How To Make Your Company Big Data Capable June 2014

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I’ll Cover

• What’s all the fuss about? • The challenges of Big Data • How can you tell how well you’re doing • Who’s doing Big Data well • How to make your organisation more “data capable” • Beware of DPA in your Big Data Programme

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IBM and Sas Definitions of Big Data

Variability and Complexity

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Yes considerably 46%

Only slightly 47%

Not at all 3%

Don't know 4%

Has your organisations database grown over the last year?

Databases get bigger in 2014

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Sas and Economist Survey

Aware of power of data but not exploiting data collected Need for defined data strategy Talent matters as much as the technology Growth in web tracking and Social media data

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Data Challenges in 2014

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0%

Don't know

Creating a single customer view

Increase in customer data

Threat of tighter data regulations

Building data and insight skills in house

Ensuring data security and data governance

Improving insight and analytics

Enhancing data quality

What data challenges do you foresee for you/your organisation in 2014?

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Why do you need Big Data?

• Recalculate entire risk portfolios • Mine customer data for new insights • Quickly (and I mean quickly) identify the customers

who matter most • Send tailored communication to mobile devices at

exactly the right time • Analyse data from new social media

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Whole new set of challenges

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You don’t know what you don’t know

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How to work out where you are

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Example Questions

• Is there a centralised, cross functional analytics unit? Such as a CoE? • Does the business have access to adequate business intelligence

analytical skill? • Are data driven market simulations in use in the organisation?

• Questions complied from a wide data bank

Data Insight & Analytics (BI) QuestionsContemporary Best Practice 6Data Identification 1Executive Sponsorship 1Future Adoption 3Information Foundation 3Insight strategy 3Optimal organisation 5Skill Base 1

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The Data Maturity Curve

Aware Have issues but doing little about them

Reactive Can fix some issues as they arise but can’t fix/identify root cause.

Proactive Can address root cause and stop issues before they occur. Conducts ongoing monitoring.

Managed Has a mature set of practices. Identifies and fixes issues but also defines strategy in a manner focussed on information development.

Optimal Centre of excellence. Information development is a core competency across strategy, people, process, organisation & technology

Based on the MIKE2.0 Methodology

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People

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Audits Benchmarking

Common Data Services

Communication Plan

Dashboard (Tracking / Trending)

Data Analysis

Data Capture

Data Ownership

Data Quality Metrics Data Quality Strategy

Data Standardisation

Data Validation

Executive Sponsorship

Functional Requirements

Master Data Management

Privacy

Security

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Technology

Data Analysis Extent to which data analysis technologies are appropriately deployed

Data Quality Metrics Considers the inclusion of automated data quality metrics

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Compliance

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Audits

Metadata Management

Data Quality Metrics

Data Analysis

Security

Issue Identification

Service Level Agreements

Data Subject Area Coverage

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Big Data in Action

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Data Strategy and

compliance

Data Team

Solving data quality at source

3rd Parties

Roadmap

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Impact of EU Legislation

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Summary

• Big Data - start collecting (but make sure you give customers a reason…)

• Get the basics locked down (quality, integration) - drive value strategically (insight, governance)

• Don’t forget the data people • Be mindful of how regulations may impact

your big data programme

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Source Material www.dataiq.co.uk/IW2014

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• Christine.andrews@dqmgroup.com • 0870 242 7788 • Register for free www.dataiq.co.uk • “dataIQ” @thedataIQ