In an era defined by rapid technological advancements, businesses are more than ever grappling with enormous volumes of data and are striving to transform this raw data into actionable insights. Big Data Analytics (BDA) is a pivotal strategy for achieving this goal, providing businesses with unique insights to exploit new opportunities, differentiate themselves in the market, and neutralize competitive threats. Ignoring the implementation of BDA strategies can not only leave potential benefits unexplored but can also pose significant disadvantages, such as:

• Losing out on potential efficiencies;
• Falling behind the competition;
• Being unable to forecast and navigate future challenges effectively, and so on.

However, the long-term strategic benefits of BDA can only be realized through an understanding of the processes that enable BDA to add strategic value, while maintaining competitiveness within the industry. This article explores how businesses can use several frameworks to assess the strategic value of BDA and avoid potential pitfalls of inadequate BDA implementation.

The VRIO framework (valuable, rare, costly to imitate, organizationally embedded) can help assess BDA’s potential to create strategic business value. This framework prompts businesses to consider if their BDA strategies provide valuable insights, are unique, hard for competitors to copy, and are supported by the organization’s strategies and culture:

1. Valuable: Does the BDA enable your organization to obtain valuable insights to exploit new business opportunities and/or neutralize competition threat?
2. Rare: Are your big data content, analytics capability, or the combination of them, rare? Can a few of your competitors acquire or possess them?
3. Costly to imitate (imitable): Do your competitors without a BDA capability face challenges or obstacles in obtaining or developing it? Is it difficult or almost impossible for your competitors to imitate what you can do with BDA?
4. Organizationally embedded: Do your organization’s business strategies and culture support the exploitation of valuable, rare, and costly-to-imitate BDA resources?

Big data analytics can be valuable

The value of Big Data Analytics (BDA) lies in its ability to provide unique insights that can be used to seize new business opportunities or counter competitive threats. These insights can enhance various business areas, including process improvement, product innovation, customer experience, and organizational performance.

For instance, BDA can improve business processes’ efficiency and productivity. It can also help firms differentiate their products/services and adjust pricing based on insights from both internal data and external user-generated content. BDA can improve customer satisfaction and loyalty, secure customer and supplier relationships, and carve out niche markets. Lastly, insights about organizational performance from BDA can enhance decision making, adapt to market changes, optimize capacity utilization, and increase return on assets.

Big data analytics can be rare

BDA can be considered rare if few competitors can acquire or possess similar capabilities. This rarity can be evaluated in two ways:

1. Data Content: While user-generated content from social media is abundant and easy to collect, it’s crucial for firms to own their internally generated data from business interactions, which are proprietary and hard for other firms to acquire. Combining internally generated data with externally available data can make a big data asset rare.
2. Analytics Capability: While big data management and analytics tools can be bought, the knowledge, talent, and experience in using these tools can be rare and unique to a business. This expertise can only be developed through actual implementation and requires continuous assessment as it may soon become a competitive necessity when more competitors gain similar capabilities.

Big data analytics can be inimitable

BDA can be costly to imitate, meaning competitors might struggle to replicate, purchase, or substitute it at a reasonable cost. This is due to several factors:

1. Time Investment: Developing a big data asset and analytics capability takes a significant amount of time and involves a “learning by doing” process, which evolves from exploratory use to a more institutionalized form.
2. Proprietary Algorithms: Firms may develop their own analytics algorithms and methods, making their analytics capability unique and hard to imitate.
3. IT Maturity and Culture: The development and implementation of BDA are influenced by a firm’s IT maturity, decision-making culture, and IT leadership, making BDA costly to replicate.

Big data analytics can be organizationally embedded

The final consideration is whether BDA can be organizationally embedded. This is possible when BDA is aligned with the firm’s long-term business strategy, facilitated by processes, policies, procedures, organizational structure, and corporate culture.

While BDA as a valuable resource may not be enough on its own to sustain a competitive advantage, its absence can lead to a competitive disadvantage. Combining BDA with other organizational resources and capabilities can provide a new way to maintain a competitive edge.

Assessing BDA is challenging due to its unpredictable nature. However, firms can improve by learning from BDA implementation, leading to better decisions, innovative products, and automated processes. For BDA to generate strategic business value, the three components – data, insights, and actions – need to work together.

How Value Is Created from BDA: A Conceptual Framework

Creating strategic value with Big Data Analytics (BDA) requires investments in data assets, technological assets, and human talent. Strategic change, as described by Pettigrew and Whipp, involves three dimensions: content (what changes should be made), process (how changes should be made), and context (conditions under which changes can be made). These dimensions are integrated when describing how BDA creates strategic business value.

The understanding of this process is framed by two concepts:

1. Dynamic Capabilities: In turbulent environments, companies build and reconfigure internal and external resources to achieve superior performance.
2. IT-Value Models: These describe how IT investments build assets/resources and create impacts on both process and variance representations.

Adapting these models to BDA, a conceptual framework can proposed that integrates key constructs into two processes: capability building and capability realization. These processes describe how BDA can be used to create value.

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Building Big Data Analytics Capabilities

Turning IT investment in BDA into valuable capabilities is a dynamic process. It involves identifying where, how, and what value will be created. These capabilities include managing and analyzing data to generate new insights. Firms need to develop a BDA strategy and understand how it can create tangible value (like increased revenue or decreased cost) and/or intangible value (like increased customer satisfaction).

Establishing a Big Data Analytics Infrastructure

Investing in BDA differs from traditional investments in structured, static, and deliberately collected data due to the dynamic nature of big data, characterized by the five Vs (Volume, Velocity, Variety, Veracity, and Value). To develop BDA capabilities, a firm needs to invest in three key infrastructure elements: big data assets, an analytics portfolio, and human talent.

Big data infrastructure includes data sources (like transactional, clickstream, social media, user-generated, external databases) and a platform for collecting, integrating, sharing, processing, storing, and managing big data, such as AINSYS. While many firms develop these infrastructures in-house, there’s a growing trend towards using cloud computing services.

Firms need to ensure the data they have is sufficient and suitable for their business goals, or if additional data needs to be collected. The infrastructure should be capable of sharing data, collecting new types of data, or integrating new data sources. Firms also need to address potential security, privacy, regulation compliance, or liability issues, especially when third-party data and personnel are involved in data analytics.

Investing in a robust analytics infrastructure is crucial for analyzing prioritized data and generating novel insights. This involves decisions about data aggregation, transformation, distributed computing, tool selection, and analytics models.

Analytical applications can include consumer sentiment analysis, financial risk modeling, marketing campaign analysis, cross-selling, fraud detection, recommendation improvement, and price and performance optimization.

However, the most critical element to leverage investments in data and analytics is the human talent infrastructure. Expertise and experience are needed to design and implement BDA strategies. Without a skilled team of big data experts, it’s impossible to develop and execute a BDA strategy. This is one of the biggest challenges for firms. Big data professionals include data scientists, developers, programmers, analysts, and modelers who play significant roles in managing and analyzing data, especially unstructured data in diverse formats. The most intensive use of people occurs during the design of the BDA strategy and the interpretation of results.

Developing Big Data Analytics Capabilities

Firms need strong capabilities to integrate, manage, share, and analyze big data in diverse formats to support various value-creating needs. BDA has evolved from the early era of business intelligence 1.0, characterized by structured content and statistical analysis, to 2.0, characterized by unstructured online content and social media analytics, to the current 3.0 era, characterized by mobile and sensor-based content and context-relevant analysis.

Modern BDA handles big data that is more diverse, granular, real-time, and iterative. BDA capabilities need to economically generate value from large volumes of varied data, enabling high-speed capture, discovery, and analysis. BDA provides a forward-looking view, enabling firms to anticipate and act on future opportunities based on real-time insights.

BDA includes all three types of analytics:

1. Descriptive analysis that reports on the past.
2. Predictive analysis that develops models based on past data for future prediction.
3. Prescriptive analysis that uses models to specify optimal behaviors and actions, with an increasing emphasis on prescriptive analytics.

Key to developing BDA capabilities is the analytics portfolio, which includes text analytics, predictive analysis, audio analytics, video analytics, social media analytics, geographic analytics, streaming analytics, and graph analytics.

Realization of Big Data Analytics Capabilities

The sheer volume of big data is not what makes it significant. The real value lies in the ability to derive meaningful and valuable insights from it. Today, the focus is shifting from the volume, velocity, and variety of data to the value of data – the ability to generate actionable insights and apply them to business practices to spur innovation, optimize operations, and improve business performance.

When used correctly, BDA can reveal previously unknown, valuable insights that can help refine business processes, develop initiatives, identify service flaws or operational roadblocks, streamline supply chains, better understand customers, predict market trends, and develop new products, services, and business models. These value creation mechanisms are key elements of sustainable business models.

Value Creation Mechanisms

The key question is how BDA capabilities create value. These capabilities generate results that must be turned into actions to impact targets, such as decisions, customers, processes, etc. BDA can create value by making information transparent, collecting accurate performance data, enabling finer customer segmentation, and improving the development of smart products and services.

In the framework, six distinct mechanisms are proposed that mediate the link between BDA capabilities and value targets:

1. Transparency and Access: Generating descriptive data and disseminating it widely across a firm allows for consistency in viewing the data and facilitates a more complete visibility of the firm’s business processes and outcomes.
2. Discovery and Experimentation: Digging into data for deep and pragmatic insights can yield important outcomes for various BDA targets. In an increasingly digital world, big data can involve many small experiments, providing insights into causality that may have strong implications for various targets, such as customer services.
3. Prediction and Optimization: Predictive analytics can determine probabilistic outcomes for the future, guiding present-day action. Optimization, on the other hand, uses big data and powerful analytics to determine the best path forward.
4. Customization and Targeting: BDA can facilitate the customization of products and services, as well as targeting different market segments with digitally versioned products, enhancing customer retention and other customer-related outcomes.
5. Learning and Crowdsourcing: Machine learning has been applied to many different contexts, while crowdsourcing is being used for predictions and leveraging innovative talent.
6. Monitoring and Adaptation: The ability to monitor situations and adapt rapidly allows preemption of future problems. This is particularly prevalent with the explosion of data from the Internet of Things, which can be used to monitor, warn, and adjust for situational abnormalities.

Targets of Value Creation

It’s crucial to integrate the views and priorities of stakeholders when determining value targets. In the proposed framework, four distinct targets of BDA value creation are identified:

1. Organizational Performance: BDA can improve decision-making by providing broad and consistent access to data across an organization. For example, analyzing real-time performance data and just-in-time inventory status can significantly impact organizational performance.
2. Business Process Improvement: BDA can enhance the effectiveness, efficiency, and productivity of business processes, leading to better execution and less time spent on process breakdowns. Business process analyses, such as process mining, can benefit from the results of BDA, helping identify the strengths and weaknesses of a business process.
3. Product and Service Innovation: This target isn’t explicitly mentioned in this excerpt, but it typically involves using BDA to generate insights that can lead to the development of new products and services or improvements to existing ones.
4. Customer Experience and Market Enhancement: Also not explicitly mentioned in this excerpt, this target usually involves using BDA to improve customer satisfaction, retention, and customer-firm relationships.

To discover the power of comprehensive big data infrastructure, you will need the right platform for data management. With AINSYS, you will be able to tap into a wealth of information from diverse data sources including transactional records, clickstream data, social media interactions, user-generated content, and external databases. With AINSYS, you’re not just collecting data; you’re integrating and sharing it in a way that brings discernible value to your business.

Our platform equips you to process, store, and manage big data effectively, transforming raw data into actionable insights. With this robust infrastructure, you can unlock a new level of understanding about your customers’ behavior, market trends, and business performance.

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