Innovating

Data is one of the most valuable assets, if not the most valuable asset in your possession. You also have the capabilities to capitalize on those assets. Because data is so valuable, you are always looking for new way data sources. Companies like Google, that make money through advertising, go to great lengths to build a better profile about their users. Google invests in free software, such as Google Chrome and Android, partially to collect more information that can be used in an advertising profile. Unstructured data is also retrieved from voice and images. Image recognition is already used to make images interpretable to computers.

• Continue to look new data sources from new products

• Leverage alternative unstructured data sources, such as voice and images

Metrics are being collected everywhere in the organization, and are being actively used to improve products and processes. KPI's are standardized across the organization. Advanced analytical KPI's are used for strategic goals, and are leading indicators for company performance. They allow organizations to better focus their efforts and measure the success. New KPI's may be introduced that are more in line with the redefined business model of the organization. KPI's have the ability to clearly communicate and incentivize priorities within the organization. Metrics are also being used more broadly to track internal processes and goals, such as employee performance or satisfaction. Metrics found from methods such as cohort analysis can be useful to measure the progress of new ventures. \textit{Innovation accounting} is a technique to measure the success of new ventures instead of relying on traditional accounting methods, which may not be suitable for measuring the success of new innovation.

• Standardize KPI's across the organization

• Track new ventures using innovation accounting methods, instead of traditional metrics

Mature data-driven organizations house a diverse set of skills. Large organizations, on the leading edge of data science, are often ahead of academic research in fields such as machine and deep learning. They tend to hire academic researchers, with a PhD in computer or data science, for advanced analytics applications. To sustain a culture of data-driven innovation, it is beneficial to have people in the organization with an entrepreneurial mindset and business development skills. Human capital is the greatest asset of an analytical company, because they form the machine that turns data into new business.

• If applicable, employ computer scientists to develop artificial intelligence features

• Develop entrepreneurial en business development skills within the organization to sustain innovation

Technology is continuously evolving. When you compete on analytics, you are competing on technology. Competitors that are able to better predict customer behavior are more likely to attract and retain customers, while also building a strong relationship that is mutually beneficial. Building and maintaining analytical systems takes a lot of work. We are seeing more software platforms that accelerate the process of building models, allowing data scientists to rapidly churn out new models and reduce the time to market \cite{analytics2018predictive}. With automation happening at every step of the data science process, the data scientist can focus on the most important task: asking the right questions.

• Invest in software platforms that accelerate the process of building analytical models

• Focus on asking the right questions

Leadership has successfully set up analytics, and has led the cultural reform of the organization. Their task is now to encourage and facilitate innovation based on the powerful analytical capabilities of the organization. New innovation can lead to a business transformation, a fundamental change to how the organization operates. For example, Netflix is known for their strong analytical capabilities, which are used to recommend a user new content. Their prescriptive capabilities have allowed them to build a loyal base of users, because those users were getting personalized recommendations. Now they are taking their analytics one step further, by using the data they collect to create their own content they know will likely succeed. They are slowly transforming, from prescribing the content you like to creating the content you like.

• Stimulate and incubate innovation efforts

• Encourage data-driven innovation; using analytics to determine and capture opportunities

Culture is the driving force behind data-driven organizations and a sustainable competitive advantage, because it cannot be easily replicated by competitors. Encourage a culture that fosters innovation and a culture of ownership, where employees feel responsibility for the welfare of the organization, without having to be assigned explicit accountability. While a data-driven culture encompasses a lot of factors, the foundation will always be building arguments based on evidence, rather than intuition, and critically looking at every process and activity.

• Encourage innovation and a culture of ownership

• Maintain a data-driven culture, where evidence triumphs intuition

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

Execution of existing processes is streamlined. Decision domains have been established, the organizational structure is flexible, and suited for knowledge sharing. The organization enables innovation by further developing innovation and change management strategies. These strategies can help breed innovation and successfully bring it to market. The goal is to determine as soon as possible if the new venture is something that sticks, or is a waste of effort. Bring new innovation to the costumers faster and see how they react. Be aware of new opportunities that can be captured quickly due to an agile business structure and culture.

• Manage innovation to increase the success rate of new ventures, and reduce the waste of creating a product nobody needs

• Bring new innovation to market faster to test the waters

• Rapidly capture new opportunities

We see analytical results further integrated into all business activities. It tells us what to do on a macro-scale: what business ventures to pursue, and on a micro-scale: what do we recommend this customer?. Analytics moves further to the point where data is generated. For example an IoT devices can already have machine learning capabilities built-in. The advantage is that the data is generated and analyzed on the same device. The computational efforts are also distributed. This architecture allows for massive scaling.

• Use analytics to make all decisions on a strategic scale and on an individual customer scale

• Integrate analytical processing capabilities into all products without centralizing data collection

Everyone has been given access to analytics, and it is now the time to do great things with it. Through organization-wide training initiatives and hiring practices, everyone will have some degree of data-literacy and the right mindset for an analytical organization. Data-driven organizations give employees access to massive amounts of data and analytical capabilities. Combined with the right knowledge and mindset these unlock new opportunities for products and services; either a new product or a new analytical feature. These analytical capabilities also allow you to continuously improve existing processes.

• Give everyone the opportunity to use data and analytical capabilities

• Trust employees with the power to innovate and improve