, and legal penalties, potentially crippling a small business. Strong data governance incorporates 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. measures and ensures compliance with relevant data privacy regulations, such as GDPR or CCPA, depending on the SMB’s operating geography and customer base.

Implementing data security for SMBs involves a multi-layered approach. This includes technical measures like encryption, access controls, and intrusion detection systems, as well as organizational measures like data security policies, employee training, and incident response plans. SMBs can leverage cloud-based security solutions and managed security services to enhance their data security posture without requiring extensive in-house IT expertise.

For example, a small healthcare clinic handling patient data must implement stringent data security measures Meaning ● Data Security Measures, within the Small and Medium-sized Business (SMB) context, are the policies, procedures, and technologies implemented to protect sensitive business information from unauthorized access, use, disclosure, disruption, modification, or destruction. to comply with HIPAA regulations. This includes encrypting patient records, implementing access controls to limit access to sensitive data, and training staff on data privacy and security protocols.

Data governance, at the intermediate level, is about transforming data from a passive byproduct of operations into an active instrument for strategic execution and risk mitigation.

Automation and Data Governance ● A Synergistic Relationship

Automation is a key driver of efficiency and scalability for SMBs. However, the effectiveness of automation initiatives is directly dependent on the quality and governance of the underlying data. Strong data governance provides the necessary foundation for successful automation, ensuring that automated systems operate on reliable, consistent, and secure data. This synergistic relationship between data governance and automation unlocks significant business benefits, enabling SMBs to streamline operations, improve decision-making, and enhance customer experiences.

Consider a small logistics company looking to automate its route planning and delivery scheduling. Effective automation requires accurate and timely data on delivery addresses, traffic conditions, vehicle locations, and driver availability. Data governance ensures the quality and reliability of this data, enabling the automated route planning system to optimize delivery routes, reduce fuel consumption, and improve delivery times. Without strong data governance, the automation initiative could be undermined by inaccurate data, leading to inefficient routes, delayed deliveries, and increased operational costs.

Building a Data-Driven Culture Within the SMB

Implementing strong data governance is not solely a technical or procedural exercise; it requires fostering a data-driven culture Meaning ● Leveraging data for informed decisions and growth in SMBs. within the SMB. This involves promoting data literacy among employees, encouraging data-informed decision-making at all levels, and creating a culture of data responsibility and accountability. A data-driven culture empowers employees to leverage data effectively in their daily tasks, fostering innovation and continuous improvement.

Building a data-driven culture starts with leadership commitment. Business owners and senior managers must champion data governance initiatives and actively promote the use of data in decision-making. This can be achieved through training programs, data sharing initiatives, and recognition programs that reward data-driven contributions. For example, a small marketing agency can conduct workshops to train employees on data analytics tools and techniques.

They can establish data dashboards to track campaign performance and encourage teams to use data to optimize their marketing strategies. They can also recognize and reward employees who effectively leverage data to achieve marketing objectives.

Advanced Implementation Strategies for SMBs

Moving beyond basic implementation, SMBs can adopt more advanced data governance strategies Meaning ● Data Governance Strategies, within the ambit of SMB expansion, focus on the systematized management of data assets to ensure data quality, accessibility, and security, thereby driving informed decision-making and operational efficiency. to further enhance their data maturity. These strategies include:

Data Cataloging and Discovery

Implementing a data catalog helps SMBs to effectively manage and discover their data assets. A data catalog provides a centralized inventory of data assets, along with metadata describing the data, its lineage, and its quality. This enables employees to easily find and understand the data they need, improving data accessibility and utilization.

For example, a small financial services firm can use a data catalog to document its various data sources, including customer transaction data, market data, and regulatory data. This allows analysts to easily discover and access the data they need for financial modeling and risk analysis.

Data Lineage Tracking

Tracking data lineage Meaning ● Data Lineage, within a Small and Medium-sized Business (SMB) context, maps the origin and movement of data through various systems, aiding in understanding data's trustworthiness. provides visibility into the origin and flow of data across the SMB’s systems. This helps to understand how data is transformed and used, improving data transparency and accountability. Data lineage tracking is particularly important for ensuring data quality and compliance. For instance, a small food processing company can use data lineage tracking to trace the origin of ingredients used in its products, ensuring food safety and regulatory compliance.

Data Governance Frameworks and Policies

Developing a formal data governance framework and policies provides a structured approach to data governance. A data governance framework defines the roles, responsibilities, and processes for managing data. Data governance policies establish rules and guidelines for data quality, data security, data access, and data usage. These frameworks and policies provide clarity and consistency in data governance practices across the SMB.

For example, a small law firm can develop a data governance framework that defines roles and responsibilities for managing client data, legal documents, and firm financials. They can establish data governance policies to ensure data confidentiality, data integrity, and compliance with legal and ethical standards.

By adopting these intermediate and advanced data governance strategies, SMBs can transform data from a potential liability into a powerful strategic asset, driving innovation, growth, and competitive advantage in an increasingly data-driven world. The journey from basic data hygiene to strategic data mastery is not a sprint, but a deliberate, iterative climb, each step revealing a broader, more advantageous vista.

Advanced

The prevailing narrative often casts data governance as a purely defensive maneuver, a shield against regulatory penalties and operational blunders. This perspective, while grounded in valid concerns, represents a fundamentally limited understanding of its transformative potential, particularly for small to medium-sized businesses poised for aggressive growth and market disruption. To perceive data governance solely as a risk mitigation tool is akin to viewing a high-performance engine merely as a means to avoid breakdowns ● neglecting its capacity to propel a vehicle to unprecedented speeds and destinations. At the advanced echelon, data governance transcends reactive compliance and operational efficiency, becoming a proactive catalyst for strategic innovation, predictive market anticipation, and the orchestration of autonomous business ecosystems.

Data Governance as an Innovation Engine

Advanced data governance, when strategically implemented, ceases to be a mere framework for control and becomes a dynamic engine for innovation. By establishing robust data foundations ● characterized by exceptional quality, accessibility, and contextual richness ● SMBs unlock the capacity to derive novel insights, identify untapped market opportunities, and develop disruptive products and services. This transformation hinges on moving beyond descriptive analytics (understanding what happened) and diagnostic analytics (understanding why it happened) to predictive analytics (forecasting future trends) and prescriptive analytics (recommending optimal actions). Data governance, in this advanced context, provides the fuel and the navigational precision for this analytical ascent.

Consider a burgeoning fintech SMB specializing in personalized investment advisory services. Basic data governance might ensure the secure storage of client financial data. Advanced data governance, however, would involve constructing a sophisticated data architecture that integrates diverse data streams ● encompassing market trends, macroeconomic indicators, alternative data sources (e.g., social sentiment, news feeds), and granular client behavioral data.

This rich, well-governed data ecosystem empowers the fintech firm to develop AI-powered 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. that anticipate market shifts, personalize investment recommendations with unprecedented accuracy, and proactively identify emerging investment opportunities for their clients. Data governance, in this scenario, directly fuels the firm’s core innovation engine, enabling them to deliver superior value and outpace competitors.

Predictive Market Anticipation Through Data Foresight

In volatile and rapidly evolving markets, the capacity to anticipate future trends and proactively adapt is a critical determinant of SMB survival and success. Advanced data governance enables predictive market anticipation by facilitating the development of sophisticated analytical capabilities. By leveraging well-governed data to build predictive models, SMBs can forecast shifts in customer demand, anticipate competitive moves, and proactively adjust their strategies to capitalize on emerging opportunities and mitigate potential threats. This data-driven foresight transforms SMBs from reactive players to proactive market shapers.

Imagine a fast-fashion SMB operating in a highly trend-sensitive market. Traditional approaches to demand forecasting, based on historical sales data alone, are often inadequate in this dynamic environment. Advanced data governance allows the SMB to integrate real-time data streams ● including social media trends, influencer activity, fashion blog analysis, and competitor pricing data ● into their analytical models.

By applying advanced machine learning techniques to this well-governed, multi-dimensional dataset, the SMB can develop highly accurate predictive models that forecast emerging fashion trends with remarkable precision. This predictive capability enables them to optimize inventory planning, design trend-aligned product lines, and proactively adjust marketing campaigns to capture fleeting market opportunities, gaining a significant competitive edge.

Advanced data governance is not about data control; it is about data empowerment ● unlocking the latent potential of data to drive strategic foresight and fuel transformative innovation.

Orchestrating Autonomous Business Ecosystems

The apex of advanced data governance lies in its capacity to facilitate the orchestration of autonomous business ecosystems. This involves leveraging data governance principles to create interconnected, self-optimizing systems that automate complex business processes, dynamically adapt to changing conditions, and operate with minimal human intervention. This vision of autonomous ecosystems is not science fiction; it is a tangible trajectory for SMBs that embrace data governance as a strategic imperative.

Consider a distributed energy SMB operating a network of smart microgrids. Basic data governance might ensure the secure transmission of energy consumption data. Advanced data governance, however, would involve establishing a sophisticated data exchange platform that integrates data from diverse sources ● including smart meters, weather sensors, grid infrastructure sensors, and energy market data feeds. This well-governed data platform, coupled with advanced AI algorithms, enables the SMB to orchestrate an autonomous energy ecosystem.

The system can dynamically optimize energy distribution across the microgrids based on real-time demand, weather conditions, and energy prices. It can proactively detect and respond to grid anomalies, minimizing downtime and maximizing efficiency. It can even autonomously negotiate energy contracts in the wholesale market, optimizing energy procurement costs. Data governance, in this context, becomes the foundational architecture for building and operating a complex, self-regulating business ecosystem.

Ethical Data Stewardship and Algorithmic Transparency

As SMBs increasingly leverage data for advanced analytics and autonomous operations, ethical data stewardship Meaning ● Responsible data management for SMB growth and automation. and algorithmic transparency Meaning ● Algorithmic Transparency for SMBs means understanding how automated systems make decisions to ensure fairness and build trust. become paramount. Advanced data governance frameworks Meaning ● Strategic data management for SMBs, ensuring data quality, security, and compliance to drive growth and innovation. must incorporate ethical considerations, ensuring that data is used responsibly, biases are mitigated, and algorithmic decision-making is transparent and accountable. This is not merely a matter of regulatory compliance; it is a fundamental imperative for building trust with customers, partners, and the broader community. SMBs that prioritize ethical data stewardship gain a significant competitive advantage in an increasingly data-conscious world.

Implementing ethical data governance Meaning ● Ethical Data Governance for SMBs: Managing data responsibly for trust, growth, and sustainable automation. involves establishing clear ethical guidelines for data collection, processing, and usage. This includes obtaining informed consent from data subjects, minimizing data collection, anonymizing sensitive data, and implementing bias detection and mitigation techniques in algorithmic models. Algorithmic transparency can be achieved through explainable AI (XAI) techniques, which provide insights into how AI models arrive at their decisions. SMBs can also establish independent ethics review boards to oversee data governance practices and ensure ethical considerations are integrated into all data-driven initiatives.

For example, a small AI-powered recruitment platform must ensure that its algorithms are free from bias and do not discriminate against any demographic groups. They must be transparent about how their algorithms work and provide candidates with clear explanations for recruitment decisions. 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. governance builds trust and ensures the long-term sustainability of AI-driven business models.

Quantifiable Metrics for Advanced Data Governance ROI

Demonstrating the return on investment (ROI) of advanced data governance requires moving beyond qualitative assessments to quantifiable metrics. While basic data governance ROI might be measured in terms of reduced operational costs and improved efficiency, advanced data governance ROI is reflected in strategic outcomes such as increased innovation velocity, enhanced market share, and higher customer lifetime value. Establishing quantifiable metrics for advanced data governance ROI is crucial for justifying investments and demonstrating the strategic value of data governance to stakeholders.

Quantifiable metrics for advanced data governance ROI can include:

1. Innovation Velocity ● Measured by the number of new products or services launched per year, the time-to-market for new innovations, and the revenue generated from new innovations.
2. Market Share Growth ● Measured by the percentage increase in market share attributable to data-driven strategies and innovations.
3. Customer Lifetime Value (CLTV) Increase ● Measured by the percentage increase in CLTV resulting from personalized customer experiences and data-driven customer engagement strategies.
4. Predictive Accuracy Improvement ● Measured by the accuracy of predictive models in forecasting market trends, customer demand, and operational outcomes.
5. Autonomous System Efficiency Gains ● Measured by the percentage reduction in operational costs and the percentage increase in efficiency achieved through autonomous business ecosystems.

By tracking these quantifiable metrics, SMBs can demonstrate the tangible business value of advanced data governance and justify ongoing investments in data maturity. The transition from viewing data governance as a cost center to recognizing it as a strategic investment with measurable ROI is a hallmark of advanced data governance thinking.

The Future of Data Governance ● Decentralization and Democratization

The future of data governance is trending towards decentralization and democratization. Traditional centralized data governance models, often characterized by rigid hierarchies and bureaucratic processes, are proving to be too slow and inflexible for the agile needs of modern SMBs. Decentralized data governance models, which empower data owners and data users to participate in data governance decisions, are gaining prominence.

Data democratization, which aims to make data accessible and usable by everyone within the organization, is also becoming a key focus. These trends are reshaping data governance from a top-down control mechanism to a collaborative, bottom-up enabler of data-driven innovation.

Decentralized data governance can be implemented through data mesh architectures, which distribute data ownership and data governance responsibilities to domain-specific teams. Data democratization can be facilitated by self-service data analytics platforms, data literacy programs, and data marketplaces that make data easily accessible and discoverable. SMBs that embrace decentralized and democratized data governance models can foster greater data agility, accelerate innovation, and empower their employees to become data-driven decision-makers. The future of data governance is not about centralized control; it is about distributed empowerment ● unleashing the collective intelligence of the organization through accessible, well-governed data.

In conclusion, advanced data governance for SMBs is not merely about adhering to best practices or mitigating risks; it is about architecting a data-powered future. It is about transforming data from a passive asset into an active catalyst for innovation, predictive foresight, and autonomous operations. It is about embracing 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. and algorithmic transparency as core business values. And it is about quantifying the strategic ROI of data governance to demonstrate its transformative impact.

The journey to advanced data governance is a continuous evolution, a relentless pursuit of data mastery that positions SMBs at the vanguard of the data-driven economy. The true benefit lies not just in governing data, but in being governed by data’s strategic insights, allowing it to guide the business towards uncharted territories of success.

References

• DAMA International. DAMA-DMBOK ● Data Management Body of Knowledge. 2nd ed., Technics Publications, 2017.
• Loshin, David. Business Intelligence ● The Savvy Manager’s Guide. 2nd ed., Morgan Kaufmann, 2012.
• Proctor, Robert N., and Kim Box. Value-Based Metrics for Improving Results ● Linking Metrics to Business Drivers. John Wiley & Sons, 2012.
• Tallon, Paul P. Corporate Governance of IT ● Aligning to Strategic Objectives. John Wiley & Sons, 2012.