Reporting

Data is fundamental to starting data-driven activities. Valuable data is hard to access in many different production systems. The critical task is to determine which data is relevant enough to collect. At this point, the number of data sources is limited. You often retrieve the data from your own systems. It can be challenging to retrieve data that resides in functional silo's. Because you are reporting over historical performance, the timing is not a big challenge. Data is often transported in a daily job to a primitive data warehouse. The quality of the data should be kept in mind but is not a huge concern at this point, because the sources are trusted and the variety of the data limited. There are no real big data challenges that must be faced.

• Determine which data needs to be visible.

• Source and report critical data.

Metrics are an essential part of reporting. Metrics provide an easy performance overview. Just like determining which data is relevant, the first task is to determine which metrics should be established. Dashboards with near real-time metrics should be made available for management to quickly identify trends. For operational activities dashboards can also be made to spot operational issues. In this stage, the focus should be on easily accessible metrics, which can later be replaced by more advanced derived metrics backed by analytical models. Overall there is a focus on current performance, not yet on long-term progression.

• Determine critical metrics.

• Create basic dashboards with the most important metrics.

At this point skills will be unavailable or underdeveloped. The focus is on sourcing talent, either internally or externally. Find people with the initiative to build and expand the analytics program in the organization from the ground up. Attracting people at this stage should be a careful process, because the first data scientists are for a large part responsible for the early success of the program. Early success will convince leadership to invest and scale, while early struggles may lead to termination of the program. Early success also spurs the interest of other employees in analytics, and a cultural shift to a more analytical culture.

• Source talent internally or externally to build an analytics program.

• Focus on early success to gather momentum.

The first step is to identify the business requirements to eventually design or acquire technology capable of delivering those needs. The selection of technology should be a careful process that analyses the many trade-offs. In the beginning stages, it is important to prove the value of analytics so it might be attractive to use a Software as a Service (SaaS) model in the cloud, where the hosting and powering of a system are taken care of by an external provider. This usually requires lower upfront costs and faster bootstrapping. Many technologies are available this way. You can also take on more burden yourself by using an Infrastructure as a Service (IaaS) model. With IaaS the infrastructure, such as servers, are provided on which you install the applications you need. The trade-off is between speed, flexibility, and costs. SaaS is often the easiest to get started with, but may no longer fit your needs over time and costs more. The technology in this stage focuses on the starting steps of an analytical platform where data is collected and made available in reports and dashboard for management. These dashboards provide an overview of historical and current performance, and leave generating insight to the ones viewing the reports.

• Identify business requirements for a basic analytics platform.

• Decide if you want to build or buy software and infrastructure.

Leadership has an important role in the early stages. It is responsible for initiating analytical programs and leading the cultural shift. In the beginning there will be a division in upper management with skeptics and believers in analytics. Someone in leadership should champion the idea of analytics and foster the program.

• Initiate analytical program by putting it on the agenda.

• Champion and foster the analytical program at the highest level.

The starting culture will largely depend on the type of organization. Companies that rely on intuitive decision-making will largely be indifferent to analytics. They are used to make decisions based on gut instinct, with the highest paid employee having the largest influence. This can also lead to opposition to a culture based on analytics. Employees who were used to being valued for their intuition-based decisions may feel threatened by the shift to evidence-based decision-making. It is necessary to remove worries by guaranteeing they will not be replaced by the transition. An excited or open attitude by the entire workforce increases the chances of success of the program.

• Warm up and excite employees for an analytical culture.

• Remove worries about the shift to evidence-based decision-making.

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.

In the beginning the execution of the chosen strategy will be largely informal and ad hoc. Moving fast and proving value is more important than adhering to strict processes. Embrace internal entrepreneurship in the early stages to maximize the early results and excitement. For a smooth start, it is however important to define basic responsibilities. For every decision, it should be clear who is responsible and accountable. Prevent projects and initiatives getting stuck because nobody is sure who is responsible for making the decisions. Without responsibilities it is hard to drive change. Defining roles and responsibilities also helps ease the shift in power balance. A new source of insight is being created, which may support or replace the previous shot-callers. If responsibilities are not clearly defined it may happen that the newly created insight is not properly used, and keeps being overruled.

• Embrace entrepreneurial spirit to move faster instead of defining strict processes.

• Define basic responsibilities and decision domains to avoid getting stuck and to keep driving change.

Integration is the beginning stages will largely be manual by delivering reports or ad hoc analysis of a specific business question. It is important to cooperate with managers to get the correct questions they need answers to. Standardized reports can be created which can be reused for monthly or quarterly updates. Software packages can provide automated integration for some specific tasks, but often big gains can already be made by manually relaying back insight to the business. Timing is, in this stage, of less importance than correctness.

• Define which information is needed by decision-makers.

• Standardize reports.

Early on employees will not be empowered by analytics. They do not have dashboards, and do not have the opportunities to do things themselves. There is little focus on Empowerment in the early stages of the process. However those responsible for analytics can create dashboards for employees to use, to follow current performance and make decisions.

• Create basic dashboards and reports to empower employees.