Enterprise Data Platform: Every CDO’s Priority
Data Fabric. Digital Thread. Data Mesh. Golden Record of Truth. Whatever you call it, a harmonized and usable enterprise data layer is an essential facet of digital transformation.
Data fabric facilitates data access across the enterprise in a unified manner with ability to scale. It is not just a matter of automating workflows and processes but also a question of how we bring together legacy and other siloed data systems, to interact with one another and make a unified data platform. It is – with an integrated set of technologies and services, designed to deliver integrated and enriched data – at the right time, in the right method, and to the right data consumer – in support of both operational and analytical workloads.
Figure: Enterprise Data Fabric Architecture
Data fabric combines key data management technologies – such as data ingestion, integration & transformation, governance, orchestration, and data catalog. Aligning with a domain-driven design, data fabric will be simply consumed by the business via data catalog.
Data Fabric Business Benefits By Persona
Business View
- Self-service data on demand without having to write a single line of code delights business users
- Reduced time to insights and data driven support to make more informed decisions when using the unified Data platform.
IT View
- Improved Quality with less tedious and repetitive data loads to perform cleansing and enhancement tasks to implement business rules and data quality rules.
- Lower total cost of ownership and increased productivity, thanks to technology agnostic tools that upgrade at the click of a button, self document, and eliminate the need to retrain resources on each new underlying technology as the organization adopts it.
Enterprise View
- Enabling enterprise-wide data consumption – including data integration, data visualization, real time streaming data, and APIs for applications
- Improves the collaboration between the business teams, and drives efficiency and productivity in engaging with IT teams.
How to Tackle Today’s Enterprise Data Fabric Challenges & Risks
As organizations seek to leverage their data, they encounter challenges resulting from disparate and legacy data sources, types, structured and unstructured data, data volume, and data quality. Code intensive platforms require deep technical skill sets and high maintenance. IT teams get caught in an endless loop of training, migrations and updates, and re-training, as they attempt to keep pace with the ever evolving technology stack, taking time away from creating business value.
This multi-dimensional data is further complicated when organizations adopt hybrid and multi-cloud architectures. For many enterprises today, operational data largely remains siloed and hidden, leading to an enormous amount of untapped business insights.
Thus, technology agnostic and no code solutions that encompass the entire data engineering, data science, and DataOps and analytics lifecycle – like Advana – are recommended.
Figure: Enterprise Data Challenges, Risks, and Recommended Solutions resulting in 30-40% Lower TCO and productivity gains.
Top 3 Data Platform Features
Critical, non-negotiable features of an enterprise data platform include centralized and automated data governance that is abstracted away from the code in a metadata layer, hundreds of common data transformations out of the box, and drag and drop analytical models to embed into data pipelines.
- Data Governance – Data quality rules and business rules are managed centrally, and referenced in pipelines, for single point of updation and ensure updation across the business. Master metadata management to consolidate and standardize data with common definitions and hierarchies to standardize reporting across the business and ensure there is summary data for enterprise defined KPIs all the way down to detailed transactional data.
- Data Engineering – Data ingestion of structured and unstructured data files of various formats from on prem and cloud storage locations. Out of the box data engineering capabilities like change data capture. Continuous data integration and delivery acceleration through DataOps.
- Data Science – ML and AI models predictive and prescriptive models can add value to data by answering specific business questions.
Data Fabric Consumption
Data only delivers business value when it is contextualized and becomes accessible by any user or application in the organization. When implemented correctly, a data fabric helps ensure those values are available throughout the organization in the most efficient and automated way possible.
- Data Catalog – Centralized, golden record of data, the single source of truth for enterprise data consumption, serving as a data dictionary to enable efficient access and reuse of data from business, and easy access to view metadata via data catalog.
- Data as a Service – Inside the data fabric, microservices help applications achieve the task of connecting to particular data subject areas in the data fabric. APIs are available for IT teams to connect to and consume data from.
- Analytics Catalog – Repository of all analytical models to enable reuse and accelerated insights by enabling ML mode sharing across the organization.
- Intelligent Apps – Applications with embedded AI to provide timely insights into business operational workflows to get the right insights, at the right time, to the right user, who can use the information to make a fast better decision.
About the Author
Jia Gao
Jia Gao is a Business Analytics Consultant at Kavi Global. She has a Masters degree in Financial Risk Management from University of Connecticut and a Bachelor of Science degree in Applied Chemistry from Northeastern University. She is proficient in feature engineering, data modeling and machine learning. Jia enjoys yoga and traveling.
