BUSINESS INTELLIGENCE

Posted: March 27th, 2020

BUSINESS INTELLIGENCE

Student’s Name

Course

Professor’s Name

Institutional Affiliation

The City and State

Date

Business Intelligence

Organizational Culture and Data-Driven Decision Making

            Organizational culture is a term that describes how a firm executes its functions and operations, which are in close association with natural behaviors and values that contribute to its unique psychological and social environment. It is important to note that a company’s culture is driven by its leadership in that the rest of the firm emulates how figureheads communicate, behave, and the values that drive their professional ambitions (Goetsch & Davis 2014). The concept also defines and represents an organization’s jointly shared internal description of self. It refers to the sum of all rituals and mores that holds the firm together through shared assumptions and beliefs that govern people’s behaviors within the organization. Barton and Court (2012, p. 79) describe organizational culture as a manifestation of civilization in the workplace that is characterized by attitudes, philosophies, practices, convictions, and mannerisms that define an enterprise both internally and externally.

            Data-driven decision-making refers to the use of empirical data and facts to inform various decision-making processes rather than basing these choices on intuition and observations alone. The permeation of science and technology in all sectors of the economy means that all industrial areas generate data and information that they should use for informative purposes (LaValle et al. 2011, p. 21). This type of decision-making is based on the notion that organizational assessments should be the result of primary data sets that illustrate their projected efficacy levels and expected outcomes. Advancements in science and technology mean that businesses have the opportunity to gather consumer data at the point of sales, and online shopping habits and preferences (Provost & Fawcett 2013). Other sources of data that users generate during their daily activities that involve interacting with technology also serve as a valuable source of information that is useful to businesses (Brynjolfsson, Hitt, & Kim 2011). Data-driven decision making improves an organization’s competitive advantage because it helps them make more accurate predictions about market trends and project consumer preferences based on facts garnered from data analytics.

Impact of Organizational Culture on the Adoption Data-Driven Decision Making

            As mentioned above, organizational culture entails the daily routines, attitudes, philosophies, beliefs, and behaviors within a firm that culminate to create a meaningful and observable way of life. Organizations seeking to establish a data-driven decision-making model must be involved in the predictive analysis of data (Donhost & Anfara 2010). This observation is because predictive data analysis comprises questions such as why, who, and what, concerning an organization’s mission and vision. Predictive data analysis also provides companies with a context that has a direct correlation to empirical data, which facilitates the establishment of an effective analytics value chain. The value chain allows for the collection and reporting of data, which provides decision-makers with valuable insights and recommendations by way of evaluations. According to Goetsch and Davis (2014, p. 56), the data analytics value chain is an iterative process that results in organizational change. The company measures the impact, and they use the outcomes to offer useful feedback pertinent to the improvement of processes. Organizations should strive to foster a data culture to ensure that they act upon insights and recommendations garnered from the value chain appropriately and promptly (Mandinach 2012, p. 74). The establishment of an organizational data culture involves practices, beliefs, and behaviors that facilitate the collection of high-quality data, sharing, hiring, and training of data analysts. These are effective communication, the establishment of an analytical, organizational structure and metric designs, and A/B testing (Brynjolfsson, Hitt, & Kim 2011). The culture plays a significant role in adopting and using data-driven decision making in an organization by setting expectations on how it should share, apply, perceive data.

            Organizational culture plays an essential responsibility in the establishment of a data culture, whereby the incorporation of a data analytics value chain is integrated into a company’s dominant customs. A data-driven decision-making organizational culture is based on information in the sense that it requires the collection of the right data using suitable means, while simultaneously minimizing collection bias and optimizing data quality (Donhost & Anfara 2010, p. 59). An organizational culture that is conducive for data-driven decision making is collaborative, inquisitive, open, and inclusive. As mentioned earlier, leadership is essential to the practical adoption of a data-driven decision making organizational culture (Davenport & Bean 2018). This form of decision-making requires visionary and progressive leadership qualities that complement the recruitment of competent data and analytical officers. The achievement of these attributes results in fact-based procedures that test existing mindsets, and do not consider the opinions or influences of the highest paid person’s views (HiPPOs) such as chief executives (Provost & Fawcett 2013, p. 55). The organizational culture ensures that a company embeds its practices in federated analytics to guarantee that all departments and personnel are involved in the data analytics value chain. The incorporation of an organization-wide data culture provides that the business equip all relevant workers with data analytics training, skills, and education (McAfee et al. 2012, p. 64). The notion also plays a vital role in information-based decision processes that allow for the efficient use of high-quality data and its useful application.

Bibliography

Barton, D & Court, D 2012, ‘Making advanced analytics work for you’, Harvard Business Review, vol. 90, no. 10, pp. 78-83.

Brynjolfsson, E, Hitt, LM & Kim, HH 2011. ‘Strength in numbers: How does data-driven decision-making affect firm performance?’ SSRN,viewed 15 August 2018, <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486/>.

Davenport, TH, & Bean, R 2018, ‘Big companies are embracing analytics, but most still don’t have a data-driven culture’, Harvard Business Review, viewed 15 August 2018, <https://hbr.org/2018/02/big-companies-are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture/>.

Donhost, MJ & Anfara, VA 2010, ‘Data-driven decision making’, Middle School Journal, vol. 42, no. 2, pp. 56-63.

Goetsch, DL & Davis, SB 2014. Quality management for organizational excellence. Pearson, Upper Saddle River, New Jersey.

LaValle, S, Lesser, E, Shockley, R, Hopkins, MS & Kruschwitz, N 2011, ‘Big data, analytics and the path from insights to value’, MIT Sloan Management Review, vol. 52, no. 2, pp. 21.

Mandinach, EB 2012, ‘A perfect time for data use: Using data-driven decision making to inform practice’, Educational Psychologist, vol. 47, no. 2, pp. 71-85.

McAfee, A, Brynjolfsson, E, Davenport, TH, Patil, DJ & Barton, D 2012. ‘Big data: The management revolution’, Harvard Business Review, vol. 90, no. 10, pp. 60-68.

Provost, F & Fawcett, T 2013, ‘Data science and its relationship to big data and data-driven decision making’, Big Data, vol. 1, no. 1, pp. 51-59.