An Ideas-Based Online Magazine of the Global Network for Advanced Management

Strategic-Level Thinking as an Engine for Growth with Big Data

The increased volume of data created every day outpaces the ability of organizations to collect and process it and to obtain value from it, so they can continue to be relevant in their sector or industry. Businesses today must design a digital strategy that takes Big Data into account and recognizes that the most valuable data is not what is stored in databases but what has not yet been created.

Big Data Illustration

We are living a Big Data revolution. Yet the huge volume of data transmitted every day over the Internet and from millions of interconnected sensors might be overwhelming to organizations that are just beginning to get started in the Big Data world.

The volume of data generated in the world is calculated at more than 2.8 zettabytes and is expected to rise to 50 times higher by 2020. Likewise, the number of nodes and sensors is growing at an annual rate of 30%. This data explosion surpasses the ability of organizations to collect and process it and to extract value from it, so they can continue to be relevant in a very competitive arena where no organization can escape from this new reality.

As it is critical to collect this data, businesses must design a Big Data digital strategy to be able to face the growing volume of information and to create competitive advantage. The strategy is always one of selection, because not all data can be used. An organization must make decisions about prioritizing and implementing a short-, medium-, and long-term portfolio of initiatives. They must be aware that the most valuable data are not those stored in databases but rather data that have not yet been created.  

In the chapter “The Strategic Business Value of Big Data,” coauthored with Dr. Jorge Ramírez and published in the book Big Data Management (Springer, 2017), we analyze how Big Data impacts business competitiveness and innovation, to identify different digital strategies to be implemented to gain competitive advantage. The disruptive power of Big Data requires businesses to get involved at the strategic level, because they can change the competitive environment by the transformation of processes, the alteration of corporate ecosystems, and the facilitation of innovation through products and services they had never imagined. Businesses such as Uber, Waze, Airbnb, and Tesla have helped some industries to transform, but not all sectors adopt Big Data at the same pace or give it the same relevance.

Many technology, media, auto, and telecommunications organizations are taking advantage of Big Data, while other types of businesses such as financial services, health, construction, energy, consumer goods, and the public sector still have a ways to go. But they should pay attention. The rapid change in sectors such as mobility and transport shows the size of the phenomenon; today auto, technology, and startup companies are both competing and complementing each other, reimagining their processes, their ecosystems, and their value creation in a world they had never considered.  

Mapping out opportunities in the algorithm age

Opportunities from Big Data can be framed through two dimensions: reengineering the value chain and reimagining the offering. Think of any product, service, or company and how its value chain is organized. It has several clients and suppliers, and many data are generated not only at the point of sale—through online, physical, or mobile channels—but also at the different stages of the supply chain—automated order and delivery processes, financial processes, or even human-resource management. Meanwhile, reimagining the offering requires creativity and vision, and Big Data offers ample opportunities for new products and services.

Essentially, the goal of a Big Data digital strategy is to turn data into information and knowledge, which over time leads the business to transform. Leading companies are constantly threatened by new players who approach client needs in different ways and force businesses and entire sectors to be reinvented. They require agile methods to test and improve prototypes, products, and strategies along with clients. One example is Amazon, which, in only ten years, introduced electronic-book readers, tablets, smartphones, cloud services, delivery services, and online markets.

Before setting up a Big Data strategy, it is necessary to identify in which maturity stage the organization is situated:

  • Enhancement: Even though it is the least radical stage, it can provide immediate value creation and fund the broader Big Data strategy growth. This stage includes predictive maintenance and streamlined digital links to help suppliers and clients make better and more accurate decisions. This enhancement includes “recommendation engines,” a useful and sometimes necessary tool.
  • Exploration: The organization investigates offerings adjacent to the current business or pursues larger adjustments of the value chain. These decisions involve management and require significant investment in infrastructure and technology, and their performance must be closely tracked.
  • Transformation: This stage is relevant for organizations that need to face deeper changes. This all-encompassing strategic move has the greatest potential to generate competitive advantage, but also implies the greatest risk. It requires major investments and the development of new partner ecosystems and key alliances.

To achieve this vision, businesses can attract scientists, experts, or organizations who can find patterns in data and translate them into useful business information. But the CIO should be the one to run this transformation and to make decisions above and beyond the technology architecture and the traditional processes.

The Big Data strategy depends on the maturity levels and on identifying and prioritizing strategic choices. An intuitive tool is the digital strategies matrix (see chart below). Organizations must take into account both the external environment and the internal capabilities. As a result, the digital strategy is an iterative process that is continually adapting and identifying new opportunities. 

The Digital Strategies Matrix, authors’ elaboration based on “IT Value Performance Tools Link to Business-IT Alignment,” Gartner Research

Big Data for decision making

It is not enough to trust intuition and experience, because we are seeing the emergence of a culture of data-driven decision making. Companies that use their data more intelligently have a better performance in financial and operational results and are better able to realize their business objectives.

Organizations with a higher maturity level are better positioned to carry out key business strategies using accurate, real-time data. Furthermore, the data tend to be fragmented and out of date at lower maturity levels, which makes it difficult to make adequate, timely decisions.

The realization that fact-based decisions are critical at every level of the organization has led to a need for technological tools and advanced analytics techniques. Groups of data on clients are analyzed—for example, their preferences, purchasing habits, website browsing, purchase histories, physical position, response to incentives, and other demographic information—and the relevant decision makers in each case receive this information. The greatest benefit of Big Data analytics is improving and speeding up the decision-making process and supporting decision making with automated algorithms.

Five Industries impacted greatly by Big Data

Several organizations from the following sectors are using Big Data to enhance, improve, or transform their value and performance:

  • Retail: With data on online transactions and client behavior, the retail industry is taking advantage of the value chain for merchandising and pricing, improving customer service or using location-based marketing strategies to send offers to their key customers’ smartphones. They are also optimizing distribution and logistics thanks to data from GPS-enabled telematics.  
  • Manufacturing: In their production processes, manufacturing companies are incorporating real-time data from sensors. There are several advantages, from reducing waste to maximizing yield throughout larger, more-complex, more-globalized supply chains.
  • Telecommunications: By analyzing network traffic in real time, businesses in this sector can optimize service quality, develop new products, identify fraudulent behavior, or prevent the loss of customers, among other things.
  • Public Sector: Big Data can help governments reduce administrative costs, collect taxes more efficiently, and increase transparency by making the most-relevant data on programs and public policies available to citizens.
  • Health Care: The integration of nanotechnology embedded in people, for example, is an innovation that could be used as a tool for monitoring and diagnosing, integrating and sharing different datasets with biological information and high-resolution tools such as X-rays, CT scans, or MRIs.

In these sectors and others, Big Data will have a profound impact on business as we know it today. To be successful in the digital transformation, businesses must link their offerings to their strengths—what they know how to do well or what makes them stand out—and identify where they can add value for their clients. In data processing, volume and speed will continue to present challenges for businesses to become more agile than their competitors, but in the end, it will be the ability to create a culture that fosters innovation and experimentation that will make the difference.