top of page

Data Engineering

Data Engineering

Data Engineering involves the holistic management of large quantities of data captured and used within an organization.  Operationally, Data Engineering involves many different facets:

laptop-computer-with-business-chart.jpg
internet-technology-concept.jpg
neon-futuristic-interior-design-3d-rendering.jpg
  • Ensuring organizational data management processes are aligned with long-term organizational strategies and routine business operational needs.

  • Designing Databases, Applications, and Systems for the collection, storage, and analysis of data at scale.

  • Managing operational databases in an effective and efficient manner, reducing downtime, maintaining high performance, and ensuring the appropriate amount of redundancy without generating excessive and costly duplication of data or performance constraints.

  • Managing User access to the data, maintaining policies of least privilege, and conducting necessary audits on a routine basis.

  • And most importantly, ensure that existing data is in the proper state to meet the needs of data scientists and business analysts within the organization, through proper ETL and statistical practices.

Many companies fall short of accomplishing these goals to the fullest extent, however, because many business operations grow in a dynamic and reactive fashion to meet the immediate or near-term needs of the business, their customers, and regulatory standards, without designing their data infrastructure for long-term analytical purposes.  Additionally, companies that have undergone mergers or acquisitions often face significant challenges in merging systems or maintaining multiple redundant systems with disjointed data structures for years on end, or even indefinitely, in some cases.

 

Often, once companies realize the importance of quality data engineering practices, and the value of data analytics, their data infrastructure is so disorganized and inefficient that their analytics teams struggle to effectively produce quality analytics.  Those that do manage to develop quality analytics, often do so through painstaking ETL processes which consume most of their valuable time, wasting company money through unnecessary effort.

industrial-integration-automation-modernization-business-internet-concept.jpg
business-man-works-with-vr-financial-artificial-intelligence-ai-cloud-computing-big-data-f
businessman-working-with-business-cloud-analytics-data-management-system-computer-online-d
big-data-technology-business-finance-concept.jpg
big-data-technology-business-finance-concept (1).jpg
business-intelligence-technology-big-data-analytic-mixed-media.jpg

This is where ChAI comes in.  Our team has 7+ years of experience in Data Engineering and can provide expert third-party insights necessary to get your analytical teams on track with the management of your data.  Our team will work with your application, database, analytics, and leadership teams to provide a holistic picture of the problems in your current data infrastructure through the completion of a thorough audit of all systems, processes, and datasets,  and develop a roadmap for your problems to be resolved.

We also recognize that most companies do not have the time and resources to fully commit to an overhaul of their data infrastructure.  Our team will work with you to prioritize efforts with the greatest ROI for your organization, and help you realign your data management processes to your long-term organizational strategy.

 

Reach out to our team using the contact information below for a free consultation.  We are eager to help you solve your Data Engineering challenges.

business-data-financial-figures-visualiser-graphic (1).jpg
programming-background-with-person-working-with-codes-computer.jpg
analyst-working-business-analytics-dashboard-with-kpi-charts-metrics-analyze-data-create-i
bottom of page