The dynamic force of innovation strategy in data-driven transformation
Considering innovation as a system and having a goal to embed it within one’s organization is neither an easy task nor an impossible one. If this is the primary objective, and if this aligns with the consensus of all stakeholders, it becomes crucial, before commencing any actions, to adopt a mindset focused on innovation, akin to how one concentrates on developing the organizational direction to enhance revenue and profit.
This implies that to succeed, the same level of effort and methodology must be directed towards developing the organizational strategy and executing the most effective and efficient innovation methods. This involves clarifying the purpose, establishing the right mission (the reason behind the initiative and the desired impact), and defining values (principles guiding all stakeholders). Internal environmental analysis (identifying organizational strengths and weaknesses related to capabilities, resources, assets, skills, and competencies) and external environmental analysis (recognizing external opportunities and threats) are also crucial. Subsequent steps include performing SWOT analysis (aligning external opportunities and threats with internal strengths and weaknesses), conducting scenario planning (suggesting strategic scenarios based on SWOT analysis alignment to set necessary objectives), and identifying value drivers (features distinguishing the value generated from the innovation strategy).
Based on the aforementioned, it’s imperative to create a vision (the long-term goal for the innovation system), establish SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) objectives, select appropriate and balanced key performance indicators (KPIs), develop sound and aligned initiatives (supporting the achievement of selected KPI targets and objectives), and consequently, disseminate the entire innovation strategy throughout the organization at all levels.
Consequently, all stakeholders must align themselves, identify their needs and expectations, and determine how to meet these through the innovation strategy. Subsequently, they should proceed with the execution process, understanding and acknowledging the clear alignment between the innovation strategy and the organizational strategy.
It is essential to view the innovation strategy as a core success domain for the organization, understanding that progress, improvement, and profit growth are interdependent with the innovation system. Moreover, it’s crucial to ensure the involvement of all stakeholders in this system, while embedding continuous improvement as the primary driver in maturing the system over time. Similar to excellence, innovation maturity is an ongoing journey that continually brings added value, which should be appreciated and built upon.
Data management in innovation strategy
The fourth industrial revolution has commenced. Linking it with innovation, transformation, future forecasting, and future change is pertinent, as they are all directly driven by and enabled by data management. Nowadays, the primary infrastructure for any company worldwide transitions from physical premises and branches to the cloud, where data are structured, organized, and interconnected, drawn from various sources such as customer interactions, product and service utilization, service and product development phases, defect management, product degradation, input and output resources.
This transition highlights that numerous data sources have been in place, yet not all have been utilized, analyzed, and transformed into information and knowledge. The shift towards big data and the advancements in artificial intelligence and conditional monitoring have changed the landscape. Decisions are now based on data, not just analyzed to reflect the current state but also organized and correlated to predict the future, facilitating decisions that secure not only the present or short-term future but also the long-term future.
This evolution underscores the importance of starting with the development of the right architecture to link various data sources, leveraging their mutual support and integration for greater benefit. It involves embedding in this architecture the correlation of data from different sources to build new components in the system architecture, adding value to the overall system. Understanding this aspect emphasizes the need to benefit from all data sources and install more sensors in development processes, products, streets, houses, cars, and everywhere, moving towards a products-as-a-service paradigm and eventually achieving the end goal of a planet-as-a-service, where data from everywhere are fed, analyzed, and used to identify new information and knowledge for the benefit of all.
Case analysis
The case study “Apple’s Future: Apple Watch, Apple TV, and/or Apple Car?” narrates Apple’s journey focusing on three products: smartwatches, smart TVs, and smart cars. It highlights how Apple has targeted the market and addressed customer needs to increase global market share and profit while enhancing the brand image. While this approach appears commendable, it aligns with the traditional viewpoint that continuous profit growth sustains a business.
However, from an alternative perspective, Apple has consistently aimed to shift from the red-ocean to the blue-ocean strategy, moving away from competition. The increasing number of competitors, open-source software, and global innovations necessitates larger leaps. Apple’s success also stems from co-creating value with its customers, understanding their needs, and embracing innovation and change.
Another facet is that Apple’s current endeavors represent a short-term strategy aimed at long-term value generation and delivery. Data serves as the primary driver, with all products and services yielding valuable data. This contradicts the notion that customers don’t know what they want; rather, it underscores the importance of understanding customer pain points and co-creating value with them.
Apple’s products evolve based on collected data and usage behaviors, generating new value with each iteration. The incorporation of health data into products like the smartwatch and analyzing consumer behaviors allows Apple to add value beyond traditional usage scenarios. Ultimately, Apple’s strategy mirrors a child playing a PlayStation game, controlling and directing the world.
While this may seem daunting and scary, proper use of data-driven strategies can benefit everyone, provided they are employed ethically and responsibly and not end up as Mikhail Kalashnikov puts it: “The fact that people die because of an AK-47 is not because of the designer, but because of politics.”
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About the Author
Malek Ghazo is a seasoned Senior Management Consultant with over 14 years of experience in the realm of organizational excellence (EFQM, 4G, Malcolm Baldrige), performance management, strategy planning/execution, and sustainability/CSR management. Throughout his career, he has cultivated expertise in developing benchmarking studies on an international scale. His clientele primarily consists of both public and private sector entities, to whom he provides invaluable services in organizational excellence, strategy planning and agile execution, KPIs and performance management models development and deployment, as well as EFQM model adoption and implementation. Geographically, Mr. Ghazo has dedicated his efforts to Europe (with a focus on the UK) and the Middle East, particularly in KSA, UAE, Qatar, and Jordan. Currently, he is engaged in pursuing his PhD at the University of Pécs in Hungary, with a focus on exploring the correlation between circular economy and organizational excellence and sustainability, aiming towards global sustainability.
Tags: Data Management, Innovation, innovation strategy
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