The Importance of Data Monetization in Retail 

Data monetization is the process of leveraging data to generate business value and long-term revenue. Retailers can analyze their existing data to extract deep insights, identify patterns, and uncover trends that help in value creation. 

For retail businesses, data monetization can be categorized into two main types: 

• Direct Monetization: Generating revenue directly from data, such as selling customer insights or market trends to third parties. 

• Indirect Monetization: Utilizing data to develop new business models, improve operational efficiencies, or enhance customer experiences. 

How Retail Businesses Can Maximize Data Monetization 

To achieve maximum efficiency in data monetization, retailers can adopt various strategies, including: 

1. Comprehensive Customer Profiling 

Creating a 360-degree customer view by consolidating data from various touchpoints where customers interact with the brand. 

• 360-Degree Customer View: Integrating both online and offline interactions to better understand customer preferences, behaviors, and purchasing patterns. This enables personalized marketing strategies and enhances customer engagement. 

• Predictive Analytics: Using advanced analytics to anticipate customer behavior and potential brand-switching tendencies. This helps businesses tailor offers and communication methods to improve customer satisfaction and loyalty. 

2. Data Integration and Accessibility 

Retail businesses should centralize their data to ensure seamless access and usability across departments. 

• Centralized Data Repository: Implementing a unified data storage system simplifies access and analysis, improving decision-making regarding inventory management, pricing strategies, and marketing campaigns. 

• Real-Time Data Processing: Leveraging real-time analytics tools enables quick responses to market changes by adjusting prices and stock levels based on current demand. 

3. Advanced Analytics Tools 

Investing in high-level analytical tools can significantly enhance data utilization. 

• Business Intelligence (BI) Tools: BI tools transform complex data into actionable insights through dashboards and reports, enabling efficient decision-making. 

• Machine Learning Algorithms: Retailers can apply machine learning to analyze large datasets, identifying key trends that optimize pricing strategies, inventory management, and targeted marketing, ultimately boosting sales and profits. 

4. Personalization Strategies 

Utilizing data to create personalized customer experiences fosters long-term brand engagement. 

• Targeted Marketing Campaigns: By analyzing demographic data and purchase history, retailers can create tailored marketing campaigns that resonate with specific customer segments, increasing conversion rates. 

• Personalized Product Recommendations: AI-driven recommendation engines suggest products based on past purchases, improving customer satisfaction and repeat purchases. 

5. Continuous Process Optimization 

Retailers can leverage data to enhance operational efficiencies and maintain business agility. 

• Regular Feedback Collection: Gathering customer feedback on shopping experiences helps retailers refine their products and services proactively. 

• Employee Training & Development: Investing in training programs for employees to effectively utilize data tools fosters a data-driven culture within the organization. 

Use Cases of Data Monetization in Retail 

1. Enhancing Internal Operations 

Retailers can optimize various business processes using data-driven insights: 

• Inventory Management: Analyzing sales data and seasonal trends helps businesses maintain optimal stock levels, reducing overstocking and stockouts. 

• Supply Chain Optimization: Identifying inefficiencies in the supply chain through data analysis enhances delivery logistics and reduces lead times. 

• Cost Reduction: Analyzing operational data helps retailers pinpoint inefficiencies and cut unnecessary costs. 

2. Improving Customer Experience 

Retailers can utilize data to provide personalized and seamless customer interactions: 

• Targeted Promotions: Analyzing purchase history and online browsing behavior enables retailers to deliver relevant promotions and personalized offers. 

• Product Recommendations: AI-powered recommendation engines suggest relevant products based on customer preferences, improving the shopping experience and encouraging repeat purchases. 

• Personalized Communication: Data-driven communication strategies enhance customer engagement and brand loyalty. 

3. Generating Revenue from Retail Spaces and Platforms 

Retailers can monetize their digital and physical spaces by offering advertising opportunities: 

• Digital Advertising: Selling ad space on e-commerce platforms, mobile apps, or email newsletters. 

• In-Store Advertising: Brands can purchase in-store ad placements, such as digital signage or printed materials. 

• Sponsored Content: Collaborating with brands to create sponsored content that promotes products in a natural and engaging way. 

4. Leveraging AI and Machine Learning 

Retailers can implement AI and machine learning to enhance business operations: 

• Predictive Analytics: AI models can analyze historical data to forecast future sales trends, enabling retailers to optimize inventory levels and pricing strategies. 

• Demand Planning: Machine learning algorithms analyze seasonal trends and external factors to improve demand forecasting. 

• Inventory Management: AI-powered solutions help maintain optimal stock levels by predicting potential shortages or excess inventory. 

5. Optimizing Supply Chain Efficiency 

Retailers can use data to assess supply chain performance and identify areas for improvement: 

• Logistics Optimization: Data analysis helps retailers streamline transportation processes, reducing delivery times and costs. 

• Supplier Performance Analysis: Retailers can evaluate supplier efficiency and negotiate better terms based on data insights. 

Unlocking Business Value with Bluebik 

As a leading digital transformation consultancy, Bluebik empowers businesses to harness data for sustainable growth through strategic solutions: 

• Strategic Planning and Project Management (PMO): Aligning data strategies with business objectives while ensuring compliance with data governance and cybersecurity regulations. 

• Comprehensive Data Ecosystem: Establishing a connected data infrastructure that eliminates silos and facilitates actionable insights. 

• AI-Powered Advanced Analytics: From fraud detection to personalized customer experiences, Bluebik leverages cutting-edge analytics to drive innovation. 

• Regulatory Compliance and Legal Strategies: Ensuring adherence to data protection laws such as PDPA and other industry regulations. 

• Holistic Data Monetization Solutions: Developing scalable revenue models that create value through both direct and indirect monetization channels. 

Data Monetization is not just an option—it is a strategic necessity. Bluebik is ready to help your organization unlock the full potential of your data and drive tangible business outcomes. 

📩 Contact Bluebik today to explore tailored solutions for your business.

📧 hello@bluebik.com 

📞 02-636-7011 

The post Data Monetization in Retail: Unlocking Maximum Value from Data appeared first on Bluebik.

• Revenue Growth: Turn insights into profitable products for partners and clients. 

• Customer Engagement: Data-driven personalization drives loyalty and retention. 

• Competitive Advantage: Institutions that prioritize data monetization stay ahead of disruptors. 

In a fast-changing landscape and competition intensifies, monetizing data is no longer optional—it is a necessity. 

From Insight to Impact 

The financial sector is evolving rapidly, driven by digitalization and fierce competition. At the center of this transformation is data monetization, a powerful tool to generate growth and redefine customer experiences. 

While financial institutions generate vast amounts of data daily, the value lies in turning raw data into actionable intelligence. According to the World Bank’s 2024 Global Economic Report, institutions that prioritize data monetization achieve 1.5x faster revenue growth and greater efficiency than their peers. 

The Strategic Case for Data Monetization 

1. Unlocking New Value Amidst Shrinking Margins 

With shrinking net interest margins—declining by an average of 12% over the past five years (IMF Financial Stability Report, 2023)—financial institutions are under pressure to identify alternative revenue streams. Data monetization presents a lucrative opportunity. 

For example, a leading Asian bank generated $15 million annually by offering anonymized consumer spending insights to retail and government partners (Statista, 2023). 

2. The Shift to Hyper-Personalized Services 

According to Statista (2023), 78% of consumers prefer banks that deliver personalized financial solutions. Leveraging behavioral data and AI enables banks to tailor products and services to individual preferences, driving customer satisfaction and retention. 

3. Staying Ahead of Fintechs 

Global fintech investment reached $180 billion in 2023 (OECD, 2023), intensifying competition in the financial sector. Traditional banks must leverage their rich data ecosystems to remain relevant, outpace disruptors, and innovate faster. 

The 5-Stage Framework for Data Monetization 

Bluebik emphasizes that achieving success in data monetization requires a structured framework that turns raw data into actionable insights and measurable business impact. 

1. Foundation: Build a Unified Data Ecosystem  

 The foundation for data monetization is a centralized and secure data infrastructure. 

• Key Actions:  

• Break down silos to integrate data into a single source of truth. 

• Ensure data accuracy, quality, and integrity through governance frameworks. 

• Comply with Thailand’s Personal Data Protection Act (PDPA) to protect customer privacy. 

2. Analytics: Extract Value from Data 

 Advanced analytics transforms raw data into actionable insights that drive revenue and efficiency. 

• Key Actions:  

• Use predictive analytics to anticipate customer needs and risks. 

• Enable real-time data processing for fraud detection and customer engagement. 

• Using Agentic AI to develop automated financial advisory models based on marketing data and customer behavior to create hyper-personalized services. 

3. Monetization Models: Choose the Right Approach 

Banks must identify monetization strategies aligned with their objectives. 

• Direct Monetization: 

• Sell anonymized insights to ecosystem partners like insurers, retailers, or government agencies. 

• Example: A European bank generated over $12 million annually by providing subscription-based data analytics (Statista, 2024). 

• Indirect Monetization: 

• Use data to enhance internal processes, improve customer retention, and reduce costs. 

4. Execution: Implement Scalable Use Cases 

Deploy practical, high-impact data monetization use cases with measurable ROI. 

• Use Case Examples:  

• Fraud Detection: AI-powered tools reduce fraud-related losses by up to 40% (Forrester, 2023). 

• Personalized Products: Tailor savings plans or loans to individual customer profiles. 

• Operational Efficiency: Automate regulatory compliance and streamline approvals. 

5. Continuous Optimization: Monitor and Scale 

Ongoing performance measurement and scalability are critical for long-term success. 

• Key Actions:  

• Monitor KPIs such as revenue growth, cost savings, and customer retention. 

• Adapt to regulatory changes and market shifts. 

• Scale proven use cases across departments or regions. 

The Impact of Data Monetization on Financial Services 

1. Fraud Prevention and Detection 

Fraudulent activities remain one of the biggest risks for financial institutions. Advanced AI and machine learning systems analyze large datasets in real-time to identify unusual patterns and flag potentially fraudulent transactions. 

• How it works: AI models compare transactional data against historical trends to detect deviations, such as rapid purchases across multiple locations or inconsistencies in account activity. 

• Impact: According to Forrester Research (2023), AI-enabled systems have reduced fraud losses by up to 40%, minimizing false positives while improving operational efficiency. 

• Example: A European bank implemented AI-driven fraud monitoring tools, reducing fraud incidents by 35% within the first year and lowering operational costs by 25% through automation of manual fraud review processes. 

2. Hyper-Personalized Financial Products 

Personalization is no longer a “nice-to-have” but a critical differentiator in banking. Hyper-personalization leverages behavioral data, spending habits, and life-stage indicators to create tailored financial products. 

• How it works: Banks use AI-powered customer segmentation to analyze individual spending behaviors, credit histories, and savings patterns to recommend or customize products like loans, investment portfolios, or credit cards. 

• Impact: Statista (2024) reports that tailored financial products increase customer retention by 30%, as customers feel more aligned with their financial institution’s offerings. 

• Example: A Southeast Asian bank launched personalized credit card offers based on spending patterns, resulting in a 20% boost in cross-selling rates and a 15% increase in active card usage within six months. 

3. Embedded Finance in E-Commerce 

Embedded finance integrates banking services seamlessly into non-financial platforms like e-commerce marketplaces, ride-hailing apps, or travel booking websites. This approach eliminates friction in the customer journey and extends financial services beyond traditional banking channels. 

• How it works: APIs and AI enable banks to offer services like instant credit approvals, buy-now-pay-later (BNPL) options, and embedded payments during the e-commerce checkout process. 

• Impact: Embedded finance is projected to drive $230 billion in new revenue opportunities globally by 2028, according to Juniper Research (2023). 

• Example: A Thai fintech integrated embedded finance into a popular e-commerce app, allowing instant loan approvals for large purchases. This led to a 25% increase in transaction sizes on the platform and expanded the bank’s reach to younger, tech-savvy customers. 

4. Predictive Risk Analytics 

Predictive analytics helps financial institutions proactively manage risks such as credit defaults, market volatility, or regulatory breaches. By analyzing historical and real-time data, predictive models generate early warnings for potential risks. 

• How it works: AI models evaluate credit histories, market trends, and external factors (e.g., economic forecasts) to predict default probabilities or market downturns. 

• Impact: According to the World Bank (2024 Financial Stability Review), predictive analytics reduces credit losses by up to 25% and enables faster decision-making in volatile environments. 

• Example: A leading bank in Thailand used predictive models to identify customers at risk of defaulting on loans. By offering preemptive restructuring options, they reduced loan defaults by 15% and improved repayment rates by 20%. 

5. Strategic Partnerships with Ecosystem Players 

Data monetization extends beyond internal operations. By anonymizing and aggregating insights, banks can create valuable data products for ecosystem partners such as insurers, retailers, and even government agencies. 

• How it works: Insights derived from consumer spending patterns, credit trends, or regional economic activity are packaged as products for subscription-based analytics platforms. 

• Impact: These partnerships not only generate new revenue streams but also strengthen relationships with key stakeholders. For example, insurers can use bank insights to fine-tune their premium pricing models. 

• Example: In 2023, JPMorgan Chase partnered with Airbnb to analyze anonymized transaction data, helping Airbnb refine its pricing algorithms and identify new market opportunities. By leveraging spending patterns and regional economic trends, Airbnb improved its dynamic pricing model, leading to a 5% increase in booking revenue. Meanwhile, JPMorgan expanded its data-driven services, strengthening its role as a financial insights provider. (JPMorgan Chase Data & Analytics Initiatives (2023) (Check JPMorgan Chase’s newsroom for related reports.)) 

Bluebik’s Expertise: Unlocking the Full Value of Data 

At Bluebik, we specialize in enabling financial institutions to unlock the full potential of their data with a strategic approach: 

✅ Strategic Planning & PMO: Align data initiatives with business goals through structured planning, governance, and execution excellence. 

✅ Unified Data Ecosystems: Break down silos and centralize data for actionable insights. 

✅ AI-Powered Analytics: From fraud detection to customer personalization, we implement advanced analytics to drive growth. 

✅ Compliance-First Strategies: Ensure adherence to regulatory frameworks, including PDPA and other local requirements. 

✅ End-to-End Monetization Solutions: Develop scalable models for direct and indirect monetization tailored to your business objectives. 

Data monetization is no longer optional—it is a strategic imperative. 

📩 Contact us today to explore tailored strategies that drive measurable results for your organization. 

 ✉ hello@bluebik.com  

☎ 02-636-7011 

The post From Insight to Impact: How Data Monetization Reshapes the Financial Sector  appeared first on Bluebik.

In Brief 

• Data Monetization Trends on the Rise: Five game-changing trends that will revolutionize how businesses create value from data in 2025 and onward 

• Data Monetization Challenges & Solutions: Address pivotal challenges in privacy, technology, and ethics, and discover strategies that differentiate industry leaders from their competitors. 

• Optimized Data Management: Leveraging emerging technologies, build powerful ecosystems, and establish standardized of governance to stay ahead 

• Successful Data Monetization Framework: Unlock data’s potential to address critical challenges and create new business opportunities for sustainable growth. 

The Evolution of Data as a Strategic Asset 

In today’s digital economy, data has evolved beyond an operational byproduct to become the cornerstone of organizational success. Leading enterprises now recognize data as a critical driver of innovation, competitive advantage, and sustainable growth. Through data monetization—the process of transforming raw data into actionable insights and tangible business value—organizations are pioneering new pathways for innovation that reshape industries and establish long-term market leadership. 

However, organizations face significant challenges in this journey. These include rapidly evolving privacy regulations, integration of complex technologies, ethical considerations, and the need to demonstrate clear return on investment (ROI). Addressing these challenges requires both technical expertise and a strategically aligned approach. 

5 Transformative Data Monetization Trends for 2025 and Beyound

1. Cross-Industry Data Ecosystems 

Opportunity: Organizations are building collaborative data ecosystems that enable secure, mutually beneficial data sharing across industry boundaries. 

Case Study: JPMorgan Chase partnered with Amazon to create a co-branded credit card program, leveraging transaction data analysis to enhance customer targeting.  The collaboration resulted in over 5 million new card acquisitions within the first year and improved customer engagement by 45% 

(Source: JPMorgan Chase Annual Report 2023, SEC Filings Q4 2023). 

2. AI and Machine Learning as Strategic Tools 

Opportunity: By 2025, advanced AI/ML models will play a crucial role, such as Narrow AI, in analyzing and uncovering specialized insights within organizations in real time. This will enhance decision-making and problem-solving, making processes faster and more accurate. 

Case Study: Wells Fargo implemented an ML-based fraud detection system that reduced fraudulent transactions by 35% while processing over 8 billion transactions annually. The system leverages federated learning to maintain data privacy across multiple jurisdictions 

(Source: Wells Fargo Technology Innovation Report 2023, Federal Reserve Financial Technology Analysis 2024). 

3. Data-as-a-Service (DaaS) Innovation 

Opportunity: Organizations can create new revenue streams through subscription-based data access models, offering everything from anonymized datasets to API-based intelligence services. 

Case Study: Walmart’s Data Ventures program monetized anonymized consumer behavior data, enabling CPG companies optimize product placement and inventory management. This initiative generated $100M in additional revenue while improving supplier inventory efficiency by 30%  

(Source: Walmart Annual Report 2023, NYSE Market Analysis 2024). 

4. Advanced Data Governance and Privacy 

Opportunity: Emerging privacy legislation is driving innovation in Privacy-Enhancing Technologies (PETs), creating opportunities for privacy-first data monetization. 

Case Study: Allianz Insurance implemented homomorphic encryption and differential privacy techniques across their European operations, analyzing over 50 million customer records while maintaining GDPR compliance. This led to a 38% improvement in cross-selling effectiveness without compromising personal data 

(Source: Allianz Digital Transformation Report 2023, EU Digital Innovation Observatory 2024). 

5. Data Democratization and Self-Service Analytics 

Opportunity: Advanced self-service analytics platforms are transforming how organizations extract value from data across all levels. 

Case Study: Target Corporation deployed a self-service analytics platform enabling real-time decision-making across 1,900 stores. This resulted in a 40% reduction in analysis time and $300M in cost savings through optimized inventory management  

(Source: Target Digital Innovation Report 2023, NASDAQ Retail Technology Index 2024). 

Data Monetization Challenges 

1. Data Privacy and Security 

Challenge: Privacy concerns and data breaches are among the top challenges in Data Monetization processes. 

Impact: Data misuse and breaches can result in reputational damage, loss of customer trust, and legal penalties. 

Solutions: 

• Implementing Privacy-Enhancing Technologies (PETs), including anonymization, tokenization, and privacy-preserving encryption 

• Investing in AI-driven cybersecurity tools for threat detection and prevention 

• Zero-trust security architectures 

• Conducting regular security audits to identify and address system vulnerabilities 

2. Advanced AI and ML Integration 

Challenge: Integrating complex AI and ML technologies with legacy systems presents significant hurdles in Data Monetization. 

Impact: Poor integration affects operational efficiency and expected ROI. 

Solutions: 

• Cloud system adoption for real-time data scaling and analysis 

• Modular approach using microservices architecture for AI and ML integration 

• Employee upskilling for effective advanced analytics tool usage 

3. Data Quality and System Silos 

Challenge: Poor data quality and siloed systems pose significant obstacles to Data Monetization. 

Impact: Poor-quality data can negatively affect analysis and decision-making, leading to unreliable results. Additionally, having siloed data can create inconsistencies in data understanding. 

Solutions: 

• Implementing Centralized Data Hubs to eliminate siloed operations 

• Deploying real-time data verification tools 

• Establishing robust data governance frameworks 

• Creating a Business Glossary and Data Catalog helps ensure that data is clearly defined and consistently understood across the organization. 

4. Selecting Appropriate Data Monetization Models 

Challenge: Many organizations struggle with choosing suitable strategies and models for Data Monetization. 

Impact: Implementing unsuitable strategies can result in missed business opportunities, rising data management costs, and exposure to legal non-compliance. Failure to obtain proper customer consent can lead to significant regulatory penalties and fines. 

Solutions: 

• Evaluating data utilization strategies to improve internal operational efficiency, generate revenue, or share data with business partners.  

• Adopting a consent-based monetization approach, allowing customers to exercise control over their own data.  

• Adopting hybrid monetization models combining direct and indirect value creation 

• Utilizing customer feedback to refine value creation approaches 

• Conducting pilot projects before full-scale implementation 

5. Ethical Balance 

Challenge: Growing concerns about ethical data use, including AI bias and customer behavior manipulation. 

Impact: Reputational risks and potential legal challenges from AI/ML ethical issues. 

Solutions: 

• Implementing transparent policies about data collection, usage, and sharing 

• Applying Ethical AI and minimizing data bias by establishing systematic and regular bias assessments of AI and ML models. 

• Selecting models designed to protect personal data, such as Federated Learning and homomorphic encryption.  

• Establishing internal ethics committees for data usage oversight 

Key Strategies for Data Monetization in 2025 

Investment in Emerging Technologies: 

• Adopting PETs like Homomorphic Encryption for secure data sharing 

• Leveraging Agentic AI to optimize data monetization through insight generation and automated actions.  

Building Data Ecosystems: 

• Collaborating with external partners like Fintech or retail companies 

• Participating in data marketplaces to expand access and revenue streams 

Customer-Centric Focus: 

• Prioritizing tangible customer benefits like personalized services or cost reduction 

• Using insights to address customer needs and concerns 

Agile Data Strategy: 

• Regular strategy review and adjustment to match market trends and regulatory changes 

• Using iterative models for Data Monetization process improvement 

Developing Reliable Metrics: 

• Implementing KPIs to measure Data Monetization success, including revenue growth, customer acquisition and retention, and operational cost savings 

Unlocking Value Through Data Monetization: A Strategic Framework 

In today’s digital economy, data has emerged as a critical driver of enterprise value creation. Based on our extensive work with global organizations, we’ve identified eight fundamental steps that enable successful data monetization initiatives. This framework helps organizations transform raw data assets into sustainable revenue streams while maintaining competitive advantage. 

Strategic Implementation Framework 

1. Design Customer-Centric Value Propositions 

Success in data monetization demands a deep understanding of market dynamics and customer pain points. Organizations must systematically identify opportunities where data-driven solutions can address unmet needs and create measurable value. This requires continuous market analysis and customer engagement to ensure solutions remain relevant and impactful. 

2. Transform Organizational DNA 

Leading organizations recognize that effective data monetization requires fundamental changes to organizational mindset and operations. This transformation involves elevating data to a strategic asset class, implementing data-driven decision-making processes, and fostering cross-functional collaboration. Our research shows that companies with mature data cultures are 3x more likely to exceed their monetization objectives. 

3. Establish Robust Data Governance 

Data quality and governance form the cornerstone of successful monetization initiatives. Organizations must implement comprehensive frameworks that ensure data accuracy, reliability, and regulatory compliance. This includes establishing clear data ownership, maintaining rigorous quality standards, and adhering to evolving privacy regulations such as Personal Data Protection Act (PDPA).    

4. Deploy Advanced Analytics Capabilities 

The integration of artificial intelligence, machine learning, and predictive analytics creates unprecedented opportunities for value extraction from data assets. These technologies enable organizations to uncover hidden patterns, automate complex decisions, and generate actionable insights that drive competitive advantage. Our analysis indicates that organizations leveraging advanced analytics achieve 40% higher returns on their data monetization investments. 

5. Focus on High-Impact Use Cases 

Successful monetization strategies prioritize initiatives that deliver maximum business impact. Organizations should evaluate potential use cases based on revenue potential, operational efficiency gains, and strategic alignment. This focused approach ensures optimal resource allocation and accelerates time to value. 

6. Build Scalable Infrastructure 

Scalable infrastructure is essential for sustainable data monetization. Organizations must invest in modern, cloud-based platforms that can accommodate growing data volumes, enable real-time analytics, and adapt to evolving business requirements. This technological foundation supports rapid innovation and ensures long-term competitiveness. 

7. Develop Market-Ready Solutions 

Transforming data into commercially viable products requires a sophisticated product development approach. Organizations should focus on creating scalable solutions such as real-time analytics platforms, predictive modeling tools, and industry-specific datasets. Successful solutions typically undergo iterative refinement based on market feedback and evolving customer needs. 

8. Cultivate Strategic Ecosystems 

The most successful data monetization initiatives leverage strategic partnerships to amplify value creation. By participating in data ecosystems, organizations can access complementary capabilities, expand market reach, and accelerate innovation. Our experience shows that ecosystem-driven approaches can increase monetization potential by up to 2.5x compared to standalone initiatives. 

Looking Ahead 

As organizations continue to expand their data assets, effective monetization becomes increasingly critical for maintaining competitive advantage. This framework provides a structured approach for organizations to unlock the full potential of their data assets while managing associated risks and challenges. 

Success in data monetization requires sustained commitment, strategic alignment, and continuous innovation. Organizations that effectively execute across these eight dimensions position themselves to capture significant value in the evolving digital economy. 

Unlock the Full Potential of Your Data with Bluebik 

In a digital-first economy, your data isn’t just a resource—it’s your most powerful asset. Are you ready to transform it into a revenue-generating engine that drives innovation, efficiency, and customer value? 

Bluebik is here to help you unlock unparalleled growth through our comprehensive Data Monetization Framework. We specialize in turning your raw data into actionable insights and sustainable revenue streams, guiding your organization through the complexities of evolving regulations, cutting-edge technology, and strategic execution. 

Why Partner with Bluebik? 

✔ Strategic Expertise: Align your data strategy with business goals to create powerful impacts. 

✔ Secure Data Ecosystems: Build compliant, privacy-first data platforms that support future growth. 

✔ Actionable Insights: Leverage advanced analytics to drive smarter decisions and better customer experiences. 

✔ Custom Solutions: Tailored strategies to uncover new business opportunities and address unique challenges. 

✔ Proven Results: With our trusted framework, we empower organizations to achieve measurable success in data monetization. 

Let’s transform your data into your biggest advantage. 

➡️ Contact Bluebik today to get started 

The post Data Monetization: Unlocking Value in the Digital Ecosystem  appeared first on Bluebik.

This article introduces Data Monetization, a strategy for turning data into business value from various perspectives, covering definitions, forms, foundational components, and real-world business applications. 

What is Data Monetization? 

Data Monetization involves using data to create long-term business value and generate revenue. Organizations can analyze available data to uncover deep insights, patterns, and trends that drive value creation. Generally, Data Monetization is categorized into two types: 

1. Direct Monetization – Generating revenue directly from data. 

2. Indirect Monetization – Leveraging data to develop new products and services. 

Why is Data Monetization Important? 

• Generates new revenue streams for businesses. 

• Transforms available data into valuable business insights. 

• Enhances customer experience through personalized interactions. 

• Improves operational efficiency and reduces costs through data-driven processes. 

Forms of Data Monetization 

• Internal Data Monetization: 

Utilizing data within the organization to improve decision-making and business processes, such as: 

• Enhancing operational efficiency through data-driven decisions. 

• Personalizing products and services to improve customer experience. 

• Identifying trends and opportunities for innovation and growth. 

• Optimizing supply chains to improve performance and reduce expenses. 

• External Data Monetization: 

Generating new revenue by offering data-related services, such as: 

• Collaborating with external partners to share deep business insights. 

• Offering data-driven products like market research services. 

• Providing advanced analytics solutions to other businesses. 

Foundational Components of Data Monetization 

• Data Strategy: 

A well-aligned data strategy should support business goals by transforming available data into deep insights, creating new business opportunities, ensuring ROI, and delivering practical use cases. 

• Data Quality and Governance: 

Robust data governance ensures data accuracy, completeness, and relevance while complying with regulatory requirements. High-quality data is essential for analysis and usage across various business processes. 

• Technology Enablement: 

Technology plays a key role in enhancing Data Monetization, including AI/ML for deep data analysis, improved data security, and APIs for seamless data integration and usage. 

• Data-driven Culture: 

Establishing a data-driven culture involves building relevant skills, fostering cross-departmental data sharing, and promoting collaboration for data-driven decision-making. 

Examples of Data Monetization in Business 

• Financial Services: 

Financial businesses can leverage large datasets from customer transactions, market trends, and risk assessments to generate both direct and indirect revenue. 

Example: Utilizing transaction data to develop credit scoring models, offering loans and financial products, or collaborating with partners for joint promotions. 

• Retail: 

Retailers can monetize customer behavior and purchase history data to analyze deep insights for marketing campaigns, product pricing, or inventory management. 

Example: Analyzing customer purchase behavior to provide personalized offers or generating revenue from anonymous data by sharing consumer trends with brands and suppliers. 

• Healthcare: 

Healthcare providers can monetize patient data, treatment histories, and service usage to enhance tailored care. 

Example: Using wearable device data to design personalized healthcare packages and promote age-specific health checkup campaigns. 

• Energy: 

Energy businesses can utilize in-depth data analysis to improve internal operations or develop new products and services. 

Example: Using energy consumption data to develop energy-saving solutions like smart home devices or technologies optimizing energy efficiency. 

Unlocking Business Potential with Data Monetization 

Data Monetization unlocks a new level of business potential, turning existing data into innovations that create added value. Bluebik believes that organizations integrating data and technology rapidly will gain competitive advantages and continuous growth opportunities. Given the complexity of technology, seeking external expert consultations can be crucial for successful and ongoing transformation. 

Businesses aiming to develop data and AI strategies to enhance competitiveness and drive growth can rely on Bluebik’s team of experts in Big Data & Advanced Analytics for comprehensive solutions, from strategy development to tailored implementation. 

Contact us at: 

✉ hello@bluebik.com | ☎ 02-636-7011 

Reference polestarllp, gooddata, stibosystems, secoda

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A key to the effective implementation of digital-first operating models is to enable businesses to make data-driven decisions on both internal and external activities. This can happen if data are centralized, have quality and reliability and are not duplicated. This is the reason why consolidated data platforms must be developed.

Without a consolidated data platform, organizations surely have problems

Will there be any problem if an organization wants to make data-driven decisions but it does not have a consolidated data platform? Organizations have used a wide range of software. This results in each organization having various kinds of software and numerous ways of data storage. Organizations have many types of databases and considerable formats of data and use many technologies to handle data. There are also proprietary data which may be stored in personal computers as well as the data that are hard copies and have not been stored in any information technology system.

The above-mentioned factors prevent companies from quickly using their data because their data are not centralized. In addition, without a proper data storage system, organizations may have problems about the quality of data because they cannot verify their data. The organizations that cannot use data to produce insights to support their business decisions may miss growth opportunities and their business can suffer lasting damage.

Consolidated data platforms turn data into insights

Consolidated data platforms are crucial to digital business. Consolidated data platforms can gather data from sources including customers’ data, financial data, sales data, marketing data, production data and supply chain data which are related to the activities of business organizations. Consolidated data platforms can centralize such data, verify them, properly sort data, protect data as required by laws and identify persons in charge of data. Consolidated data platforms are user-friendly, provide executives and decision-makers with comprehensive and clear data related to their business and let them see business trends quickly. Consequently, business organizations can better serve their customers.

10 steps to build consolidated data platforms

Consolidated data platforms are created to enable businesses to use data to produce the insights which facilitate strategic decisions, improve efficiency and develop competitive edge. There may be various ways to build consolidated data platforms, depending on the types of business, the sizes of organizations and requirements. However, Bluebik sees 10 basic steps to develop effective consolidated data platforms.

1. Situation assessment

Before creating consolidated data platforms, business organizations should examine their own data infrastructures, data quality, data-related knowledge and expertise and the data analytical processes of their staff. The examination will show the loopholes and new business opportunities that consolidated data platforms can address to improve efficiency.

2. Clear strategies and visions

Important factors in the creation of consolidated data platforms to support decisions are clear strategies and visions. These refer to business goals, measureable objectives and realistic development roadmaps. This will justify support and encouragement from executives and key stakeholders.

3. Data governance

Strong frameworks for data governance maximize organizations’ efficiency in data management. They cover data collection, data storage, data use and analyses, data supervision, data quality and data security. Organizations should have their data governance working groups responsible for data management.

4. Investment in carefully chosen technologies

It is important to choose and invest in the technologies that suit organizations. This concerns infrastructures, tools and platforms for the collection, processing, storage and analyses of data. They include databases, data lakes, business intelligence (BI) tools and advanced data analytic instruments like machine learning (ML) and artificial intelligence (AI).

5. The improvement of data quality and relevance

Organizations should have processes and standards for the improvement of data quality which covers accuracy, reliability and relevance so that their data can be analyzed and turned into correct and practical in-depth data.

6. The development of data-driven organizational culture

To be able to use data to support strategic decisions, organizations must have, among others, data-driven culture. This means that staff members base their decisions on data, share data across departments and have data literacy. Organizations must have data-related training sessions and workshops for their staff and include data analyses in the regular work processes of staff.

7. The formation of data excellence teams in organizations

Organizations should form special teams to handle data-related work. Such teams comprise data analysts, data scientists and specialists who can study in-depth data, develop data analysis models and create return on investment in data. Such data excellence teams may collaborate with departments to address challenges facing businesses.

8. The continuous promotion of data-driven initiatives

Though having their own consolidated data platforms, data-driven organizations should never be complacent. They should always encourage new data-related initiatives to promote learning and experiment. Organizations should give advice to further improve processes in accordance with their business objectives and technological capabilities.

9. Follow-up and evaluation

For the development of consolidated data platforms, organizations should have key performance indicators (KPIs), apply them regularly to verify progress and use findings to develop strategies and further improve work processes.

10. Communicating success and sharing insights

When organizations share the concrete outcomes and success of their data-related projects, they prove to their staff clearly that data-related initiatives and investment are valuable and bring about success. By this means, their data-driven organizational culture will be strong for good.

These 10 steps are basic ways to build consolidated data platforms and turn businesses into data-driven organizations. As a leading consultancy on end-to-end digital transformation, Bluebik is of the view that many challenges may arise in the process of building of consolidated data platforms. They include failure to hire the professionals who can design and install digital systems, the unsuccessful transformation and improvement of existing systems at organizations, the poor management of IT and business teams that have different experiences, and the lack of understanding about business trends and operations which is necessary to create the use cases that suit business objectives. Consultancies with relevant expertise can ensure the fast and truly successful transformation of business organizations to data-driven ones.

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AI and ML are the technologies that many business organizations want to use to enhance their competitive edge. In fact, the use of AI and data analytics for advanced data analyses is a latter stage in data use. Most organizations focus on this part and overlook the important stage of having data architecture to possess strong bases and valuable data. If unverified or substandard data are processed or analyzed with AI, results may be inaccurate or even wrong and lead to the business decisions that may cause unexpected loss or damage in the future.          

Bluebik would like to show how data architecture can give the quality data that will unlock AI potential and turn businesses into data-driven organizations as expected.

What is data architecture?

Data architecture is like a blueprint for the direction, data quality, and other factors that will guarantee efficient and organized data management. It also makes data use agile and flexible in the way that business requirements will be fulfilled accurately and precisely. The important elements of data architecture are as follows.

1. Data sources – Organizations have various sources of data and their data are in many forms including structured data, semi-structured data and unstructured data such as data from databases, data from applications and pictures.

2. Data modeling – It refers to the design of data models to satisfy business requirements.

3. Data integration – It concerns channeling raw data from their sources to data lakes and data warehouses. 

4. Data pipeline and data flow – This is to determine the natures of input – batches or real-time input, regulate input and transform input into the designed data models.

5. Data storage – Data can be stored in the data lakes that are the sources of raw data, the data warehouses which keep the data that passed a data cleansing process and are readied for analyses or the data marts which gather data for presentations, reports and executives’ dashboards.

6. Data serving – It refers to the channels that offer ready-to-use data to organizations. The channels can be databases and application programming interface (API), among others.

The development of data architecture is all for the “data of truth” 

1. Business understanding – It will lead to the data modeling which creates the attributes of data for data categorization. It will also identify the data that meet the demand of organizations.

2. Data storage standard – It facilitates data use and data integration and supports future systems.

3. Methodology for data storage and operation – It sets the data sources that will be the cores of data models and determines the flow of data to be processed. There must be a clear picture of this process and the clear components of data management consisting of data storage places, the technologies that are applied and the sources of data to be processed.

4. Data connection and exchange – Technological application is designed to support the connection and exchange of data among many data sources which may require different methods of connection. Some applications may require data connection through API and other applications can connect directly to databases.

5. Data security – Authority to access data is set with data classification. There are public data, the internal data of individual business units and exclusive data for executives. The design of data access authority is aimed at offering basic data security to organizations.

6. Data categorization – There are master data and reference data. With data categorization, data can be acquired more accurately and precisely. For example, without reference data, a company that needs the exchange rates that are always changing may have different rates among business units because the rates come from different sources.

7. Data cleansing – It guarantees the quality of data. However, data cleansing cannot be done for all attributes of data. Therefore, data architects together with users must set critical data elements (CDE) to specify the data that are important and need absolute accuracy for the sake of advanced analyses.

8. Design of data acquisition for analyses – This is another important step towards the creation of data architecture. The acquisition of stored data must be flexible and ensure that subsequent data analyses will be convenient and smooth.

Professionally designed data architecture means organizations’ advantage and reduced risks

Good data architecture is not just a framework of the best practices for organizations. Its design requires understanding of both business and technology. The vendors that mainly have technology expertise may design the data models that serve the demand of industries. However, data modeling has a frequent problem. Some necessary attributes do not exist in common models. This results from the lack of knowledge and understanding of business. Therefore, the design of the architecture that truly supports data analyses, reduces human errors and cuts operating costs and workers’ hours need the experienced designers who have thorough business knowledge and technological expertise to eliminate risks.

It is now undeniable that data are the heart of business. Previously used instinct and experience may compromise the growth of organizations in the future especially when competitors become data-driven organizations. The first organizations that can handle data professionally with data architecture have their competitive edge in the era of accelerating change.

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Today worldwide businesses attach importance to and seriously invest in data storage and analysis to remove limitations and increase growth potential for the sake of profit. Data-driven marketing is a marketing tool that helps present the products and services that impressively meet the demand of customers.

Data-driven marketing needs the processes of data storage, analysis and synthesis to understand thoroughly the needs of customers in all dimensions. To guarantee successful data-driven marketing, there are four things that marketers need to know and understand.

1. Business Objectives

Businesses or marketeers should have clear objectives for their use of analyzed data, such as a objective to expand a customer base to cover 1 million buyers in three years. A clear objective is the first and foremost success factor in data-driven marketing. With clear objectives, businesses can determine the scopes of their data analysis that will ensure the achievement of their objectives.

After there is a clear objective, the next step is the use case generation which varies according to the types and contexts of business and has to suit respective business objectives. Examples of use cases are as follows.

• For an objective to enhance efficiency in business operations – Customers will be stimulated to consistently buy products and services to maintain revenue. For example, cross-selling can increase revenue compared with the purchase of a single product. The products or services that exactly interest individual customers can be introduced in a timely manner (personalized promotions/products).
• For an objective to improve customers’ experience – Customers will gain good experience and be willing to consistently buy products and services. For example, customer segmentation and the calculation of customer lifetime value can be applied to offer privileges to the groups of customers who generate high income for companies.

After initial use case generation, the next question is which use cases will help businesses complete their objectives. There must be use case prioritization based on these two factors:

• business impacts such as financial outcomes, new business opportunities, return on investment (ROI) and compatibility with the overall strategies of organizations; and
• the feasibility of, for example, data, systems, work processes and external factors such as the Personal Data Protection Act (PDPA), laws and regulations.

2. Data Readiness

The most important element of data-driven marketing is, of course, data. Therefore, businesses must evaluate their data to find out the quality of the data and check if there are enough data for analysis and what kinds of data they have.

Today there are more kinds of data than those in the past. Data can be roughly divided into three kinds:

1. unstructured data including picture, video and audio files
2. semi-structured data such as files of the XML (extensible markup language) format
3. structured data such as tables of data in databases.

The design of use cases based on data can really create competitive edge for businesses. Therefore, business organizations should consider how their data were collected, what were the sources of their data, whether there are any missing part and how the data should be used. For example, there can be gamification campaigns to gather data about customers’ behaviors and also develop interaction with customers. Customer relationship management (CRM) can be used to help efficiently collect data about customers. Besides, centralized data lakes or data warehouses can be applied to obtain data from various sources.  

3. The design and development of data analysis models (Design & Development)

Before designing a data analysis model, businesses should first realize what they want to know and why they want the knowledge so that they can choose a suitable data analysis model. There are four basic data analysis models:

1. Descriptive analytics – the analysis of what happened in the past including sales and the behaviors of the customers who used to buy products;
2. Diagnostic analytics – investigation into factors behind what happened through correlation analysis to see, for example, reasons behind increasing sales, which can be promotional campaigns or not;  
3. Predictive analytics – predictions of what are likely to happen in the future based on the analysis of data from the past; and
4. Prescriptive analytics – analysis to find what should be done in the future.

Businesses should create data analysis models after having clear business objectives, choosing use cases and readying essential data. Data analysis models can be created by the following stages:

• creating features for data analysis processes (Feature Engineering)
• inputting created features to the Model Training stage to find the patterns of data that give insights which can be further developed and applied for business purposes
• evaluating models through tests including Confusion Matrix, F1 Score and AUC – ROC(Model Evaluation)
• adjusting parameters to increase the efficiency of models (Model Optimization)

4. Turning insights into execution  

This is another important stage which follows the reception of desirable data from an analysis process. This concerns how insights can be turned into tangible business outcomes.

Before launching a marketing campaign, businesses should draft its outline which describes what is its target group of customers, what will be presented to customers and how businesses can contact the group of customers. Then acquired insights are used to design a marketing campaign. Here are the basic tips that can be applied.

• Attracting target groups of customers – The spending behaviors of each group of customers which are acquired through customer segmentation and the customer lifetime value (CLV) can be used to determine which groups of customers our promotional campaigns should target. For example, customers with high CLV should regularly receive promotional campaign news and discounts or giveaways may be offered to customers with less CLV to encourage them to buy more.
• Searching for particular products and services worth presentation – Data are used to develop the personalized promotions that serve the demand of individual customers. For example, discount coupons on baby products are offered to pregnant women.
• Using the communication channels that reach the most customers – Today customers have numerous purchase channels. Businesses can collect data from these channels and find which channels are mostly used by customers. Then businesses know which channels will guarantee the best impacts of their content and campaigns and can thus direct their marketing campaigns through the channels.

In sum, successful data-driven marketing must begin with business objectives, followed by strategy formulation, data evaluation, the design and development of analysis models and the translation of insights into business outcomes and then data will help improve marketing effectiveness and create long-term growth for businesses.

The post 4 Things Business Need to Know for Successful Data-Driven Marketing appeared first on Bluebik.

Business competition was driven mainly by data which were used in the design of new products to follow trends and generate more revenue and were analyzed to cut unnecessary operating costs. However, many organizations cannot achieve their goals because of their poor data. Their data may be incomplete, duplicate or irrelevant to the products they wanted to study. Consequently, business units are unable to really use the data and have to bear the unjustified costs of storing and maintaining the unnecessary data which may affect confidence in their business in the future.

The root cause of the problem is substandard data management. So, data governance must take priority at organizations to establish a standard of data management and supervision because it increases potential for the efficient use of data and supports the formulation and implementation of policies concerning intra-organizational data, personnel management and processes to reduce risks. If organizations effectively manage data and fully use them, they will have competitive edge

Data governance is aimed at clearly setting the rights, duties and responsibilities of stakeholders in the data of organizations. It varies to suit the contexts of individual businesses because different organizations have different sources of data and the types of their data and units responsible for their data are also different. Bluebik has offered consulting services to many large-scaled businesses. They include the organizations that already have standard systems to handle data but still need advice to improve their systems and the organizations that never have a standard data system. Data governance covers three areas – data policy, data governance team and process. 

1. Data policy

It is the first step which concerns basic rules and metrics that are essential for data storage. Data policies must comply with relevant laws and regulations. They fall into the following categories:

• Data standardization – This is to set a standard for data storage which includes the naming, formats and lengths of data to guarantee the highest efficiency in data use. 
• Data quality – Organizations may need data of different qualities for different situations. The qualities of data can be categorized by their precision, completeness and times needed for their acquisition.   
• Security policy – This concerns authority to access different levels of data, the disclosure of data to external organizations, privacy protection and data management processes.
• Compliance with laws and regulations including the Personal Data Protection Act (PDPA). There must be clear guidelines for data creation, correction and deletion. 

2. Data governance team

After designing data management policies, organizations should form specific teams to handle data. The following teams will have their own duties.

• Data governance council – It is tasked mainly with determining policies on data management and solving relevant problems. In general, the council includes the chief executive officer, the chief information officer and the information department head of an organization. 
• Data steward team – It may consist of the information service heads of business units, the information technology department and the information department. The team is duty-bound to advise on the definitions and standards of data and set the quality standards of data.
• Data stakeholder – This concerns stakeholders in data, namely data owners, data management teams, data users and data creators. Apart from creating and using data, a stakeholders’ team is also responsible directly for maintaining data and managing data systems.\

3. Process

This integrates all elements to make sure that system development and data management comply with policies and standards. The process can begin with the “data architecture” which covers the whole data structure and then proceed with data modeling, meta data, data security and the installation of data warehouse and data lake facilities to store data at one place where they can be acquired for analyses.

Data governance also helps unlock business potential in various areas. For example, it raises income opportunities for businesses. With advanced analytics, standard data increase precision in business decisions and work efficiency. Thanks to data analyses, project implementation can be accelerated. Organizations can be quickly aware of problems, solve them right away and also prevent risks.

In the era when competitiveness depends on data, good standards of data management certainly create competitive edge for businesses. For example, a world-renowned fast food restaurant chain used properly stored data for its customer services and efforts to woo customers with the promotional campaigns and privileges that better impress target groups of customers. As a result, its sales soared by 35%. The Economic Intelligence Center of Siam Commercial Bank reported that in 2017, 56% of leading Thai companies in many industries used big data to develop sale and marketing processes and improve products and services. Their use of data is likely to grow by 20-25% annually and within 2022, the marketing value of big data can reach 13 billion baht. Moreover, setting data management standards is crucial to organizations’ transformation to increase potential and create new growth opportunities in the future

For more information about Bluebik, please visit www.bluebik.com. The company also posts its news on its Facebook page, Bluebik Group, and its LinkedIn account, Bluebik Group

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There are 5 steps to make the most use of data.

1. Data analytics and use case generation

Firstly, we must develop a mindset so that all departments will understand the importance of data and their efficient use for business gains. For example, findings from data analytics can be used to increase the revenue of businesses, cut operating costs and improve sales methods to raise sales.

To see how an organization can use its data, we must understand the operations of all its departments, its products, its services and its communications with customers and study the data that are generated in each business process. Then we will analyze the data and develop them into data analytics use cases to find opportunities and gaps for development to reach business goals. With the use of data, organizations can see the paths of their development clearly and can use data to efficiently support their business strategies.

2. Gap analysis

After business organizations see and understand the goals of their data analytics, they will find the gaps that get in the way of their system development. They may have to look at their foundations which are data infrastructures so see if the foundations are ready to support data analytics. For example, a common problem of most organizations concerns data quality. Data may be stored in the formats that obstruct analyses. After problems are analyzed, the discovered gaps must be considered to develop the action plans that will truly enable organizations to make the maximum use of data and develop use cases as planned.

3. Data foundations

After we are ready in terms of data and the use cases that meet business goals, we must have a unit that will prepare data use from the stage of planning to the action plans that will efficiently support the development of use cases and data analytics. Data foundations comprise these three main parts.

• People – They must be readied. This begins with a structure of data supervision which includes data administrators, data service teams and stakeholders. The readiness ensures the efficient and reliable management of data.
• Process – This concerns the initiation of a data management process which covers the planning of data use, data storage, backup data, data restoration, archives, data migration and data destruction. The process ensures that data will be always ready for use and accurate.
• Technology – Organizations must have their data platform or data lake to store central data, reduce duplications and disorganized data and create compatibility with various instruments for data analytics such as the data warehouses, which support the compilation of understandable reports (visualized dashboards), machine learning and real-time data analytics.

4. Roadmap

After organizations are ready in terms of strategies, business goals and the three foundations, they must work out action plans to efficiently achieve business goals. Tasks must be prioritized. Normally a priority matrix is applied with two axes of impacts and readiness and their high, medium and low levels. When tasks are put on such matrixes, business organizations can conveniently and efficiently draw their action roadmaps which cover short and long-term tasks.

5. Data science implementation

The ultimate goal is to translate data into real business opportunities. To find patterns and achieve business goals, data are processed with analytic models which include algorithms such as classification, regression and clustering according to business purposes set in the first step. Outcomes from data analytics must be useful for business operations. For example, outcomes from product recommendation engines can be fed into a marketing system which will present better personalized promotions to prospective customers.

The future use of data will reveal the behavior of things

Many business organizations have realized that data are precious assets and can be used to create opportunities and prevent mistakes. Large-scale organizations worldwide begin to adapt and find new business opportunities by using “the data that display the behaviors of consumers”. Obviously electronic devices are collecting consumers’ data. They include highly developed wearable devices like smartwatches and smart glasses. There are also the applications with which consumers willingly share the information of their everyday life. They include mobile banking applications and the applications that require facial recognition before transactions. Besides, IoT devices are providing organizations and businesses with new kinds of data about consumers. The organizations that are planning to use data or installing data application systems should update their plans and systems to follow trends.

Product development may target not only customer experience but also total experience

Another significance of data analytics, apart from customer experience, is that it supports the creation of good experience for all parties, from staff to customers (total experience). This greatly strengthens business because business concerns not only consumers. The creation of positive experience from within will result in the valuable experience of end users. This can lead to the sustainable growth of revenue and profit.

The attractiveness of data consulting

Apart from learning new technologies and interesting behaviors, data consultants also gain experience in business. This is because the good data analytics that will make business grow efficiently should begin with business goals. This work suits the people who are enthusiastic about technologies and good at noticing and have creativity in the analyses of new formats of data. These qualifications mean considerable work of quality.

What skills and mindsets does data consulting require?

Work with data and analyses may require basic knowledge about the use of instruments and data analyses (technical skills), ability to interpret data and see the causes and effects of behaviors (logical thinking), the  business understanding which leads to the understanding of data and credible presentations, the mindsets that help build strong teams (leadership), the open-mindedness which is necessary for collaboration with experts from various fields and the empathy that supports understanding, concerted efforts to solve problems, happiness at work and progress.

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Digitizing Work Processes for Starters

Banking business is considered to involve a complicated work process with large documentation. When entering the digital world, not only do we have to turn paperless, but we also need to adjust our work processes such that everything runs smoothly, and can be managed automatically within a short period of time with the minimal number of procedures required as everything goes online.  One example about a first step banks took in digitizing operations was the implementation of Internet Banking. Many applications have been developed to enable the customers to do transactions on their smartphones conveniently, as can be seen today. Digitizing not only helps reduce the complicated process, it also provides Data in the digital form that can be further used or applied more easily.

Once Consumers Have Fully Stepped into the Digital World

One of the technological developments implemented by banks in the earlier period was the electronic payment system called PromptPay, by which the transferor can transfer money to the recipient’s bank account number tied to their telephone number, helping reduce the difficulty in remembering the recipient’s bank account number. Nowadays, a growing number of PromptPay users can be seen and people are less worried about doing transactions via the digital system. Over the past 10 years, various FinTech firms have offered new technologies to the financial industry with the clear trend of increased users, such as e-wallet, cryptocurrency, or AI-based credit lending, which is based on the analysis of customers’ personal behaviors (personalization).    At this moment, banks can no longer give an excuse for not adjusting themselves, although there are some customers who still want to use services at physical bank branches. Any bank that still bases its success on this old feature and cannot develop a new system or feature or devise a new strategy in response to the customers’ changing behaviors is likely to stumble or be forced to lose its market share to other FinTech firms in the end.

Tech Company, A New Player to be Watched

Another challenge facing the banking industry is the need to pay attention to the big players or experts in using data and technologies like Tech Companies. Currently, many tech companies are becoming players in the financial industry in the category of Non-Bank. For example, Apple has developed a payment solution called Apple Pay or a credit card called Apple Card which has gained attention from numerous users. Another example is the debut of Ant Financial by Alibaba to expand footprints in the financial industry, allowing the granting of credit facilities without the presence of loan officers by adopting Big Data and AI to run data and assess the risks of customers before granting loans. In the past, banks gained advantages from their large customer bases, but nowadays Tech Firms also have large pools of customers and are gaining advantages in terms of the ability to apply technologies faster. It is therefore not hard for those firms to steal market share from the banks. In the future, we will be likely to see a trend of a new, small-scale bank that starts its business on a full-scale digital banking platform.

How can banks adapt themselves?

The answer is that if banks want to survive this era, they must take serious actions and proactively adopt technologies and apply big data to achieve greater efficiency. This is because banks are organizations that store a large number of data of customers which can be used to create value added. Also, the banks must seek new sources of information that can add value to the business, since the use of traditional data such as transaction data may no longer be enough to create any difference to the organization. In addition, the banks must not ignore the platforms that cover the different lifestyles in order to collect data of the customers’ behaviors. There are 4 primary suggestions as follows:

1. Identifying business issues based on “pain points” derived from the traditional ways of work: Businesses may identify current business issues that can be solved by the application of Big Data, Machine Learning, and AI technologies to achieve greater efficiency or work solutions. For instance, AI can be used to analyze and predict trends of increases of interest rates based on communications from the central bank; this allows banks to plan or manage their interest rate policies in the most efficient manner. Another example is the use of AI together with Geo-Spatial Analytics for planning of locations of ATMs, and closures and mergers of branches in an efficient manner.

2.Identifying new ways to create value added: Businesses can increase existing income or generate new sources of income by way of targeted marketing; customer segmentation and selection of the commensurate products; development of new products from insights gained from data such as new types of credit cards, loans, or deposits that better respond to market needs; customer interactions through personalization; and data monetization through, for example, creation of data products from financial transactions in which customer behaviors have been analyzed.

3. Strengthening alliance: This is another key mechanism to be readied for an open banking platform. What the banks need to prepare are:

(1) APIs to connect to applications and functions inside and outside of the business and the maintenance of API security;

(2) Creation of an eco-system with FinTech or business allies to create new innovations that lead to mutual business opportunities through an API platform; and

(3) Joint creation of services or products between allies as a key factor for expanding the scope of services to cover a wider scale of customers. This helps collect more information and leads to the development of a business model towards an open banking platform for further connection with data or functions of other retail service providers in the future through Open API.

4. Taking into consideration Data Governance and other applicable laws: For example, data security, data privacy, and rights of data owners shall be taken into consideration to ensure compliance with the Personal Data Protection Act (PDPA). Also, the integrity and quality of data shall be maintained to ensure the benefits directly gained from the further analysis of the data.

Regardless of the size of a bank or no matter how large its market share or customer base, the coming technologies can always be disrupting the existing work systems or strategies. What matters most in the era of digital disruption is how businesses can make best use of available data so that they can adapt themselves to changes and best suit their customer needs. Don’t think that a giant cannot fall. The bigger they are, the harder they fall (if they do not adjust themselves.)

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