Empowering

Data is already being collected on a massive scale. You probably collect everything there is to collect about your customers. Keep looking out for new opportunities to learn more. Third-party data is another opportunity to get access to valuable data. External companies, specialized in data collection, can sell you market data that complements your own data. Third-party data is often expensive, so make sure it is going to be worthwhile. Data governance is becoming very important if you are starting to grant users, across the organization, access to data. You want employees to have access to all the relevant data, but you also have security concerns. With many access points and a large data volume a clear governance strategy is necessary.

• Look for new data opportunities, including from third-party sources

• Create a clear data governance strategy that scales

Instead of focusing on data that is externally oriented, e.g. data about customers or prospects, you can also collect data that is internally oriented, toward your own processes. These metrics can be used to steer the company, and improve the quality of the processes. Quality management concepts, such as Six Sigma, heavily rely on accurate collection of metrics. Amazon uses analytics to perfect their logistics processes, and they collect a lot of data to do so. But, you can also look on how we can collect data to analyze employee performance. Are there predictors for future performance, or if an applicant will be a good hire? Google is known for quantifying their performance management program. The important thing to ask right now is how you can correctly measure these kinds of metrics. Collecting data is one thing, but collecting the right data can be hard. A mix of domain knowledge and statistical knowledge is necessary to come up with the right kind of metrics.

• Collect metrics about internal processes if you have not done so already

• Steer and optimize based on internal metrics

• Look for new opportunities to apply metrics and performance management

Data science skills will continue to be needed to analyze the data and build analytical models. In addition to this, it is necessary to bring analytical talent to the rest of the organization. Departments, across the organization, should be creating their own roles that analyze and improve the department. They might still rely on a central analytics department to facilitate the technical side, but also start to take on some of the burden themselves. It is beneficial to combine domain knowledge with an analytical background. Business-IT skills come into use, to capitalize on the opportunities of analytics in new products and services. To build a data-driven culture all new hires across the organization can be assessed for their knowledge analytics and data. To get the most out of your data and analytics efforts it is beneficial that the whole workforce is data-literate to some degree.

• Train employees to be more comfortable with basic data analytics

• Democratize and distribute basic analytics across the organization, bring it closer to the work floor

• Bring in the skills to optimally align the business and IT to capitalize on new business opportunities made possible by analytics

Technological developments in this phase allow business users to do their own form of analytics. You provide the tools and they provide the domain knowledge. Self-service business intelligence is the concept of granting tools to business users to build basic analytics functionality themselves. They can build their own reports or analyze data. The caveat is that these users may not be well versed with data and statistics. It is easy to draw the wrong conclusions when you misinterpret statistics. We are also increasingly building in analytical features into our products and services. Features that add value to the customer by providing them with analytical insight based on the data the product or service collects, e.g. a smart thermometer that learns your preferences.

• Focus on a modular and scalable infrastructure that can rapidly support new opportunities

• Augment products with integrated analytical features (smart products)

• Create more opportunities to use self-service business intelligence

The data-driven mindset has now become mature enough to stand on its own legs. The attention of leadership shifts from taking analytics by the hand to encouraging adoption across the organization. Make sure the analytics department delivers on the tools that enable general adoption, and make sure the tools are being utilized. A data-driven mentality is being adopted by the entire organization, this should go all the way to the top. Incentivize and encourage an experimental and analytical approach to work.

• Shift focus from nurturing analytics to encouraging widespread adoption

• Encourage the empowerment of employees and an entrepreneurial spirit

An analytical culture is slowly becoming a reality. Analytics has found its way to many daily processes. Data and analytics are seen as powerful tools that can be used to accomplish the tasks you are facing. Metrics are used to continuously improve and track progression over time. The sentiment in the organization has shifted from skepticism to optimism. It is important to build on this sentiment to truly become innovative in the future.

• Hire and retain employees with a data-driven mindset

• Build on optimistic sentiment to become a truly data-driven organization

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

With a growth of analytics across the organization, it is important to keep everything in line. Common processes concerning data usage and analytics should become standardized so everyone follows the same rules. Standardized processes are easier to manage and deploy. A standardized plan allows you to capture new opportunities immeditately. Because analytics is not practiced in a central location it is also important to create a platform for sharing expertise. Organizational models, such as that of Spotify, accommodate organization-wide knowledge sharing in so called \textit{guil.toString()}, which consists of workers sharing a specific interest. Keep evaluating if the existing organizational structure is effective or can be improved. Flexibility in organizational structure is valuable to adapt to new opportunities or initiatives.

• Standardize analytics processes and data access in order to deploy in new areas faster

• Share expertise across the organization

Analytics is spreading everywhere in the organization. From centrally coordinated integration efforts we are moving to analytics being performed closer to the source. Analytics close to the related domain gives the advantage of better domain knowledge and stronger integration. Departments can respond faster to opportunities when they are in charge of their own analytics. Systems with a highly scalable architecture provide easy access to data and analytics, for applications anywhere in the organization. These central hubs allow rapid and easy integration of existing analytical capabilities into new products and services.

• Distribute analysis closer to the relevant departments

• Provide easy integration of analytical capabilities into new offerings

This stage is focused on empowering the organization to engage in analytics. The main way to scale analytics organization-wide is to provide infrastructure centrally and distribute the analysis. Basic analysis can be done with self-service BI tools to create reports and dashboards. Users have access to on-demand reports that they have compiled to their own needs. Advanced analytics can be performed by data scientists with domain knowledge that belong to a specific team or department. Training will empower employees with the knowledge to systematically approach challenges in a data-driven way.

• Scale analytics with self-service BI

• Combine domain knowledge with analytical knowledge to better suit to the needs of departments