Getting complete, quality views of data about subjects of interest is essential to nearly every business decision or action, and the demand for analytics continues to grow. More organizations want to advance beyond dashboards to self-service analytics and more sophisticated algorithms such as machine learning.

Analytics can only be as good as the data, which is why it’s important to keep the end in mind, as data and higher-level information integration are the foundation for reaching data-driven objectives. These recommendations can help guide your modernization efforts:

Multiple data ingestion techniques allow data to move at its own speed or generation frequency. That way, data arrives in target data platforms as soon as possible and is available for immediate business use in dashboards, reports, and analytics.

Self-service data access helps users work with spontaneity and speed because they aren’t waiting for IT or a data management team to construct a data set for them. This is key to modern practices such as agile development, data exploration, and data discovery.

Data preparation. Many of the problems business users and analysts in organizations confront when working with data is due to poor, ill-defined data. Data preparation processes focus on determining what the data is and improving its quality and completeness, standardizing how it is defined and structured, collecting and consolidating it, and taking transformation steps to make it useful, particularly for reporting and analysis. Data preparation processes need to address data governance, especially as user self-service becomes more prevalent. Which leads us to the next point, COE.

COE. IT is the function primarily responsible for making it easier for users to find relevant data and understand how to use it properly. However, communication through a CoE can help business and IT spot weaknesses in data preparation and identify where training or technology investment is needed. The CoE can also contribute to data governance, including the development of rules, policies, and standards for data use, along with transformation, data lineage, and the user development of content such as dashboards.

The best analytics environments allow executives, managers, and operational personnel to ground their decisions in the latest, greatest, and most comprehensive range of information available to the organization.

The key to success is providing all users with self-service, contextualized, visual access to discover, explore, and analyze all data at any time. This includes the ability to access and utilize all forms of data, including ERP and MES.

DELMIAOperationsConsulting ​​​​​​​