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Ernst & Young Advisory
Powering Data-Driven Innovation
Cao Hong
These innovations can accelerate the process of turning data into information, knowledge and insight, and make them available at the right time, at the right place and to the right people. This is set to transform our current way of “live, work, and play” smarter in many ways. To an organization, this also means tremendous opportunities to escalate their information system and strategically lift their performance and business competencies.
Starting the journey
Data-driven innovation is no doubt the technological driver for digital transformation. Despite its good prospect, starting the journey with proper planning is important for it to take shape. First, there should be a strong commitment from C-suite to understand its promises and risks, and the real meaning of the data to their business as part of the strategy development.Those that do not risk being left behind by their peers.
At the execution stage, a dedicated innovation task force can be involved to bring forth a smooth process. The company can also expect organizational changes beyond upgrade of IT infrastructure and data platform,such as a cultural shift towards openness and acceptance of new ideas, background and new skill sets, employees empowered to innovate, and multi-level collaboration. For a smooth transition, organizations should see this as a continuous performance improvement journey, where they have the opportunity to create a fact-driven and experimentation friendly environment, identify quick wins to start with, effect the changes progressively into the business pipeline, and move up the value chain.
Given the risks associated with any innovation projects, a de-risked approach is recommended. Innovation project should start small; incrementally build up, so that the organization continues to stay agile and vibrant. If a concept needs to fail, let it fail fast, fail small and take on the learning to start again.
It is also important to empower the entire workforce and encourage bottom-up innovation ideas. Business case and its potential value can be demonstrated starting from early prototyping stage.
Trust in data
The growth of data-driven innovation has resulted in concerns over trust in data systems. As a result of the wide sources and types of data, there is a lack of trust in the data quality and huge effort is typically required for data cleaning before usage.
Addressing this, organizations need to work on the technology and process management in data acquisition. For example, organizations can consider appointing data stewards in the organization who are incentivized to be accountable for maintaining quality of data sources. Also, the growing complexity of artificial intelligence has resulted in a lack of understanding and trust in the system. To this end, transparency and user experience and subsequent education should be factored into design of a data system. In addition, amid data fraud concerns, there should be security technologies to detect and alert fraudulent data manipulations.
Deliver value
According to Data & Advanced Analytics: High Stakes, High Rewards, a study by EY and Forbes Insight, only seven percent of global respondents have a well-established enterprise-wide analytics strategy that is central to their business. Out of these, 66 percent achieved revenue growth of 15 percent or more, while 63 percent reported that operating margins had increased 15 percent or more in 2016. In addition, 60 percent of them said they improved their risk profiles.
Data-driven innovation is no doubt the technological driver for digital transformation
Despite the strong value proposition, numerous organizations and industries are still at the early stage of digital transformation. The majority of data engineering works still focus on infrastructure upgrade, data preparation, and integration of silo-ed data sources for centralized governance. There has been limited focus on the real value that can be generated by advanced analytics.
For now, data-driven innovations typically happen at the entry level of descriptive analytics. However, for data-driven innovations to deliver true business value and performance improvement, there is a need to move up the value chain to predictive and prescriptive analytics.
(Source: Data & Advanced Analytics: High Stakes, High Rewards by EY and Forbes Insight)
Predictive analytics aims to predict the future happenings. Prescriptive analytics takes into account fu-ture predictions and their implication to key performance indicators to prescribe recommended actions. For this, simulation and optimization are typically leveraged to support real-time decision making. In years to come, we expect more data-driven innovations will climb up the value chain to deliver high-value solutions.
A powerful concept of data-driven design is to digitally recreate complex real-world systems. Technology advances today are driven towards mixed reality where boundaries between physical and digital, human and machine have blurred. This incubates the creation of mix-reality virtual simulator, an integration of mixed types of data models wrapped within an immersive human interaction interface.
This simulator can resemble the behaviors of a real system, such as a vehicle. Such a data-driven technology will enormously accelerate the design iterations of a complex system by offering a virtual experimentation environment with humans in the loop. The environment can also prepare a trainee to gain marketable skills for working with a complex system.
Innovation has often been thought to be a driver to business success. Indeed, innovation can be a revenue center when it is embedded into the business. With the ubiquitous data generated from the business, data-driven innovation might just be the way to take the organization forward.
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