The desire to move from intuition-based decision-making to evidence-based decision-making should be reflected in the Strategy during the early stages. Additionally, a long-term vision for analytics should be developed. How will analytics transform the way business is generated in this company? For some companies, this might seem too far into the future, but this exercise provides both a starting point for analytical activities and a future you can work toward. It makes no sense to start the analytical activities with logistics, if you do not plan for it to be a competitive strength in the future. In the beginning, there are two main approaches. Starting small and proving value before moving bigger, or going all-in on analytics. Starting small is a slower approach, but might be an easier transition for the organization and the employees. This approach is also useful if upper management is not yet fully convinced.
Identify core competencies of the organization.
Develop a long-term strategic vision for analytics.
After carefully setting the first steps toward a data-driven strategy, it is time to expand the efforts and give analytics a permanent place in the organizational strategy. The focus expands from reporting on past events, to also analyze why things occurred. The goal is to use this insight to better predict what will happen in the future. Data-driven activities should also be utilized to improve existing processes.
Give analytics an official place in the organizational strategy.
Expand focus from reporting aggregated data to analyzing underlying behavior.
Use analytical insight to decide what to do on a strategic level.
Analytics is continuing to fulfill a more prominent role in the strategy of the organization. We started with analytics in a supporting role in the organization. First as a method to report on current performance, later to further analyze and improve performance. We are now seeing analytics becoming more integral to all processes by augmenting them with analytical insight. In the Reporting and Analyzing stages, we were concerned with how we can we use data to improve the blueprint of a process. In the Optimizing stage and beyond, we are increasingly looking at how can we use analytics to augment the working of a process. The difference is that if you were to stop collecting data in the first scenario, you would still have a better blueprint, say a better performing customer journey, that no longer requires data to further optimize. In the second scenario, the process would break down because you are actively using data, for example to recommend a customer the best product. Analytics has become an active component of the process.
Position analytics as an integral component of global strategy.
Use analytics to optimize existing products and processes.
Use integrated analytics to create an optimized customer experience.
The strategy of the organization should strive for the adoption of analytics in all business processes, and new products and services. Data and analytical activities form a central part of the strategy. Data is recognized as a valuable asset that can used or potentially sold. Analytics is used to optimize existing processes, products, and services. New products and services are augmented with `smart' capabilities that add value for the customer. A strong analytical culture is a major strength that competitors might not have. Continuous improvement and innovation through analytics allow the organization to be competitive. A comprehensive customer view provides a way to better fit products and services to the customer.
Strive to adopt analytics in every process and product
Compete with strong analytics and continuous improvement
Analytics and data become the most important component of the global strategy. Data-driven innovation is the greatest source of competitive advantage. A strong analytical foundation, comprised of data, technology, people, and processes, allows an organization to successfully introduce new services, and compete in new markets. This foundation allows an analytical company like Amazon, traditionally an internet company, to confidently enter a new market with the acquisition of Whole Foods. Amazon has the ability to innovate faster, and make decisions based on evidence backed by data analysis \cite{deloitte2018amazonwhole}. Expanding into new markets can also be a strategic move, to get access to more data and integrate existing services with new markets. Amazon can use their existing customer insight to provide a better service in a new market, while also having the ability to collect more data; both on a individual customer level, as new customers in general. Because of a network effect combining data sources can lead to better individual customer insight. This effect fuels the pursuit of growth in large internet companies \cite{franklin2009continuo.toString()}. Data-driven principles are universally applicable and can be internally oriented toward business processes and externally toward consumers and markets. Where we start with the question, `How can we capture the most value out of our data?', we now ask ourselves: `How can we generate the most value out of our analytical capabilities?'
Analytics as a cornerstone of organizational strategy and vision
Expand into new markets successfully through strong analytical capabilities
Tailor and optimize the customer experience