ContextiTM Big Data Framework

Maturity Model

Scale

Embed

Transform

Acquire

Organise

AnalyseIntelligence Function

Data Supply Chain

As the organisation matures in an analytical context, an independent Intelligence function should be established to support all areas of the business. A federated model allows analytics to be embedded in the front-lines and enables data-driven decision-making incorporating business context to be done at scale across the organisation.

The data supply chain illustrates the journey through the stages of transformation from data to information to insights and into decisions. In order for the final stage of data-driven decision-making is done by the Business, all previous stages of the data supply chain must be operating effectively.

Sponsor

Focus Analytics BusinessTechnology

Data as a Strategic Asset for Competitive Advantage

Data Management as a Cost of Business

Business

Information Technology

DatabaseData

WarehouseInformation

AnalyticsInsights

BusinessDecisions

Volume, Velocity, Variety Value

GM Level CXO Level

The Big Data sponsor is initially at the GM level for projects within their own BUs. However, in order to scale, there must be a sustained Big Data program sponsored from the C-Suite level.

Whilst there is always training and investment spend across all functions, it is initially focussed on Technology which then moves into Analytics and finally to the Business.

Organisation-wide

Capture data from all internal sources and augment with relevant external sources.

Process the data and organise into information with drill-down access to the raw data.

Perform analytics made possible by big data infrastructure to turn information into insights.

Make data-driven decisions based on insights and execute to create business value.

Use data as a strategic asset for competitive advantage and achieve business transformation.

Multiple Business Units

Contexti Pty Ltd

Action

Scale out successes across the organisation and consolidate data and expertise into a Center of Excellence.

Embed analytics within the Business who understand context and federate the function across the Org.

Analytics

Capture data from all internal sources and augment with relevant external sources.

Process the data and organise into information with drill-down access to the raw data.

Perform analytics made possible by big data infrastructure to turn information into insights.

Make data-driven decisions based on insights and execute to create business value.

Scale out successes across the organisation and consolidate expertise into a Center of Excellence.

Embed analytics within the Business who understand context and federate the function across the Org.

Use data as a strategic asset for competitive advantage and achieve business transformation.

Embed TransformAcquire Organise Analyse Action Scale

What is the goal?

At phase

What are the

challenges?

What are the key

questions?

What use-cases

can deliver

ROI?

ContextiTM Big Data Framework

Framework Guide

How can they be

overcome?

Traditional database limits are often exceeded by the growing volume, velocity and variety of modern datasets. Moreover, costs of OLTP and storage can be prohibitive due to the increasing scale.

The dramatic increases across the 3 Vs of big data has stretched traditional OLAP systems beyond their limits, resulting in poor performance and high costs, as well as loss of quality via aggregation.

The enormity of datasets and solution spaces exceed the memory and computing capacities required to perform analytics across the full ranges. Data Science talent is difficult to find.

Developing data-driven decision-making requires a drastic shift in organisational culture and often require significant structural and process changes to execute effectively.

Breaking down silos within an organisation requires standardising processes and aligning BU/Functions across the organisation. This requires C-Level support and sustained change programs.

Centralised services via a Center of Excellence provide standardisation but can be too inflexible and bottleneck progress. Business context is also lost when services are removed from frontlines.

Disrupting business-as-usual within an organisation requires creativity and boldness from leadership who are highly data-literate and have a vision for the future of the organisation.

NoSQL databases for scaling OLTP beyond traditional RDBMSs

Processing machine-data for monitoring

Data Lake as cheap redundant storage

Data Warehouse Augmentation with Hadoop

Distributed ETL with Hadoop

Data Lake providing access to raw data.

Data Sandbox for analytical purposes.

Distributed processing for Statistical Engines e.g. R on Hadoop

Analytics via web or mobile interfaces

Pricing Customer

Segmentation Sentiment Analysis Recommender Engine Propensity Modelling Fraud Detection

Enterprise Data Warehouse to break down data silos

Center of Excellence consolidating expertise to create standard processes

Interactive visualisation tools

Real-time dashboards with live updates

Consolidated self-service analytics platform

Packaged datafeedscreated by massaging data assets for use internal and external

Customer facing analytics self-service portal

Big Data Technologies like Hadoop and NoSQL utilise distributed computing to handle growing OLTP in a cost-effective manner.

Big Data Technologies offer low-cost scalability and can be used to augment existing OLAP to deal with growing data.

Distributed processing infrastructure allows advanced data science techniques to be used within time constraints.

Big Data Analytics training programs increases the overall data-literacy within a company and enables cultural change.

Establishing a Center of Excellence consolidates expertise and data assets and by standardising processes allows access.

After standard processes have been established, de-centralising analytics allows the flexibility to respond to changes.

A visionary leadership who understands the transformative power of data and analytics and inspires and empowers.

Are all Internal Data sources being captured?

Are we augmenting with External Data?

Can our systems accommodate future growth?

Are we able to capture unstructured data?

What is our daily ETL processing time?

What are our DW costs per TB?

Are we losing Data Quality?

Are we able to process unstructured data?

Is our data accessible in a timely manner?

Do we have real-time , interactive capability?

What are the unknown unknowns?

Can we perform analytics across our full datasets?

Does the business have access to insights at the speed required?

Do we make decisions based on data or instinct?

Do we proactively engage analytics to identify opportunities

Are we asking the right questions?

Are we able to execute on insights?

Are there data silos within BU/Functions or is there an Enterprise Data Warehouse available?

Are analytical services available to all parts of the Business, or is it siloed under a BU/Function?

Is analytics being done using the relevant business context?

Are constituent units using the established standardised process?

Do we have a systematic process of measurement and improvement?

How can we use our strategic data assets for competitive advantage?

Are we able to monetise our data assets and create sources of revenue?

How can we innovate using Big Data?

Contexti Pty Ltd

Capture of data across: Semi/Unstructured Online/Offline Internal/External

Efficiency across: Cost Performance Scalability

Purposed for: Strategic Tactical Operational

Analytics usage: Proactiveness Pervasiveness Continuity

Org-wide available: Data Warehousing Analytics Business Functions

Levels of: Decentralisation Self-service Feedback

Disruption to: Organisations Customers Markets

Peak

level of

Big Data

maturity

Not yet

started

the Big

Data

journey

ContextiTM Big Data Framework

Self-Assessment

Factors to consider

Embed TransformAcquire Organise Analyse Action ScaleAt phase

Most relevant internal data sources are captured but a full diagnostic of possible useful sources not yet investigated.

Proof-of-concept level projects in defined use-cases. Eg. Data Warehouse Offloading, Hadoop for ETL.

Diagnostic analytics is used to find correlations and causalities between events to explain past outcomes.

Analytics is used to measure performance and explain behaviours and outcomes of past decisions.

CIO support has aligned IT to create an EDW that provides a central repository and access for organisation-wide data.

Embedded teams in niche areas utilise standards established by the CoEbut also use datamartsfor domain-specific use.

New services or capabilities are made possible using data.E.g. Personalised realtimeoffers to customers

Level 3

Critical transactional data is captured and exploring Big Data Technologies to capture lower value machine/web data.

Investigating and training on use of Big Data Technologies for potential use-cases in existing systems.

Descriptive analytics is used to provide insights into past events and outcomes in the historical dataset.

Reports are consulted periodically to track progress and perform course-correction when necessary.

Initiatives span BUs/Functions as BU Heads collaborate to benefit from scale and increased ROI.

Standard processes and procedures have been adjusted to allow for embedded teams within the Business.

The organisation has improved existing services or spend.E.g. Marketing based on Single Customer View

Level 2

No investigation of how Big Data Technologies provides faster, cheaper capture and storage.

No investigation of how Big Data Technologies could improve costs, scale or performance.

Reporting only on specific measurements and KPIs for use in management reporting.

Decision making is still primarily based on management experience and intuition.

There is no support at the Executive level and any initiatives are siloed within Business Units.

There are no embedded analytics teams and lack of business context limits effectiveness.

Investments in Big Data Technology has not yet realised value creation at the business level.

Level 1

All relevant internal data sources are captured including unstructured data but there is no external augmentation.

Production level projects that are operationalized and providing ROI by improving performance, scale or costs.

Predictive analytics is used for forecasting outcomes and predictive models are used to guide decisions.

Initiatives are generally undertaken based on analytical insights and decisions-making is supported by data.

A Big Data Analytics Center of Excellence is in its early stages of consolidation and standardisation.

A strong feedback loop iteratively improves IT, Analytics and Business processes as embedded teams increase.

New sources of revenue based on data assets, analytical capabilities and business executionE.g. Packaged Datafeeds

Level 4

All relevant internal data sources are captured and there is some adhocaugmentation from external sources.

A re-architected datawarehouse solution with multiple points of Big Data Technology utilisation.

Prescriptive analytics at the tactical and operational level provide actionable insights in often in real-time.

The business proactively engages analytical insights to discover and explore potential new opportunities.

The CoE has been established and provides analytical services using the EDW to all areas of the business as needed.

Business priorities and outcomes are the primary drivers of change in Analytics Models and Data Warehousing.

Organisation disruption occurs as Big Data enables transformative innovations.E.g. Data Brokerages

Level 5

All relevant internal and external data sources are captured cost-efficiently with scalability to allow for future growth.

Solution architecture incorporates Big Data technologies to achieve optimal blends of Cost, Scale and Performance.

An Intelligence function provides strategic-level decision support for the purposes of competitive advantage in the market.

Business structures and processes have been redefined in order to utilise and execute on data driven decisions.

Analytics operates as an independent Intelligence function across the organisation with a C-Level leader.

A federated model combines the strengths of CoE standardisation, flexible constituency and self-service tools.

Customer disruption as Big Data enables changes to how customers and markets behave.E.g. Virtual Shopping

Level 6

Contexti Pty Ltd