Fundamentals

Consider the small bakery owner, Sarah, whose online orders suddenly plummet. She scratches her head, bewildered. Is it the new sourdough recipe? A glitch in the website?

Perhaps, but maybe it is something far more fundamental ● the customer data, riddled with errors, leading to misdirected promotions and abandoned carts. This scenario, amplified across countless small and medium businesses (SMBs), highlights a silent profit killer ● poor data quality, and its impact is measurable long before the cash register stops ringing.

The Unseen Drain ● Data Quality Demystified

Data quality, in its simplest form, refers to the fitness of data for its intended uses in business operations, decision-making, and planning. Think of it as the raw material fueling your business engine. If that fuel is contaminated, expect sputtering performance, misfires, and eventually, a breakdown. For SMBs, often operating on tight margins and limited resources, the consequences of 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. can be particularly acute, impacting everything from 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. to strategic growth.

Key Business Metrics Under Siege

The insidious nature of poor data quality is that it doesn’t always announce itself with flashing lights and sirens. Instead, it subtly erodes business performance, manifesting in a range of metrics that, when examined closely, tell a story of inefficiency and lost opportunity. These metrics act as early warning systems, indicators that something is amiss beneath the surface of day-to-day operations.

Customer Relationship Metrics ● The Fading Connection

For many SMBs, especially in the service and retail sectors, customer relationships are the lifeblood of the business. Poor data quality directly attacks these relationships, leading to tangible metric declines.

• Customer Churn Rate ● This metric, representing the percentage of customers who stop doing business with you over a period, is a critical indicator of customer satisfaction and loyalty. Incorrect contact information, leading to missed communications or irrelevant offers, irritates customers and pushes them towards competitors. Imagine Sarah sending out a promotion for vegan cupcakes to customers flagged as having gluten allergies due to outdated data. The result? Wasted marketing spend and alienated customers.
• Customer Acquisition Cost (CAC) ● Acquiring new customers is expensive. When marketing campaigns Meaning ● Marketing campaigns, in the context of SMB growth, represent structured sets of business activities designed to achieve specific marketing objectives, frequently leveraged to increase brand awareness, drive lead generation, or boost sales. are based on flawed data ● targeting the wrong demographics or geographic areas ● CAC skyrockets. If Sarah’s bakery uses inaccurate location data to target online ads, she is paying to reach people outside her delivery zone, effectively throwing marketing dollars away.
• Customer Lifetime Value (CLTV) ● Understanding the long-term revenue potential of a customer is vital for strategic planning. Poor data quality skews CLTV calculations. Inaccurate purchase history or demographic data leads to underestimation or overestimation of customer value, resulting in misguided investment decisions in customer retention and engagement strategies. Sarah might undervalue a loyal customer who has moved but still orders occasionally because her address data is not updated, missing opportunities to nurture that relationship.
• Net Promoter Score (NPS) ● This metric gauges customer loyalty and willingness to recommend your business. If surveys are sent to incorrect email addresses or phone numbers due to outdated data, the NPS score becomes unreliable. Furthermore, if customer feedback is miscategorized or ignored due to data entry errors, opportunities to improve customer experience are lost, ultimately impacting NPS negatively. Imagine Sarah receiving negative feedback about delivery issues but attributing it to the wrong customer segment due to data inaccuracies, hindering her ability to address the real problem.

Deteriorating customer relationship metrics, such as rising churn and acquisition costs, are often the first visible signs of underlying data quality problems in SMBs.

Operational Efficiency Metrics ● The Drag on Productivity

Beyond customer-facing metrics, poor data quality significantly impacts internal operations, creating inefficiencies and driving up costs. These operational metrics offer a different lens through which to view the impact of flawed data.

• Order Fulfillment Time ● Inaccurate inventory data or customer address details directly impact order processing and delivery times. If Sarah’s inventory system shows she has enough chocolate chips when she is actually out, orders get delayed, customer satisfaction dips, and operational costs increase due to expedited shipping or handling of complaints.
• Inventory Turnover Rate ● This metric measures how efficiently inventory is managed. Poor data quality in inventory management systems ● incorrect stock levels, misplaced items ● leads to inaccurate turnover calculations. This results in either overstocking, tying up capital in unsold goods, or understocking, leading to lost sales and frustrated customers. Sarah might over-order flour based on inflated sales projections derived from inaccurate past sales data, leading to storage issues and potential spoilage.
• Employee Productivity ● Employees spend significant time correcting data errors, searching for missing information, or working with inconsistent data. This wasted effort directly reduces productivity. Sarah’s staff might spend hours manually verifying customer addresses or correcting order details due to errors in the customer database, time that could be spent on more value-added activities like baking or customer service.
• Error Rates in Key Processes ● Whether it’s invoicing, shipping, or customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. interactions, poor data quality increases error rates across all business processes. These errors lead to rework, delays, customer dissatisfaction, and increased operational costs. Sarah might send out incorrect invoices due to pricing errors in her product database, leading to disputes with customers and additional administrative overhead.

Consider the table below, illustrating the interconnectedness of data quality and operational metrics in a hypothetical SMB retail setting:

Financial Metrics ● The Bottom Line Bleed

Ultimately, the cumulative effect of poor data quality manifests in financial metrics, directly impacting profitability and long-term financial health. These metrics paint the clearest picture of the economic consequences of neglecting data quality.

• Revenue Leakage ● Lost sales due to inaccurate inventory data, missed opportunities from flawed marketing campaigns, and customer churn Meaning ● Customer Churn, also known as attrition, represents the proportion of customers that cease doing business with a company over a specified period. driven by poor communication all contribute to revenue leakage. Sarah’s bakery might experience revenue leakage from abandoned online orders due to confusing product descriptions or inaccurate pricing information on her website.
• Increased Operating Expenses ● Rework, error correction, wasted marketing spend, and inefficient processes all inflate operating expenses. Sarah’s operational expenses increase due to the time spent correcting data errors, handling customer complaints arising from inaccurate orders, and re-running marketing campaigns that initially failed due to poor targeting data.
• Lower Profit Margins ● The combination of revenue leakage and increased expenses directly squeezes profit margins. Sarah’s bakery sees its profit margins shrink as costs rise and revenue stagnates due to the compounding effects of poor data quality across various aspects of her business.
• Return on Investment (ROI) on Technology Investments ● SMBs invest in technology ● CRM systems, marketing automation platforms, e-commerce platforms ● to improve efficiency and drive growth. However, if the data fed into these systems is of poor quality, the ROI is significantly diminished. Sarah might invest in a new CRM system hoping to improve customer engagement, but if her 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. is inaccurate and incomplete, the system’s effectiveness is hampered, and the expected ROI is not realized.

Financial metrics, such as shrinking profit margins and reduced ROI on technology, are the ultimate financial statement reflecting the hidden costs of poor data quality.

Taking Action ● A Practical First Step for SMBs

Recognizing the metrics impacted by poor data quality is the first step. For SMBs like Sarah’s bakery, the next step is not to panic but to take a pragmatic, phased approach. Start with a data quality audit of key data sets ● customer data, product data, inventory data. Identify the most critical data quality issues and prioritize addressing them based on their potential business impact.

Simple tools and techniques, like data cleansing and standardization, can yield immediate improvements in data quality and, consequently, in the business metrics Meaning ● Quantifiable measures SMBs use to track performance, inform decisions, and drive growth. that matter most. Sarah can begin by training her staff on proper data entry procedures and implementing simple data validation Meaning ● Data Validation, within the framework of SMB growth strategies, automation initiatives, and systems implementation, represents the critical process of ensuring data accuracy, consistency, and reliability as it enters and moves through an organization’s digital infrastructure. rules in her systems. The journey to data quality improvement Meaning ● Data Quality Improvement for SMBs is ensuring data is fit for purpose, driving better decisions, efficiency, and growth, while mitigating risks and costs. is a marathon, not a sprint, but even small steps can lead to significant gains for SMBs.

Intermediate

Beyond the immediate, easily observable symptoms of poor data quality, lies a more intricate web of interconnected business metrics subtly undermined by flawed information. Consider a growing e-commerce SMB, “TechGadgets,” experiencing plateauing sales despite increasing marketing spend. Superficially, marketing inefficiency might be blamed.

However, a deeper analysis reveals a more fundamental issue ● inconsistent product data across various sales channels, leading to customer confusion, abandoned carts, and ultimately, diminished revenue growth. This scenario underscores the necessity to move beyond surface-level metrics and examine the more sophisticated indicators that reveal the true extent of poor data quality’s impact.

Strategic Metrics ● Gauging the Ripple Effects

At the intermediate level, the focus shifts from basic operational and customer-facing metrics to strategic metrics that reflect the broader, longer-term consequences of data quality issues. These metrics are crucial for SMBs aiming for sustainable growth and competitive advantage.

Marketing and Sales Effectiveness Metrics ● Precision Vs. Spray and Pray

In today’s data-driven marketing landscape, precision targeting and personalized customer experiences are paramount. Poor data quality renders these strategies ineffective, leading to wasted resources and missed opportunities.

• Marketing Return on Investment Meaning ● Return on Investment (ROI) gauges the profitability of an investment, crucial for SMBs evaluating growth initiatives. (MROI) ● MROI measures the profitability of marketing campaigns. However, inaccurate campaign performance data, flawed attribution models due to inconsistent customer IDs across platforms, and targeting errors stemming from demographic data inaccuracies all distort MROI calculations. TechGadgets might overestimate the ROI of a particular campaign because sales attributed to it are actually driven by organic traffic or other marketing efforts not properly tracked due to data integration Meaning ● Data Integration, a vital undertaking for Small and Medium-sized Businesses (SMBs), refers to the process of combining data from disparate sources into a unified view. issues.
• Conversion Rates Across Sales Funnels ● Analyzing conversion rates at each stage of the sales funnel ● from website visits to lead generation to sales closures ● provides valuable insights into customer behavior and sales process effectiveness. Poor data quality, such as inaccurate lead scoring Meaning ● Lead Scoring, in the context of SMB growth, represents a structured methodology for ranking prospects based on their perceived value to the business. data or incomplete customer journey information, obscures funnel bottlenecks and hinders optimization efforts. TechGadgets might struggle to identify why potential customers are dropping off at the checkout stage if their website analytics data is skewed by bot traffic or inaccurate session tracking due to data quality problems.
• Sales Cycle Length ● The time it takes to convert a lead into a paying customer is a critical indicator of sales efficiency. Poor data quality, like incomplete lead information or inaccurate sales activity tracking, makes it difficult to accurately measure and optimize the sales cycle. TechGadgets might experience longer sales cycles because sales representatives spend excessive time verifying lead information or correcting data errors in the CRM system, diverting them from actual selling activities.
• Lead Qualification Rate ● The percentage of leads that are actually qualified prospects for sales engagement directly impacts sales efficiency and resource allocation. Inaccurate lead scoring models, based on flawed demographic or behavioral data, lead to sales teams chasing unqualified leads, wasting time and resources. TechGadgets might have a low lead qualification Meaning ● Lead qualification, within the sphere of SMB growth, automation, and implementation, is the systematic evaluation of potential customers to determine their likelihood of becoming paying clients. rate because their lead scoring system incorrectly flags website visitors from irrelevant industries as high-potential leads due to inaccurate industry classification data.

Strategic marketing and sales metrics, such as MROI and conversion rates, become unreliable indicators of performance when undermined by poor data quality, hindering effective resource allocation Meaning ● Strategic allocation of SMB assets for optimal growth and efficiency. and strategic decision-making.

Decision-Making and Analytics Metrics ● The Mirage of Insights

Data-driven decision-making relies on the assumption that the data being analyzed is accurate and reliable. Poor data quality undermines this assumption, leading to flawed insights and potentially detrimental business decisions.

• Accuracy of Sales Forecasts ● Sales forecasts are crucial for inventory planning, resource allocation, and financial projections. However, inaccurate historical sales data, skewed by data entry errors or incomplete records, leads to unreliable forecasts. TechGadgets might overstock certain product lines based on inflated sales forecasts derived from inaccurate past sales data, leading to inventory write-offs and reduced profitability.
• Reliability of Business Intelligence (BI) Reports ● BI reports provide a consolidated view of business performance across various metrics. If the underlying data feeding these reports is flawed, the insights derived are misleading. TechGadgets’ BI dashboards might show misleading trends in customer demographics or product preferences if the data is riddled with inconsistencies or inaccuracies, leading to misguided strategic initiatives.
• Validity of A/B Testing Meaning ● A/B testing for SMBs: strategic experimentation to learn, adapt, and grow, not just optimize metrics. Results ● A/B testing is used to optimize website design, marketing messages, and product features. However, if the data collected during A/B tests is compromised by tracking errors or data inconsistencies, the results become unreliable, and incorrect conclusions might be drawn. TechGadgets might incorrectly conclude that a particular website design change is effective based on A/B test data that is skewed by data quality issues, potentially damaging the user experience.
• Precision of Predictive Analytics Meaning ● Strategic foresight through data for SMB success. Models ● SMBs are increasingly leveraging predictive analytics for tasks like demand forecasting and customer churn prediction. The accuracy of these models is heavily dependent on the quality of the training data. Poor data quality leads to biased or inaccurate models, resulting in flawed predictions and suboptimal business outcomes. TechGadgets’ customer churn prediction Meaning ● Predicting customer attrition to proactively enhance relationships and optimize SMB growth. model might misidentify loyal customers as high-churn risks due to incomplete or inaccurate customer behavior data, leading to ineffective retention efforts.

Consider the following table illustrating how data quality issues can cascade through the analytics pipeline, impacting decision-making metrics:

Automation and Implementation Metrics ● The Stalled Engine of Efficiency

SMBs are increasingly turning to automation to improve efficiency and scale operations. However, automation initiatives Meaning ● Automation Initiatives, in the context of SMB growth, represent structured efforts to implement technologies that reduce manual intervention in business processes. are critically dependent on high-quality data. Poor data quality can derail automation projects and negate their intended benefits.

• Automation Error Rate ● Automated processes, such as order processing or customer service chatbots, rely on accurate data to function correctly. Poor data quality leads to increased error rates in automation, requiring manual intervention and negating the efficiency gains of automation. TechGadgets’ automated order processing system might fail to process orders with incomplete address information or invalid product codes, requiring manual intervention and slowing down order fulfillment.
• Data Integration Success Rate ● Successful automation often requires integrating data from various systems. Poor data quality, particularly inconsistencies in data formats and definitions across systems, hinders data integration efforts, leading to project delays and increased costs. TechGadgets might struggle to integrate its CRM and marketing automation systems due to inconsistencies in customer data formats, limiting the effectiveness of personalized marketing campaigns.
• Process Automation Throughput ● Automation aims to increase the speed and volume of process execution. However, if automated processes are constantly encountering data quality issues and requiring manual correction, the throughput is significantly reduced. TechGadgets’ automated customer onboarding process might be slowed down by data validation errors and manual data cleansing requirements, limiting the number of new customers that can be efficiently onboarded.
• System Downtime Due to Data Issues ● In extreme cases, poor data quality can even lead to system failures and downtime. Data corruption or data integration errors can destabilize systems, causing disruptions to business operations. TechGadgets might experience downtime in its e-commerce platform due to data corruption issues in the product database, resulting in lost sales and customer dissatisfaction.

Automation and implementation metrics, such as automation error rates and data integration success, highlight the critical dependency of successful automation initiatives on high-quality data, revealing the hidden costs of data neglect.

Moving Towards Proactive Data Quality Management

For SMBs like TechGadgets, recognizing the impact of poor data quality on these intermediate-level metrics necessitates a shift from reactive data cleaning to proactive data quality management. This involves implementing data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. policies, investing in data quality tools, and fostering a data-centric culture within the organization. A crucial step is to establish data quality metrics Meaning ● Data Quality Metrics for SMBs: Quantifiable measures ensuring data is fit for purpose, driving informed decisions and sustainable growth. and regularly monitor them to track progress and identify areas for improvement. TechGadgets could implement data quality dashboards to monitor metrics like data completeness, accuracy, and consistency across key data sets.

By proactively addressing data quality issues, SMBs can unlock the full potential of their data assets and achieve sustainable growth and competitive advantage. The journey from data chaos to data mastery requires commitment and investment, but the long-term benefits far outweigh the initial effort.

Advanced

The discourse surrounding data quality impact frequently defaults to tactical operational inefficiencies or readily apparent customer service failures. However, to truly grasp the pervasive influence of substandard data, one must ascend to a strategic vantage point, examining its insidious erosion of organizational agility, innovation capacity, and ultimately, competitive resilience. Consider a technologically progressive SMB, “DataWise Solutions,” ironically specializing in data analytics, yet internally grappling with fragmented data silos and inconsistent data definitions across departments.

Despite outward expertise, DataWise’s own suboptimal data landscape impedes its ability to develop cutting-edge AI solutions, limits cross-functional collaboration, and ultimately constrains its market leadership potential. This paradox illuminates a critical, often overlooked dimension ● poor data quality as a strategic liability, subtly undermining an SMB’s capacity for future growth and innovation.

Strategic Agility and Innovation Metrics ● The Stifled Potential

In the contemporary business ecosystem, characterized by rapid technological advancements and dynamic market shifts, strategic agility Meaning ● Strategic Agility for SMBs: The dynamic ability to proactively adapt and thrive amidst change, leveraging automation for growth and competitive edge. and innovation are not merely desirable attributes; they are existential imperatives. Poor data quality acts as a significant drag on these crucial organizational capabilities, often in ways that are not immediately quantifiable but profoundly impactful in the long run.

Time to Market for New Products/Services ● The Innovation Bottleneck

Innovation hinges on the ability to rapidly iterate, test, and deploy new offerings. Poor data quality introduces friction into this process, extending development cycles and delaying market entry. Inconsistent product data across R&D, manufacturing, and marketing, for instance, leads to delays in product launches and missed market windows. DataWise Solutions might experience protracted development cycles for new AI products due to the time spent reconciling disparate data sets and resolving data inconsistencies across its internal systems, hindering its ability to capitalize on emerging market opportunities ahead of competitors.

Adaptability to Market Changes ● The Rigidity Trap

Market conditions are rarely static. SMBs must be able to swiftly adapt their strategies and operations in response to evolving customer needs and competitive pressures. Poor data quality impairs this adaptability.

For example, inaccurate market intelligence data or delayed insights due to data processing bottlenecks hinder timely strategic adjustments. DataWise Solutions might be slow to respond to shifts in customer preferences for AI solutions if its market analysis is based on outdated or incomplete market data, losing ground to more agile competitors who leverage superior data insights.

Cross-Functional Collaboration Efficiency ● The Silo Effect

Innovation often emerges from the synergistic interplay of diverse expertise across different functional areas. Poor data quality erects barriers to effective cross-functional collaboration. Inconsistent data definitions and lack of data accessibility across departments lead to misunderstandings, duplicated efforts, and fractured workflows. DataWise Solutions might struggle to foster seamless collaboration between its R&D, sales, and marketing teams if each department operates with its own siloed data and inconsistent data standards, hindering the development and go-to-market strategy for integrated AI solutions.

Employee Morale and Engagement Related to Data Processes ● The Human Cost

The impact of poor data quality extends beyond purely quantitative metrics, affecting the human element of the organization. Employees frustrated by dealing with data errors, manual workarounds, and unreliable systems experience decreased morale and engagement. This, in turn, negatively impacts productivity and innovation. DataWise Solutions’ data scientists and analysts might experience frustration and decreased job satisfaction if they spend a disproportionate amount of time on data cleansing and preparation rather than on actual data analysis and model building, stifling their creativity and innovation potential.

Strategic agility and innovation metrics, while less directly quantifiable, are profoundly impacted by poor data quality, revealing its role as a strategic impediment to long-term SMB growth Meaning ● SMB Growth is the strategic expansion of small to medium businesses focusing on sustainable value, ethical practices, and advanced automation for long-term success. and competitive advantage.

Risk Management and Compliance Metrics ● The Hidden Liabilities

Beyond lost opportunities and operational inefficiencies, poor data quality introduces significant risks and compliance liabilities for SMBs. These risks, if unaddressed, can lead to financial penalties, reputational damage, and even legal repercussions.

Data Security Breach Rate Attributable to Data Errors ● The Vulnerability Multiplier

Poor data quality can indirectly increase the risk of 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. breaches. For instance, inaccurate access control data or incomplete audit trails due to data logging errors can create vulnerabilities that cybercriminals can exploit. Furthermore, data quality issues can hinder effective incident response and breach detection. DataWise Solutions might be more susceptible to data security breaches if its access control systems are based on inaccurate employee data or if its security monitoring logs are incomplete due to data logging errors, making it harder to detect and respond to security threats effectively.

Compliance Violation Rate Related to Data Regulations ● The Regulatory Minefield

Increasingly stringent data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. regulations, such as GDPR and CCPA, impose strict requirements on data quality. Inaccurate or incomplete data can lead to compliance violations, resulting in hefty fines and reputational damage. For example, inaccurate customer consent data or incomplete data subject access request fulfillment due to data retrieval challenges can lead to regulatory breaches. DataWise Solutions, handling sensitive client data, faces significant compliance risks if its data quality practices are inadequate, potentially incurring substantial fines and legal liabilities for non-compliance with data privacy regulations.

Financial Audit Findings Related to Data Integrity ● The Accountability Gap

Financial audits increasingly scrutinize data integrity and data governance practices. Significant audit findings related to data quality weaknesses can raise red flags for investors, lenders, and regulatory bodies. Material data errors in financial reporting systems or inadequate data validation controls can lead to adverse audit opinions and erode stakeholder confidence. DataWise Solutions, as a publicly traded SMB, could face negative investor reactions and increased scrutiny from financial regulators if its financial audits reveal significant weaknesses in data quality and data governance practices, impacting its stock price and access to capital.

Reputational Damage Score Due to Data Quality Issues ● The Trust Erosion

In today’s interconnected world, negative experiences related to data quality issues can quickly escalate and damage an SMB’s reputation. Inaccurate product information online, data breaches stemming from data vulnerabilities, or compliance violations all erode customer trust and brand image. Negative online reviews and social media backlash can have a significant and lasting impact on customer acquisition Meaning ● Gaining new customers strategically and ethically for sustainable SMB growth. and retention. DataWise Solutions might suffer reputational damage if clients experience issues with its AI solutions due to underlying data quality problems or if it faces public scrutiny for data privacy violations stemming from inadequate data quality controls, impacting its brand reputation and future business prospects.

The table below illustrates the interconnectedness of data quality, risk, and compliance metrics, highlighting the potential for significant business liabilities:

Risk management and compliance metrics, often overlooked in data quality discussions, reveal the potential for significant financial, legal, and reputational liabilities stemming from inadequate data quality practices in SMBs.

Embracing Data Quality as a Strategic Imperative

For advanced SMBs like DataWise Solutions, recognizing the strategic implications of poor data quality necessitates a fundamental shift in perspective. Data quality is not merely a technical issue to be addressed by IT departments; it is a strategic imperative that must be championed at the highest levels of the organization. This requires establishing a robust data governance framework, investing in advanced data quality management Meaning ● Ensuring data is fit-for-purpose for SMB growth, focusing on actionable insights over perfect data quality to drive efficiency and strategic decisions. technologies, and fostering a data-driven culture that prioritizes data quality across all business functions. Furthermore, SMBs must proactively monitor strategic data quality metrics, integrate data quality considerations into strategic planning processes, and continuously adapt their data quality strategies to the evolving business landscape.

DataWise Solutions, to maintain its market leadership, must transform its internal data landscape from a liability into a strategic asset by embracing data quality as a core organizational value and investing in comprehensive data governance and data quality management initiatives. The journey to data excellence is a continuous process of refinement and adaptation, but it is a journey that advanced SMBs must undertake to thrive in the increasingly data-centric competitive arena. The future belongs to those who not only collect data but also master its quality and harness its strategic power.