Data baselining, also known as data landscaping, is our approach to processing and visually organizing data in an intuitive and easy-to-understand way. This involves developing diagrams, maps, charts, and dictionaries to show the relationships between different data elements and entities. After identifying the appropriate data (see "Minimum Viable Data"), we process, prepare, clean, and align it for analysis. This typically includes data profiling, cleansing, and transformation to ensure accuracy, consistency, and completeness of the data. 

We use strategic data analysis to identify and target our clients' Minimum Viable Data (MVD), which is the minimum amount of data needed to achieve a specific objective or goal. The concept of MVD is often used in agile development methodologies to quickly develop and release products by focusing on the minimum set of features required to meet customer needs. 

Regarding the application of data analytics, MVD refers to the minimum amount of data necessary to gain insights or make informed decisions. This involves identifying the most important data variables, reducing data redundancy, and eliminating extraneous data points that do not contribute to the analysis. 

The use of MVD can streamline the data collection process, reduce the cost and time required for data storage and processing, and improve the accuracy and relevance of the analysis. By focusing on the minimum amount of data required, data analysts and decision-makers can avoid information overload and being distracted by irrelevant or inconsequential data. 

However, it is important to note that MVD should not be seen as a rigid or fixed requirement. As business needs and objectives change, the minimum viable data may also change, and additional data may be required to gain deeper insights or achieve more ambitious goals. 

We employ data governance as the process of managing and ensuring the quality, availability, integrity, and security of an organization's data. We work with clients to define policies, procedures, and standards for data management, as well as assigning responsibilities for data management and ensuring compliance with regulatory requirements. 

The data governance process typically includes the following steps: 

• Defining data policies: Establishing policies that govern the management of data, including data quality, data security, data privacy, and data retention. 

• Establishing data standards: Defining data standards that outline the rules for data collection, storage, and use, such as data formats, data definitions, and data validation rules. 
• Assigning data ownership: Identifying data owners who are responsible for managing specific data sets and assigning data stewards who are responsible for ensuring the quality and accuracy of the data. 
• Establishing data processes: Developing processes for managing data throughout its lifecycle, including data collection, data storage, data processing, and data dissemination. 
• Ensuring data security: Implementing measures to protect data from unauthorized access, such as encryption, access controls, and data backup and recovery. 
• Monitoring data quality: Regularly monitoring data quality to ensure that data is accurate, complete, and consistent. 

• Auditing data compliance: Conducting regular audits to ensure compliance with regulatory requirements and internal policies and procedures. 
• Continuously improving: Continuously reviewing and improving the data governance process to ensure that it remains effective and efficient in meeting the organization's data management needs. 

Our approach is a true end-to-end process, from the initial identification of value through to the implementation of applied analytical solutions. We work with our clients from initial discovery to POC (Alpha) and MVP (Beta) to full-scale implementation and support. 

Our analytics development process is a framework for designing, developing, and implementing analytics solutions for specific business problems or opportunities. The process involves several stages that build on each other to create a successful analytics solution. These include problem definition, data collection, data preparation, data exploration, modeling, evaluation, and deployment. 

Overall, the analytics development process is iterative and involves multiple cycles of refining and improving the analytics solution. The key to success is to ensure that the analytics solution is aligned with the business problem and stakeholders' needs and that the insights generated are actionable and can drive business value. 

The IoT digital core for industry is a framework for building out the value of data that customers already have. Integrating and exposing this data with ease, and creating value use cases and analytics without the focus on the technology, allows accelerators to quickly begin the value of data. 

The IoT digital core and data use cases & analytics are two essential components of IoT technology. They work together to collect and analyze data from connected devices and sensors in real-time. 

IoT Digital Core: The IoT Digital Core is the foundation of an IoT system. It consists of the hardware and software that manage the flow of data from connected devices to the cloud. The digital core includes the devices themselves, as well as the gateways and edge devices that are used to collect data and transmit it to the cloud. In addition to hardware, the digital core also includes software such as operating systems, security software, and data management systems. Instead of focusing on technology, this is an accelerated framework approach to bringing value from your data, with blueprint integration adapters, data models, and workflows. 

Data Analytics: Once data is collected by the digital core, it is processed and analyzed using data analytics tools. Data analytics involves applying statistical and machine learning algorithms to identify patterns and insights in the data. These insights can be used to optimize industrial processes, improve product quality, and reduce downtime. Data analytics can also predict equipment failures and maintenance needs. 

Together, the IoT Digital Core and Data Analytics form a powerful system for managing and analyzing IoT data. By collecting and analyzing data in real-time, businesses can gain valuable insights into their industrial processes and make data-driven decisions to improve efficiency, productivity, and profitability.