Data, Networking, Analysis (DNA)

DICE works with existing companies to help them adopt new technologies such as the Internet of Things (IoT, which generates lots of data that will need to be stored) or new companies as they are planning out their technology stack. The importance of data as a corporate resource and the purposeful storage of that resources is now at the forefront of most clients’ requirements.

Data

The ways in which data is generated, captured, transmitted and analyzed are on a staggering growth trend that is not slowing. DICE knows that data can help guide industry solutions using expert data integration, being mindful of data integrity and proper data storage.

Data integration

Data integration is the process of bringing data together from different sources into a single, coherent data store. When consolidated, data is easier to consume and process, and can better provide a unified view of an entire system. Data integration encourages collaboration between internal and sometimes external stakeholders, allowing for a complete understanding and mapping of relationships that exist between different data sources. DICE analyzes not just individual aspects of systems but the whole to see how the individual pieces affect and interact within the system.

Data integrity

Data integrity is the overall accuracy, completeness and consistency of data. It also refers to the safety of data—its regulatory compliance and security. Data integrity is maintained by a collection of processes, rules and standards that start at the planning stages and continue throughout the life-cycle of any project. Data integrity aims to prevent unintentional changes to information especially important during the data integration, storage and analysis aspects of any project. In the analysis phase of projects, data is often purposely manipulated while maintaining its meaning. DICE collaborates with industrial partners to ensure the integrity of our analysis and that th data is correct and maintained. In addition to data manipulation, cleaning of data for machine learning or artificial intelligence algorithms is another step where data integrity must be kept. Our researchers work with our partners to ensure integrity by discussing what determines a skew, or erroneous data point, and whether it should be included.

Data storage

Data storage is the recording of information. Paper and pencil or Ribonucleic Acid (RNA) and DNA may be considered forms of storage; however, DICE is more concerned with the electronic storage of information. Electronic data storage allows back up and protection of important information. For decades relational databases were considered the standard form of data storage. With the explosion of big data, traditional, more constraining forms of data storage have been replaced by cloud storage, data warehouses, data lakes, NoSQL and others. Edge computing even allows that some data, where appropriate, be stored right at the sensor. DICE helps to guide partners through this landscape to determine the best option.

Networking

Data transmission is the transfer of data from one digital device to another across a network. This may occur over a physical media such as fiber optic or copper cables or it may be transmitted wirelessly. There may be a single point-to-point style of communication, or it may involve point-to-multi-point communication styles depending on the requirements. Within Saskatchewan, the transmission of data within a rural agricultural setting has many unique challenges. Similarly, the transmission of data within a mining environment also has significant challenges. Data transmission for remote and harsh environments is an area where DICE has multiple projects.

Analysis

Data analysis is the practice of working with data to glean useful information that is used to make informed decisions. Data provides the necessary information used in the analysis process. Although the information provided by data is comprehensive, its dense form often makes it less useful. Data analysis gives context and provides actionable insight useful to companies and partners.

Methods of analysis include data mining (statistical analysis and knowledge discovery—what does my data contain), business intelligence, machine learning, artificial intelligence, and data modeling (digital twin) to name a few.

Data integration is often a precursor to data analysis. DICE works with our partners through each of the steps to ensure their requirements are met.