Why are People the Most Important Aspect in an Analytics Driven Culture?

Why are People the Most Important Aspect in an Analytics-Driven Culture?

Today’s business environment is largely digital thus the analytics culture is an essential component of gaining a competitive advantage. Indeed, organizations use data analytics to support decision-making. The decision-making should be embedded in organizational culture to inform people about their responsibilities in the wake of rapid changes witnessed in the business world. Organizational processes have considerably changed and managers today rely on data and algorithms to adapt to these changes to improve management and enhance human resource actions. However, in establishing a data analytics-driven culture, it is important to examine the abilities of the people to utilize the new analytic tools and algorithms at all levels of management. Additionally, managers need to establish the willingness of these people to embrace and support organizational operations anchored on data analytics. People play essential roles in organizations today meaning that their voices should be heard for the culture to thrive. Indeed, people are the most important element in analytics-driven culture because they play fundamental roles in organizational support and relationship building.

Support and Advisory Paradigm

The analytics-driven modules can easily be bought or manufactured. However, once the analytics modules have been acquired, people are needed to nurture and further develop the culture to address current business needs. It is disappointing to managers who only think that they can utilize technology to catch up with enterprises and competitors leveraging on data solutions. Hence, despite having the right technology, it is important to address the challenges posed by the new culture. Hsinchun et al. (2012) posit that businesses are struggling to address the challenges posed by analytics-driven culture. It has been established that people pose 62.5 percent while process presents 30 percent, and technology 7.5 percent of the challenges (Brynjolfsson & McElheran, 2016).  The revelation implies that managers need a more comprehensive approach to nurture data-driven culture by addressing the challenges posed by people.

People are important components in data-driven culture because they support and advocate for the implementation of new methods of operation anchored on data and algorithms. As revealed by Sarkar (2018), data-driven culture does not only depend on technological tools but also relies on the skill sets and behavior of people.  Therefore, people, including heads of departments and company executives, play critical roles in advocating and supporting change processes. It is argued that the skills and behavior of people are essential in helping them treat data as a capital resource capable of helping an organization achieve its goals (Rogers, 2016). Besides, the people within an enterprise can align their skills with resources to determine how analytics and algorithms are likely to support organizational change initiatives.

The formation of teams to initiate and implement data-driven operations depends on the willingness of the people and their ability to embrace data-driven change. Therefore, it is necessary to address the concerns of workers and other stakeholders before committing resources to implement a new organizational culture (Manpreet, 2019). Jaguar is a notable example of a company that shifted its operation to analytics-driven after having a constructive conversation with its workers and stakeholders. In this company, the analytics-driven culture has succeeded due to the power of board reporting that ensures all people are aligned with organizational goals (Ismail, 2018). Essentially, data visualization has worked in most of Jaguar’s departments as workers are committed to the cause. Hence, people are the building units of any possible analytics-driven culture because they are directly involved in the processes and procedures associated with data-driven culture.

Proficiency skills and operations

Over time, companies assemble broad arrays of data modules and tools to support decision-making and enhance business performance. However, having tools and systems may only hinder the implementation of analytics-driven culture if a company fails to focus on the proficiency and skills of a culture’s solid foundation of the people. Therefore, analytics-driven culture in organizations depends on the proficiency of the workers. Jahn et al. (2019), outline that for workers to effectively analyze data in their jobs, they need to be able to demonstrate proficiency in the utilization of data to make strategic decisions. Here, not only having the right skills for a job but also showing the desire and inclination towards data-driven decisions matter. Therefore, to address the ever-increasing business needs, business managers are encouraged to recruit people with the right skills and train them to increase their proficiency. Nevertheless, a data driven-culture only succeeds when the employees are trainable (Brynjolfsson & McElheran, 2016). In essence, the people encourage and support business executives to challenge the traditional practice and embrace new work methods to move a company forward towards profitability. Without the help of officers who are directly in contact with customers, it is difficult to come up with customized decision support. Managers are only concerned with policy formulation and do not directly accomplish the various tasks as envisaged in innovative processes.

Ideally, employees support the improvement of data-driven operations through curiosity and discovery of new customer data elements as part of their work activities. Unlike processes and technology that cannot be empowered, people can be trained to utilize their curiosity to solve complex daily tasks involving big data analytics. For instance, priority and proficiency, applicable to people, has worked for the Charles Schwab Company. The enterprise has flourished since it began improving the experience of employees using data through training and resource support. The approach that the enterprise has implemented encourages both senior data analysts and novice users to come together and advance new work methods and procedures (Brynjolfsson & McElheran, 2016). Hence, people can be trained to attain proficiency skills, which makes them an important element of an analytics-driven culture pursued by companies across the globe.

Confidence with New Work Methods 

Stakeholders may not invest in a company’s operations if they are unsure of a newly implemented culture. They are only happy if their demands regarding the delivery of quality products or generation of huge profit margins are met. Therefore, workers play essential roles in utilizing data to create added value for customers, suppliers, and investors. For instance, to build sustained competitive advantage, workers attempt to leverage the use of big data to answer strategic questions and concerns raised by customers. The workers (people) are integral in the entire process of creating long-term customer relationships because analytics culture not only triggers the next purchase but also focuses on building customer loyalty to sustain a buying culture.

In essence, having confidence in new work methods and structure is the initial step towards building relationships anchored on loyalty. Effective implementation of analytics culture by workers is critical in offering value-for-money products to customers. Per Kotorchevikj (2019) boosting confidence in an organization’s operations and procedures is difficult without the presence of people working on big data to leverage business services and solutions. Anderson (2015) posits that investors are often quick to invest in a business with many customers because it guarantees more profits. Hence, the first step is to have competent people implement the analytics culture to instill confidence in the customers, attract new customers and provide value-added goods and services to establish loyalty. Subsequently, the needs of other suppliers and investors are met once a business gains a competitive edge against the competitors.

Data Enthusiasm

The implementation of an analytics-driven culture, which is a change process, may experience challenges because some workers are rigid thus they resist change. Therefore, data enthusiasts drawn from the human resource section have to be enlisted to support and encourage the workers to embrace new work methods. The data enthusiasts can then create communities or teams to promote the use of analytics in solving business problems. Bartlett (2014) affirms that enterprises thrive if leaders are able to engage all employees and convince them to accept change. Indeed, leaders should influence the workers that may resist the use of analytics as a culture to advance their data skills to actively participate in decisions and operations that are essential for the success of an entity. Once the majority of staff acquire adequate skills, the adoption and growth of business analytics can be realized both individually and communally.

People encourage others to share a passion for data analytics. Data enthusiasts in an organization can pull together the other staff members to streamline a company’s efforts to institute a data-driven culture and align operations to business metrics. Such enthusiasts can dispel misconceptions and fears about new company processes and culture likely to derail business performance (Hsinchun et al., 2012). Cargill has succeeded in the conglomerate industry because it relies on few data enthusiasts to drive its new business model and culture. In this case, the data devotees invite others to join them, inspire novice data users, and develop internal community support frameworks.

Conclusion

Analytic-driven culture is the gateway to competitive advantage. Businesses often aspire to stay ahead of the rest when it comes to operations, customers, and profit generation. Therefore, business executives have discovered that having an analytic-driven culture allows a firm to embrace a top-down approach to leadership. As such, managers are poised to invest in people and technology that is capable of helping them achieve short and long-term goals. Human assets in organizations utilize technological tools to glean data which is then used to make decisions. Notably, the analytics-driven culture relies on data-driven people who attempt to communicate and establish business relationships. Therefore, the success in the implementation of the analytics-driven culture is dependent on how managers train and empower their workers. As such, managers are aware that if they want these people to do things differently, they have to support them acquire new skills. Essentially, workers must support colleagues understand new systems and the capabilities to do analytics in the future. These people are integral as they provide support and advice, enhance proficiency, guarantee confidence, and support others to move forward as a team and embrace new cultures. Therefore, the other components like processes and technology cannot work without the input of the people. Nevertheless, future studies should examine the requisite skills required to support the establishment and implementation of analytics-driven culture in business organizations.

References

Anderson, C. (2015). Creating a data-driven organization: Practical advice from the trenches. Sebastopol, CA: O’Reilly Media Inc.

Bartlett, R. (2014). Analytics-driven culture. Analytics Magazine. Retrieved from http://analytics-magazine.org/analytics-driven-culture/.

Brynjolfsson, E. & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133-139.

Hsinchun, C., Chian, R. & Storey, V. (2012). Business Intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.

Ismail, N. (2018). How are data roles and skills expected to change in 2018? Information Age. Retrieved from https://www.information-age.com/data-roles-skills-expected-change-2018-123470403/.

Jahn, J., Rasmussen, T. & Messenbock, R. (2019). Unlocking change management with people analytics. BCG. Retrieved from https://www.bcg.com/publications/2019/change-management-with-people-analytics.aspx.

Kotorchevikj, I. (2019). The role of Agile Analytics in instilling a data-driven culture. Hyper Right. Retrieved from https://read.hyperight.com/the-role-of-agile-analytics-in-instilling-a-data-driven-culture/.

Manpreet, R. (2019). Why is people analytics so important for HR? My HR Future. Retrieved from https://www.myhrfuture.com/blog/2019/10/1/why-is-people-analytics-so-important-for-hr.

Rogers, S. (2016). How and why to build an analytics-driven culture. TDWI. Retrieved from https://tdwi.org/articles/2016/06/24/building-analytics-driven-culture.aspx.

Sarkar, S. (2018). Role of leadership towards building a data-driven culture. Analytics Insight. Retrieved from https://www.analyticsinsight.net/role-of-leadership-towards-building-a-data-driven-cultur