In recent years Banks have initiated important transformational paths to evolve their models of government information;
the pressures for change are essentially grouped into three types:
- Regulatory: recent wave of regulations in the field of data and reporting (eg BCBS 239, Anacredit, …) are characterized by the increasingly pressing need to have granular, timely and high reliability data (both in analytical terms and in terms of descriptive underlying computing processes) and therefore functions dedicated to the “government” of the life cycle of information
- Technological: in recent years we are witnessing a technological revolution unprecedented characterized by a multiplicity of components (eg. Blockchain, Artificial Intelligence, Virtual Reality / Augmented Reality, Analytics, Cloud, IoT, Big Data, …) with relatively quick introduction within the financial sector, and particularly focusing on new and increasingly sophisticated methods of collection, enrichment and analysis of large volumes of data
- Competitive: the complex current economic situation and the changing market environments than in the recent past (eg. Fintechs) have prompted Banks to rethink / drastically revise their business and operational models evolving towards IT organizational structures much more responsive, light, open and focused on knowledge “deeper” behavior, needs and intentions of its customers
Therefore, in order to respond effectively to the complex challenges that the market presents, the Banks are definitely considering the “data” as a strategic asset to know, preserve and above all exploit their value deriving meaningful insights and persistent to convert knowledge into action, according to evolutionary paths toward what we call “Info-driven company” or else a company that is able on real time basis to make sense of the data arising from a digital and fast iperconnected world and gaining powerful insights delivering promptly to human or unhuman supervised points of action.
By our point of view, to succeed into the evolutionary “insight driven” path, 5 are the key success factors:
- Leveraging the proliferation of data and new technologies to get a new perspective on the business
- Applying machine learning and data science techniques to deconstruct and predict potential customer behaviour and enterprise performance
- Providing asset-powered agility to address constantly shifting analytics needs
- Embedding analytics into the operating model and aligning the organization, processes and technology to enable a scaled, data-driven enterprise
- Mobilize the organizations to embrace and fulfill the promise of analytics help them gain a better understanding of the scope and meaning of this revolution
Regulatory, technological and competitive pressures have pushed most developed financial intermediaries to launch important Programs of transformation in the “government” of the information, become increasingly a source of competitive advantages determinants: high-performance companies recognize that data is a strategic asset and strive to adopt a data-driven culture to foster data-based decisions that lead to clear business outcomes, yielding a measurable return on investment. We are seeing several imperatives in our clients’ journeys to become data-driven and insights-powered organization. We believe that it is imperative that enterprises:
- RIDE THE DATA WAVE: Identify precise data, create new data management strategies, and build partnerships to share data in new ways.
- ACTIVATE PLATFORMS: Employ a platform ecosystem as the foundation for operating and new value creation.
- EMBRACE THE MACHINE AGE: Seize augmented intelligence to improve asset efficiency, transform customer interactions and enable the enterprise to operate in new ways.
- BECOME DESIGN LED: Embrace human-centered design so that analytics is contextual, intuitive, immersive and engaging to drive consumption.
- THRIVE ON INSIGHTS: Reorient people to continuously adapt, learn, create and drive relentless change, fueled by insights. Replace business as usual with a new standard of agility.
In our point of view the “big” data layer should be completely re-thought and re-build, allowing data to regain their core role against the single vertical applications. Leveraging new and more robust technologies, data should be always accessible, coherent and consistent and “nearest to the digital touchpoints” in order to enable real time interactions and fast analytics powered managerial decision: we believe in a future based on a unified, secured, cognitive data platform used via compelling and real time UIs.