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Enabling Digital Transformation with Analytics Ops

Jan 17

4 min read

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Introduction

I wrote this article in 2019 as a white paper for the organization I worked at. It is worth re-iterating the concepts as analytics and AI become increasing complex based on the tools integration, data volume and diversity, evolving skills sets in analytics and technology.


As enterprises increasingly adopt Big Data, the roles of those managing and using data have become highly specialized. The challenges of Big Data—characterized by velocity, variety, volume, and veracity—have created a knowledge gap between data managers and data users. This gap has hindered the successful adoption of enterprise-level Big Data initiatives.


Analytics operations teams (Analytics Ops) serve as the crucial link, connecting specialized teams and ensuring the successful deployment of analytic assets in production environments. This document explores the pivotal role of Analytics Ops teams in bridging this gap and enabling the ROI of Big Data technologies. Analytics Ops offer a global path for Data and Analytics (D&A) teams to harness transformation technologies for advanced analytic solutions enablement that sustain enterprise growth.


Existing Operational Models

Technology teams often rely on DevOps practices to achieve speed and quality in complex environments. Similarly, ML operations (MLOps) optimize the lifecycle of AI/ML assets, using semi-automated processes to ensure agility and scalability.


However, for broader analytics beyond ML, “everything else analytics”, no well-defined operational approaches exist to integrate technology and analytics. Analytics Ops teams address this gap by blending platform, software engineering, and analytics expertise to streamline and enhance production enablement.


In a world of GenAI applications, there is a greater need for an Analytics Ops team to ensure the connectivity across users-domain-tech ecosystems.


The Role of Analytics Ops Teams


  1. Production Enablement


    Analytics Ops teams combine technological and analytical knowledge to overcome production delays caused by iterative translation, testing, and validation. Their hybrid expertise ensures faster, more accurate implementations by adopting best practices from both domains, enabling enterprises to harness the power of Big Data for transformative outcomes.





  1. On-Time and On-Demand Analytics


    Traditional analytics teams often face delays due to extended handoffs and iterative processes during production enablement. Analytics Ops teams mitigate these risks by working collaboratively with data science teams to create production-ready assets from the outset. Using modern tools and CICD processes, they reduce the timeline for updates and ensure analytics assets respond flexibly to data and strategy changes. Like ML models, analytic algorithms require deeper understanding of business and statistical knowledge domains.


3.      Aligning Data and Analytics with Technology


Modern analytics requires an understanding of data structures, compute platforms, and enterprise tools. Analytics Ops teams bridge these technological gaps, ensuring analytics teams can adapt and align with evolving enterprise environments. Acting as connectors, they maintain the balance between analytics and technology without compromising either domain's evolution.  Questions that this team helps their partners answer include:


  • What data is good?

  • How and what data should be stored?

  • How can the data be used for business growth?

  • How should the data -analytic pipeline be built to realize the ROI of this data?



Coexisting of All 3 Systems
Coexisting of All 3 Systems


  1. Balancing Automation and Intervention


    Regulated industries often require a balance between automation and manual oversight to ensure compliance. Analytics-led business solutions include lead generation, call center optimization, Identity authentication, credit risk and decisioning. Analytics assets in regulated industries require intervention steps within decisioning algorithms that are monitored, adjusted, or changed at regular intervals.  Analytics Ops teams combine business domain expertise with data engineering skills to design processes that accommodate necessary interventions while maintaining efficiency.


  2. Supporting Innovation and Production


    While analytics teams focus on innovation and advanced techniques, Analytics Ops teams take ownership of tasks like production monitoring, governance, and troubleshooting. By relieving analytics teams of these responsibilities, they enable faster innovation cycles and more effective analytics-driven products.



Challenges in Building an Analytics Ops Team


Information value chain can be long, complex and be managed across tiered organizational structures. Any type of transformation, digital or platform updates, brings a variety of challenges. Technology, Telecom, Financial Services or Fintech are examples of industries that I have personally experienced these challenges.


During transformations, challenges include:

  • Integrating multi-domain data across legacy, lake, and cloud platforms.

  • Standardizing workflows for compliance with encryption protocols and dynamic environments.

  • Bridging knowledge gaps between software engineering and analytics teams.

  • Adopting best practices like SDLC for analytics workflows.

  • Creating processes for bug fixes, refactoring, and advanced design planning.


Analytics Ops teams must identify missing processes, develop metrics, and establish templates to streamline the development and deployment of analytics assets. Their unique role enables them to act as translators between analytics and technology teams, ensuring optimal use of analytic assets within production platforms.


Case Study: In Financial services, Identity and Fraud solutions require multi-domain data ingestion across legacy server platforms, newer lake, and cloud platforms. With expanding volumes of financial data, migrating data-driven products and services to cloud-based platforms can be a challenging process.  As a data science leader, working with new technology systems the question of re-designing a modern asset came under my Analytical Ops team! Updated data processes on new platforms require new product definitions. During such widespread transformation migrating sophisticated analytic assets is a challenge that can fall right into the “chasm”.


Analytic Ops team was critical in enabling the Data-Analytics assets mapping to new capabilities, migrating them to the new platform and ensuring their successful operability. A bottom-up effort to change “immediate-partner” culture and re-structure of internal data science roles required a large dose of Emotional Intelligence practices. This is an important aspect of the analytics ops team.

 

 

Key Takeaways


Analytics Ops teams are essential for enterprises aiming to unlock the potential of Big Data and analytics. By bridging technical and analytical domains, they enable faster, more efficient production, align analytics with evolving technologies, and drive measurable business outcomes. The role of Analytics Ops will continue to grow as organizations adopt more complex, data-driven strategies.


In 2024, I met with a few individuals who have adopted the role of Analytic Translator or Analytics Bridge team doing the role I describe above. This will become an important piece of the cultural change that will accelerate with the adoption of GenAI tools.

 

Jan 17

4 min read

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8

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