Unleashing the Power of Analytics: A Guide for Business Success

Analytics involves scrutinizing data to reveal patterns, trends, and insights, blending statistics, mathematics, and technology to guide decision-making across diverse fields. By converting raw data into actionable intelligence, it fuels business strategies, optimizes operations, and boosts performance. However, varying definitions complicate quick explanations, making a clear understanding crucial for leveraging these concepts effectively.

16/07/2024 - 5 min read

What Are Analytics?

In essence, #dataanalytics can be defined as the science of fusing heterogeneous data from various sources, drawing relations and causalities among them, making predictions to gain insights, and supporting decision-making. More generally, analytics refers to any #datadriven decision-making process.

The recent emergence of #bigdata and possibilities to gain a deeper understanding of processes and extract useful insights has elevated the role of data analytics, presenting both significant opportunities and challenges (Semanjski, 2023).

Why Use Analytics?

Numerous studies have demonstrated the relationship between implementing analytics and creating a competitive advantage. For example, research by O'Neill and Brabazon (2019) empirically investigates the link between Business Analytics capabilities—rooted in Governance, Culture, Technology, and People—and their impact on organizational value and competitive advantage. Survey results from 64 professionals across 17 sectors reveal a significant correlation between higher capability levels and enhanced organizational impact.

Organizations excelling in information management and data-driven approaches consistently achieve better performance. Analytics enhance various business processes and are crucial for developing distinctive products, capabilities, and making effective decisions Harris (2017); Mithas (2011).

The exponential growth in data use and promising market forecasts for the coming years highlight the increasing value of leveraging analytics. This presents a compelling reason for businesses to adopt analytics, positioning them to capitalize on significant future revenue opportunities.

Exhibit 1: Global Data Generated Annually (Duarte, 2024)

Exhibit 2: Analytics as a Service (AaaS) Market Size Forecast Worldwide in 2021 and 2028 (Vailshery, 2023)

Davenport (2006), Davenport and Harris (2007), and Davenport et al. (2010) suggest the Business Analytics Competency Center as a structural device that might make the business analytics-enabled insight generation process more effective.

Conversely, several studies indicate that the benefits are realized only if the insights are clear, actionable, shared effectively, and can be swiftly integrated into decision-making processes. Wright (2020) suggested that future markets will require data-enabled learning for product enhancement but will rely on network effects and proprietary data for sustained value. It is also highlighted that competitive advantage might be obtained only if it generates insights that can be quickly incorporated. Sharma et al. (2010) presented a case study illustrating the complexity involved in converting insights into options and decisions.

Top-performing companies also excel in analytics by attracting and retaining top talent, with 58% having deep expertise in key areas. They have four times as many analytics professionals, retain three times more talent, and offer strong career development, ensuring clear paths and growth opportunities for their staff (Miranda, 2018).

How to Implement Analytics

Effective implementation of data analytics starts with proper data extraction and analysis, leading to informed decision-making. It aids in risk management and offers a competitive edge through technologies like AI and machine learning. Effective implementation requires strategic planning, expert consultation, and patience. Companies should partner with experts, be patient, and invest in analytics to gain future technological advantages (Gordon, 2022).

Large and small businesses need a new architecture—a data fabric—to become truly data-driven and put AI to work to meet their KPIs. With a data fabric approach, user-friendly business intelligence platforms can connect the right people to the right data sets at the right time to promote agility, predict outcomes, and personalize experiences IBM (2024). While other research supports this approach, Harris (2017) emphasizes the importance of starting with clear goal definition, and Barton D. & Court D. at Three keys to building a data-driven strategy highlight the necessity of a well-defined strategy for business transformation. However, effective analytics also requires robust data collection and preparation to succeed within these models.

Connecting the right data with the right people is essential and one of the most challenging steps in implementing an effective analytics organization. Top-performing organizations in advanced analytics (AA) succeed through deep expertise, strategic partnerships, and a structured talent organization. They use various organizational models—centralized, decentralized, or hybrid—with clear governance to integrate analytics across business units. Centralization often helps in data governance and partnership management, while decentralization can empower individual units. AA units should be strategically located for enterprise-wide influence, often within business intelligence or strategy departments. Outsourcing analytics should be limited to non-critical tasks, keeping key analytics in-house to maintain competitive advantage (Miranda, 2018).

4. Conclusion

The integration of analytics into business operations clearly offers significant advantages, as supported by multiple studies. High-performing organizations that prioritize information management and data-driven strategies consistently outperform their peers, benefiting from improved decision-making and distinctive product development.

The growing importance of data and promising market forecasts underscore the critical need for businesses to adopt analytics. Establishing a Business Analytics Competency Center can streamline the process of generating actionable insights. However, the real challenge lies in ensuring these insights are effectively shared and integrated into decision-making processes.

Top-performing companies not only leverage analytics but also excel in attracting and retaining top talent. These companies are characterized by a high concentration of analytics professionals and robust career development opportunities. To build an effective analytics organization, businesses must focus on strategic planning, expert consultation, and the development of a supportive architecture, such as a data fabric, to enhance data connectivity and agility.

Implementing analytics successfully requires a well-defined strategy and clear goal definition. Whether through centralized, decentralized, or hybrid models, effective governance and strategic placement of analytics units are essential. Maintaining critical analytics functions in-house ensures sustained competitive advantage, while non-critical tasks may be outsourced judiciously. Ultimately, businesses that invest in building comprehensive analytics capabilities will be well-positioned to capitalize on future technological advancements and market opportunities.

To resolve business problems effectively, solutions must balance both quantitative and qualitative assessments. It is evident that the advantages of analytics can significantly aid managers in navigating the pressures and challenges of the decision-making process. However, this article does not address the ethical issues arising from the use of analytics or the complexity involved in selecting the right models or approaches, which can create additional challenges in their effective use.

References

Barton, D, Court, D., 2013, Three keys to building a data-driven strategy. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/three-keys-to-building-a-data-driven-strategy [Accessed 2024].

Duarte, F., 2024. Exploding Topics-Amount of Data Created Daily (2024). [Online] Available at: https://explodingtopics.com/blog/data-generated-per-day

Gordon, L., 2022. FORBES-The Data Analytics Implementation Journey In Business And Finance. [Online] Available at: https://www.forbes.com/sites/forbestechcouncil/2022/10/14/the-data-analytics-implementation-journey-in-business-and-finance/ [Accessed 2024].

Harris., T. H. D. &. J. G., 2017. Competing on analytics. The new science of winning.

IBM, 2024. IBM-Business intelligence and data-driven architecture. [Online] Available at: https://www.ibm.com/analytics?utm_content=SRCWW&p1=Search&p4=43700076252130520&p5=p&p9=58700007558655828&gad_source=1&gclid=Cj0KCQjw1qO0BhDwARIsANfnkv_moxbYWFWrAunQx3CNqXdRBDhUnj0DCcW6vZzpXqmNTKWRA9en4lUaArNPEALw_wcB&gclsrc=aw.ds [Accessed 2024].

Miranda, G. M.-L., 2018. Building an effective analytics. McKinsey on Payments, Volume Volume 11 Number 28.

Mithas S, R. N. a. S. A. V., 2011. How information management capability influences firm performance.. MIS Quarterly, pp. 35(1), 237–256.

O'Neill, M. a. B. A., 2019. 'Business analytics capability, organisational value and competitive advantage'. Journal of Business Analytics, 2, 2, pp. pp. 160-173.

S HARMA R, R EYNOLDS P, SCHEEPERS R, S EDDON P and S HANKS G., 2010. Business analytics and competitive advantage: a review and a research. s.l., Challenges for the Next Decade, DSS 2010.

Semanjski, I. C., 2023. Transport Planning in the Age of Big Data and Digital Twins.. s.l.:s.n.

Vailshery, L. S., 2023. Statista-Analytics as a service (AaaS) market size forecast worldwide in 2021 and 2028. [Online]  Available at: https://www.statista.com/aboutus/our-research-commitment/2816/lionel-sujay-vailshery

Wright, A. H. a. J., 2020. When Data Creates Competitive Advantage And when it doesn’t. Harvard Business Review.