Fundamentals

In today’s rapidly evolving business landscape, even the smallest of enterprises are generating vast amounts of data. From customer interactions and sales figures to website traffic and social media engagement, this data holds immense potential. For Small to Medium-Sized Businesses (SMBs), harnessing this potential through a Data-Centric Business Approach is no longer a luxury but a necessity for sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. and competitive advantage. But what exactly does it mean to be data-centric, especially for an SMB just starting out?

At its core, a Data-Centric Business Approach signifies a fundamental shift in how an SMB operates and makes decisions. It moves away from relying solely on intuition, gut feelings, or outdated industry norms, and instead emphasizes the use of data as the primary driver for strategic and operational choices. Imagine a local bakery, for example. Traditionally, they might decide to bake more of a certain type of pastry based on what they think is popular.

A data-centric bakery, however, would analyze sales data from their point-of-sale system to identify actual customer preferences, track which pastries sell best on which days, and even correlate sales with weather patterns or local events. This shift from guesswork to data-informed decisions is the essence of a Data-Centric Business Approach.

Understanding the Basics

For SMBs, the initial steps towards becoming data-centric can seem daunting. It’s not about immediately investing in complex AI systems or hiring a team of data scientists. It starts with understanding the fundamental principles and taking incremental steps. Let’s break down the core components:

What is Data-Centricity?

Data-Centricity is more than just collecting data; it’s about embedding data into the very fabric of your business operations. It means:

• Data-Driven Decision Making ● Making key business decisions based on data analysis Meaning ● Data analysis, in the context of Small and Medium-sized Businesses (SMBs), represents a critical business process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting strategic decision-making. rather than assumptions. This could range from marketing strategies to inventory management.
• Data Collection and Organization ● Systematically gathering relevant data from various sources and organizing it in a way that is accessible and usable.
• Data Analysis and Interpretation ● Analyzing collected data to identify trends, patterns, and insights that can inform business strategies.
• Data-Informed Actions ● Translating data insights into concrete actions and strategies to improve business performance.
• Continuous Improvement ● Using data to monitor the impact of actions, measure progress, and continuously refine strategies for better outcomes.

For SMBs, a Data-Centric Business Approach is about using data to make smarter decisions, improve efficiency, and drive growth, starting with simple, manageable steps.

Why is Data-Centricity Important for SMBs?

SMBs often operate with limited resources and tighter margins compared to larger corporations. A Data-Centric Business Approach can be a powerful equalizer, enabling them to compete more effectively and achieve sustainable growth. Here are some key benefits:

1. Enhanced Customer Understanding ● Data can reveal valuable insights into customer behavior, preferences, and needs, allowing SMBs to tailor products, services, and marketing efforts more effectively.
2. Improved Operational Efficiency ● By analyzing operational data, SMBs can identify bottlenecks, optimize processes, reduce waste, and improve overall efficiency.
3. Data-Driven Marketing and Sales ● 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. can help SMBs target the right customers with the right message at the right time, maximizing marketing ROI Meaning ● Marketing ROI (Return on Investment) measures the profitability of a marketing campaign or initiative, especially crucial for SMBs where budget optimization is essential. and boosting sales.
4. Informed Decision Making ● Data provides a solid foundation for making strategic decisions, reducing risks and increasing the likelihood of success in areas like product development, market expansion, and resource allocation.
5. Competitive Advantage ● In today’s data-driven world, SMBs that embrace data-centricity gain a significant competitive edge by being more agile, responsive, and customer-focused.

Getting Started ● Practical Steps for SMBs

Embarking on a Data-Centric Business Approach doesn’t require a massive overhaul. SMBs can start with small, manageable steps:

1. Identify Key Business Questions ● Start by defining the critical questions you need to answer to improve your business. For example ● “What are our best-selling products?”, “Who are our most valuable customers?”, “Which marketing channels are most effective?”.
2. Identify Data Sources ● Determine where relevant data is currently being collected or can be collected. This could include point-of-sale systems, website analytics, CRM software, social media platforms, customer feedback forms, and even simple spreadsheets.
3. Start with Simple Tools ● You don’t need expensive, complex software to begin. Spreadsheet programs like Excel or Google Sheets can be powerful tools for basic data analysis and visualization. Free or low-cost analytics platforms like Google Analytics can provide valuable insights into website traffic and user behavior.
4. Focus on Actionable Metrics ● Identify 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 are directly relevant to your business goals. Track these metrics regularly and analyze trends to identify areas for improvement.
5. Build a Data-Driven Culture ● Encourage a mindset within your team that values data and uses it to inform decisions. Share data insights with employees and empower them to use data in their daily work.

For instance, a small retail store might start by tracking daily sales data by product category. They could use this data to identify slow-moving inventory, optimize product placement, and plan targeted promotions. A service-based business, like a cleaning company, could track customer feedback and service completion times to identify areas for service improvement and efficiency gains. The key is to start small, focus on relevant data, and gradually build your data capabilities over time.

In conclusion, for SMBs, embracing a Data-Centric Business Approach is about taking a pragmatic and incremental journey. It’s about leveraging the data you already have, or can easily collect, to make smarter decisions and drive sustainable growth. It’s not about being perfect from day one, but about starting, learning, and continuously improving your data capabilities to unlock the immense potential hidden within your business data.

Intermediate

Building upon the fundamentals, SMBs ready to advance their Data-Centric Business Approach can explore more sophisticated strategies and tools. Moving beyond basic data collection and descriptive analysis, the intermediate stage focuses on leveraging data for deeper insights, process automation, and proactive decision-making. This phase is about transforming data from a reactive reporting tool into a proactive strategic asset that fuels SMB Growth and operational excellence.

At this level, SMBs begin to integrate data across different departments and functions, creating a more holistic view of their business. They start to employ more advanced analytical techniques to uncover hidden patterns, predict future trends, and personalize customer experiences. Furthermore, Automation becomes a key enabler, streamlining data processes and freeing up valuable time for strategic initiatives. This intermediate stage is crucial for SMBs aiming to achieve significant scalability and competitive differentiation in increasingly data-driven markets.

Deepening Data Integration and Analysis

The intermediate phase of a Data-Centric Business Approach involves moving beyond siloed data and basic reporting to integrated data systems and more advanced analytical methods. This allows for a richer understanding of business performance and customer behavior.

Data Integration and Centralization

Many SMBs initially collect data in disparate systems ● sales data in a POS system, 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. in a CRM, website data in analytics platforms, and marketing data in various tools. The first step towards intermediate data-centricity is Data Integration, bringing these data sources together into a centralized repository. This could involve:

• Implementing a CRM System ● A robust CRM system can centralize customer data, sales interactions, and marketing activities, providing a unified view of the customer journey.
• Data Warehousing Solutions ● For SMBs with larger data volumes, a data warehouse can be a more scalable solution for centralizing data from multiple sources. Cloud-based data warehouses are particularly accessible and cost-effective for SMBs.
• API Integrations ● Utilizing APIs (Application Programming Interfaces) to connect different software systems and automate data flow between them. For example, integrating your e-commerce platform with your accounting software.

Advanced Data Analysis Techniques

With integrated data, SMBs can leverage more advanced analytical techniques to gain deeper insights:

• Customer Segmentation ● Moving beyond basic demographics to segment customers based on behavior, purchase history, engagement levels, and other relevant data points. This allows for more targeted marketing and personalized customer experiences.
• Predictive Analytics ● Using historical data to forecast future trends and outcomes. This could include predicting sales demand, customer churn, or potential risks. For example, predicting inventory needs based on seasonal sales patterns.
• Regression Analysis ● Identifying relationships between different variables to understand cause-and-effect. For instance, analyzing the impact of marketing spend on sales revenue or the correlation between customer satisfaction Meaning ● Customer Satisfaction: Ensuring customer delight by consistently meeting and exceeding expectations, fostering loyalty and advocacy. and retention.
• Data Visualization Tools ● Utilizing interactive dashboards and 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. tools to explore data, identify patterns, and communicate insights effectively. Tools like Tableau, Power BI, or even more user-friendly options are available for SMBs.

Intermediate Data-Centricity for SMBs means integrating data across systems and using advanced analytics Meaning ● Advanced Analytics, in the realm of Small and Medium-sized Businesses (SMBs), signifies the utilization of sophisticated data analysis techniques beyond traditional Business Intelligence (BI). to predict trends, personalize experiences, and drive proactive decision-making.

Automation for Efficiency and Scalability

As SMBs become more data-driven, Automation plays a crucial role in streamlining processes, improving efficiency, and enabling scalability. Data-driven automation can be applied in various areas:

1. Marketing Automation ● Automating marketing tasks such as email campaigns, social media posting, lead nurturing, and personalized content delivery based on customer data and behavior.
2. Sales Automation ● Automating sales processes like lead qualification, follow-up reminders, sales reporting, and CRM updates, freeing up sales teams to focus on building relationships and closing deals.
3. Customer Service Automation ● Implementing chatbots, automated email responses, and self-service portals to handle routine customer inquiries and provide faster, more efficient support.
4. Operational Automation ● Automating tasks like inventory management, order processing, report generation, and data entry, reducing manual effort and minimizing errors.

For example, an e-commerce SMB could automate personalized product recommendations Meaning ● Personalized Product Recommendations utilize data analysis and machine learning to forecast individual customer preferences, thereby enabling Small and Medium-sized Businesses (SMBs) to offer pertinent product suggestions. based on browsing history and purchase data. A service-based SMB could automate appointment scheduling and reminders based on customer preferences and availability. Automation not only improves efficiency but also enhances customer experience Meaning ● Customer Experience for SMBs: Holistic, subjective customer perception across all interactions, driving loyalty and growth. and allows SMBs to scale operations without proportionally increasing headcount.

Implementing Intermediate Strategies ● Practical Examples

Let’s consider practical examples of how SMBs can implement intermediate data-centric strategies:

Example 1 ● Data-Driven Marketing Personalization for an Online Retailer

An online clothing boutique wants to improve its marketing effectiveness and customer engagement. They can implement the following:

1. Data Integration ● Integrate their e-commerce platform with their CRM and email marketing system.
2. Customer Segmentation ● Segment customers based on purchase history (e.g., frequent buyers, first-time buyers, specific style preferences), browsing behavior (e.g., categories viewed, products added to cart), and demographics.
3. Personalized Email Campaigns ● Automate personalized email campaigns based on customer segments. For example ●
   • Welcome emails with personalized product recommendations for new subscribers.
   • Abandoned cart emails with reminders and potential discounts.
   • Promotional emails featuring products similar to past purchases or recently viewed items.
   • Birthday emails with special offers.
4. Dynamic Website Content ● Personalize website content based on customer segments. Display relevant product recommendations, banners, and promotions based on browsing history and preferences.
5. A/B Testing ● Continuously A/B test different marketing messages, email subject lines, and website layouts to optimize for engagement and conversion rates.

By implementing these strategies, the online retailer can significantly improve marketing ROI, increase customer engagement, and drive sales growth through personalized experiences.

Example 2 ● Predictive Inventory Management for a Restaurant Chain

A small restaurant chain wants to optimize its inventory management Meaning ● Inventory management, within the context of SMB operations, denotes the systematic approach to sourcing, storing, and selling inventory, both raw materials (if applicable) and finished goods. to reduce food waste and ensure ingredient availability. They can implement:

1. Data Centralization ● Centralize sales data, inventory data, and supplier data from all restaurant locations.
2. Predictive Analytics ● Use historical sales data, seasonality trends, local events data, and weather forecasts to predict demand for different menu items at each location.
3. Automated Inventory Ordering ● Implement an automated inventory ordering system that uses predictive demand forecasts to generate optimal order quantities for each ingredient at each location.
4. Real-Time Inventory Tracking ● Implement a system for real-time inventory tracking to monitor stock levels, identify potential shortages or surpluses, and adjust orders dynamically.
5. Waste Tracking and Analysis ● Track food waste data to identify menu items or processes that contribute to high waste levels and implement strategies to reduce waste.

By leveraging predictive analytics and automation, the restaurant chain can significantly reduce food waste, optimize inventory levels, improve operational efficiency, and enhance profitability.

In summary, the intermediate stage of a Data-Centric Business Approach for SMBs is about deepening data integration, leveraging advanced analytics, and implementing automation to drive efficiency, personalization, and proactive decision-making. It’s about moving from simply understanding past performance to predicting future trends and shaping business outcomes through data-driven strategies. This phase sets the stage for more advanced data capabilities and sustained competitive advantage.

Advanced

The Data-Centric Business Approach, when examined through an advanced lens, transcends simple operational improvements and emerges as a fundamental paradigm shift in organizational strategy and competitive dynamics, particularly for Small to Medium-Sized Businesses (SMBs). At this advanced level, we move beyond practical implementation and delve into the theoretical underpinnings, diverse interpretations, and long-term strategic implications of data-centricity. This section aims to provide an expert-level, research-backed definition and meaning of the Data-Centric Business Approach, exploring its multifaceted nature and its profound impact on SMBs in the contemporary business ecosystem.

Scholarly, the Data-Centric Business Approach can be defined as a strategic organizational philosophy that prioritizes data as the primary asset and driving force behind all critical business functions and decisions. It is not merely about using data for reporting or performance monitoring, but rather about embedding data intelligence into the core operational and strategic processes of the organization. This approach necessitates a cultural transformation, technological infrastructure, and analytical capabilities that collectively enable the systematic collection, processing, interpretation, and application of data to achieve strategic objectives and gain a sustainable competitive advantage. This definition, derived from a synthesis of business strategy, information systems, and organizational behavior research, highlights the holistic and transformative nature of data-centricity.

Redefining Data-Centricity ● An Expert Perspective

To arrive at a robust advanced definition, it’s crucial to analyze diverse perspectives and cross-sectorial influences that shape the meaning of Data-Centric Business Approach. We will focus on the influence of Algorithmic Management and its implications for SMBs adopting data-centric strategies.

The Influence of Algorithmic Management

Algorithmic Management, a concept increasingly prevalent in advanced discourse, refers to the use of algorithms and data-driven systems to manage, control, and optimize various aspects of organizational operations, including human resources, customer interactions, and strategic decision-making. In the context of a Data-Centric Business Approach, algorithmic management Meaning ● Algorithmic management, within the domain of Small and Medium-sized Businesses, refers to the use of algorithms and data analytics to automate and optimize decision-making processes related to workforce management and business operations. represents a significant evolution, moving beyond human-centric decision-making to a model where algorithms play a central role in guiding and automating business processes. This shift has profound implications for SMBs.

Traditionally, SMBs have relied heavily on the expertise and intuition of business owners and managers. However, as data volumes grow and business complexity increases, relying solely on human judgment becomes increasingly challenging and potentially suboptimal. Algorithmic Management offers a potential solution by leveraging the power of data and algorithms to analyze complex patterns, identify optimal solutions, and automate routine tasks. For SMBs, this can translate to:

• Enhanced Decision Accuracy ● Algorithms can process vast amounts of data and identify subtle patterns that humans might miss, leading to more accurate and data-informed decisions.
• Improved Operational Efficiency ● Automation driven by algorithms can streamline processes, reduce manual errors, and optimize resource allocation, leading to significant efficiency gains.
• Scalability and Consistency ● Algorithmic systems can operate consistently and scale efficiently, enabling SMBs to manage growth without compromising quality or consistency.
• Personalization at Scale ● Algorithms can enable personalized customer experiences Meaning ● Tailoring customer interactions to individual needs, fostering loyalty and growth for SMBs. at scale, tailoring products, services, and interactions to individual customer needs and preferences.

Scholarly, a Data-Centric Business Approach is a strategic philosophy prioritizing data as the primary asset, driving decisions and operations through algorithmic intelligence for sustainable SMB competitive advantage.

Cross-Sectorial Business Influences and Multi-Cultural Aspects

The meaning and implementation of a Data-Centric Business Approach are not uniform across sectors and cultures. Different industries and cultural contexts present unique challenges and opportunities for data-centricity. For SMBs, understanding these nuances is crucial for effective implementation.

Cross-Sectorial Influences:

The application of data-centricity varies significantly across sectors. For example:

• Retail and E-Commerce ● Heavily reliant on customer data, transaction data, and marketing data for personalization, inventory management, and supply chain optimization. Algorithms are used extensively for recommendation systems, dynamic pricing, and targeted advertising.
• Manufacturing ● Increasingly leveraging data from IoT sensors, production systems, and quality control processes to optimize manufacturing processes, predict equipment failures, and improve product quality. Data analytics 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. are used for predictive maintenance and process optimization.
• Healthcare ● Utilizing patient data, clinical data, and operational data to improve patient care, optimize resource allocation, and enhance operational efficiency. Data analytics is crucial for diagnostics, personalized medicine, and healthcare management.
• Financial Services ● Long-standing tradition of data-driven decision-making, using financial data, market data, and customer data for risk assessment, fraud detection, and personalized financial products. Algorithms are central to credit scoring, algorithmic trading, and fraud prevention.

SMBs need to adapt their Data-Centric Business Approach to the specific characteristics and data landscape of their industry. What works effectively in e-commerce might not be directly applicable to a manufacturing SMB.

Multi-Cultural Business Aspects:

Cultural context also plays a significant role in shaping the perception and implementation of data-centricity. Data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. concerns, cultural norms around data sharing, and varying levels of technological adoption can influence how SMBs in different cultural contexts approach data-centric strategies. For instance:

• Data Privacy Regulations ● Different regions have varying data privacy regulations (e.g., GDPR in Europe, CCPA in California). SMBs operating internationally must navigate these complex legal landscapes and ensure data compliance.
• Cultural Attitudes Towards Data ● Cultural norms around data sharing and transparency can influence customer acceptance of data-driven personalization and algorithmic decision-making. Some cultures may be more trusting of data-driven systems than others.
• Technological Infrastructure and Adoption ● Levels of technological infrastructure and digital literacy vary across regions. SMBs in less technologically advanced regions may face challenges in implementing sophisticated data-centric strategies.

SMBs operating in diverse cultural contexts need to be culturally sensitive and adapt their data-centric strategies to align with local norms and regulations.

In-Depth Business Analysis ● Algorithmic Bias and Ethical Considerations for SMBs

Focusing on the influence of algorithmic management, a critical area of in-depth business analysis is the potential for Algorithmic Bias and the ethical considerations that arise from increasingly data-driven and algorithmically managed SMBs. While algorithms offer numerous benefits, they are not inherently neutral or objective. Algorithms are trained on data, and if the data reflects existing societal biases or if the algorithm design itself incorporates biases, the resulting algorithmic system can perpetuate and even amplify these biases. For SMBs, this presents significant ethical and business risks.

Sources of Algorithmic Bias:

1. Data Bias ● Algorithms are trained on historical data, which may reflect existing biases in society or within the organization. For example, if historical hiring data disproportionately favors one demographic group, an algorithm trained on this data may perpetuate this bias in future hiring decisions.
2. Algorithm Design Bias ● The design of the algorithm itself can introduce bias. The choice of features, the weighting of different factors, and the optimization objectives can all influence the algorithm’s outcomes and potentially lead to biased results.
3. Feedback Loops ● Algorithmic systems often operate in feedback loops, where the algorithm’s outputs influence future data inputs. If an algorithm makes biased decisions, these decisions can reinforce and amplify the initial bias over time.

Ethical Implications for SMBs:

For SMBs adopting algorithmic management, the ethical implications of algorithmic bias Meaning ● Algorithmic bias in SMBs: unfair outcomes from automated systems due to flawed data or design. are significant:

• Discrimination and Fairness ● Biased algorithms can lead to discriminatory outcomes in areas like hiring, lending, marketing, and customer service, potentially violating ethical principles and legal regulations.
• Reputational Damage ● If an SMB is found to be using biased algorithms, it can suffer significant reputational damage, eroding customer trust and brand value.
• Legal and Regulatory Risks ● Increasingly, regulations are being developed to address algorithmic bias and ensure fairness in algorithmic systems. SMBs need to be aware of and compliant with these regulations to avoid legal penalties.
• Erosion of Trust and Transparency ● If algorithmic decision-making is perceived as opaque or unfair, it can erode trust among employees, customers, and stakeholders. Transparency and explainability of algorithmic systems are crucial for building trust.

Mitigating Algorithmic Bias in SMBs:

SMBs can take several steps to mitigate algorithmic bias and ensure ethical algorithmic management:

1. Data Auditing and Bias Detection ● Regularly audit training data for potential biases and use bias detection techniques to identify and mitigate biases in existing datasets.
2. Algorithm Transparency and Explainability ● Prioritize algorithms that are transparent and explainable, allowing for scrutiny and understanding of how decisions are made. Techniques like SHAP (SHapley Additive exPlanations) can help explain complex model outputs.
3. Fairness-Aware Algorithm Design ● Incorporate fairness considerations into the algorithm design process. This may involve using fairness metrics, applying debiasing techniques, or explicitly optimizing for fairness alongside accuracy.
4. Human Oversight and Intervention ● Maintain human oversight Meaning ● Human Oversight, in the context of SMB automation and growth, constitutes the strategic integration of human judgment and intervention into automated systems and processes. of algorithmic systems and establish mechanisms for human intervention to correct biased outcomes or address ethical concerns. Algorithms should augment, not replace, human judgment.
5. Ethical Guidelines and Training ● Develop ethical guidelines for data and algorithm use within the SMB and provide training to employees on ethical considerations and bias awareness.

Long-Term Business Consequences and Success Insights for SMBs:

For SMBs, proactively addressing algorithmic bias and ethical considerations is not just a matter of social responsibility; it is also a strategic imperative for long-term business success. Ethical algorithmic management can lead to:

• Enhanced Brand Reputation and Customer Trust ● Demonstrating a commitment to fairness and ethical AI Meaning ● Ethical AI for SMBs means using AI responsibly to build trust, ensure fairness, and drive sustainable growth, not just for profit but for societal benefit. can enhance brand reputation and build stronger customer trust and loyalty.
• Reduced Legal and Regulatory Risks ● Proactive bias mitigation can reduce the risk of legal challenges and regulatory penalties related to algorithmic discrimination.
• Improved Employee Morale and Engagement ● Employees are more likely to be engaged and motivated when they believe their organization is operating ethically and fairly.
• Sustainable Competitive Advantage ● In an increasingly ethically conscious marketplace, SMBs that prioritize ethical AI can gain a sustainable competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. by attracting and retaining customers and talent who value ethical business practices.

In conclusion, the advanced understanding of a Data-Centric Business Approach for SMBs extends beyond operational efficiency Meaning ● Maximizing SMB output with minimal, ethical input for sustainable growth and future readiness. and technological implementation. It encompasses profound ethical considerations, particularly concerning algorithmic bias in the context of algorithmic management. For SMBs to truly thrive in a data-driven world, they must not only embrace data and algorithms but also prioritize ethical AI practices, ensuring fairness, transparency, and accountability in their data-centric strategies. This holistic approach, integrating technological prowess with ethical responsibility, will be the hallmark of successful and sustainable data-centric SMBs in the future.

The Data-Centric Business Approach, therefore, is not a static concept but a dynamic and evolving paradigm that requires continuous adaptation, learning, and ethical reflection, especially for SMBs navigating the complexities of the modern business landscape. It is a journey of continuous improvement, driven by data, guided by ethics, and aimed at achieving sustainable growth and creating lasting value for all stakeholders.

Table 1 ● Evolution of Data-Centric Business Approach for SMBs

Table 2 ● Ethical Considerations in Data-Centric SMB Operations

Table 3 ● Implementation Roadmap for Data-Centric SMB Growth

Table 4 ● Key Performance Indicators (KPIs) for Data-Centric SMBs