The digitization of business has decreed the explosion of available information, offering companies the opportunity for better strategic decisions. Triggering a virtuous cycle of data-driven innovation means exploiting the immense information assets to obtain helpful evidence for the business through advanced analytical tools, artificial intelligence solutions, and data management systems. The objectives go in several directions: recovery of efficiency, automation of processes, innovation of the offer, refinement of marketing and so on.
Especially in an emergency context, such as the current pandemic and economic recession, it is essential to seek alternative ways to operate and present oneself to the market. We need to position ourselves at the starting blocks with differentiating models, making it possible to generate savings, margins and profit by attracting new audiences. A practical and “creative” use of corporate data, too often used incorrectly and reductively, can become the engine of a recovery that would otherwise be a dubious success.
Streamline Processes And Prepare People
How to get started? Developing a data-based strategy requires intense and coordinated work on three main aspects: processes, people and technologies. Before venturing into any initiative aimed at data-driven innovation, a careful organizational evaluation and review are required. It is essential to define the areas of intervention (which activities are to be improved through the conscious use of data?) And to rationalize the operating procedures, thus optimizing the processes of information collection, selection and management. At the same time, people will be trained to acquire the following:
- Mastery of the digital tools involved in the data-driven innovation process.
- A reasoning model based on analytical insights.
- The ability to exploit the automation enabled by artificial intelligence, delegating low-value tasks and taking on roles of greater responsibility.
The Technological Pillars For Data-Driven Innovation
Keeping process optimization and change management as an indispensable premise, the technological areas to be monitored for data-driven innovation strategies concern:
- In the on-premise or cloud version, big data platforms allow real-time integration of any data, structured, semi-structured, or unstructured. In this way, a unified view of multi-format information is obtained, coming from a plurality of sources: mobile apps, social media, IoT devices and so on;
- advanced analytics, which processes continuously updated data to predict future trends, behaviors and events, outlining what-if scenarios and anticipating the impact on corporate strategies of any changes;
- Data governance solutions ensure data quality throughout their life cycle, managing all processes aimed at ensuring their availability, usability, consistency, integrity, standardization and security.
- Artificial intelligence technologies, from machine learning to natural language processing, combine cognitive science with information technology, allowing computational systems to simulate human reasoning, analyze phenomena and activate automatic response mechanisms.
The Data-Driven Approach In The Corporate DNA
Any data-driven innovation project, from IoT initiatives for optimizing processes to constructing recommendation systems to intercept new sales opportunities, requires a skilful mix of the technologies above. Only in this way can the information sea magnum be managed correctly and provide helpful evidence to those who have to make decisions (a human professional or an artificial intelligence system). The combination of techniques for extensive data integration, governance and analytics represent the necessary substrate for the digital transformation of companies and the realization of data-driven strategies.
The Journey Towards Data-Driven Innovation
However, it is a path that organizations must face gradually, passing through different stages of technological, methodological and cultural maturity. The ability to exploit information and analytics will lead the company to expand its knowledge progressively, developing the results for the benefit of the business and achieving true data-driven innovation. In the initial phase, historical data is analyzed to monitor a stock’s performance retrospectively. In the second stage, analytics allows the extraction of hidden evidence to deepen the knowledge of a phenomenon. The next step is to use analytical insights to optimize operational and strategic processes.
Monetizing the data analysis, taking advantage of greater efficiency and decision-making improvement, is the fourth step. Finally, the top step is the corporate metamorphosis, creating new organizational and business models. Only then can we talk about data-driven innovation. The process is not trivial and must be tackled gradually. However, the crisis we face must be read as a stimulus and an opportunity to incentivize the journey towards a data-driven organizational model. Accelerating the roadmap means guaranteeing continuity for the company itself, which can overcome difficulties and differentiate itself from the competition thanks to the powerful insights engine.
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