It is believed there is more data moving across the Internet every second than stored in the entire Internet 20 years ago (Brynjolfsson & McAfee, 2012). Our society’s technological, social, and cultural transformation into an era of big-data and analytics, introduces a new horizon of challenges and novel opportunities for competitive advantages. Leaders in organizations across a variety of industries, are realizing the utility and benefits of big-data analytics in addressing their problems and revealing innovative solutions (Marshall, Mueck, & Shockley, 2015). However, with this advent of unfathomable volume, velocity, veracity, and variety of data, many are realizing the challenges surrounding the world of big-data do not necessarily originate from a lack of technology but, instead, a lack of leadership (IBM, 2013).
Organizations must align their culture to embrace big-data and data analytics in order to successfully reap the potential benefits (Brynjolfsson & McAfee, 2012). In this chapter, we will define and discuss big-data and analytics, how leading organizations support and maximize benefits from big-data and analytics, and finally, address how big-data is revolutionizing the healthcare industry. We primarily aim to address the characterizations and attitudes of organizational culture that readily embrace big-data, data analytics, and how leaders can emulate these stances.
Interestingly, many of these values have been reiterated as ideal traits and qualities throughout our leadership education. However, it appears this unique cocktail of a culture of empowerment, trust, transparency, and inquiry adequately prime organizations to successfully embrace and maneuver the world of big-data and analytics. We also aim to highlight the benefits and challenges recognized in healthcare organizations that have adopted big-data and analytics into their processes. Our guiding question asks what core values must a leader emulate to guide their organization to stand as competitive and effective players in the big-data and analytics era?
What is big-data and analytics and why is it important?
It is vital to begin with the building blocks to set context and realign ourselves with the same foundation and definitions. Data can be defined as “basic, discrete, objective facts about something such as who, what, when, where” (Jennex, 2017). The knowledge pyramid, designed originally in 1986, stacks data as the base, leading to information, knowledge, then finally, wisdom (Jennex, 2017). This structure was revised and inverted in 2000 to acknowledge that there is more information than data. Wisdom is also replaced by intelligence, which accounts for actionable knowledge, and intelligence ultimately leads to organizational learning in this revised version (Jennex, 2017). How we think about knowledge represents the added complexity as we move from simply “data” to the advent of “big-data.”
Big-data is entirely changing how we obtain knowledge and transform from intuition-based to evidence-based decision making (Jennex, 2017). Big-data is primarily used to translate data into business advantages and is described as ‘big’ in four or sometimes five key components (Brynjolfsson & McAfee, 2012). These four dimensions of big-data are referred to as the 4 V’s: ‘volume, velocity, variety, and veracity’. ‘Value’ has been added recently added as a fifth dimension by some thought leaders (“Infographics Master of Science in Leadership Big Data’s Growing Role in Organizational Leadership & Development,” n.d.).
Volume refers to the sheer scale of data created and available. IBM estimates that 2.5 quintillion bytes of data are generated every day (IBM, 2013). Walmart is estimated to collect 2.5 petabytes of data each hour which, for reference, is equivalent to about 20 million filing cabinets of text (Brynjolfsson & McAfee, 2012). One interesting driver to this quantity of data is the advent of genomic technology and whole genome sequencing (WGS). WGS allows a much more robust and phylogenetic perspective and introduces a new world of possibilities. Overall, the volume of data impedes many technological systems from readily accessing data and restricts humans from using this information without implementing any analytics. This volume is exponentially growing, supported by the estimation that 90% of existing data has been created in the last two years (IBM, 2017).
Velocity is the near real-time speed related to data creation and processing. Rapid insights provide the most useful advantage and therefore act as the gold standard to data digestion and output (Brynjolfsson & McAfee, 2012; Jain, 2016). Variety accounts for the diversity of incoming data, originating from images, text files, social media, videos, sensors, GPS signals, and more. Challenges in this diversity come in organizing and standardizing structured databases as well as processing multiple types of data in single databases (Brynjolfsson & McAfee, 2012; Jain, 2016). Veracity refers to the unknown, or uncertainty, of data. Beyond a lack of trust in data quality from one in three business leaders, poor data quality costs the United States $1.3 trillion per year (IBM, 2017). Finally, value “through insights from superior analytics” is the desired outcome associated with big-data usage (IBM, 2017). Data collection and data generation must have accurate and substantial value to serve any purpose (Jain, 2016).
Leaders are recognizing that big-data and analytics enable better prediction capacity to closer meet customer needs which gives their organizations a competitive edge (Marshall et al., 2015). Marshall et al. estimates that when organizations exploit big-data and analytics to drive innovative decisions, they are 36% more likely to outperform competitors who do not (Marshall et al., 2015). This revolution is transformative and allows data-driven decision making to largely replace intuitive, gut-based decision making.
It is indisputable that this era of big-data and analytics will affect every aspect of our society. Therefore, it is vital to address the key characteristics and attitudes that support and enable leaders to embrace big-data and analytics as integrated parts of their organizational culture. The following section will enumerate these characteristics and describe how leaders can adopt and emulate these to transform their culture.
Key Characteristics and Attitudes of a Big-Data Leader
Managing and extracting valuable meaning from big-data is not only a science challenge, but more than anything, a leadership challenge (Bolling & Zettelmeyer, 2014). Becoming a big-data enabled organization requires a culture of empowerment, trust, transparency, and inquiry. These qualities allow analytics to be woven throughout the fabric of an organization which elevates and reiterates the investment and commitment to analytics (Bolling & Zettelmeyer, 2014). Across the literature, it is acknowledged that the managerial and leadership challenges of big-data outrank the technical challenges associated with utilizing big-data to solve business goals (Bolling & Zettelmeyer, 2014; Michael S. Knapp, Juli A. Swinnerton, Michael A. Copland, & Jack Monpas-Huber, 2006; Woods, 2012).
It is vital to empower leaders to have the capacity to promote data-driven decision and analytics. One way this can be accomplished is through the creation of C-level individuals such as Chief Data Officers or Chief Analytics Officers (Stadolnik, 2014). By including positions such as these, an organization clearly commits to the pursuit of analytics and their priority of using data analytics to address problems (Davenport & Bean, 2018). Creating these roles also increase the odds that analytics will become integrated into the organizational culture because strong analytics leaders now hold influence and power. Prior to these positions, data management was typically reserved exclusively for the IT department or was isolated into disparate departments (Stadolnik, 2014). Today, big-data and analytics are a pervasive component of high-performing companies. Surveys by McKinsey & Co revealed that “highly engaging, evangelizing leaders” should convene a data team to drive desired data agendas (Stadolnik, 2014). This theme of empowerment is supported when the right individuals are provided the opportunity to have a seat at the table and exhibit to the company the importance of embracing and leveraging data (Stadolnik, 2014).
There have been four distinct leadership roles that take on the challenges of navigating big-data and analytics for organizations. The Chief Data Officer is a senior position rising in popularity, as it is estimated that 90% of large companies will hire a CDO by 2019 (Stadolnik, 2014). A CDO should act as the data owner and architect and should set data definitions and strategies. Typically, the position of the CDO is primarily focused on finding data initiatives that will add to the business and understanding the rollout speed of which to integrate these initiatives.
Data scientists tend to be highly technical individuals and classically trained as data engineers, mathematicians, computer scientists, or statisticians (Stadolnik, 2014). Data scientists will excel as leaders if they are proficient in their understanding of business and are capable of asking questions relevant to the domain of work (Bolling & Zettelmeyer, 2014). Analytic positions serve a primary role of integrating real-time data to develop business insights. High-performing companies are creating Chief Analytic Officer roles to engage with the C-suite board and offer their expertise to the executives. The CAO often owns a board realm of responsibilities and functions to maintain forward-thinking progress. Finally, the leadership role of the data manager or data leader serves as the organizer and architect of data. The data manager oversees a fluid connection between the data agenda and technology agenda (Stadolnik, 2014). Again, this position can only reach its full potential if given a seat at the executive table.
Empowerment for Data and Data Analytics
Another way to develop an organizational culture that emphasizes empowerment towards analytics is to invest in employee trainings in analytics. This can create a data literate company that is capable of infusing analytics throughout the organization (Marshall et al., 2015). According to the survey by Marshall et al., individuals categorized as leaders are 110% more likely to support training all employees in analytics than those categorized into strugglers. If an organization creates a culture where all individuals have a working knowledge of data science, they will be able to ask the right questions and make stronger data-driven decisions (Bolling & Zettelmeyer, 2014). This emphasis on data literacy can also be promoted by adding analytics competencies to every employee role in some manner so that the organizational culture is one with a steady foundation of analytics (Marshall et al., 2015).
An organizational culture is more likely to embrace analytics and big-data if employees feel empowered to implement drastic changes based on their findings in data. Often, resistance towards change comes at a top-down pattern due to historical norms or hierarchical structure. Big-data may introduce challenge to what is thought to be dogma, and therefore, all individuals in an organization must feel empowered to speak up and address these findings regardless of their implications (Bolling & Zettelmeyer, 2014). Employees must feel empowered to take manageable risks or follow leads using data-driven information. Without a culture founded in empowerment, big-data and analytics goals will often be blocked or inefficient.
Distributed Leadership Roles
Consistent with themes of empowerment, building an organizational culture that elevates analytics can be achieved through distributed leadership roles. As discussed, there are several positions that can be added to emphasize the commitment to big-data and analytics, such as the CDO and CAO. Organizations can also hire positions with analytic objectives across the board and invest in analytic training opportunities for their employees. One way this can be accomplished is if the organizational culture rewards expert authority over hierarchical authority, inherently distributing the typical structure of leadership (Michael S. Knapp et al., 2006). These types of leadership will be discussed in later parts of the chapter.
Transparency, Trust, and Relational Transparency
Key to priming an organization to be leaders in big-data and analytics is creating a culture that values transparency and trust. This has been a theme we see not only in terms of analytics but throughout leadership theory. Building an organizational culture that values transparency of information supports an atmosphere of trust and openness. Scholars also refer to relational transparency as a primary component of authentic leadership (Walumbwa, Avolio, Gardner, Wernsing, & Peterson, 2008). This concept addresses presenting one’s authentic self by openly discussing thoughts and feelings, within appropriate limitations (Avolio, Walumbwa, & Weber, 2009). The benefits of transparency within an organization are also highlighted in the Cleveland Clinic case study (Porter & Teisberg, 2016). By displaying metrics openly, organizations hold themselves accountable to improve weak areas and encourage members to present new, innovative solutions (Porter & Teisberg, 2016).
Along these same lines, transparency is only meaningful if data is easily accessible in a relatively useful manner. Organizations can support analytic driven culture by creating systems that put company metrics at the fingertips of many users (Michael S. Knapp et al., 2006). Individuals throughout an organization should be able to access data, and understand the implications of said data, in a relatively reasonable time and manner (Michael S. Knapp et al., 2006). This relates back to building an organization that has is data-literate through empowerment and training of all employees so that big-data and analytics is woven throughout the organization (Zettlemeyer, n.d.). According to a survey of executives, 56% stated that the largest obstruction to reaping the benefits big-data offers is that the information is siloed or trapped in certain departments or by certain individuals (Capgemini, 2012). By educating employees on how to access and appropriately use metrics, leaders can feel assured that big-data adoption and benefits will be met efficiently and effectively.
Oftentimes, transparency in metrics is thought to present damaging results. However, companies should consider if and how the benefits outweigh the risks (Groves, Kayyali, Knott, & Kuiken, 2013). A report conducted on using survey data at universities states that, “In order to use data to initiate institutional change, transparency is crucial” (Benson & Trower, 2012). Transparency allows organizations to both confirm where their strengths are concentrated and identify weaknesses for improvement (Benson & Trower, 2012). When an organization fosters a culture of transparency, they must also be prepared for open discussion and debate in order to welcome creative and innovative solutions (Benson & Trower, 2012).
Similarly, they must recognize that revealing up data in a transparent way may incite demand for more data and more information (Benson & Trower, 2012). The disclosure of data in a “warts-and-all-approach” allows leaders to breed an atmosphere of trust among employees and clients by showing them that, regardless of the implications, honesty is a shared value of the organization (Benson & Trower, 2012). Organizations should feel confident that, “the right data to make the right decisions that support the right outcomes in the right moment” is the intention and goal of all members and leaders (Maxwell, Rotz, & Garcia, 2016).
Inquiry and Innovation
The final critical component of any leaders and leading organization that aim to weave big-data and analytics into its culture is the acceptance of innovation and inquiry. Those that are given tools to manipulate data to glean insights and inspire innovative solutions will excel as leading organizations (Marshall et al., 2015). Key to this is the component of acceptance, as employees of all statuses should feel comfortable proposing innovative solutions and confident that their voice is valued and highly regarded. This again relates to building a culture with empowered employees that have access to quality and true data.
Marshall et al. coins the term “quantitative innovation culture” (Marshall et al., 2015) which adequately suits the goals of leaders who want to drive big-data initiatives in their organizations. Leaders, in particular, invest energy into encouraging a culture of innovation by outlining metrics of innovation. Successful organizations accomplish innovative aims by promoting collaboration and allowing space and time for creative and imaginative thought (Marshall et al., 2015).
If employees have been trained to be data-literate and are allowed open access to company metrics, they should have space to submit innovative ideas and feel assured that they have resources to achieve those results. Part of a quantitative innovation culture is measuring the success and failure of innovative solutions and programs. Careful measurement of these metrics allows insight into where future efforts should be concentrated. It may be beneficial for leaders to set big-data goals of various scales to encourage success and positive attitudes while driving momentum towards transformative goals (Groves et al., 2013). Leaders should implement innovative efforts that have short, medium, and long-term goals of accomplishments (Groves et al., 2013). Such early goals could deal with the use of real-time data to stay relevant and on top of current desires or trends. Medium goals may deal with data-storage and organization efforts so that information is more accessible and manageable in a useful manner. Long-term goals could involve a total shift in the data collection process or a re-appropriation of resources into more efficient software or technology.
Leaders can drive a culture that supports inquiry, defined as an organization with an embedded atmosphere of trust and physiological safety in which all feel safe to raise questions pertaining to how the company functions (Michael S. Knapp et al., 2006). Knapp et al. has designed their own version of the “culture of inquiry” cycle related to using data-informed leadership in the educational system (Michael S. Knapp et al., 2006). This cycle, as well as many other aspects that prime organizations to embrace big-data and analytics, is founded on the idea of distributed leadership (Michael S. Knapp et al., 2006).
Distributed leadership moves away from a centralized hero-figure towards collaborative shared leadership system that views leadership as a group activity (Bolden, 2011). The theory of distributed leadership rests on the principle that responsibility does not fall on one individual and therefore is shared throughout an organization (Bolden, 2011). This system of distributed leadership means that the organization functions less in a hierarchical system and invites input from all. Distributed leadership, or frequently termed as shared leadership, pays homage to the idea that the influence process is shared between many individuals rather than limited to a centralized leader (Avolio et al., 2009; Pearce & JA, 2003). Skills in data analytics may be dispersed throughout various individuals and not necessarily nestled in one department or one section of an organization so this mechanism of distributed leadership embraces and maximizes the capabilities of all (Michael S. Knapp et al., 2006).
Similar to this concept of distributed leadership, Groves et al. recommends “setting a top-down vision and stimulating creation of bottom-up innovation” (Groves et al., 2013). This appropriately sums up how ownership of innovative ideas and implementation should be a shared entity. Leaders should encourage innovation as an embedded part of the company by constantly being open and receptive to change, new ideas, and solutions. They need to display their commitment by supporting and adequately funding innovative efforts. Typically, top-down visions can disseminate guidelines and serve as a roadmap for others to follow, but they frequently stifle innovative thinking if they are too rigid (Raffaelli, 2017). Bottom-up leadership and innovation can lead to higher buy in and a greater diversity of expertise and ideas but require the support from top leaders (Raffaelli, 2017). Therefore, the dynamic of an organization wanting to embrace big-data and analytics to explore innovative solutions should balance leadership and distribute power to all members.
‘Values’ Relative to Organizational Change
As we have discussed, the three major components leaders and leading organizations can instill to efficiently embrace analytics and remain competitive in an era of big-data, we must refer back to the basis of organizational culture. The reciprocal relationship between organizational culture and leadership exists; organizational culture is developed from leadership and the culture can also impact how leaders are developed (Bass & Avolio, 1993). Culture is defined as an abstract force that motivates, drives, and influences action (Schein, 2010). The authentic leader is an emerging leadership theory that relies heavily on transparency and positive self-development (Avolio et al., 2009). Similarly, transformational leadership theory revolves around the idea that followers feel elevated and motivated in a positive manner by their leader, further motivating positive behavior and results (Avolio et al., 2009). These values closely align with the discussion here about employee empowerment and building a data literate company that embraces innovative thinking and problem solving.
A key tenet of authentic transformational leadership behavior is maintaining and promoting high ethical standards (Bass & Steidlmeier, 1999). Ethics, in this case, play into the role of honest and transparent data distributed to all members of community or organization. These individuals must feel confident that they trust the data and feel safe and empowered to point out controversial revelations that may stray from organization dogma. Attentive leaders can influence a change in organizational culture towards embracing big-data by consistently addressing empowerment, data transparency, and accepting inquiry. It is important for actions to match statements and for a consistent message to be relayed in order to properly drive organizational change (Bass & Avolio, 1993). Again, these components will be best accepted in atmosphere of distributed leadership where all individuals have a share in the actions and practices of an organization (Bolden, 2011; Michael S. Knapp et al., 2006). This involves weaving analytics into the objectives and priorities of every role and offering opportunities for innovative thinking to every participant.
Big Data in Healthcare
Shifting gears, it is vital to address how healthcare industries have already benefited from big-data and the opportunities still to come. Leaders in the healthcare industry have recognized these opportunities and have primed their organizations in exactly the ways previously discussed to embrace and reap the benefits big-data and analytics have to offer. With the advent of electronic health records (EHR), the volume of U.S. healthcare data is reaching yottabyte scale (1024) (Raghupathi & Raghupathi, 2014). These complex datasets are comprised as images, insurance claims, clinical data and therefore, a variety of datatypes that are difficult to store, manage, and interpret (Groves et al., 2013). However, if organizations manage to adequately take advantage of big-data, the potential to “improve care, save lives, and lower costs” are significant (Raghupathi & Raghupathi, 2014). When organizations embrace capacities to synthesize and aggregate big-data by creating cultures of openness with data-literate employees, insights can lead to better informed decision making and better health outcomes. Our future will undoubtedly involve real-time decision making using individual and population data to best inform physicians of the most efficient and effective, cost and outcome, treatment of patients.
Advantages of Electronic Health Records and Big-Data in Healthcare
One significant advantage of EHR in the healthcare realm is the network capacity to share and disseminate patient data within hospital networks or accountable care organizations. In this way, duplication is avoided and an individual can receive more consistent and safer treatment regardless of location. Patients are also able to readily access their medical data in new ways, such as with apps or mobile fitness tracking devices (FitBit, etc). This further empowers patients to take ownership and accountability of their own healthcare. Healthcare analytics can improve predictive capabilities by understanding population behavior and thereby estimating average length of stay which is a strong indicator for medical complications or other hospital-acquired illnesses (Raghupathi & Raghupathi, 2014).
It is estimated that big-data and analytics can save the United States an estimated $300 billion per year (Raghupathi & Raghupathi, 2014). For example, the way we are able to analyze historical patterns of disease and track outbreaks can help us understand how to mitigate and halt these outbreaks faster. The rise of big-data will also support genomic analytics and allow us to begin to elucidate how the human genome can be used to make medical decisions (Raghupathi & Raghupathi, 2014). Groves et al. describes how big-data is allowing new value pathways: right living, right care, right provider, right value, and right innovation. Essentially, with changing capabilities towards analytics and big-data in the healthcare field, we can better align decisions to the expectations and benefit of the patient (Groves et al., 2013). Another advantage of adopting EHR is being able to disaggregate results to look at underrepresented groups or populations and determine areas of health disparities (Benson & Trower, 2012).
Challenges of Big-Data in Healthcare
Central to challenges of healthcare data is securing patient privacy while still sharing clinical data for its value and insights. Certain groups attempt to exploit healthcare data for their own benefit and thus, are adding to misuse and privacy concerns appropriately feared by many (Groves et al., 2013). This also explains the strong resistance towards adopting big-data in the healthcare industry. Unlike shopping preferences, personal health information is highly valued as private information so there are groups wary and skeptical about using complex technology to store and disseminate their information. In addition, there are currently limited systems to aggregate data from various sources from siloed departments to a single source. It could prove highly beneficial to integrate pharmaceutical, provider, and payer data together but systems must be designed with this capacity (Groves et al., 2013).
Thanks to a variety of legislation and governmental incentives, the resources for transitions towards EHR was much less burdensome. For example, the Health Information Technology for Economic and Clinical Health Act in 2009 authorized $40 billion for providers to adopt EHR and train staff (Groves et al., 2013). However, the time and money required to train employees and build this infrastructure are still cumbersome. Raghupathi et al. states that big-data analytics in the healthcare sector should be “menu-driven, user-friendly, and transparent” (Raghupathi & Raghupathi, 2014). Again, we recognize the necessity for transparency and a culture of openness with data-literate and empowered members owning a role in acknowledging avenues of improvement.
It is vital to recognize that an organization will not successfully integrate big-data and analytic agendas without first addressing their leadership values and organizational culture. To truly exploit the unrealized potential that big-data and analytics have to offer, leaders must verify that they have prioritized designing an organization founded on employee empowerment, transparency, and receptivity to innovative problem solving and thinking. This can be accomplished by giving analytic positions a seat at the executive table and investing in employee training opportunities. Furthermore, it is important to create a safe space that takes the time and resources to address findings of employees that are using and generating the data. Analytics should be woven throughout a company and involved in the objectives of every employee instead of siloed into IT departments as in the past.
Big-data provides the capacity to shift the world on its axis, as poignantly stated by Bolling and Zettlemeyer, “previous disruptions challenged the way things were done; big data challenges what we think we know.” (Bolling & Zettelmeyer, 2014). Overall, the time has come for leaders to apply the theories and values of transparency, empowerment, and innovative thinking to embrace the new world of big-data and data analytics and truly transform our society.