Can Your C-Suite Handle Big Data

Many companies have created C-level roles in response to changing business conditions in the last three decades. The position of CFO grew in importance in the mid-1980s due to the demand for more efficient asset management and increased communication with investors. The need for a CMO increased as new channels and media made developing a brand more challenging, while CSOs were brought on board to help navigate complex and ever-changing global markets. Now, with the impact of data and analytics on business operations, companies may once again require additional top-level assistance. This has led to the emergence of new executive roles such as the Chief Digital Information Officer, Chief Analytics Officer, and Chief Technology Officer[1]. These new roles are a testament to the growing recognition of information technology as a valuable asset that can drive strategic initiatives within organizations.

Organizations may consider establishing a new executive position to effectively implement their analytics agenda. It is becoming more evident that, in order to seize data-driven opportunities, companies will need senior executives who possess a deep understanding of the potential of data and can lead the necessary organizational changes. These emerging executive roles, such as Chief Digital Information Officer, Chief Analytics Officer, and Chief Technology Officer, require a unique combination of business acumen and expertise in information technology to successfully harness the power of data and analytics for steering strategic initiatives and facilitating digital transformation within the organization.[2][3][4]

Data-driven Decisions & Insights

Organizations must acknowledge the value of making data-driven decisions, which can offer deeper insights than traditional methods. This necessitates a cultural shift within the organization that involves embracing data-driven decision-making, being open to exploring new approaches, and having the courage to question existing assumptions. As part of their strategic planning process, organizations should develop a roadmap that clearly outlines goals, objectives, and processes for their data-driven transformation. This plan should take into consideration the necessary technology, resources, and talent needed while also identifying relevant sources of data from both internal and external channels. It is crucial for companies to have comprehensive and up-to-date analytics capabilities by effectively collecting and organizing this diverse range of data.[5]

In order to analyze data effectively, companies need to collect and organize it in a centralized repository or platform. This will allow them to gain valuable insights and develop models that can inform decision-making processes. Additionally, companies should use the insights generated from their data analysis efforts to guide the development, implementation, and optimization of data-driven strategies. Senior teams should equip themselves with knowledge about data analytics in order to understand its potential applications and then lead initiatives within different business units or functions aimed at identifying areas where data analytics can significantly improve performance. It is crucial for this exercise to be led by a senior executive who possesses the necessary influence and authority needed to motivate action.[2][6]

Why do Organisations fail to become Data-driven?

In order to fully harness the potential of data analytics, companies must allocate necessary resources including personnel, time, and funding. Without a clearly defined strategy and alignment on priorities, many businesses fail to capitalize on the benefits provided by data analytics. It is crucial for companies to carefully consider whether they should build their own infrastructure or rely on external vendors for core data, models, and tools. Additionally, establishing baselines and benchmarks for success is essential in measuring progress towards organizational goals. [7][5]

Strategic decisions regarding gathering data and developing advanced analytics models should be made by experienced senior leaders with authority to ensure improved performance. External sources have highlighted the importance of fostering a culture that values evidence-based decision-making as well as integrating big data technology into business practices. This will not only improve operational efficiency but also drive innovation within these organizations.[8][9]Organizations often struggle to transition into data-driven entities due to a multitude of factors. Here are some insights gathered from various sources:

Declining Engagement: Over the years, the percentage of firms identifying as data-driven has decreased from 37.1% in 2017 to 31.0% in 2019, indicating a trend of declining engagement with data-driven practices​.[10]

Cultural Hurdles: For an organization to be genuinely data-driven, a data culture needs to permeate the entire firm, not just at the executive level or within certain functional areas​2​. Moreover, it's essential for the leadership team and management to lead by example in employing data-driven decision-making, but almost half of the employees tend to rely on gut feeling over data-driven insight, even when data is available​.[11]

Acceptance of Data Science: Embracing a data-driven approach in decision-making requires organizations to accept data science almost religiously, implying a thorough understanding and adoption from the top to the lowest level of management​​.[12]

The journey towards becoming data-driven may be laden with challenges, mainly cultural and managerial, but with the right approach, leadership, and acceptance of data science and analytics, organizations can overcome these hurdles and reap substantial benefits.

Pioneering C-Suite Success Stories

The examples of how C-suite handles Big Data can be seen in the digital transformation efforts by companies such as Nissan and Sprint. Nissan utilized big data analytics, AI-enabled chatbots, machine learning, and robotics to streamline operations, improve the work environment for over 240,000 employees, and enhance customer experience by facilitating online sales and test drives. This approach not only enhanced operational efficiency but also proved beneficial from a profitability perspective as it minimized operational waste in selling and marketing cars​​.[13][14]

Similarly, Sprint, a telecom company, embarked on a digital transformation journey to overcome competition and improve customer experience. The company devised a five-year turnaround plan, with a substantial focus on gathering and analyzing data. By processing vast volumes of data from various sources like emails, databases, and logs, Sprint could identify bugs affecting different customer actions such as browsing behavior, device upgrades, reviews, and customer feedback. This data-driven approach helped Sprint catch up with industry giants like Verizon and AT&T and significantly improve its customer and tech experiences. Also, Sprint's initiative to go paperless resulted in over 83% of its customer base using paperless billing, the highest in the industry​​.[15][16]

These cases illustrate the essential role of the C-suite in navigating the challenges and opportunities presented by Big Data. The C-suite's active involvement in crafting and implementing big-data strategies is crucial for effecting widespread change in how a company conducts its day-to-day business. Mobilizing the C-suite for big data analytics is about more than just mining data for hidden trends; it's about transformative change that can significantly impact a company's operations and bottom line​​.[17][18]

Furthermore, it's highlighted that Big Data presents both a unique challenge and incredible opportunity for success for businesses worldwide. There might be a need to re-evaluate or even expand the C-suite roles to stay ahead of the curve in harnessing the potential of Big Data. This could include raising the mandate of chief information, strategy, marketing, or risk management officers or even creating new roles entirely to ensure that the organization can effectively leverage Big Data for strategic advantage​​.[19][20]

These narratives underscore the necessity for C-suite leaders to be proactive and adaptive in leveraging big data analytics to drive digital transformation, enhance operational efficiencies, and improve customer satisfaction and engagement.

Conclusion

In conclusion, the journey towards becoming a data-driven organization is often impeded by a variety of factors. These hurdles largely revolve around the organizational culture, managerial and leadership practices, and the level of acceptance and understanding of data science across the organization. Despite these challenges, numerous success stories highlight the transformative benefits that adopting a data-driven approach can bring to an organization across various sectors. Overcoming these barriers requires a concerted effort from leadership and a holistic change in organizational mindset and practices towards data and analytics.

References

1. Sibanda, Mabutho, and Durrel Ramrathan. Influence of Information Technology on Organization Strategy. 23 Feb. 2017, https://scite.ai/reports/10.1515/fman-2017-0015.

2. Building an effective analytics organization | McKinsey. https://www.mckinsey.com/industries/financial-services/our-insights/building-an-effective-analytics-organization.

3. Insight-driven organization | Deloitte Insights. https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html.

4. How to Structure Your Data Analytics Team - Harvard Business School Online. https://online.hbs.edu/blog/post/analytics-team-structure.

5. Data ethics: What it means and what it takes | McKinsey. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/data-ethics-what-it-means-and-what-it-takes.

6. What Is Data and Analytics: Everything You Need to Know | Gartner. https://www.gartner.com/en/topics/data-and-analytics.

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8. What matters: How to scale advanced analytics in corporate functions. https://www.mckinsey.com/capabilities/operations/our-insights/what-matters-how-to-scale-advanced-analytics-in-corporate-functions.

9. Three keys to building a data-driven strategy | McKinsey. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/three-keys-to-building-a-data-driven-strategy.

10. Why Becoming a Data-Driven Organization Is So Hard. https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard.

11. 10 Reasons Why Your Organization Still Isn’t Data-Driven - Forbes. https://www.forbes.com/sites/brentdykes/2021/06/01/10-reasons-why-your-organization-still-isnt-data-driven/.

12. Data-Driven Decision-Making | SpringerLink. https://link.springer.com/chapter/10.1007/978-3-031-19554-9_11.

13. Interview: How Nissan is transforming in the digital world. https://www.artefact.com/blog/interview-how-nissan-is-transforming-in-the-digital-world/.

14. Digital Transformation Success Stories for C-Suite Leaders. https://imaginovation.net/blog/digital-transformation-success-stories-c-suite-leaders/.

15. DX success: Sprint focuses on telecom customer experience. https://www.digitaljournal.com/business/dx-success-sprint-focuses-on-telecom-customer-experience/article/521695.

16. The Secret Behind AT&T’s Digital Strategy Success - Forbes. https://www.forbes.com/sites/servicenow/2021/08/31/the-secret-behind-atts-digital-strategy-success/.

17. Assessing the complexity of C-suite priorities | Deloitte Insights. https://www2.deloitte.com/us/en/insights/topics/leadership/c-suite-business-priorities.html.

18. How to reshape the C-suite for a better working world | EY - US. https://www.ey.com/en_us/consulting/how-to-reshape-the-c-suite-for-a-better-working-world.

19. Big data business models: Challenges and opportunities. https://www.tandfonline.com/doi/full/10.1080/23311886.2016.1166924.

20. How Big Data Collection Works: Process, Challenges, Techniques - TechTarget. https://www.techtarget.com/searchdatamanagement/feature/Big-data-collection-processes-challenges-and-best-practices.