The potential of big data is growing. Taking advantage of this advantage means that companies combine their strategic perspectives with big data to make better, faster decisions.
Is big data just hype?,
Quite the contrary: early research may have only partially affected the final outcome. McKinsey Global Institute (McKinsey Global InsTItute, hereinafter referred to as MGI) in a new report, the era of data analysis: business competition in big data environments, indicating the scope of application and opportunities of big data is still expanding. Given the rapid growth of technology, many companies now need to face the integration of big data into their operations and strategies – identifying where their big data analytics may subvert the entire industry.
Big data potential continues to grow, business competition is becoming increasingly fierce
A report from MGI pointed out in 2011 that big data will make a big difference. Today, five years later, we still believe that the potential of big data is still very large. In fact, the trend of technology convergence is accelerating. Information flows are doubling into data platforms, wireless sensors, virtual reality applications, and countless smartphones every three years. Data storage capacity has increased while costs have fallen dramatically. Data scientists now have unprecedented computing power and are committed to developing more complex algorithms.
In the early days, we expected the potential market value of big data and data analysis to exist in five main areas. Looking back, the chart below demonstrates that developments in all areas are uneven and that there is still room for further development (Exhibit 1). The biggest developments are location-based services and the US retail industry, where competitors in both areas are digital natives. In contrast, the potential value of manufacturing, the European public sector, and the US health care sector is less than 30% of the estimated value of the article five years ago. In addition, since the emergence of new opportunities in 2011, the gap between leading companies and laggards has increased.
By using their capabilities, large companies have not only improved the efficiency of their core business operations, but have also introduced new business models. The network effect of digital platforms has created a winner-take-all situation in some markets. Leading companies already have experienced analytics to deal with issues, and they are actively looking for ways to enter other industries. These companies can use their volume and depth of data to add new business, and these expansions are gradually eroding the boundaries of traditional industry separation.
When digital native companies build systems based on data analysis, it is not easy for established companies to make great efforts to reform or transform existing systems to adapt to data-driven decision making. Some companies have invested heavily in technology, but they have not changed their organizational structure to make these investments work. Many companies are working hard to identify talent, upgrade business models, and adjust organizational structures to capture the results of data analytics.
The first step is to incorporate data and analysis into the company's core strategic vision. The second is to develop appropriate business processes and build capabilities, including data infrastructure and corresponding talent pools. This is not simply superimposing a powerful technology system on top of existing business processes. These transformations need to combine all aspects to realize the full potential of data and analysis. The challenges faced by the old company in the transformation are the reasons why the vision mentioned in the MGI 2011 report has not been achieved.
The transformation of existing enterprises is imminent, because the pioneering enterprises have already occupied an absolute advantage, and hesitating will only make reform more difficult. For many reasons, reforms are now facing resistance. Introducing new types of data sets ("orthogonal data") can bring some competitive advantages; for example, large-scale data integration can break through the constraints of organizational structure, making new perspectives and model applications possible. The ultra-large digital platform [6] can match buyers and sellers in real time, increasing market conversion rates. Fine data can be used to personalize products and services, including the special area of ​​healthcare. New analytical techniques can promote new discoveries and innovations. Most importantly, companies no longer need to rely on intuition, they can use data and analytics to make quick decisions, and through a lot of empirical evidence to achieve more accurate predictions.
The tools of the next generation may cause even greater changes. New machine learning and deep learning techniques can extend enormous possibilities and generate economic benefits in many areas. The system provides customer service, logistics management, analysis of medical records, and even news reporting through machine learning.
These technologies can increase productivity and improve quality of life, but they can also lead to unemployment and confusion. MGI's previous research found that 45% of current human labor can be replaced by robotics [7]; 80% of which is achieved through machine learning. The breakthrough results of natural language processing can further expand this impact.
Big data and data analytics have shaken across multiple industries, and as data analytics applications reach critical mass—the machines will gain unprecedented capabilities to solve problems and understand language, and these effects will become more apparent. Companies that can effectively leverage these capabilities will be able to create tremendous value and differentiation, while the rest will find themselves increasingly disadvantaged.
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