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Data is at the very heart of digital transformation and vital to business strategy. Every company has proprietary data assets in addition to domain expertise. Acting logically, industry leaders seeking to protect existing markets rely on data; however, without continual data innovation, market leadership is unsustainable, and creating new markets unlikely. Over time, static incumbents create disequilibria in which existing markets, either perceived as unattractive or unidentified, are underserved. Such inefficiencies risk dislocation in the competitive landscape and market disruption. Data-driven innovation in digital transformation underlies disruptive strategy and is the ultimate competitive differentiator. Data is incredibly valuable in global real estate and infrastructure; it is revolutionizing the industry’s future.
Data is the fuel that powers multiple use cases and strategies. A data strategy contains both defensive components aimed at maximizing operational efficiency and making data-driven decisions, as well as offensive components including establishing new markets, creating new revenue streams, increasing retention and win rates of the core business. Failing to identify subtle data trends and leverage data insights results in significant revenue loss, increased risk, and operational process deficiencies. Conventional analysis of structured data sources makes it challenging to generate clear hypotheses and build robust commercial use and integration.1 Traditional variables are already highly predictive of real estate values and other informational outcomes, but the predictive power of nontraditional variables is an essential driver of insights. Unconventional data sources are increasingly relevant. Transforming raw data into actionable information requires processing, aggregation and organization, generating predictive insights, and delivering prescriptive outcomes. Data innovators have the potential to turn a compelling opportunity into a substantial competitive advantage. It is not the raw data that creates value; instead, it is the ability to extract patterns, forecast and use those predictions to serve existing markets better and create new ones.
1 Gabriel Morgan Asaftei, Sudeep Doshi, John Means and Aditya Sanghvi, “Getting ahead of the market: How date is transforming real estate,” https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/getting-ahead-of-the-market-how-big-data-is-transforming-real-estate (McKinsey Insights Oct. 8, 2018).
Data Innovation: Driving Digital Transformation and Sustainable Competitive Advantage
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The leadership mandate for digital transformation is becoming evermore critical with a granular focus on Customer Experience (CX) as a potent driver of growth. Analytics can change the relationship with the customer, from a product and service offering to a partnership offering, laser-focused on outcome-orientated solutions. Advanced analytics swiftly yield robust and essential insights that challenge common intuition. Companies should begin by employing analytics in identifying strategic imperatives, pursuing data-cleansing efforts, and establishing transparent processes for data governance, interpretation and decision-making. For example, an asset manager may expand and optimize a portfolio of multifamily buildings using machine-learning algorithms, which can rapidly combine macro and micro forecasts to prioritize cities and neighborhoods with the highest demand for the asset class. Failure to leverage this data risks disruption. In another case, innovative uses of Operational Intelligence Data that track a global portfolio in real-time can maximize investor returns and identify new markets.
Ultimately, data innovation needs strategic direction with long-term roles and goals beyond pilot projects. Data focus is indispensable at every level of the organization, including the C-Suite. Deploying an effective strategy requires advanced analytics, depth of data, and native cloud solutions that focus on three primary pillars: Predictive Data, Data Mining, and Big Data. The information gleaned, in conjunction with Application Programming Interfaces, Embedded Analytics, R Analytics, Python for Data Science, and Apache Hadoop, is essential. To accelerate the industry’s path to digital transformation, one must incorporate hyper-automation using Artificial Intelligence (AI) and Machine Learning (ML), Augmented Reality (AR) and Virtual Reality (VR), Geospatial Intelligence, Edge Analytics, and integrate Smart and Sensor Technology, and other connected IoT devices. These tactical initiatives can only be achieved by attracting and retaining visionary data experts and technologists.
Leading innovators consistently outcompete their peers in analytical capabilities. Those who lag consistently fail. Digital transformation requires a multidisciplinary top-down approach spearheaded by an organization’s leaders. Exemplifying this, predictions show that 20% of all chief executive officers will have a chief data officer background by 2030.2 Data innovation is imperative and will prove to be integral to achieving a sustainable competitive advantage.
2 According to Charles Holive, Managing Director at Sisense, Inc. (2019).
Ferguson Partners' Innovation + Technology practice brings your organization the precise talents, skillsets and capabilities it needs to achieve digital maturity. Using multiple lenses to explore your future-ready state and identify risks and opportunities, we deliver a comprehensive, elegant solution that reflects your strategic vision and respects your culture.
In a globally connected, data-driven world, the difference between keeping up with the competition and outpacing it is talent. Technology is the platform, but human expertise and ingenuity is the driver. With our deep understanding of what’s ahead, we can streamline your path to innovation.
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Linda J. IsaacsonManaging Director
Global Head of Innovation + Technology
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