In today’s data-driven economy, use of big data analytics is becoming imperative for business success. Some assert that a company’s big data capabilities can actually protect it from competition. But is this really true?

Can big data act as a shield against rivals and support sustainable competitive advantage? Or is big data just a temporary strength that competitors can quickly mimic? This article will examine the realities around big data’s ability to fortify a firm’s market position.

Big Data Fuels Competitive Advantage

On the surface, big data appears well-positioned to help companies differentiate from the competition. When utilized effectively, big data analytics can confer strategic advantages in many ways. Some are mentioned here.

Customer Insights

Analyzing granular customer data from sources like CRM systems, website analytics, mobile apps, email campaigns and social media conversations can reveal valuable insights regarding customer preferences, behaviors and pain points. These insights inform efforts like product/service refinement, personalized promotions, micro-segmentation and churn modeling to boost engagement.

Competitors without access to comparable high-fidelity customer data or the analytical skills are not able to utilize these differentiating insights.

Operational Efficiency

Applying big data analytics to internal operational data flows can bring optimization opportunities throughout an organization’s business processes, supply chain logistics, inventory management, HR functions and IT systems.

By identifying bottlenecks, redundancies, unnecessary costs and using data to streamline operations, companies can achieve productivity gains and cost savings that competitors lack. Companies use IoT sensors to constantly monitor operations. The sensors create lots of data.

Advanced analytics helps make sense of all that sensor data to find valuable information. Companies that are really skilled at getting insights from the sensor data can run much better operations than their rivals.

Data Monetization

Data assets of companies are accumulated over time. Monetizing the data through data products, services or marketplaces represents a lucrative opportunity that the competitors find hard to replicate. Usage data from connected products or operational data from proprietary systems can be packaged into value-added proprietary data feeds and analytics tools.

Data assets can also be monetized by granting access via APIs to promote broader data ecosystem effects that prevent rival entry.

Predictive Modeling

Advanced predictive analytics techniques utilizing the abundance of big data enable companies to model outcomes like forecasted market demand, price fluctuations, equipment failures, healthcare risks and customer churn with greater accuracy than competitors.

This data-driven strategic foresight about potential futures is based on statistical algorithms. These algorithms are trained on more robust datasets, therefore, these are beyond the access of rivals.

Sustainable Competitive Advantage Requires More

Big data analytics can provide temporary competitive advantages, whereas, rivals can eventually replicate data assets and develop comparable analytics capabilities over time. Sustaining differentiation requires specialized big data capabilities that are difficult for competitors to imitate.

Proprietary Data Assets

Truly proprietary data assets accumulated over the course of years or decades of operations provide lasting differentiation because competitors lack access. These may include unique operational data from proprietary systems and processes, customer transactional activity, usage patterns and behavior from connected products and services, and niche datasets.

However, most big data is either readily purchased through external data providers, accessed via APIs, or replicable through digitizing internal operations. Without continued cultivation of proprietary data streams, competitive advantage erodes over time.

World-Class Analytics Talent

While off-the-shelf analytics software can be easily purchased, a world-class analytics team with creative data science and engineering skills is much harder to replicate. Skills like advanced machine learning, causal inference, optimization, and ability to cleanse messy data provide enduring advantage — provided the talent can be retained as demand for these scarce skills intensifies. Due to high turnover, proprietary processes and documentation around analytics workflows creates partial insulation.

Data Integration Capabilities

The ability to seamlessly integrate complex enterprise systems generating messy internal data alongside external open and licensed data feeds into a flexible and scalable architecture provides lasting advantage. But with more modular cloud-based data tools and maturing industry standards, integration barriers are lowered which allow followers to catch up.

Agile Data Governance

Balancing data access and innovation against risks requires governance models that evolve as technology changes and new regulations arise. But standards bodies and regulatory efforts also codify basic governance expectations lowering this barrier over time. True differentiation arises from nuanced governance processes tailored to enterprise risk patterns.

Continuous Improvement Capabilities

Mature capabilities for rapid testing and iterating analytics practices, continuous monitoring data veracity, and incentivizing insights compound over time making emulation harder. But analytics operations management processes are being increasingly codified into tools and practice frameworks eroding this long-term edge.

The most enduring data advantages arise from deeply rooted and evolving propriety assets, culture, and talent that layer over time.

Big Data Alone Is Not Enough

Although big data analytics is crucial for competitiveness but relying on big data alone is insufficient for impenetrable strategic advantage.

Here’s why:

Data Commoditization: As data aggregators proliferate, more external data becomes a low-cost commodity. Internally, similar business processes create comparable data.
Tool Democratization: Powerful cloud-based analytics tools are increasingly accessible to all. Big data analytics has been democratized.
Talent Churn: Skilled talent frequently moves between competitors. Expertise disperses quickly.
Concept Diffusion: Ideas around data governance, culture, architecture and other differentiators diffuse over time.
External Shocks: Market disruption from outside the industry like technology shifts or Black Swan events eclipse historical data.
Law of Diminishing Returns: After early easy wins like cost cutting, incremental data ROI declines over time. Predictive power has limits.

For sustained strategic advantage, companies must weave big data into broader differentiators like brand, culture, intellectual property, ecosystems, and barriers to imitation. Big data is best embedded as part of a broader tapestry of competitive capabilities.

The Verdict on Big Data As a Barrier

In summary, here is the verdict on whether big data can erect barriers against competition:

Big data analytics provides temporary competitive edge. Foresight and efficiency gains outpace data laggards initially.
However, most big data capabilities diffuse quickly allowing rivals to catch up. Sustainable advantage requires specialized data assets, talent and culture tough to replicate.
Big data itself is rarely enough to provide durable separation from competitors. It must integrate within the organization’s broader differentiating capabilities.
Managers should view big data as an essential but insufficient factor in sustaining market leadership. Competitive advantage depends on crafting an ensemble of strategic complements to big data analytics.

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