Data Analytics

The ability to rapidly obtain insights to guide decision-making is critical to the future of an organization.   This can be enabled using advanced Business Intelligence and Artificial Intelligence methods and tools. 

Data Discovery and Inventory

Before deploying any data analytics solution, the current state of data needs to be established.  Developing an understanding of the current use cases, security requirements, and any existing data integrations for each data asset is a critical first step.

Typical questions to be addressed are: 

  • Identification and documentation of available data sets/sources 

  • Medium-term and long-term plans for datasets (e.g., merge, archive, shutdown, enhance) 

  • Business requirements and usage associated with datasets 

  • Business risks are satisfied by integrity checks and data backups 

  • Nature of your current data sets (locations, applications, systems, formats, encryption) 

  • Access to the data sets (permissions, owners, security, governance) 

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  • Data collection method (collection mechanisms/applications) 

  • Integration with other data sets or applications 

  • Current data usage (reports, dashboards) 

  • Data currency (how often does the data change) 

  • Data classification (what data is confidential or regulated) 

  • PIPEDA implications (is there a requirement for deletion if requested by customer) 

  • User population (How large is the user set? What application is used?) 

Data Perceptions works with our clients to complete an inventory of data assets including the data architecture, and sources for each data asset.   

Data Strategy

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Data Management Plan

Every data strategy needs a corresponding data management plan that addresses data classification, data governance, and data security.   

Data Perceptions can assist in developing the: 

  • Framework for the evaluation and classification of data.   

  • Data governance policy and process. 

  • Data Security policy and implementation plan. 

  • Deployment strategies (CD/CI) for development, staging, and testing, and production. 

Data Analytics Pilot

Developing a data strategy is the next critical step to developing a data analytics solution. Leverage the data sets that will create value for the organization, enable data-driven decisions, and promote business transformation. 

  • How can this be accomplished? 

  • Who needs the information and how frequently?

  • What is the best way to present the information?

  • Are there opportunities to better inform the organization through improved access to data?

  • Is there data duplication that can be addressed?

  • Can some data strategy requirements be achieved by modifying the scope of existing or planned projects?

  • Is there an opportunity to define a master data management strategy? 

Data Perceptions can guide your team through the process of answering these questions and developing an actionable data strategy.   

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There is often an iterative process with data analytics between strategy and pilots.  With cloud-based data repositories (data lakes), transformation and ingestion platforms (ETL), and data analytics platforms, this iterative process is accelerated.  Data analytics pilots are great to test theories, validate assumptions, and explore options.  Data analytics pilots also require an iterative process to be successful.  Typical iterative pilots can follow an agile sprint-based approach: 

  • Engaging key business and technology stakeholders to identify desired future state of data analysis, report development, and information use cases. 

  • Development of a data model design and roadmap 

  • Iterative development and testing of data analytics dashboards and reports with users 

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Machine Learning and Artificial Intelligence (ML & AI)

Many organizations are considering ML & AI to augment their data analytics solutions.  Data Perceptions can assist with an ML and AI strategy when larger data sets are required. 

Data Perceptions has experience leading data analytics pilots using platforms such as: 

  • Microsoft PowerBI  

  • Tableaux 

  • Metabase (open source) 

  • Microsoft Azure Synapse (ETL and Data Lake) 

 
 
 
 
 
 
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