Six Steps to Analytics Modernization

The first step to implementing analytics modernization in your organization is to define and document your use cases. The use cases should be tangible, as they will help to measure the value of analytics and encourage business unit leaders to adopt the new technology. After the use cases are documented, the next step is to build a backlog of analytics solutions. You should also designate an evangelist, who will coach employees and act as a technology product owner. This person should have both technical skills and emotional intelligence. The evangelist can be a single person or a group of people, depending on the size of the company.

One of the key elements of analytics modernization is building a strong learning culture. Building a learning culture has been associated with improved morale and retention rates. Organizations should create a plan to attract and retain highly engaged employees with marketable skills. They also must develop a data mart that defines business logic and makes data accessible to all business units. Analytics modernization should be a team effort between IT and business units. In many cases, the two teams work hand-in-hand to implement analytics, so implementing these two components will have a positive impact on the company's bottom line.

The next step in the analytics modernization process is deciding how best to train your employees. There are two main types of training available - developer-focused training and end-user training. Developer-focused training is aimed at the technical staff who will build and maintain analytics solutions and end-user training. Neither approach will be ideal for every organization, and the training should be tailored to the individual needs of the staff. To make the process as seamless as possible, you should also consider hiring a consultant with expertise in analytics modernization.

Data migration is another challenge that may stall analytics modernization. You will likely need to migrate several years of data. If you have a plan in place, it can be useful to identify which reports and apps to migrate first. In the short run, this can help you focus on the short-term goals while identifying which reports should retire. It can also be an opportunity to triage requirements. After all, not everyone needs ten years of historical data.

Data management is a critical component of Analytics Strategy. The process requires standardized data management, and the organization needs to consider the reliability of data sources. Regardless of data format, the standardized management of data is crucial to the success of analytics modernization. A good data management architecture can help you avoid several potential issues and ensure an optimal level of data quality and speed. It is imperative to automate data management so that you can achieve business value from your analytics.

Data transformation is essential to improve analytic processes. For example, data transformation should include the first phase of data type conversion. Flattening hierarchical data and transforming it to a standard format will increase compatibility with analytics systems. Subsequent transformations should be implemented additively by data scientists and analysts. The purpose of each layer of processing is to perform specific tasks. Once you have streamlined your data transformation process, it is time to move on to the next phase of modernizing your analytics. You can learn more about this article at: https://www.britannica.com/topic/modernization.