How Can Organizations Develop Strong Big Data Analytics Capabilities?

In today's data-driven world, organizations are increasingly investing in Big Data Analytics (BDA) to gain a competitive edge. Let's look at the essential resources and capabilities organizations need, along with the common challenges they face in gaining value from BDA initiatives.

Key Resources for Big Data Analytics Capability

To successfully develop a BDA capability, organizations need to focus on three main types of resources:

Tangible Resources

• Technology: Investments in advanced technologies like Hadoop, NoSQL databases, and cloud services (e.g., AWS, Azure) are crucial for handling large volumes and diverse data.
• Data: Access to high-quality data from multiple sources is essential. This includes internal data, open data, purchased datasets, and historical data.
• Financial Resources: Significant financial investments are needed for technology infrastructure and skilled personnel.

Human Skills

• Technical Skills: Expertise in data architecture, engineering, and analytics is vital. This includes proficiency in managing data acquisition, storage, cleansing, and analysis.
• Managerial Skills: Competencies to interpret analytics results and apply them strategically are important but often underdeveloped initially.

To augment these skills, organizations can either upskill existing staff or recruit skilled talent.

Intangible Resources

• Organizational Learning: A culture of continuous learning is necessary to keep pace with evolving technologies.
• Data-driven Culture: Commitment from top management to make decisions based on data insights is critical for success.

Differences Between Dynamic vs. Ordinary Capabilities

To leverage BDA effectively, organizations need to invest in both dynamic and ordinary capabilities. Understanding the difference between them is essential, as BDA impacts and gets supported by both of them.

Ordinary Capabilities

These are operational capabilities that help organizations manage day-to-day activities efficiently. BDA can enhance these by improving decision-making accuracy and operational efficiency.

Dynamic Capabilities

Dynamic capabilities empower organizations to adapt and innovate in response to evolving environments. BDA enhances these capabilities by delivering insights that fuel strategic changes and foster innovation.

Common Challenges in Generating Business Value

Organizations frequently face numerous challenges when trying to extract business value from BDA initiatives. These challenges include:

Data Governance

Establishing robust data governance frameworks for data management is crucial to ensure quality and efficiency.

Skill Gaps

The scarcity of skilled professionals in data science and analytics presents a major obstacle.

Lag Effects

The advantages of BDA investments frequently take time to materialize, which can foster doubt regarding their worth. It is crucial for top leadership to maintain unwavering commitment and to enforce a data-driven decision-making approach within the organization.

Resistance to Change

Organizational inertia can impede the adoption of big data analytics (BDA) practices, often stemming from resistance from both upper management and IT personnel.

Conclusion

Developing a robust Big Data Analytics capability requires a strategic focus on acquiring the right resources, fostering a culture of learning and data-driven decision-making, and understanding the role of dynamic capabilities. Overcoming common challenges such as skill shortages and resistance to change is essential for realizing the full business value of BDA initiatives.

Note: This blog is based on

Mikalef et al. (2017). Big Data Analytics Capability: Antecedents and Business Value.