Despite the continuous hype around data analytics and the rapid acceleration of data technologies such as machine learning (ML) and artificial intelligence (AI), most companies are lagging behind with low data capabilities and no in-house data team in place. These companies have their data either fully unleveraged or marginally analyzed by executives on the side of their jobs to produce limited reports.
In such a situation, pushing the organization up the hill of data maturity would require building a team of data-specialized personnel. Building such a team can be daunting, as every company would have different conditions and no one way can fit all cases. However, covering the following main grounds can help cut miles on the road to building a data team from the ground up.
First, nurture the environment and plant the seeds. Data teams cannot grow in a vacuum. To prepare the organization to become data-driven with a data team, enhancing the organizational data culture is a good starting point. Having employees at all levels with a data-driven mentality and an understanding of the role of data analytics can significantly prepare the room for the planned team.
Second, connect with stakeholders and recognize priority needs. Carrying out data culture programs inside the organization can open up opportunities to have meaningful discussions with stakeholders on different levels about their data needs, what they already do with data, and what they want to achieve, in addition to having better insights into the pre-existing data assets. This is a good stage to recognize the organization’s data pain points, which would then be the immediate and strategic objectives of the future data team.
Third, define the initial structure of the team. According to the scale of the organization and the identified needs, data teams can have one of three main structures:
Centralized: This involves having all data roles within one team reporting to one head, chief data officer (CDO), or a similar role. All departments in the organization would request their needs from the team. This is a straightforward approach, especially for small-size companies, but can end up in a bottleneck if not scaled up continuously to meet the organization’s growing needs.
Decentralized: This requires disseminating all data roles and infusing them into departmental teams. This mainly aims to close the gap between technical analysis and business benefits as analysts in every team would be experts in their functional areas. However, the approach may lead to inconsistencies in data management and fragile data governance.
Hybrid: This consists of having governance, infrastructure, and data engineering roles within a core team, along with embedding data analysts, business analysts, and data scientists in departmental teams. The allocated personnel would report to the respective department head as well as the data team head. This approach combines the benefits of both centralized and decentralized structures and is usually applicable in large organizations as they require more headcount in their data teams.
Fourth, map the necessary tech stack and data roles. As the previous stages have uncovered the current uses and needs of data in an organization, it should be easier to start figuring out the tech tools that the team would be initially working with. Mapping the needed tech stack would be the first pillar before moving on to the hiring process. The second pillar would involve defining the roles that the team would need in its nascent stage to meet the prioritized objectives.
Several data job titles can be combined in a data team, with many of them having specializations that intersect with or bisect each other. However, there are three main role areas that should be considered for starting data teams:
Data engineering: implementing and managing data storage systems, integrating scattered datasets, and building pipelines to prepare data for analysis and reporting
Data analysis: performing final data preparation and extracting main insights to inform decision-making
Data science: building automated analysis and reporting systems, usually concerned with predictive and prescriptive machine learning models
Fifth, follow step-by-step team recruitment. Hiring new employees for the data team is one option. The other option can be upskilling existing employees with an interest in a data career and with minimum required skills. Even employees with just interest and no minimum required skills can be reskilled to fill some roles, especially within an initial data team.
The team does not need to take off with full wings. It can start small and gradually grow. Typically, data teams would start with data analysts who have extra skills in data engineering, data engineers who have experience with ad-hoc analyses and reporting, or a limited combination of both. In later stages, other titles can join onboard.
The baby-step-building approach is more convincing for stakeholders as it can be more efficient from a return-on-investment (ROI) perspective. Starting with a full-capacity team may end up being too costly for the organization, which could lead to the budding project being cut off in its prime.
Sixth, deliver ad-hoc analyses, heading towards long-term projects. In the beginning, data analytics experts at the organization would be expected to answer random requests and solve urgent data-related problems, like developing quick reports and reporting on-spot metrics. This is a good point to prove how data personnel can be of direct benefit to the organization.
However, along with delivering said ad-hoc requests, the data team should have strategic goals to enhance and develop the overall data maturity of the organization, like organizing, integrating, and automating the analytics processes and installing advanced predictive models. These long-term projects should foster the organization’s data maturity, which should result in ad-hoc requests being less frequent as all executives should be self-sufficient in using the installed automated reports and systems. In such a data-mature environment, the team would have time to advance their data products continuously, opening up new benefit opportunities.
Seventh, fortify the team’s presence. Strategic projects with shorter implementation periods and more immediate impact should be prioritized over longer ones, especially in the beginning. That would help continuously prove the benefits of the data team and the point of its foundation. Owning the products of the data team by having its name on it can help remind decision-makers of the team’s benefit. In addition, it is highly useful for the data team’s head to have access to top managerial levels to keep promoting the team’s presence and expansion.
Building a data team from scratch requires careful planning, investment, and commitment from organizational leadership. By following these guidelines and adapting them to their specific needs, organizations without prior data capabilities can establish a robust data team capable of driving innovation and offering a competitive advantage through data-driven insights.
Over the recent years since Vision 2030 of the Kingdom of Saudi Arabia (KSA) has been initiated, the massive changes within the operations of government entities have led to a rise of expectations for better communications with the stakeholders to achieve effective citizen engagement. Communication strategies and initiatives have been developed and launched with the initiation of KSA’s Vision 2030 in order to streamline the strategic objectives and clarify the roles of stakeholders and staff as well as identify the target audience and communicate with them more effectively.
To implement the communication strategy of any public entity effectively and efficiently, the communication plans should include what information should be communicated, who should receive that information, when that information should be delivered, and how those communications are tracked. Also, some actions need to be considered within the implementation of communication strategy, such as opening two-way communication means, using technology to streamline the communications, and focusing more on engaging with the audience–not just listening to them and answering.
The business world is dynamic, and crafting a winning strategy is often merely the first step. The true test lies in successfully translating that strategy into tangible results. This is where the often-overlooked power of change management comes into play.
While strategies may be meticulously planned and crafted on paper, they can often fall flat in the face of organizational inertia and resistance to change. This is where change management steps in, acting as the bridge between well-defined aspirations and their successful implementation.
Understanding the why of change management
At its core, change management focuses on the human aspect of organizational transformation. It recognizes that successful implementation hinges not just on revised processes or new technologies, but also on the willingness and capacity of individuals within the organization to adapt and embrace the change.
By employing various strategies and frameworks, change management fosters a supportive environment that facilitates individual and collective buy-in to the proposed changes. This involves:
Clear communication: Effectively communicating the “why” behind the change, not just the “what,” is crucial.
Building confidence: Addressing concerns and providing training equip employees with the necessary skills and knowledge to navigate the change effectively.
Empowering employees: Fostering a culture of ownership and encouraging participation in the change process can enhance engagement and motivation.
The benefits of embracing change management
This ability to transform and thrive in a dynamic business landscape becomes a key differentiator in today’s competitive environment. Thus, integrating change management principles into strategy execution is not just a “nice to have.” It is a strategic imperative with numerous benefits, such as:
Boosted effectiveness: By proactively addressing resistance and fostering a sense of shared ownership, change management significantly increases the adoption rate of new strategies. This translates to a smoother transition and ultimately, faster realization of the desired outcomes.
Elevated morale and engagement: When employees feel valued, informed, and involved throughout the change process, it cultivates a more positive and productive work environment. This leads to increased employee engagement, which is directly linked to higher levels of performance and innovation.
Enhanced organizational agility: By fostering a culture of adaptability and continuous learning, organizations become better equipped to navigate future changes and challenges.
Change management in action
Here are some actionable ways to incorporate change management into strategy execution:
Craft a compelling communication plan: Develop amulti-channel communication strategy that clearly articulates the vision, goals, and rationale behind the change. This could involve town hall meetings, targeted emails, internal newsletters, and Q&A sessions. The goal of this plan is to ensure transparency and consistent messaging across all levels of the organization.
Invest in building capabilities: Equip employees with the necessary skills and knowledge to navigate the change effectively. This could involve providing training programs, workshops, and mentorship opportunities. Remember, addressing knowledge gaps and fostering a learning culture is crucial for building confidence and encouraging active participation.
Empower change champions: Identify and cultivate champions within the organization who are passionate about the change and possess strong leadership skills. Empower them to act as advocates and peer mentors, providing support and guidance to their colleagues throughout the transition.
Embrace feedback and iterate: Regularly monitor progress and solicit feedback from employees at all levels. This data-driven approach allows you to identify potential roadblocks, adjust the implementation strategy as needed, and ensure that the change is aligned with employee needs and preferences.
By recognizing the crucial role of change management and actively incorporating its principles, organizations can bridge the gap between strategy and action, turning plans into tangible results and paving the way for lasting success in an ever-evolving business landscape.
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About the author
This article is written by Rami Al Tawil, the General Manager of Organizational Excellence at Al Saedan Real Estate Company. He holds a master’s degree in industrial engineering from Jordan University of Science and Technology. With 19 years of expertise spanning strategy planning, performance management, business improvement, and more, he excels in aligning employees with strategic visions for consistent performance improvement.
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.
Artificial intelligence (AI) has emerged as a transformative force in corporate strategic management, fundamentally altering the way companies make strategic decisions. AI is crucial in driving innovation even in the face of dynamic business environments and data abundance.
The integration of AI into corporate strategic management offers a myriad of benefits for businesses seeking to navigate the complexities of the modern market, namely:
Data-driven decision-making: AI empowers companies to transform raw data into actionable insights to identify market trends, assess customer preferences, and predict future outcomes more accurately. AI supports data-driven strategy, leading to better resource allocation, risk mitigation, and operational effectiveness. For instance, a company can leverage AI predictive analytics capabilities to forecast future revenue, competitive threats, the likelihood of expansions succeeding, and other core strategic considerations years in advance.
Enhanced strategic planning: AI’s capabilities extend beyond data analysis to encompass strategic planning and scenario modeling. AI-powered tools can simulate tens of thousands of realistic scenarios per minute, allowing companies to evaluate the potential impact of strategic decisions and identify potential risks and opportunities before committing to major investments. Maersk, for example, uses cutting-edge AI algorithms to revolutionize its container shipping operations. These algorithms optimize vessel routes for efficiency, predict equipment maintenance needs to minimize downtime, and provide real-time insights into cargo location and status, ensuring unparalleled transparency and efficiency.
Customer-centric strategies: AI plays a pivotal role in understanding and anticipating customer needs, enabling companies to develop customer-centric strategies that foster long-term customer loyalty and enhance brand reputation. AI-supported tools can analyze customer behavior, preferences, and feedback; thus, AI can provide valuable insights to personalize marketing campaigns, improve product offerings, and optimize customer service experiences. For instance, H&M Group uses AI to create personalized shopping experiences, optimize product offerings, and improve customer satisfaction by analyzing customer data and preferences.
Competitive advantage: AI adoption provides companies with a competitive advantage in a rapidly evolving market. AI-driven strategies enable organizations to adapt quickly to changing market dynamics. By leveraging AI’s capabilities, corporations can outstand competition and establish themselves as leaders in their respective industries. Unilever, for example, leverages a system to analyze sales data, marketing campaigns, economic trends, and weather patterns to predict future demand more accurately. This enables the company to optimize production planning, reduce waste, and improve profitability.
AI challenges within corporate strategic management
While AI presents immense opportunities, it is crucial to address the ethical considerations surrounding its implementation, such as the potential for AI to perpetuate biases and discrimination. AI algorithms can be trained on biased data sets, leading to partial decision-making and deeper inequalities. Transparency and accountability are other ethical concerns in AI decision-making as it is important to foster trust and understanding of how decisions are made. This is to ensure that everyone involved can act on a just and well-informed strategy.
Successful AI integration also requires a cultural shift within organizations. Companies need to develop training and educational programs to equip employees with the needed skills and to work with AI systems and capabilities in harmony. This should improve communication and collaboration, leading to better alignment with corporate strategic objectives.
Companies that embrace and adapt AI strategically will be well-positioned to navigate the complexities of the modern market and achieve sustainable competitive advantage. For effective adoption of AI capabilities, companies need to develop transparent and accountable AI systems and establish clear ethical guidelines for AI use. Additionally, companies need to engage in ongoing dialogues with stakeholders to build trust and ensure that AI is used responsibly and ethically.
