Is Your Big Data Thinking Backwards?

Is Your Big Data Thinking Backwards?A guide on adopting customer-driven data flows as core business strategy.

Devin BostBlockedUnblockFollowFollowingAug 11, 2018Forget about expanding your company through initiatives that will increase customer value.

Think about expanding your company through initiatives that will give you data that will tell you how your customer can be served in ways that nobody imagined.

This is the paradigm that must be recognized to be successful in adopting Big Data as a culture of corporate strategy.

This article is the second part of an article series starting from: Big Data Analytics: The Best Disruptive Thing You Can Do To Your OrganizationBig Data is more than a technological issueKrotov (2017) explained this phenomenon with an analogy:“In March 1875, then-obscure inventor Alexander Graham Bell offered Western Union Telegraph Company President William Orton a patent for Bell’s telephone invention at the price of $100,000, roughly $2 million today (Carlson, 1994).

William Orton turned down the offer.

What happened next is history.

At the time of Bell’s offer, Western Union was the most dominant telecommunications company in the U.


Within a few years, smaller companies started to use Bell’s telephone invention to cut into Western Union’s market share.

In an attempt to catch up, Orton attempted to develop Western Union’s own version of the telephone.

However, this was too little, too late.

Western Union was never able to return to the level of prominence it had achieved.



The story of Western Union is often used in business texts as an anecdotal proof of how blind business leaders can be in relation to the future potential of a technology (Christensen, Anthony, & Roth, 2004) .



Carlson (1994, p.

161) explained this bias [among leaders] in that:“Both historians and the public often assume that the ‘end use’ of a new technology is embedded in the technology itself.

It is assumed that once a device is invented, it is clear how it will be used and by whom .



Instead .



the ‘end use’ of technology is created or constructed by a variety of participants in a technological enterprise .



”[1]The world is not the same place that it was when most contemporary management theories were formulated.

Technology has advanced far more rapidly than management thinking has evolved, as was alluded to by Braganza, Brooks, Nepelski, Ali, and Moro (2017), who stated:“Various estimates of sources and volumes of data being produced include CISCO’s estimate “by 2020, the gigabyte (GB) equivalent of all movies ever made will cross the global Internet every 2 minutes” (Cisco Visual Networking Index, 2016, p.

4) .



Big data has led to the creation of new technologies, methods, data capture applications, visualization techniques, and data aggregation capabilities.

Drawing on established business intelligence, data mining and analytics practices, big data methodologies spawn new generations of algorithms and renew interest in mathematics, statistics and quantitative analysis.



Scholars recognize big data is more than a technological issue and, to be fully effective, big data needs to become part of the fabric of organizations (Davenport, Barth, & Bean, 2012).

Big data should be incorporated into strategic activities such as marketing and new product development (Xu, Frankwick, & Ramirez, 2016).

Others recognize big data affects organizational culture, as decision making becomes more evidence-based (Erevelles et al.

, 2016, Irani, 2010).

”[2]The most profitable companies are figuring out ways to allow their customers to create data creation flows that drive core business processes.

Pigni, Poccoli, and Watson (2016) reported:“The emergence of digital data streams is creating strategic opportunities for existing firms and enabling the formation of new enterprises.

The catalyst for this seismic change is the massive generation of real-time structured and unstructured data streams that organizations can leverage for decision making and operational change.

Emblematic of these new enterprises is Uber, the world’s largest “taxi” company, speculatively valued at $50 billion.

(1) Uber owns no vehicles, but harnesses a real-time digital data stream of its drivers’ cars and matches them with real-time demand for rides.

Existing organizations are also successfully leveraging the real-time flow of “big data” for new value creation.

Consider San Francisco’s SFPark, a private-public partnership of the San Francisco Municipal Transportation Agency (SFMTA).

By installing magnetometers to detect a vehicle in each of the city’s paid parking bays, SFMTA creates a real-time flow of parking data.

By doing so, it supplies real-time visibility of available parking spaces, reducing both the average time motorists spend searching for a parking spot (43 percent), the average cost of on-street parking (4 percent), and garage rates (12 percent).

Furthermore, the city is also able to appropriate some of the value created by introducing demand-response pricing.



As a result, parking bay occupancy targets are reached 31 percent more often, and there has been a 30 percent reduction of greenhouse gas emissions because of the fewer miles travelled by drivers circling for parking”[3]Consider examples of the data flows created by companies like Google, Amazon, Facebook, Twitter, Instagram, Pinterest, Uber, and LinkedIn.

These companies thrive on the data flows created by their customers as part of their core business strategies.

From Pigni, F.

, Piccoli, G.

, & Watson, R.

 (2016)Leveraging customer-driven data flows as core business strategyAmazon created a global marketplace[4] that allows their customers to upload their own products and leverage Amazon’s platform and market to sell to a global audience.

In a way, their corporate strategy creates a type of self-serve business model where customers are able to pick and choose the products and features they want to utilize, leverage the tools and data curation processes offered by the company to meet their own needs, and provide the data that allows Amazon to better enhance their offerings for other customers and expand into new markets.

Mazzei & Nobel (2017) said it this way:“Amazon offers an iconic example of how a firm might apply data and analytics to evolve strategically.



Starting as an e-commerce firm focused on books, Amazon was able to gain information and apply analytics to the mouse clicks of consumers viewing its inventory of books.

The firm captured browsing history, including search terms, books purchased, those not purchased, those placed on wish lists, and the length of time items were viewed.

This led to increased selection, improved target marketing, and ultimately an expansion into additional market segments by the e-retailer.

Amazon now sells virtually any product on the e-commerce website, including electronics, sports equipment, apparel, and even construction materials.

Improved analytic capabilities helped reinforce the power of big data, catapulting Amazon into a nascent industry as a cloud computing services provider.

Amazon Web Services (AWS) is now a $5 billion business that leads the cloud computing and analytics infrastructure market, offering flexible and comprehensive services to companies of all sizes (Novet, 2015).

”[5]Is it possible that through analyzing the search queries and traffic behavior of the users who were searching for books and products on their website, Amazon realized that the next big area where they had an opportunity for a new market was through cloud computing and web services?.Think about it.

Why would a company like Amazon reach into a market that in many ways was totally foreign to them?.A move like that was not an impulsive decision that happened in one grandiose step; rather, it required careful planning over a period of time.

One of Amazon’s first big data acquisitions occurred back in 1998 with the acquisition of Junglee Corp.

[6], an XML data mining company that extracted online product prices to allow web shoppers to compare prices across millions of products[7].

As we look back on Amazon’s decisions, clearly, they moved into the right direction; and as a consequence of their choices, they have become a major leader in this new market of self-serve web service technologies.

Google is another major example of a type of self-serve business model.

Google’s products thrive on the ecosystem of data flows created by their customers, including organizations that maintain websites and web applications, as well as individuals who are searching for content amidst these organizations[8].

Back in 2012, Google alone was processing approximately 24 petabytes (or 24,000 terabytes) of data every day, according to Davenport, T.

, Barth, P.

, & Bean, R.


Mazzei & Nobel (2017) also gave the example of Progressive Insurance, who leveraged real-time analytics to drive their core business processes in novel ways without substantially changing their market focus:“Progressive Insurance is using real-time analytics from in-vehicle telecommunications devices to monitor driving activity, creating a competitive advantage by identifying risky behaviors.

This allows the company to rate each driver more accurately based on their actual driving habits, while also encouraging positive changes in the driving behaviors of its consumers (National Association of Insurance Commissioners, 2015).

”[5]So even when it’s not appropriate for a company to dive head-first into a completely unfamiliar market yet, becoming data-driven can take place in incremental steps.

As Progressive Insurance began leveraging real-time data to more accurately rate drivers’ actual driving habits, additional insurance markets began to emerge, such as Pay-As-You-Drive (PAYD), Pay-How-You-Drive (PHYD), and Pay-As-You-Go (PAYG)[11].

These are yet still examples of allowing streaming data to drive market decisions, allowing companies in many instances to create new markets that address customer needs in better ways.

Big Data is not an independent process or technologyIt is now error for organizations to think of Big Data as simply a technology or process that independently influences the organization and adds value.

Many big data initiatives fail for this exact reason[12], but the organizations that interweave big data throughout their organization and business processes are the ones that obtain the greatest value and revenue gains from their investments[13].

However, integrating big data into existing business processes can require a reversal of many processes and complete transformation of management perspectives on decision making.

If big data has been introduced into your organization but your process for making decisions has not substantially changed, you are missing the opportunity and creating a bottleneck in your organization’s performance.

Mazzei & Nobel (2017) declared:“There are a number of visionary executives who are dedicated to building data resources that allow their firms to develop radically innovative business models that wed traditional and modern strategic thought.

In essence, these leaders focus on data as central to their organizational strategy and choose to concentrate on data flows rather than data stocks (Davenport et al.

, 2012).

These companies develop ecosystems devoted to their products and services based on the data they are able to accumulate.



Many of the traditional constraints to expansion and diversification are devalued as these learning organizations dynamically evolve based on trends uncovered through data analyses.

These companies create leverage– — due to their access to data, knowledge, and resources gained from past and current revenue sources– — that leave traditional competitive barriers (e.


, bargaining power of buyers/suppliers, barriers to entry) meaningless in many instances (Porter & Heppelmann, 2014).

The primary issues surrounding expansion and diversification for these firms are whether (1) their existing data collection and analyses inform new opportunities; (2) exploration allows for richer, more insightful data collection and analyses; or (3) the expansion effort improves the organization’s data ecosystem, wherein the end customer is viewed as a living, breathing data source.

These organizations perceive the compilation of data as a source of value creation in and of itself.

They do not need to monetize data immediately, for if they capture enough data it can be leveraged in innumerable– — and perhaps currently unrealized– — ways in the future as they broaden and navigate new industries as part of the development of their dynamic capabilities and digital ecosystem.



Among the firms mentioned here, there are numerous acquisitions or product line expansions that at first blush appear to be misguided, unless you envision the data flows as the output that the firm seeks.




that view data in a strategic capacity are able to experiment with their offerings without the immediate need for profit (e.


, Facebook into virtual reality, Apple into automotive, Alphabet into space travel and self-driving cars), so they can continuously innovate and learn what they do not know.

Strategically, this is a far different conceptualization than that of data as a tool, as data flows and new knowledge become the driving force behind strategic policy and decision making.



[These types of companies] continually develop their platforms into expansive ecosystems that permeate consumers’ lives, build data stocks, and increase the number of data flows that will be monetized through later products and technologies, which in turn are likely to continue adding data flows.

”[5]When we consider this new perspective that may seem like a radical transformation of traditional management theory, it is important to remember that the world is a very different place that it was when most contemporary management theories were developed, and the businesses that don’t catch the wave will be left behind.

How data driven were your most recent business decisions?Think back on the last several business decisions you made.

How many of those decisions were made primarily due to the data you discovered from another initiative?.If the process of making true data-driven decisions is not already part of your corporate culture, you are way behind.

No longer is it appropriate for companies to make decisions before leveraging the data.

Mazzei & Nobel (2017) stated it this way:“Exploration and exploitation decisions in [data-driven] organizations are not solely predicated on profitability; instead, these firms are concerned with enhancing data flows, with the intent to develop innovative service modules that can be easily combined with existing platforms to execute increasing levels of service (Morabito, 2015) .



At the time most contemporary strategic management theory was formulated, we did not have text messaging, chat, email, essentially-free long distance telephonic access, video conferencing, digital photos and movies, nearly unlimited storage of data, and the myriad of other digital tools that are now taken for granted.

Instead, deals were done using facsimiles, in-person meetings, typewriters, and “snail” mail.

Data processing was a long, expensive, and arduous task.



How [data-driven] firms choose to explore new markets is not done through traditional strategic planning, but instead evolves through opportunity recognition based largely upon information gleaned from consistently analyzing more and richer data flows.



Big data, digitalization, and automation are predicted to eliminate 47% of current human jobs over a 10–20 year period in the United States alone (Frey & Osborne, 2013)”[15]Think about what your company would be like if 47% of your employees were replaced by big data, digitalization, and automation.

Think about who would remain and who would be replaced.

Think about what types of roles might be replaced.

It’s important to recognize what is happening in the industry because if you haven’t already embraced it, the industry and your competition will do it for you, and you may be among the 47% who get replaced.

Is your data dictating your business strategy yet?From Mazzei, M.


, & Noble, D.

 (2017)The global perception of data is changing, and data is becoming increasingly important as a strategic priority.

Let’s think about strategy for a moment in a game sense.

In various games, you might have used strategy by making certain choices to see how your opponent will react so that you can estimate their plans.

You might have used strategy to explore certain opportunities or decisions to gather more information about the rewards or risks involved.

You might have used strategy to leverage your resources to obtain a competitive or positional advantage.

Now, in how many of those circumstances were your choices or confidence limited by the amount of available information you had?.Imagine if you had ten times the information about the decision you were considering.

Can you see how it could make the difference between a choice that was advantageous and a choice that would have been fatal?.Brandenburger and Nalebuff (2002) stated, “Successful business strategy is about actively shaping the game you play, not just playing the game you find.

”[19] Companies leveraging data flows as a strategic priority are actively shaping their markets and creating new markets while their competitors struggle to catch up.

However, not all existing business processes fit this modern paradigm of putting data first, so many companies are needing to seriously re-examine their assumptions, priorities, and thinking.

Massi & Nobel (2017) also stated:“Access to massive amounts of data and advancing analytic capabilities requires a reexamination of prior assumptions.



Does a firm’s business-level strategy dictate how it uses data to exploit current markets, or do data flows generated from a firm’s positioning play a more important role in diversification and the development of corporate strategy?.Whereas traditional views in the field of strategic management suggest that a chosen strategy determines the metrics of value and the selection or applicability of data, we argue that numerous firms have altered this approach.

Rather than corporate strategy dictating which data should be collected and analyzed, our observations suggest that in some instances the data collected and analyzed is having a dramatic influence on corporate strategy (see Figure 1).

Companies that embrace the opportunities for innovation and exploration presented by big data are realizing new value creation and improved firm performance (Lavalle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011), and are doing so on a scale not seen before.

We are witness to a movement in practice that has begun to unravel much of the known strategic management theory developed over the last 40 years by eviscerating traditional value chains and competitive forces (Evans, 2013).

The uses for data are shifting as collected data helps to determine what markets to explore and how consumer trends are changing, and the data can drive these determinations in real time.

We are seeing firms take on non-traditional markets, leveraging their data and analytic resources– — in conjuncture with massive amounts of human and financial capital– — to upend traditional barriers to entry.

The ultimate goal of big data movers and innovators is to build greater knowledge and dynamic capabilities and to apply the benefits of big data analytics in a way that creates unique and sustainable competitive advantage through the development of diverse ecosystems and data flows.

”[5]Putting the data first — How Big Data should affect strategyWhen was the last time that your company started a customer-facing initiative on the understanding that the initiative wouldn’t directly provide additional revenues but instead would provide data that would give executive management the insight required to determine if they should enter a new market that they hadn’t previously considered?.When was the last time your company risked your bottom line by starting a project purely intended to get the data to show you if your customers will be more interested in getting certain potentially high-impact new product features instead of others?.When was the last time your company created a controversial, “high-risk” project (that might even appear at first glance as misguided) to determine if the company should risk entering a completely new market that is either barely emerging or that they have no current experience in?.If none of these apply to you, then your company is behind.

Companies who focus too much on hitting their immediate revenue targets will miss the opportunities for the greatest long-term revenue gains and market opportunities that can only be obtained from insights gained from experimental data collection efforts.

Shah, Soriano, and Coutroubis (2017) reported:“Big data projects usually fail because organization [efforts] are on technologies and their capabilities instead of the business opportunity [9].

Companies, which are taking innovative approaches and giving a chance to exploration, are creating new values and reaching [accelerated] performance [25].

According to [15], a common mistake is to treat big data like a delimited over time project, rather than a continuous exploration exercise.

”[17]Making big data a core strategic priority means that it cannot be done in isolation.

Leveraging data cannot be solely handled by an isolated group, even an influential group, of experts within the company.

It must become a systemic organizational practice that pervades every aspect of management thinking and decision making.

Even companies with centralized big data analytics operations can struggle to create value from analytics efforts if it has not become part of management thinking across the organization, as stated by Fleming, Fountaine, Henke and Saleh (2018):“As with any major business initiative, analytics should have its own strategic direction.



We have observed that organizations with successful analytics initiatives embed analytics capabilities into their core businesses.

Those organizations struggling to create value through analytics tend to develop analytics capabilities in isolation, either centralized and far removed from the business or in sporadic pockets of poorly coordinated silos.

Neither organizational model is effective.

”[18]Companies that have yet to embrace analytics company-wide may require significant changes to become fully data-driven, but the first major step is to reverse our thinking to put data before the strategic planning occurs.

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