idc retail insights - what's possible with a modern data architecture?

From Big Data to Real-Time Business Value

Greg Girard, Program Director World-Wide Omni-Channel Retail Analytics

BDA is Transformational, not Trendy

§  Data-driven society

§  New business models

§  A medium of exchange

§  Balance sheet asset

© IDC Visit us at IDC.com and follow us on Twitter: @IDC 2

Where’s your big data?

© IDC Retail Insights Visit us at IDC-ri.com 3

Sales Analysis $ 5,000,000,000 Total Revenue

$14 $/Transac6on 357,142,857 Transac6ons

20 A<ributes/Transac6on 7,142,857,143 Transac6on A<ributes

3 Items/Transac6ons 10 A<ributes/Item

10,714,285,714 Item Transac6on A<ributes

Marke.ng Decisions 4,000 Products

10 Promo6on Types 20 Channels/Touch Points 20 Customer Segments

16,000,000 Intersec6ons/Promo6on Event 100 Events/Year 1,600,000,000 Intersec6ons/Year

Reaching the top—never a sprint


§  Where are we? §  What do we need? §  Why climb higher?

§  How did they do it?

can we

Big Data and Analytics Priorities of North American Retailers

0 20 40 60 80

Other

Plant, Facilities, and Equipment Design,

Human Capital Management

Regulatory Compliance, Financial Controls

Operational, Fraud, and Malicious Risk

Process and Operations Optimization and

Information Technology Optimization and/or

Product, Service, or Program Improvement and

Supply Chain Management and Logistics

Customer Service and Support

Market and Competitive Intelligence

Pricing Strategies and Programs

Customer Acquisition and/or Retention

Basis: % of Retailers Who are Pursuing BDA Initiatives

Non-Food Retail CPG manufacturer or retail grocer

© IDC Visit us at IDC.com and follow us on Twitter: @IDC 5 N=200 Source: IDC's 2013 BDA Survey, July 2013

Q10. Which of the following areas have been identified as significant drivers of your organization's big data and analytics initiatives over the next 12 to 24 months?

Dissecting BDA Maturity: “5 by 5”

Dimensions Levels

Ad Hoc Opportunistic Repeatable Managed Optimized

People

Process

Data

Technology

Intent


~90 Sub-‐dimensions

Big Data and Analytics Maturity Levels


Ad hoc Experimental, siloed proof-‐of-‐concept or pilot projects, undefined processes, lack of resources, and individual effort

Opportunis.c Inten.onal, defined requirements and processes, unbudgeted funding, and project management and resource alloca.on inefficiency

Repeatable Accepted, recurring projects, budgeted and funded program management, and documented strategy and processes with stakeholder buy-‐in

Managed Measured; project, process, and program performance measurement influences investment decisions and standards emerge

Op.mized Opera.onalized and con.nuous and coordinated BDA process improvement value realiza.on

Some have it, some not so much


Source: IDC, Business Strategy: IDC MaturityScape Benchmark — Big Data and Analytics in Retail in North America, #RI246594

Figure 1: Big Data and Analytics Maturity Distribution Across Stages: Retail

IDC Big Data and Analytics Dimensions of Capabilities


Dimension Enablers

Intent • Level Strategy Set • Justification • Impact on Roles and Functions

Data • Analytics • Data Quality • Integration

Technology • Fit for Purpose • User Satisfaction with Tools • IT Responsiveness to Business Needs

Process

• Strategy and Planning • Information Mgmt and Apps Development • Data Analysis and Information Consumption • Measurement and Management • Governance

People

• Skills on Hand and Human Capital Mgmt Thereof • Collaboration, Communications, and Coordination • Location of BDA Tasks • Sponsorship

Dimension Enablers

Intent

Data

Technology

Process

People

BDA Maturity Increases Success Rates


n = 200 Source: IDC's Big Data and Analytics Maturity Benchmark Survey, 2013

Definitions •  Highest achievers.

Recent BDA projects met or exceeded expectations

•  Low achievers. Recent BDA project did not meet expectations or delivered no benefits

1st

3rd

5th

2nd

4th

Skew right

Skew left

*Differential = (Repeatable + Managed + Optimized)Highest Achievers - (Repeatable + Managed + Optimized)Low Achievers

Figure 3: Comparison of Big Data and Analytics Maturity Between High and Low Achievers Dashboard: Retail

New Metrics, Techniques, and Data Drive Higher Achievement


Q. How has the type of data analysis performed in your organization changed in the past 12–24 months?

Q. In aggregate, what type of benefits has your organization achieved from recent big data and analytics projects?

Scenarios New Capability 1 2 3 4 5 6 7 8 New metrics or KPIs Y Y Y Y

New analytic techniques Y Y Y Y

New data Y Y Y Y

None Y

% High Achievers 26% 23% 21% 10% 15% 19% 15% 6%

n = 700 Source: IDC's Big Data and Analytics Maturity Model Survey, July 2013

High Achievers Excel in All Five Dimensions of BDA Competence


Maturity Dimension Key Traits Differen.a.ng High Achievers

Intent Fund and budget BDA program at the enterprise level.

Data Ensure 6mely, trusted, and clean data.

Data Integrate customer-‐interac6on data with other data types.

Technology Balance development of structured repor6ng tools and advanced analy6cs (predic6ve analysis and data mining).

Technology Enable mobile decision making and data consump6on.

Process Address strategy and planning, governance, applica6on development, and data analysis and informa6on consump6on.

People Recruit and develop skills for data and content collec6on, integra6on, prepara6on, and management in addi6on to skills for performing mul6dimensional analysis, predic6ve analy6cs, and data discovery.

People Build culture of collabora6on, communica6on, and coordina6on across analy6cs groups and lines of business.

People Recruit and development skills for administra6on and maintenance of big data and analy6cs–related IT hardware.

People Engage execu6ve management in BDA efforts and extend BDA insights to customer-‐facing associates.

Source: IDC Retail Insights, 2014

Essential Guidance §  Intent

•  Align, align, align •  Focus on Collaborative Governance and Closed-Loop Management •  Set and Manage BDA Strategies at the Enterprise Level •  Fund and Budget BDA Programs Annually with Flexibility

§  Data •  Exploit Enterprise Data Assets •  Introduce New Data Types and Analytic Methods

§  Technology •  Ensure Users are Satisfied with the Tools of Their Trade •  Complement BDA Technology with Mobility

§  People •  Complement Skills and Expertise with Culture Collaboration •  Embed BDA Insights into Decision-Making and Customer Interactions

§  Process •  Start with Information Mgmt, App Dev, and Strategy and Planning •  Finish with Information Consumption—The Last Mile of BDA Success


Contact Information


Greg Girard, Program Director [email protected] @gregorydgirard www.idc-ri.com https://idc-insights-community.com/retail