Abstract
This research finds that people are the most important aspect in an analytics driven culture. This research also finds that the right data analytics-focused leaders and practitioners (i.e., data analysts and experts), managed within the right organizational personnel structure or chain of command, are essential for developing and maintaining an analytics driven culture. Each of the two core role subgroups (i.e., leadership and management and data analysts and data experts) has distinct responsibilities and requires the application of analytical understanding and prowess along with communication skills, specifically in translating the analytical findings at the tactical level (e.g., data analysts) and heralding the value that the utilization of analytics provides at the strategic and operational levels (e.g., leadership and management). Members of an organization’s leadership and management team also need to recognize and pursue the clear strategic advantages that the successful deployment and execution of data analytics efforts provides, particularly in the form of improved quality and speed of judgment and/or decision making. The right practitioners need to be skilled and experienced in multiple analytical techniques, but they must also be willing or motivated to apply the full suite of their own capabilities. This research finds that the combination of these efforts, including selecting the right leaders, identifying and communicating the strategic advantages data analytics can provide, establishing the right personnel management structure, and selecting and supporting the right practitioners, forms the core of a successful data analytics program. This research also finds that some of the contemporary best practices in appropriately managing personnel and leveraging their skills and abilities, or talents, can be found in the Old and New Testaments of the Holy Bible.
Of Math and Men: The Importance of People in an Analytics Driven Culture
People are the most important aspect, the most important resource, in an analytics driven culture because “without analysis and processing, data is useless” (Niu et al., 2021). A baseline philosophical argument may suggest that without people, there can be no data and therefore no analytics, but that does not advance this discussion in a focused and meaningful manner. This essay explores the importance of the role of people in the development and sustainment of an analytics driven culture as a strategic advantage, reviews the structure within which the people function, and considers two key levels of the organizational strata where critical analytical personnel reside, including leadership and management and data experts and analysts. This essay integrates several Biblical principles supporting the designation and utilization of personnel in these roles as well.
Strategic Advantage
“The objective of data analysis is to provide facts that will support better judgment” (or decision making), and to support this, “the business strategy needs to create an environment where decision makers can sort through the information and quickly obtain reliable facts” (Bartlett, 2013, p. 50). From a decision-making perspective, Bartlett (2013) employs a four-act system “to illustrate a process for analytics-based decision making” (Bartlett, 2013, p. 55). Each of these four acts includes “causes of death”, or factors that lead to failure at each discrete phase, and the majority of these causes include people-centric issues (Bartlett, 2013, p. 57-58).
For example, in “Act I: Framing the Business Problem” the first cause (of death) listed is “the necessary qualifications were absent or underutilized in framing the problem” (Bartlett, 2013, p. 57). “Act II: Executing the Data Analysis” includes a cause (of death) regarding support for the essential personnel: “The business analysts and quants were insufficiently resourced with analytics software, adequate time, and/or access to appropriate or reliable data” (Bartlett, 2013, p. 57). The third act, “Interpreting the Results”, designates a cause wherein “the necessary qualifications were absent or underutilized in interpreting the results” (Bartlett, 2013, p. 58). In the finale or “Act IV”, we find an extremely similar cause, but this time, it falls on members of the leadership team. “The necessary qualifications were absent or underutilized in making the decision” (Bartlett, 2013, p. 58). These points illustrate the critical importance of selecting and placing the right people with the right qualifications in the right positions throughout the organization to establish and sustain an analytics driven culture. People are the pillars in this environment.
To create this data-driven supportive environment, organizations need to invest in individuals with specific data-focused competencies, whether in leadership and management or technical experts and analysts. “Making strategic data-relevant decisions requires respective competencies”, and furthermore, “adequate competencies to use data appropriately can be seen as (an) important investment” (Klee et al., 2021).
Why? Because “business analytics is used as a strategic resource, and it supports strategic innovation” (Bartlett, 2013, p. 83). In order for data analytics to be strategically integrated within an organization’s operating model and structure, the associated resources, efforts, expenditures, and benefits (typically bundled in project frameworks) need to support and advance the overarching strategic plan. “The projects must align to the organization’s strategic objectives by creating value for the organization” (Ahmed & Pathan, 2019, p. 220).
The created value may include discrete analytical results, the results from a single or limited scope analysis, ranging from reduced inventory levels and carrying costs to facility closures (or openings), but the primary worth to the organization is improved decision support. “Firms have invested in data analytics tools to generate and share knowledge which may help firms to improve the quality of their decisions” (Ghasemaghaei, 2019). While strategic decisions are important, the value data analytics provides is amplified at the operational level where decision frequency can be hour-to-hour or even second-to-second, organization- and industry-dependent. “Data analysis allows managers to trickle all facts into decisive operational decisions” (Niu et al., 2021).
While the value of data analytics is recognized, organizations may struggle with the initiation and maintenance of successful data analytics programs, departments, and/or teams. A primary reason for this is grounded in the people that support the program. “Many organizations lack skills to implement analytics and some businesses that attempted it lack the knowledge to apply the results” (Nweke, 2019). Many of the implementation, integration, and application challenges confronting data analytics programs and practices stem from skillset shortfalls at the individual level, with people who are selected, either by proxy or situation.
In light of this and “as part of digital transformation, organizations go through changes in business models, operating models, human talent and skill requirements” (Mitra et al, 2019). Reconsideration and revision of the human requirements, the training, skills, experience, and the roles needed to support the successful utilization of data analytics are critical activities in this pursuit. Stated succinctly, the solution requires organizations “to find the right people and place them in the right roles” (Bartlett, 2013, p. 83). Two of these roles include two groups, leadership and management and data experts and analysts.
Leadership and Management
Changes in the business environment have driven requirements for organizational change. “Over the years, corporations have needed to adapt to changing technology. To keep up, their assets have shifted towards more specialized knowledge-based professionals” (Bartlett, 2013, p. 17). These specialized professionals include tactical, technical experts (i.e., data analysts) and operational managers, the decision makers, to support and lead them. The latter group requires initial attention, because “the first component in this environment is selecting a decision maker” (Bartlett, 2013, p. 50).
The decision maker in this sense must possess experience and demonstrate expertise in the successful application or integration of data analytics within organizations. Beyond mathematical prowess, this position requires people who possess assimilation, translation, and communication skills because “many decision makers today underutilize the available information because it is either not simple enough or arrives in an inconvenient form and hence is not readily usable” (Bartlett, 2013, p. 51). Underutilization in this sense may also include incorrect utilization (e.g., improper analytical techniques) or complete abandonment of the available data coupled with a sole reliance on the decision maker’s industry experience. The right leaders and managers, those with the right qualifications and skills, know how to solve this issue, and they understand and emphasize that “data analysis should be applied deliberately to simplify the information in order to avoid brain-dead decision making or guessing” (Bartlett, 2013, p. 55).
The question becomes: Where to begin? The answer, from academia, experience, and from the Bible, is at the top. After their departure from the organization known as Egypt, the Hebrew people grew in number and new management structures were needed and applied. “Moses chose able men out of all Israel and made them heads over the people, chiefs of thousands, of hundreds, of fifties, and of tens. And they judged the people at all times” (English Standard Version Bible, 2001, Exodus 25-26). This is reminiscent of a traditional or military organization chart replete with tiered management structures and responsibilities. “The organizational structure of the military is comprised of a power hierarchy known as the chain of command that revolves around a succession of commanding officers differentiating superior and subordinate roles” (Atuel & Castro, 2018).
These people who were selected for the leadership team (the able men and chiefs), were designated to exercise and espouse God’s will (judgment) and to manage the organization (the Hebrews) at strategic and operational levels. Their assignment was not to execute tasks at a tactical level - initial attention was applied to leadership and management. Similarly, contemporary organizations who are interested in deploying successful data analytics programs can benefit from understanding the required leadership roles (i.e., the chiefs) and their functions along with interactions in internal and external networks. “The chain of command is a social network of interdependent roles within an ordered power hierarchy” (Atuel & Castro, 2018). The purpose of the establishment and maintenance of this management or organizational structure is simple: it frames a people-centric management plan, similar in several ways to a Data Management Plan, which “facilitate(s) data sharing, data curation, and optimum reuse of data” (Gajbe et al., 2021). The right chain of command, established by and including the right leaders, facilitates two-way information sharing (e.g., reporting up and delegating down) and optimum use of people through controlled (functional- or department-based) management networks.
This management system (the chain of command) requires people to understand, codify (in rules, regulations, policies, etc.), communicate, and enforce the lines and levels of decision authority and the responsibilities for each position (or level) in the organization. The rigor and discipline involved in these efforts requires leaders and managers who are committed to a methodical and repeatable management approach. But what specific roles should these leaders play?
“There are a number of leadership roles that enhance or retard a corporation’s analytical capabilities”, ranging from “Enterprise-Wide Advocates” to “Expert Leaders” (Bartlett, 2013, p. 19). Enterprise-Wide Advocates are senior leaders in the corporation and as such they have considerable influence. They understand and endorse the value analytics (and analysts) provide for the organization and emphasize this at the strategic level. This role is intended to “promote examples of applying analytics-based decision-making” and “remove conflicts of interest and encourage objective analysis”, along with other tasks and responsibilities (Bartlett, 2013, p. 19, 20). The individuals who fill this role do not have to be analytical specialists (e.g., statisticians, etc.), but they do need to understand and support analytical efforts and programs within the organization. They need to communicate the value their subordinates, the expert leaders in particular, provide.
Expert Leaders “possess the training and experience to understand what the analytics team produces and how to fit it into the business” and “yet less leadership authority” (Bartlett, 2013, p. 88, 21). This is the level of operational integration where analytics are encouraged by the Enterprise-Wide Advocates and directly applied by Expert Leaders. This role manages the tactical integration and application of analytics and supports the doers, the data experts and analysts, as well as the program and associated systems.
Using a vehicle-based acquisition and use analogy, the Enterprise-Wide Advocates select a vehicle (or data analytics program, approach, etc.) for its speed and quality and possibly cost or other factors, the Expert Leaders drive the vehicle, and the data experts and analysts continue to design, maintain, and improve the vehicle over time. Without these engineers and mechanics, the wrench turners, the vehicle may breakdown or crash.
Data Experts and Analysts
With this in mind, it is easy to conceive that ignoring or neglecting the hire of data experts and analysts has consequences. “Many organizations are lavishly rich in industry knowledge but paupers in analytics capabilities” (Bartlett, 2013, p. 52). This problem is not becoming less impactful as time passes, and “as the variety and volume of our data increase, more sophisticated methods are required to unveil the valuable information hidden in it” (Ahmed & Pathan, 2019, p. 173). These methods require trained and often retrained individuals; hence, the root of this issue lies in the analytical qualifications of individual practitioners. “Lack of skilled analysts is (a) critical challenge towards handling the extensive data set of any organization” (Maheshwari et al., 2021).
Who are the right people to fill these roles? In general, they are described as analysts, data scientists (in the case of Big Data), and analytics professionals. These resources “are trained to accept their ignorance, value humility in presenting results, and qualify their statements (Bartlett, 2013, p. 68). Their job, in a nutshell, is “challenging current industry knowledge, validating the more accurate legends, and replacing the fairy tales” (Bartlett, 2013, p. 74).
However, analytical skills in and of themselves are not enough. These skills must be accompanied by motivation. “Humans are lazy by nature. In order to realize the benefits of having more accurate predictive models, analysts not only need to possess skills, they must also be willing to expend the effort necessary to exercise those skills” (Gerhart et al., 2022).
This concept, the encouragement and direction for individuals to use their skills to benefit others, including organizations, is emphasized by the Apostle Paul in the Holy Bible when he stated, “having gifts that differ according to the grace given to us, let us use them” (English Standard Version Bible, 2001, Romans 12:6). Christians are also directed to use their God-given skills and talents as a form of worship. “Let your light shine before others, so that they may see your good works and give glory to your Father who is in heaven” (English Standard Version Bible, 2001, Matthew 5:16). The secular benefits stemming from an individual’s application of analytical capabilities, including better judgment or improved decision making, are paired with spiritual gains as well.
Conclusion
People are the most important aspect of an analytics driven culture. Organizations need the right people with the right skills and experience in the right roles throughout the right structure to survive and thrive, and this is particularly applicable in the development and maintenance of a successful analytics driven culture.
The pursuit of this fact-based culture requires the organization to comprehend, understand, and appreciate the strategic advantage that a data analytics driven culture can provide. This advantage is multifaceted and includes improved understanding of the operating environment (marketplace), the organization, and the interaction between the two, at a minimum. Even more, the strategic advantage includes the application of this improved understanding and manifests within the organization as improved judgment and decision-making capabilities. However, this strategic advantage requires the right people to capitalize upon it.
Capitalizing on this strategic advantage requires the identification, selection, deployment (or placement), and utilization of the right leaders and managers. According to Bartlett (2013), the spectrum of suggested leadership and management positions includes Enterprise-Wide Advocates and Expert Leaders (and others), each with specific roles and requirements. This demonstrates the importance of having leaders with different, focused skills at different levels in the organizational strata to continuously emphasize and exercise the distinct advantages that data analytics provide.
This also requires the identification, selection, deployment (or placement), and utilization of the right doers, typically represented by data experts and data analysts. These experts must have the right analytical skillsets, mathematical competencies and communication skills that complement and support the organization’s leadership (decision makers) and employees in general.
More so, this set of professionals must also be willing to apply the skills they have attained in order to maximize returns to the corporation. “Relying purely on assembling of data without transitioning into analytics to inform organisational decisions is more likely to lead to underutilisation of ‘assets’ and consequently business failure” (Amankwah-Amoah & Adomako, 2019). It is people, not programs, systems, nor methods, that provide these analytical capabilities. The systems, etc. merely support these people.
This recognition of people as key resources in an organization (or in an instance or situation) is a recurring theme in the Holy Scriptures and in data analytics best practices (and again in lessons learned, for example, Bartlett’s (2013) causes of death). Successful organizations have effectively integrated data analytics into their cultures by identifying the strategic advantage data analytics can provide, the people needed to lead the effort, the structure within which the people can effectively and efficiently perform, and the people needed to do the work.
References
Ahmed, M., & Pathan, A. (2019). Data analytics. CRC Press. https://doi.org/10.1201/9780429446177
Amankwah-Amoah, J., & Adomako, S. (2019). Big data analytics and business failures in data-rich environments: An organizing framework. Computers in Industry, 105, 204-212. https://doi-org.ezproxy.liberty.edu/10.1016/j.compind.2018.12.015
Atuel, H. R., & Castro, C. A. (2018). Military cultural competence. Clinical Social Work Journal, 46(2), 74-82. https://doi.org/10.1007/s10615-018-0651-z
Bartlett, R. (2013). A practitioner's guide to business analytics: using data analysis tools to improve your organization’s decision making and strategy. McGraw-Hill.
English Standard Version Bible. (2001). ESV Online. https://esv.literalword.com/
Gajbe, S. B., Tiwari, A., Gopalji, & Singh, R. K. (2021). Evaluation and analysis of Data Management Plan tools: A parametric approach. Information Processing & Management, 58(3), 102480. https://doi-org.ezproxy.liberty.edu/10.1016/j.ipm.2020.102480
Gerhart, N., Ogbanufe, O., Torres, R., Sidorova, A., & Evangelopoulos, N. (2022). Effort minimization theory in the data analytics era. Journal of Computer Information Systems, 62(4), 837-849. https://doi.org/10.1080/08874417.2021.1924092
Ghasemaghaei, M. (2019). Does data analytics use improve firm decision making quality? The role of knowledge sharing and data analytics competency. Decision Support Systems, 120, 14-24. https://doi-org.ezproxy.liberty.edu/10.1016/j.dss.2019.03.004
Klee, S., Janson, A., & Leimeister, J. M. (2021). How data analytics competencies can foster business value– a systematic review and way forward. Information Systems Management, 38(3), 200-217. https://doi.org/10.1080/10580530.2021.1894515
Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of big data analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, 59(6), 1875-1900. https://doi.org/10.1080/00207543.2020.1793011
Mitra, A., Gaur, S. S., & Giacosa, E. (2019). Combining organizational change management and organizational ambidexterity using data transformation. Management Decision, 57(8), 2069-2091. https://doi.org/10.1108/MD-07-2018-0841
Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725. https://doi-org.ezproxy.liberty.edu/10.1016/j.ipm.2021.102725
Nweke, H. F. (2019). Big data and business analytics: trends, platforms, success factors and applications. Big Data and Cognitive Computing, 3(2), 32. https://doi.org/10.3390/bdcc3020032