OVERVIEW

For many companies today, there is a real desire to become data-driven. A key first step in achieving this, is to create a data strategy that sets out a roadmap on how to get there.  When defining a data strategy, most companies are faced with two broad challenges. The first is how to reduce risk to get data under control, govern it and build trusted data products that they can consume and use. This needs to happen in an environment where regulation is increasing, data complexity is growing with thousands of data sources and many different types of data stores both on-premises, in SaaS applications and in one or more clouds. The second is how to maximise the use of data and analytics for competitive advantage to disrupt the market(s) you compete in.  Data strategy therefore needs to be defensive as well as offensive.  So how do you do this?  What do you need to do to build a data strategy? What things should be in a data strategy? What should you include in a defensive data strategy versus an offensive one? This class looks at this problem and seeks to find a way to build a data strategy that enables both defensive and offensive data programmes and brings them together to create competitive advantage in a data driven enterprise.

AUDIENCE

Chief Data Officers, Chief Information Officers, Heads of Data Governance, Data Architects, Data Engineers, Solution Architects, Heads of Analytics,  Data Warehouse Managers, Data Administrators, Business Unit Managers responsible for Data

LEARNING OBJECTIVES

Attendees will learn how to build a data strategy for a data driven enterprise that delivers business value while improving data governance

MODULE 1 – INTRODUCTION

• What is a data strategy?
• Why is it needed?
• Key stages in building a data strategy
  o Business strategy alignment – a critical success factor
  o Understanding your current setup
  o Defining a future setup to reduce risk and improve competitive advantage
  o Providing a roadmap to help achieve your business goals, priorities, and targets

MODULE 2 – BUSINESS STRATEGY ALIGNMENT

• Understanding the components of a business strategy
  o Businessvision
  o Strategic business objectives o Key performance indicators (KPIs)
  o KPI Targets
  o Executives accountable for achieving targets and goals
  o Strategic business priorities
  o Business initiatives and budgets

• Defining a vision for becoming a data- driven enterprise

MODULE 3 – UNDERSTANDING AND ASSESSING YOUR CURRENT SETUP

• Understanding and assessing how your business currently works:
  o Critical operational business processes
  o Critical managerial analytical business processes
  o Identifying problems caused by data

• Understanding and assessing existing:
  o Organisational structure currently managing and governing data
  o Data and analytical projects
  o Skillsets
• Understanding and assessing:
  o Existing data estate: Data sources, types of data stores on-premises, in multiple clouds, SaaS applications and at the edge
  o Existing data capabilities in use: File systems, content management systems, transaction data stores, data warehouses, data lakes, lakehouses, graph databases, streaming data systems, master data management
  o Existing analytics capabilities in use: BI reports, ML models, AI-bots
  o Existing data architecture
  o Critical operational business process data flows within and across systems
  o Existing analytical system data flows
  o Existing business unit data capture and processing
  o Existing data and analytics technologies: Data stores, data integration tools, data catalogs, application and business process integration tools, BI and data science tools
  o Existing data governance setup: Data governance disciplines, people, processes, policies, technologies, data governance capabilities, regulatory obligations, policy enforcement techniques, and current projects
  o Identifying the impact of ungoverned data on business processes, business performance and compliance

• Documenting and ranking business cases where data issues restrict business from achieving its goals
• SWOT and Gap analysis
• Gauging where you are on a maturity model

MODULE 4 – DEFINING YOUR FUTURE SETUP

• Defining future organisational structures for:
  o A Data Program Office o DataGovernance
  o Implementing operational data flows
  o Building data products for use in analytics
  o Knowledge management

• Defining future data architecture foroperational and analytical data flows,content and knowledge management
• Recommending new data capabilities
• Recommending new data technologies
• Defining a defensive data strategy
  o Defining operational data flows to reduce risk
  o Use data and analytics to reduce risk
  – Defining what data products, analytical products needed to reduce different types of risk
  – Align with business strategy compliance and risk objectives and priorities
  o Defining a data governance framework
  o Definingdatagovernance disciplines and policies for compliance and risk reduction, e.g., data privacy, data access security, data loss prevention
  o The role of data observability, data intelligence and active real-time data governance
• Creating an offensive data strategy to drive competitive edge
  o Align data products, analytical products, and decision services with business objectives and priorities to improve profitability
  o Growing revenue
  – Define what data capabilities,data products, analytical products, decision services needed to grow revenue
  – Creating a Customer 360o data platform and analytics to drive revenue
  – Integrating customer data and insights into marketing, sales and customer service
  o Reducing cost
  – Defining common application and business process integration capabilities
  – Defining new operational data flows to improve efficiency and reduce costs
  – Using master data to reduce costs and increase agility
  – Define what data products, analytical products, decision services needed to reduce cost
• Creating a data platform and changing your data architecture to support both

MODULE 5 – IMPLEMENTATION ROADMAP

• Managing expectations – the promise of building data and analytical products using rapid data integration and machine learning
• Identifying high value business cases and quick wins
• Defining the data projects needed to implement your data strategy
• Classifying data projects by business objective to align with business strategy
• Identifying accountable business executives to sponsor your strategy
• Defining business and IT personnel needed
• Defining a roadmap to incrementally bring you data strategy to life
• Implementing data governance as part of your data strategy
• Defining how to measure success
• Communicating and evangelisingbusiness value