Decisively Digital. Alexander Loth
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“My concern, however, is that decision-makers are too often caught in traditional, linear (and nondisruptive) thinking or too absorbed by immediate concerns to think strategically about the forces of disruption and innovation shaping our future.”1
Organizations that have already embarked on their transformation journey are seeing the advantages. Disney, for example, produced hand-drawn movies in the past, relying on a very costly distribution system. Today Disney has gone completely digital, from movie production to the screen, leveraging the entire value chain. Disney's new digital endpoint, the streaming service Disney+, is even outperforming its own forecasts.2
With cloud-powered technologies, all organizations have access to AI-infused tools and modern work capabilities tailored to their needs. With scalability of implementation and speed of adoption, these organizations are seeing increased cost savings and more productive employees across all departments.
This is called digital maturity and is basically the sum of digital capabilities that are available in the organization. Every organization in every industry will increasingly need to level up their digital maturity to be successful and grow.
Strategic Topics
An organization's digital strategy is characterized by the application of new technologies to existing business activities and a focus on the enablement of new digital capabilities. These new digital capabilities can be clustered into five strategic topics (which form the structure of this book).
Modern Work (Part II): How does technology change the way we work and communicate, and how does this change interfere with culture and strategy?
Data Democracy and Analytics (Part III): How can every person achieve more by being enabled to access, understand, and communicate data?
Big Data Processing and Cloud Computing (Part IV): How do we retrieve, store, and process vast amounts of data?
Artificial Intelligence (AI) (Part V): How do intelligent agents take actions that maximize the chances of successfully achieving our business goals?
Process Automation, Blockchain, and the Internet of Things (IoT) (Part VI): How does direct integration of the physical world into computer-based systems result in efficiency improvements, economic benefits, and reduced human exertions?
Data is treated as a key strategic asset, and organizations are committed to realizing value from it. Therefore, data democracy and analytics and big data processing and cloud computing can be collectively referred to as data strategy.
Culture
Henry Ford said, “Culture eats strategy for breakfast.” This statement has probably never been as relevant as it is today. Many employees started working from home because of the COVID-19 pandemic as this book was being written. Micromanagement, which is characterized by mistrust, became obsolete, giving rise to a new, digital culture of trustful cooperation. Crafting and fostering culture within an organization is essential. Culture supports the digital strategy, enabling and empowering all people in an organization during transformational change.
Organizations are best fitted to go through a transformation when employees are unified and working with shared values and ideas. They have a culture that keeps their team connected, and an organizational mindset rooted in flexibility and openness: openness to new ideas, technologies, and digital capabilities.
Organizations whose culture accepts the diversity of personalities, abilities, ideas, and those approaches that are requirements for driving an organization forward are those that do best with adopting new digital capabilities.
Culture is not mapped 1:1 to a single strategic topic. Culture spans multiple strategic topics, which also influence each other, as shown in Figure 1.1.
Figure 1.1 Strategic topics and culture
Collaborative Culture
Collaborative culture helps organizations maximize employee knowledge and capabilities. Ideas and information can spread more easily when employees communicate and collaborate freely across functional and departmental lines, which will have tremendous impact on the organization's performance. Amy Djeridi, group head of Workplace Products at AXA, explains: “Now that working together can be seamless, employees no longer struggle to make teamwork happen with time-consuming tools and technology. Today, we're focusing on the business stakes.”3
Adopting a collaborative culture breaks up knowledge silos. Employees collaborate on documents, spreadsheets, dashboards, and presentations, all while using chat and video call features. This enables employees to quickly exchange ideas and help each other to achieve more and achieve it much more quickly.
All kinds of information — from raw-data sources to polished presentations — are shared and searchable by everyone in the digital organization. This means employees seldom need to start from scratch but instead can leverage existing assets.
The collaborative culture is based on the strategic topics of modern work and data democracy and analytics and supports data-driven decision-making.
Data-Driven Decision-Making
Data-driven decision-making is the culture of making organizational decisions based on actual data rather than on observation or intuition alone. Data-driven decision-making involves collecting data based on measurable goals or key performance indicators (KPIs), analyzing patterns and facts from these insights, and using them to refine business strategies and activities that benefit the organization's goals.
The culture of data-driven decision-making is based on the strategic topics of data democracy and analytics and big data processing and cloud computing, and supports collaborative culture and the culture of citizen data science.
Citizen Data Science
While an academic education is necessary for data science, citizen data science is more a matter of attitude. In principle, every employee, even one without knowledge of statistics and programming, can become a citizen data scientist. Therefore, citizen data science should be viewed as a culture.
According to the analysts from Gartner, a citizen data scientist defines a citizen data scientist as a person who creates models that use advanced analytics and predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.4
Citizen data scientists tell stories about a company based on company data by translating this data into a language that everyone can understand. Most of all, citizen data scientists need to be curious. They have to be able to recognize potentially useful information in a large amount of data and to highlight and translate key findings for other employees and departments.
The culture of citizen data science