Analyzing

The focus in the Reporting stage was on making data insightful for users by making it available in reports and operational dashboards. In the Analyzing stage, you go deeper into the data to find insight not visible from the surface. Where you previously collected data already available, you now might want to look at creating additional sources of data. You will start to collect data that is not only relevant for reports, but also for further analysis. Collecting more data, and from different sources, brings its own challenges. Data quality might become an issue if the sources are too unreliable. Monitor data quality and assign responsibility for certain domains to ensure it never drops below a minimum. Security and privacy should be a great concern and a priority. User data should be anonymized and secured.

• Determine which data is relevant for deeper analysis.

• Collect and analyze relevant data.

• Monitor and ensure data quality.

• Define a strategy for keeping data secure and private.

In this stage, basic metrics are collected and displayed in dashboards. Because you are collecting metrics for a longer time you can now also track KPI's over time. This provides additional insight over the progression of the organization. Make sure to fully realize what drives the KPI. KPI's are the outcome of a process. Focusing on a correct process will provide long-term results, while artificially inflating KPI's only serves the short-term. You now also have the capability to create KPI's backed by analytical models. For a customer lifetime value (CLV) analysis you need a lot of historical knowledge. If you can create an analytical model that predicts the customer lifetime value for a new customer, you can estimate how much you can spend to attract this customer. The CLV can also serve as a KPI that can be tracked.

• Define more advanced KPI's.

• Track KPI's over time; focus on long-term results.

• Create analytical models to back analytical KPI's.

Analytics evolves from showing numbers to calculating numbers, and the required skills evolve with it. Data mining and analytical skills are the most important skills in this phase. The current team has to be trained for more advanced analysis and the creation of analytical models that can be used for predicting future events based on new incoming data. In addition to basic statistics, advanced knowledge of data and data mining techniques is necessary. The retention of current talent is also a priority, because it can take a lot of resources to bring newly hired employees up to speed. A small team in the beginning means that a few individuals will have critical knowledge about the systems. Expanding the team will spread the critical knowledge and help overcome a sudden departure.

• Train or attract data scientist to dive deeper into the data.

• Retain talent with critical knowledge.

• Introduce employees to basic data literacy skills.

Where in the Reporting stage the infrastructure was largely ad hoc, a professional analytics platform will be built during this stage. The implementation details will largely depend on the organization's requirements. There has to be a standardization of technology, and a modular platform that is capable of easily integrating new features and components. All data will be stored, in a standardized format, in a data warehouse or data mart, and is easily accessible for services. Invest in tools that help you analyze and mine the available data. A lot of technology is being developed to help the data scientist in making the available data more explorable and increasing their efficiency.

• Professionalize the data infrastructure.

• Standardize technology, integrations, and warehousing.

• Acquire technology that helps data scientists analyze data.

Leadership continues to be an important driving force for data-driven maturity, as long as not everyone in the organization is convinced. Early success has hopefully convinced the majority of upper management. If leadership praises the improvements through analytics, the rest of the organization will more likely embrace the changes. Basing decisions on data means no longer the most influential employees are taking the decisions, but decisions are made based on the correct use of analysis. Leadership has to follow through on this all the way to top level decisions. You can not force low-level decisions to be based on data while continuing to base high-level strategic decisions on intuition.

• Continue to drive home the importance of a data-driven organization.

• Show the results of data-driven efforts to the organization.

• Lead by example and base high-level strategic decisions on data analysis.

The Culture inside the organization is still lukewarm to the use of analytics. They may know of some activities that are being started, but it is still isolated from the rest of the organization. The successes may inspire some early adopters to also try more experiments to confirm their beliefs. Some struggle may occur between believers in a data-driven way of working, and those who `have always worked this way', and continue to do so. Clear direction from leadership and management is needed to resolve this.

• Use early success as inspiration to the rest of the organization.

• Set a clear direction from the top down to move away from the old way of working.

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.

Where agility in the first stage is characterized by its ad hoc nature, this stage will be focused on the formalization of roles and responsibilities. Struggles and conflicts with the old way of working should be smoothed out as much as possible. For every part of the organization someone should be responsible for advocating and integrating the use of analytics and a data-driven mindset. The questions the analytics team is solving are, for a large part, still determined by upper management. They have specific problems that need further analysis. A process should be in place to manage the requests by other departments to the analytics team.

• Formalize roles and responsibilities.

• Assign ambassadorship for analytical initiatives across the organization.

Further integration takes place in this stage. The analytics team is coming up with new recommendations by digging deeper into the available data. They deliver detailed reports with their findings and recommendations to upper management, whose task it now is to put the changes on the agenda for implementation. Automated reporting and dashboards are getting more sophisticated. Where in the past they had to wait on a report, they can now query a standardized report on demand whenever it suits them.

• Create a process to implement recommendations backed by analytical evidence.

• Create on-demand available reports and dashboards with the right information.

Reports are already providing a way to monitor performance. Managers now might also be interested in improving this performance through a data-driven way of working. However they lack the knowledge and tools to take action. The analytics team should help, educate, and provide tools, to others in the organization that wish to take action. They can help with the implementation of experiments or other activities that can help improve the performance of certain processes. Wide adoption of a data-driven mindset should be nurtured at every opportunity.

• Educate and provide tools to allow managers to use data and create dashboard.

• Nurture the adoption of a data-driven mindset; how can we use data to create success?