Right Data Strategy: The Foundation for any AI Implementation

Artificial intelligence (AI) has changed and helped organizations and their employees be more innovative, creative, and efficient. So, I ask one question – Is AI built into your data strategy?

There are many ways to view your overall data strategy and calculate your AI ROI – And – The #1 advice learned in my many years is focusing on the following, no matter what stage you are in your digital transformation:

  • Know your use cases and real-world pitfalls
  • Construct a long-term data strategy
  • Collaborate between IT and other departments when building the data strategy

Once you process the above, here are some thoughts for further insights on the strategy and how it can help your organization.

Data platform modernization for digital transformation will continue to accelerate

  • Organizations need to continue to invest heavily in data platform modernization
  • Focus on migrating away from proprietary,special-for-purpose hardware & software to more flexible, scalable, cost-effective platforms that support diverse data types, in-memory computing, real-time analytics, and high-performance machine learning & graph analytics

Increasingly automate data management processes to enforce Data Governance

  • Organizations need to focus heavily on governing and stewarding the usage of data to bring efficiencies, increase adoption and meet regulatory & compliance requirements
  • Focus on initiatives as Data Lineage, MDM/RDM, Data Quality Management, Metadata management, Date Self-Service, Security, Audit & Access Control

Data Quality is a crucial differentiator to leverage important assets

  • Organizations must implement an automated data quality framework with appropriate control mechanisms to define, measure, and monitor data quality at an enterprise scale
  • Organizations should leverage statistical & ML techniques to score data quality, preempt and implement corrective actions proactively

Data protection will have the highest priority

  • Organizations must take a holistic approach for data protection to address GDPR and similar regulations
  • Global regulations will redefine how organizations collect, store and use data.

Cloud-enable data systems are growing faster than ever

  • Cloud is the preferred option for running Big Data storage and computing
  • Data Gravity encompasses what happens to big data in cloud services. For any organization, the associated costs are crushing. Large data sets can increase the fees to access your data — doubling the costs to host, replicate, and sync duplicate data sets impedes budget and business success. Data Gravity will pull analytical processes to the cloud as well

More implemented production-grade AI/ML programs

  • Organizations have accelerated and moved AI/ML initiatives to Production
  • Use cases for AI/ML have become more complex and broad-based, requiring a better understanding of larger data sets and more collaboration.

Democratization of Data Science programs

  • Data Science programs have started to leverage well-defined processes, platforms, and playbooks for mainstream adoption, bringing an element of standardization at an enterprise level
  • Data Science is not restricted to the niche Data Scientist community but is available to all data enthusiasts

New opportunities for data monetization

  • Organizations explore different options, including Data Products & Partnerships, Data as a service (DaaS), and Data API’s
  • Organizations build analytical products leveraging internal and external data.

DataOps culture will drive more agility, quality & governance

  • DataOps will eliminate several inefficiencies in the path of innovation
  • Organizations of all sizes will have to adopt DataOps, continue to realign themselves, and revamp processes for achieving better efficiency

Lowering the cost of operations and improving productivity is critical drivers

  • Organizations look to lower costs by adopting cost-effective models and leveraging more open-source software
  • Enhancing productivity through accelerators and reusable assets is high on the agenda.

Data platform modernization: Key drivers and challenges

  • Innovation
    • Legacy stifles innovation (e.g. mainframes)
    • Unable to deliver reports on time
  • Regulation
    • Lack of governance, stewardship
    • Poor data quality
  • Time to Market
    • Agility in integrating additional data sources
    • Lack of robust processes and automation (Devops)
  • Cost efficiencies
    • Higher cost (RTB – run the business) – extensive on-prem data estate
    • Higher TCO – with proprietary platforms
    • Multiple DWBI tools across LOBS that require standardization

Structured approach for bridging the gaps between data & insights

Consulting & Co-Innovation

  • Engagements are consulting-led
  • Co-innovate between consultants and clients with a clear focus on business value from inception
  • Use of Frameworks, including Data Maturity Framework, Data Asset Transformation, and BI Rationalization

Enabling Agility in Data Platforms

  • Drive agility through DevOps, accelerators, test automation & innovation focused model
  • Use of Frameworks, including ETL conversion utility, Code Reviewer, and Rules driven development

Data Governance

  • Govern data assets with efficient MDM, RDM, DQ, Metadata Mgmt., Data Lineage framework
  • Use Frameworks, including Data Quality Framework, Data Lineage, Dynamic Data Masking Framework, and Data Profiling tool

Lowering Cost through Factory Model

Meet the demands of large data programs by adopting Factory Model in Core-Flex resourcing driven by a rolling quarterly demand forecast to ensure high flexibility in resource availability and better resource utilization.

Driving outcomes through AI & Cognitive

Use and understand the chief elements of Cognitive & AI: Machine Learning, Speech & Vision recognition, Natural Language Processing, and delivering measurable outcomes from AI & Cognitive.

ConsultingData EngineeringData Governance
  • Data Asset Rationalization
  • Data Maturity Framework
  • Data Migration to Google Cloud
  • ETL Conversion Tool
  • ETL Code Review Tool
  • Data Migration Framework
  • BI Rationalization Framework
  • Data model
  • Data Lineage Utility
  • Data Profiler
  • Data Masking Framework
  • Data DeDuplication framework
  • ML Driven Metadata to Business Glossary Matching

AI is the tool for organizations to make smarter decisions, boost productivity, improve analytics, and provide partners and customers insights. In short, AI is a tool that every organization should consider using in their data strategy for 2021 and beyond as follows:

  • AI ROI targets projects that predict, not report: Instead of reporting on customers that have not paid their outstanding invoices or customers whose leases will expire within a set period, the best AI use cases help predict which customers will not pay their invoices or whether a customer with an expiring lease will buy out the equipment, return it, or extend the term of the lease
  • A close relationship between IT and the business is critical when identifying pain points: Discovering the pain points in the organization that AI can best address comes down to developing and fostering a close relationship between IT and other business stakeholders. Once you identify those pain points, it’s incumbent on IT to determine what to do about it

Thought Leadership