Data mastery: beyond human scale

Many aspects of data management – from organizing and preparing it for AI analytics, to using it for insights – are increasingly beyond human capability. 

As Figure 2 shows, the ratio between the volume of work performed by humans as opposed to machines continues to turn in favor of machines, particularly when it comes to data organization, complex decision support and rules-based decision making.

Consider that today, the main types of data integrated into AI applications are Internet of Things (IoT), customer and internal data. In many cases, this is simply because of the sheer volume of accessible data generated by sensors and customer interactions. But other forms of data are where the most extraordinary insights often lie, particularly when multiple forms of data are combined. 

This means getting past the reports and spreadsheets and bringing in data that’s not always structured and formatted and not always owned by the business itself, including publicly available drone and camera images or social media sentiment, as well as geolocation and psychographic data. It also means combining this data in new ways, such as taking video from street cameras and merging it with traffic data and local tweets to ascertain the business revenue of a particular geography or even what people are buying in that area. 

More work pivots to machines as process data explodes

Respondents were asked to what extent the following activities are executed by machines vs. employees, now and in 2023. (Percent of work that is or will be conducted by machines)

Figure 2

Response base: 4,000 Source: Cognizant Center for the Future of Work

Respondents were asked to what extent the following activities are executed by machines vs. employees, now and in 2023. (Percent of work that is or will be conducted by machines)

Imagine pulling insights from millions of customer interactions with geolocation or psychographic data and making accurate, ongoing predictions regarding consumer needs and desires. What if you could add human insights into the results (warmth, empathy, creativity), with the ability to craft engaging, insight-driven customer journeys that work at scale? 

Retailers, for example, could create immersive product catalogs with a “virtual try before you buy” feature; educators could offer personalized and effective learning paths for any subject; doctors could spot opioid addiction or a patient’s withdrawal from the physical world. The possibilities for work are endless. This shift is not science fiction; it is happening now and is generating achievable outcomes across a host of processes and industries.

Data really is the new oil

The key question is whether your business and current technology infrastructure can handle this deluge of data. Volumes will only increase, especially with a second wave of IoT solutions coming online, and the advent of 5G set to transform these

solutions with greater bandwidth and lower latency. IoT sensors embedded into products would enable better user experiences, or give process owners the ability to monitor assets virtually, continually adjusting them for peak performance and applying data insights from third-party sources. Is your workforce ready for the advent of these new technologies? How will you cope with the deluge of data? This is why the use of machines is on the rise. 

In our study, it’s clear businesses recognize that handling today’s data volumes cannot be done by human workers alone. Businesses need help organizing their data more effectively, using machine learning software targeted at databases to cleanse and organize data so it can be of business value. According to respondents, machines will perform a greater portion of this task, from 17% of this work today to 26% by 2023.

The second and third areas where the transition toward machines is set to accelerate are both encompassed by the function of decision support. “Execution of complex decisions” and “execution of routine, rules-based decisions” are both areas in which respondents expect to see a significant transition toward machines in the next three years (from 16% to 24% and from 15% to 23%, respectively). Executives are increasingly turning to AI to process large data sets (just as most stock trading is now commonly undertaken by machines, complex decision making will be done more quickly and effectively by machines). As this shift occurs, businesses will need to more fully consider the best ways for machines and human workers to partner together.


by 2023

Machines will perform a greater portion of data management tasks, from 17% of this work today to 26% by 2023.