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The power of emerging technologies: Finding value through data

To get more value from their data, companies need to upgrade their IT architecture and invest in emerging technologies. Here are four best practices to successfully implement the necessary changes.

By Jannik Podlesny, Henning Soller, and Marvin Wenzel

Many companies want to extract more value from their data so they can improve products and make better business decisions. IT architecture is a critical component in this endeavor, but to date, companies have been reluctant to commit resources to it without seeing a clear return on investment. The good news is that, across industries, we have seen increased instances of use cases that justify investments in IT architecture and data tools. A recent McKinsey Global Survey on data and analytics finds that companies can typically reduce 30 percent of their data spending and 40 percent of their time to market for data products through next-generation use cases, including real-time demand estimations and pricing in e-commerce and advanced analytics models.

However, many companies struggle to understand emerging data solutions and their respective advantages and disadvantages. Based on our experience, we have developed a four-step process that can help companies chart a clear path to get more from their data.

How industries are getting more from their data

Industries can select from a range of different technologies to extract value from their data (exhibit). An organization’s approach to building out technologies or creating use cases depends on its level of digital maturity.

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High-performance computing in healthcare

A large healthcare regulator is employing supercomputing to build a database that houses information on the country’s entire population for a genome-mapping project. The intent is to build a system that can hold a foundation of data and build targeted patient groups. Over time, doctors can use this biological data to better diagnose and treat people with different disease profiles. Advanced technologies can also be used in healthcare to enable personalized medicine through tailored treatment plans sensitive to each patient’s characteristics and needs, thus helping to reduce the risk of drug side effects. Such large-scale personalization efforts have already been implemented in other sectors, such as digital advertising (adtech), where interest patterns and buying behavior are individually predicted.

Digitization and automation in pharma

In pharma, digitally enabled labs can use advanced real-time data analytics to optimize scheduling, planning, and processes; identify trends across these three areas; and make recommendations. Improvements in these three areas could reduce the time it takes for a lab to process a sample and release test results by 10 to 20 percent. Those efforts are not completely new and have been successfully pursued in other sectors, such as retail and aviation. Leading e-commerce companies, for instance, continuously work on automating their backbone processes, even shifting to shelves that move toward the worker instead of having workers search the warehouse for pallets.

Advanced analytics in retail and insurance

In retail and e-commerce, companies could use machine learning to develop a method or model to identify or predict potential cannibalization of products or items. One retailer used geospatial analytics to analyze store performance in a specific market and found that its outlet stores were cannibalizing each other as well as its full-price stores and website. The company used that information to adjust its portfolio of stores in the market and to update its product displays.

Insurers can use advanced analytics to assist with sales and customer targeting; for instance, carriers can receive reminders from an AI-enabled system to reach out to customers about an insurance product at the right moment. They can also use analytics at the first notice of loss to quickly identify and route claims to the appropriate handler. Machine learning can also help improve processes and classify claims predictions.

While data-driven decision making is a more recent trend in the insurance sector than in retail, both can actively benefit from the experiences of the aviation industry or financial institutions. Airlines, for example, continuously strive to reduce turnaround times to maximize utilization of aircrafts and use advanced analytics to estimate demand and predict routes.

Making the transition to getting better value from data

Collectively, market demands are pushing organizations to handle even more extensive data sets and to achieve even faster query and analytics responses. Like the healthcare authority, companies are looking to build systems that store massive amounts of data. This speed can be a competitive advantage, but the pressure to deliver can also create complications around data management, computational performance, and scale of use cases—and the necessary technology investments to support them. Further, this exponential growth of data not only complicates certain aspects of IT applications but also raises governance concerns around an organization’s ability to react quickly to changing business requirements or to ensure data-security parameters are in place.

When assessing technologies for investment in order to support potential data use cases, organizations often face three common challenges. First, many lack the capabilities to accurately assess emerging technologies, which leads them to rely on traditional solutions rather than make additional efforts to implement new ones.

Second, most large organizations feel locked into their existing vendor relationships. Often these companies are stuck with rather expensive incumbent technologies and fear that replacing them with emerging next-generation tools implies risk. Organizations may also have failed to negotiate favorable contractual terms when renewing contracts and thus stick with unfavorable technologies for required data infrastructure instead of breaking their contracts.

Finally, incumbent technologies are typically rather expensive to maintain compared with emerging technologies, despite similar use cases and features. A failure to negotiate more favorable terms when renewing contracts has left many companies saddled with a high cost base for required data infrastructure.

Viewed collectively, these challenges highlight the struggle that companies experience in trying to balance the business and technical value of emerging technologies, such as the cloud, with the costs and capabilities required to do so. Thus, to address these challenges and fully unlock the value of data, companies should pursue four best practices.

Set a data strategy and road map

Companies planning a wholesale replacement of existing technologies will need a clear strategy for fitting the new technology into the overall business plan. This requires an analysis of current and potential use cases. A clear funding plan and a road map in line with the additional costs of the use cases will also help focus their efforts and resources. For selective technology updates, where the enterprise system is left intact, organizations could assess potential areas for improvements in their existing information architecture and technology landscape. This will help them make better decisions regarding which incumbent technologies to replace with relevant emerging ones.

Build information architecture and technology

Provide transparency across business units on existing information architecture and technology. Organizations should also identify the gaps in their current systems that might deter a data strategy or the implementation of use cases.

Clarify data governance

Companies should define roles, responsibilities, and committees in charge of leading the data-strategy implementation—the output of which is data democratization. In addition, setting a clear ethical framework for aggregating and using data can ensure that information is used appropriately.

Define a talent strategy and culture

Organizations will need a hiring strategy for talent who can support the overall data strategy and build the internal capabilities needed to become a data-driven organization. Investing in capability building requires organizations to attract and develop technical talent focused on data technology and use-case implementation. As part of this effort, leadership will also need to promote cultural change to ensure that data is deeply embedded in decision making across the entire organization.


Whether or not companies intend to invest in emerging technologies right now, they should strive to stay up to date. Specifically, companies should consider how they can incorporate emerging technologies to reduce costs and speed their products’ time to market. The demand for data integration and application will only increase—so understanding how to select and incorporate new technologies is critical.

Jannik Podlesny is a specialist in McKinsey’s Berlin office, Henning Soller is a partner in the Frankfurt office, and Marvin Wenzel is a consultant in the Cologne office.