Data Management

At Cognitell, we recognize “there can be no AI without data” thus the foundation of our problem-solving efforts must be good data management. Regardless of your organization’s current capabilities regarding managing your data, Cognitell stands ready to help get your data ready to solve problems.  

Data management includes activities such as:

Data Collection 

Data collection activities are the crucial first step to getting data into your organization. Care needs to be taken to address issues related to data quality at this early stage.  Issues with field sensors or measurement protocols can greatly impact the usefulness of data and are not readily fixed in later stages.  Even simple data entry methods need to be standardized to ensure various data fields have the same information and meaning based on the individual entering the data. Cognitell can help address any issues you may have at the data collection stage by helping develop data protocols and standards as well as helping to evaluate the quality of field sensors and IoT-connected devices.  

Data Transmission 

Data transmission is the next critical stage in data management as data travels from wherever it is collected to a storage solution.  These issues come up again when transmitting data to other steps of the process such as data modeling or a reporting platform. Using correct data transfer protocols and well-designed APIs ensures no loss of quality during transmission and can greatly reduce costs for edge IoT devices relying on cellular or satellite transmissions. Quality control and security are high priority concerns at this stage as are monitoring activities to ensure data pipelines are functioning efficiently. Cognitell works with customers to understand their needs and can develop APIs and other solutions to ensure the efficient, secure, and timely transmission of data. 

Data Storage 

The days of storing everything in spreadsheets until you are big enough for a SQL database are behind us. Modern data storage solution design requires an understanding of available options such as SQL and No-SQL. No-SQL solutions need to be evaluated to ensure the right implementation is selected such as document, key-value, column-oriented, graph, or other types of solutions. In addition, various data types may work better in purpose-built databases such as time series or geospatial databases. Once the data storage solution is designed, administration, optimization, and security need to be addressed for the unique solution for your data. Cognitell can help with the design of your data storage solution as well as help you move forward from an existing solution to a more appropriate design.  

Data Cleansing 

Another critical component of data management is ensuring the data are correctcomplete, accurate, and relevant. Correct data means having the right data; complete data means having all of the data available; accurate data means the data represents reality; and relevant data means only paying to store and transmit data relevant to your organizational needs. Cognitell can help develop QA and validation processes as well as develop a program of exploratory data analysis to ensure your organization has access to clean data.

 

At Cognitell, we recognize “there can be no AI without data” thus the foundation of our problem-solving efforts must be good data management. Regardless of your organization’s current capabilities regarding managing your data, Cognitell stands ready to help get your data ready to solve problems.  

Data management includes activities such as:

Data Collection 

Data collection activities are the crucial first step to getting data into your organization. Care needs to be taken to address issues related to data quality at this early stage.  Issues with field sensors or measurement protocols can greatly impact the usefulness of data and are not readily fixed in later stages.  Even simple data entry methods need to be standardized to ensure various data fields have the same information and meaning based on the individual entering the data. Cognitell can help address any issues you may have at the data collection stage by helping develop data protocols and standards as well as helping to evaluate the quality of field sensors and IoT-connected devices.  

Data Transmission 

Data transmission is the next critical stage in data management as data travels from wherever it is collected to a storage solution.  These issues come up again when transmitting data to other steps of the process such as data modeling or a reporting platform. Using correct data transfer protocols and well-designed APIs ensures no loss of quality during transmission and can greatly reduce costs for edge IoT devices relying on cellular or satellite transmissions. Quality control and security are high priority concerns at this stage as are monitoring activities to ensure data pipelines are functioning efficiently. Cognitell works with customers to understand their needs and can develop APIs and other solutions to ensure the efficient, secure, and timely transmission of data. 

Data Storage 

The days of storing everything in spreadsheets until you are big enough for a SQL database are behind us. Modern data storage solution design requires an understanding of available options such as SQL and No-SQL. No-SQL solutions need to be evaluated to ensure the right implementation is selected such as document, key-value, column-oriented, graph, or other types of solutions. In addition, various data types may work better in purpose-built databases such as time series or geospatial databases. Once the data storage solution is designed, administration, optimization, and security need to be addressed for the unique solution for your data. Cognitell can help with the design of your data storage solution as well as help you move forward from an existing solution to a more appropriate design.  

Data Cleansing 

Another critical component of data management is ensuring the data are correct, complete, accurate, and relevant. Correct data means having the right data; complete data means having all of the data available; accurate data means the data represents reality; and relevant data means only paying to store and transmit data relevant to your organizational needs. Cognitell can help develop QA and validation processes as well as develop a program of exploratory data analysis to ensure your organization has access to clean data.