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Big Data Must be Humanized to Fully Benefit Companies

Ein Beitrag von: Kara Xenia Tucholke, Scientific Research Analyst

"Where is the Life we have lost in living? Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?"

T.S. Eliot, 1934

With the above passage of his poem “The Rock”, the poet T.S. Eliot foresaw what would characterize today’s information age. The advent of computer technology and the internet has transformed how we gather information to generate knowledge. Knowledge is know-how that transforms “information into instructions”(1). And while information is structured data, knowledge “is actionable information” (2). Accordingly, data builds the basis for generating information, refined to bring some sort of greater value to the ones who are investing in it. In the quest for greater insights, the mass of data has become a problem, not a solution: “There is obviously plenty of data in the world, but not a lot of wisdom” (3). The replacement of human judgement by algorithms and the belief in the superiority of data-driven decisions did not only change how business is done but how we think. Big data and its technologies excel in optimization and business improvement. However, when it comes to the complex environment and behavior of human beings, context, creativity, and ingenuity is needed to keep a competitive advantage.

Introduction: A Value Shift from Tangible to Intangible Properties

Throughout history, “mankind has wanted to know to be able to act” (4) and as such businesses which had or have the right knowledge – to make better decisions leading to rational actions based on facts, rather than on intuition – have a real competitive advantage.

Data are particles of information (4) something that can be recorded, analyzed, and reorganized (5). For decades to stay competitive businesses have exploited data to keep up with a continuously faster-pacing world.

In the 20th century, value has shifted from the physical, like factories or land, to intangible properties, such as a brand or intellectual property. This now encompasses data, a vital asset, and the basis for new business models, innovations, and economic value. Data is seen as indispensable for our “information society” to learn from and hence its use is expanding vastly to a point of impacting all aspects of our human endeavors. What Mayer-Schöneberger and Cukier (5) call “big data consciousness” is the presumption of a quantitative component in everything we do. What the authors term “datafication” means recording and quantifying information of everything under the sun: from natural phenomena to essential acts of living (like a person’s location), now accelerated through embedded sensors in everyday objects (Internet of Things). By applying math and inferring probabilities from information, the long-term goal is prediction. This aspect is characterized through the use of advanced technologies like artificial intelligence (AI) or more specifically, machine learning, and supersedes the mere definition of “big data”. Information technology is still linked to people transforming information into knowledge by applying context and meaning (6), however, it can be observed that human judgement can be replaced by computer systems (7).

The following sections aim to give an overview of how big data has evolved – what came before it and what comes after it – and what role human beings play in the future of big data. Furthermore, it will be set out why specifically human judgement is the new competitive advantage in the world of big data.

 

The Growth of Data before Big Data: E-commerce and Better Marketing

As mentioned, the datafication of all aspects of life goes hand in hand with the advent of big data (5). Even before big data, humans have always attempted to quantify the world (like population censuses) as well as to measure and track production and business transactions (ibid).

An increasing recording and datafication of everyday life created the promise of a better understanding of how humans think, what they do and what is shaping their behavior (8). This was a real value and differentiation to organizations, to better understand their customers, to create better strategies – better services, products and marketing (5,8). What is now known as business intelligence (methods for managing business data to improve competitiveness, monitoring, etc) used to be called decision support systems at the time before e-commerce (4).

According to Kurzer (9), the first gathering of data started with e-commerce. Merchants in stores would observe details of customers, what they bought, and when, which would then form the basis of marketing plans. Although one could correlate between advertisements and customer purchases, this was nevertheless speculative. Once it was possible to record information of customers electronically, businesses were able to create more personalized marketing. In the 1980s, businesses would create direct mail campaigns based on personalization, and through digitalization in the 1990s, a download of an e-mail coupon could be correlated with in-store behavior. (ibid.) Marketing expert Don Peppers (10) wrote in a Forbes article in 1995 that emerging computer technology would transform traditional marketing to mass-customized marketing. Based on past purchases, a business could send personalized offers to customers. A flower shop, where you once bought a Mother’s Day gift, could record your transactions and send you a personalized offer the following year, asking if they should send the same bouquet to your mother for the coming Mother’s Day. If companies can track customers individually, they are going to focus on their share-of-customer – trying to sell them as many products and services as possible – as opposed to their market share.(ibid.)

 

The Impact of the Internet and the Start of the Digital Era

The advent of the internet has changed consumer behavior as well – enabling them to access more information and different products from wherever they are located (4). As they became more individualistic, consumers did not care for marketing that did not appeal to their specific needs (11). This meant that they preferred companies that incorporated technology as they appeared more consumer-centric and ‘cooler’ (ibid). Paradoxically, consumers also become increasingly concerned about the protection of their privacy as well (the so-called privacy paradox) (8,12). As governments started to regulate the use of data to tackle the increasing number of data breaches, companies find better solutions and technologies to collect and analyze data (9). As data-driven companies like Facebook and Google are faced with increased scrutiny on their data collection and processing (13), businesses have to find ways to monitor, follow-up, and optimize their insight-generation, based on an increasingly complicated environment of complex consumer behavior (4). Digitization and the implementation of cloud computing in the 2000s, and ubiquitous use of computers and smartphones in the 2010s caused our life processes to become increasingly mediated by digital systems (15). Also, the relationship between a business and its consumer has changed: businesses now needed to monitor and anticipate clients’ expectations (4). Every imaginable insight into a consumer context became datafied: their location, identity (demographics, financial information, etc.) search history, purchases, biometrics (apps tracking health and sleep) (8,17) as well as their relationships and sentiments on social media platforms (5). Such information could give businesses valuable insights for customer feedback or evaluate the success of marketing campaigns (5,8).

Defining Big Data

The term “big data” was first used in 2008 and quickly gained momentum (14). However, even now the meaning of big data differs widely (5,7,8,15,16). Further, its extensive use by industry leaders and media coverage led to big data becoming a hyperbole, a buzzword with inflated meaning (8,16). What characterizes big data is generally Volume (vast amounts), Velocity (created in near real-time), and Variety (diverse) (15), and what big data is currently defined as, varies from a “marketing approaches derived from information technologies (IT)” to “a new reality linked to the growth of the internet” and “a phenomenon that has intensified with the digitalization of the world” (4). In an attempt to clarify: big data can be referred to as data government, the management, or data mining (16), to generate new insights or create new forms of value – goods and services (5). As such, the growth of big data goes hand in hand with business intelligence (4).

The Value of Big Data

Before the age of big data, the means of a competitive advantage lay in actually possessing the new tools and large amounts of data (only affordable to big companies) (4,5). Now, differentiation relies on having the right mindset and innovative ideas. This mindset included that data was valuable beyond its primary use – product recommendation or website optimization – and could be reused and analyzed for the discovery of unexpected patterns. An example of this is Walmart: In 2004, the company analyzed historic transactional data from its retail database to find correlations between weather phenomena like hurricanes and the purchase of Poptarts. Knowing this, Walmart was able to boost its sales by placing these products at the right time at the right place (the entrance of their store). (5)

The ‘big’ in big data transformed how we ‘know’ about the world: being less concerned with causality – why something happened – and moving towards correlation – using the data to discover that something is happening. Where before, decisions were based on the intuition of an executive, they now became increasingly data-driven, which means the data has to be consulted before decisions can be taken (5,8).

The increasing affordability of storage power and that data is mined with no specific question in mind (5,15) has added to the abundance of data that now piles up in organizations’ warehouses. At the same time, newer data is constantly mined in real-time (4) and the variety of data makes it messy to deal with (5). This calls for new tools and technologies to cope with big data (4,15).

After Big Data: Smart Data & Data Analytics

When Kitchin (15) talks about the “Data Revolution” in 2014, he notes that while it had a substantial impact in a short amount of time, this is still only in its infancy. In 2020, big data forms a part of any data-driven enterprise and, at the same time, connected devices generate massive amounts of data every day (18). The three formerly described “V’s” of big data now graduate to also include value (4) and veracity (18). This can be seen as the next step of differentiation for a company, where the new winners are the businesses that process their messy data on time, in order to generate high-quality data, leading to useful insights (ibid.). So, while the first chapter of big data can be seen as the age of gathering and storing data, the second chapter is that of analysis. What terminology would replace big data, however, is not clear (19). De Goes (16) expressed in 2013 that terms from “Smart Data” to “Data Science” and “Predictive Analysis” have common attributes with the aim of applying advanced techniques – from processing to artificial intelligence (AI) – to generate further insights from big data.

Smart Data Explained

The term that has been used increasingly in the last years is “Smart Data” (4,17,20,21). What makes data ‘dumb’ is its un-usefulness to the specific context of an organization – contrary, ‘smart’ data is processed to “self-identify” itself if used with the appropriate software (20). To achieve this, vast amounts of raw contradictory, unstructured, and scattered data are cleaned, reduced, and sorted (called pre-processing) to reveal its underlying structure (21). This process is time-consuming, costly, and difficult (20,22) and still accounts for a large part of the work in a data-driven organization (23). Through automation and algorithms, these processes can be done by machines (15). Where ‘small’ data can be analyzed by humans, large data sets pose a bigger challenge. Advanced technologies based on AI like machine learning (ML) automatically mine, detect patterns, and build predictive models (ibid). Such predictive models are not concerned with why something is happening, but rather use historical data to predict future outcomes (24). The technologies have the capability to group customers into familiar groups and, by predicting their behavior, can automatically send purchase recommendations or execute personalized advertisements with the aim to maximize conversions (15,16,25). At the same time, the increasing sophistication of sensor-based devices (Internet of Things) enables them to “process out the noise” of big data in real-time, automatically generating smart data (21). This has direct implications on the timing and efficiency of automated day-to-day processes: machines can gather big data and create predictions and optimize decisions in real-time (25).

Introducing the above technologies gives organizations a tactical advantage over their competitors (ibid). The transformation from a data-driven to a learning organization through smart data is challenging, however, saves an organization valuable time that will differentiate it from its competition (25). Reaching this state may pose some challenges to organizations to date (26), but businesses that are still struggling with processing and generating value from their data will be worse off in the future (4).

How Smart is Smart Data? What AI and Automation Can’t Do

Big data, AI, machine learning, and algorithms work well for optimizing business processes (27) by seeing through the chaos of a company’s many data transactions and supplying new correlations – this is something no human can do. In the last ten years, companies invested in these new technologies with the quest to become better (28,29). However, they are not experiencing better decisions (29). Instead, companies are becoming more bureaucratic: for example, AI decides based on rules – inferred from past data (28). Additionally, the reliance on data gives the illusion and comfort of control (29,30), while not really knowing anything. Big data is framed as accurate and objective, but it is only a selection of possible available data, making it inherently subjective (15). Judging people on the basis of data rather than their actual behavior is a risk that comes with the increasing sophistication of predictions (5). Where applications for universities (27), jobs, or credits are now carried out by algorithms and AI, a bias in past data can result in the exclusion of certain ethnicities or postal codes because they are seen as less skilled or not creditworthy (7,27,28). The argument that these decisions are objective because all rules apply equally to everyone (28) creates an echo-chamber of “good candidates”, where the organization may not even be informed about this (7). Automating a brand’s social media interaction is a risk as well, because a machine can misread emotions or languages, where a formulaic response can make a customer feel unappreciated (31). While purchase predictions and product recommendations may be an easily automatable process, a machine cannot understand a context – resulting in further purchase recommendations for burial urns (28). The greater amount of companies is dissatisfied with the investment they have made in big data and its technologies, the higher the likelihood of a slower adaptation of big data technologies by companies (28,29,32). Understanding that technologies like big data remain only a tool and do not offer ultimate answers (5), is the first step to betterment. 18% of the companies that focus 80% of their initiatives on the efficiency of their decision-making (instead of trying to save money) experience growth (28). With the question of who is accountable for the decisions taken by algorithms (7,28,33), new challenges arise. Inferring from the difficulties that machines have in the complex environment of human behavior, the need for a more human component in our work arises.

Humanizing Big Data: the Future of Big Data

As the previous sections show, big data and automation of tasks promised a competitive advantage, but these have also led to the increasing replacement of human judgement and created a black box without a deeper understanding of our world. Companies who take the chance and invest in resources beyond data science and analysis will be the successful ones. The technological evolution surrounding big data has resulted in the tools becoming easier to use and skill for it is widely available (e.g. programming and customer insights are off-shored to third-party providers) (5). As data collection and AI technologies become more affordable, the demand for and value of more frequent decision-making will rise (34). This means an application of human judgement and emotional understanding regarding customer support, promotions, product customization, and product development (35). During a time when society is constantly and increasingly applying big data tools to explore new leads, the place for human creativity, instinct, and ingenuity is carved out to produce innovations (5).

Humanized Data Requires Organizational Changes in Companies

How is differentiation achieved? Companies have to invest time and resources to make the cross-disciplinary collaboration between business, data science, marketing, and human resource teams possible. This means deciding on a protocol of how decisions are made based on data prediction models (28) and asking the right (second-order) questions (8) based on data: Where did it come from? In what context, and what kind of analysis was conducted? (36) This means a new culture of decision making, where important decisions are not taken by the highest-paid executive (35,36), who may be subject to bias, lack the cognitive ability, and discard valuable information (8). This means training employees in big data literacy to make automated processes transparent in order to challenge any biases in prediction models (7). As data scientist jobs are becoming increasingly replaced by automated machine learning tools (37) training computational (38), statistical (39), and skeptical thinking (40) is necessary to ensure that all employees can see through the ‘black box’ of algorithms (7). Organizations that democratize and decentralize data access, tools, and judgement power will have a sustainable competitive advantage (35). As real-time data gathering and modeling picks up speed, requiring the approval of decisions of many levels can boggle down any advancements made (41). As such, enabling more people with access to data management tools and decision-power can result in the right decisions for a particular context on time (35). An example of this is the credit union Affinity (ibid.), which enables its employees to decide freely – with or without the basis of data models and company frameworks – as long as it is in the best interest of the customer in that particular context. This resulted in a reduction of fees by 50%. Where one would assume the contrary, machines can lead to more empathy: Where a job requires emotional labor – like managing a brand’s social media – algorithms can interpret sentiments and help prioritize attention: which users are overly complaining? Which user did interact with the brand but never received a reply? Which user is linked to a broad network of followers? (31) The end-goal is not more data but to understand what the customer thinks and what the market demands (42). This means that in order to acquire the nuances behind quantitative data, direct communication with consumers has to be routinized. One example to achieve this is by reserving a certain amount of time for employees to regularly have direct conversations with their customers (43) – by working as a customer service representative for example (42). Data visualization and analysis has been a popular method for the basis of decision-making and analytical reasoning (15). This includes adding qualitative to quantitative data to not only show what is happening but why it is happening, much less how you can improve it. (42) The ability to visualize qualitative data (e.g. through journey analytics) leads to in-depth insights that can be handed down to more employees (ibid).

Humanized Big Data also Leads to Greater Employee Engagement – a Win-Win

Big data and AI have freed up people from repetitive and operational tasks and the responsibilities of future employees now revolve around creative and complex problem-solving. With employees being tied in closer into an organization’s decision making, they have a higher stake in its success. This builds a committed workforce always in search of new insights – consequently producing innovative products with the customer in mind. By following the idea that all people have the creativity to innovate shows the example of Facebook. The company has created organizational structures, where every employee can contribute with his own ideas. The results are re-defining features like its websites’ Timeline layout or the Like button. (43)

Conclusive Remarks

Throughout history, organizations have evolved from being data-driven to learning to insight-generating. From historical data to future predictions, companies soon have to realize that true value lies in human decision-making based on good data. Not the other way around. Just as humans have struggled with the messiness of their data, machines will always struggle with the complexity of human behavior. “After all, messiness is an essential property of both the world and our minds; in both cases, we only benefit by accepting and applying it”(5). Looking back, the most human traits are the source of progress: if Henry Ford would have consulted big data algorithms about what the customer wants, it would have answered: “a faster horse” (ibid.).

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Kara Xenia Tucholke

Kara Xenia Tucholke

Written by Kara Xenia Tucholke, Scientific Research Analyst