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IAPA – December 2014Ricky BarronDirectorInfoStrategy Pty Ltd

The World of Analytics by 2020

+Topics

What will the industry look like in 2020?

Will data sharing be part of every aspect of day-to-day life?

What implications will greater data streams have for analytics?

How should analytics professionals prepare for this new world?

+POV

Analytics will become far more pervasive across business by 2020

No longer the exclusive domain of Data Scientists, Actuarial Depts, and Statistical Modellers Although they still play an important part

Business users want more predictive and prescriptive analytics beyond simple forecasting, slice 'n dice OLAP capabilities.

Driven by more sophistication on behalf of business analysts and advances in self-service data collection, preparation, blending and analytics software tools

+Analytics – the capability, processes and tools to enable Businesses to:

Look Back Look Across Look Forward

Yesterday Today Tomorrow

Descriptive AnalyticsDiagnostic Analytics

Data ExplorationDiscovery Analytics

Predictive AnalyticsPrescriptive Analytics

What has happened?Why did it happen?

What is likely to happen?What should I do about it?

What new opportunities exist?

What processes can be improved?

What is happening right now?

• Provides information workers with a view of historical data for analysis and reporting.

• Determine the facts about the performance of the business across a number of dimensions, including time.

• Ability to slice and dice the data in a number of ways to determine patterns of behaviour which may provide insights into why a particular event happened.

• Enables information workers to blend and explore different types of data and analytics

• Look for ways of improving business processes and to discover new business opportunities.

• It provides faster time to value because in a data discovery system the data does not have to be integrated into a data warehouse before it can be analysed.

• Traditional analytics to mine the data and apply predictive algorithms / models to forecast and predict potential outcomes based on propensity, etc.

• Includes seasonality, forecasting, outliers in trends.

• Prescriptive analytics enables information workers to apply reasoning to the analysis to determine a number of possible outcomes based on altering variables across a range of scenarios.

+Where change in decision making is starting to happen.

Business Executives have already started to change the way they approach 'big' decision making as a result of big data or advanced analytics...

Relying on enhanced data analytics such as simulation, optimisation, or predictive

analytics

Employing a dedicated data insights team to inform strategic decisions

Changing the way data or analytics is presented to management

+The 3 Stages of BI Evolution

Source: Data Exploration and Discovery: A New Approach to Analytics. (BI Research 2013)

Business Value

Complexity

Statistical, text & graph analysis.Forecasting.Predictive modelling & analysis.Optimisation.

Fixed and interactive analytical dashboards.Ad hoc analysis (OLAP)

Fixed & interactive reporting

To be competitive and

increase business

efficiency you need to plan for these options

These options are no longer sufficient to

meet business requirements.

+Driving prescriptive analytics

Insight

Action

OptimiseMove a metricChange a productChange behaviour/process

Hindsight

Realtime

Foresight

Trusted informationAct on insights gainedExecute theories

MeasureOutcomesSentimentFeedback

Explore datasets, discover correlations, patterns.Undiscovered facts

Information Value

Data

Volu

mes

Forecasting, planning & trendingStatistical Analysis

Operational reporting, SCADA controlAlerts & Events

Historical reporting, Proof of operationRegulatory, statutory, financial

+Asking multiple questions on the same dataset will only provide limited insights

Moves by big corporations to build 'data lakes' will only go so far large single data source of all production data, uncleansed,

untransformed, granular, transactional level for mining of insights.

once you have gleaned some insights – and actioned them – what is next?

Information innovation through enrichment is critical Finding, Collecting, Preparing, Blending Data into

AnswerSets for: Exploration and Discovery Analytics

+So where will all this data come from?

The Internet of Things (IOT)

Commercialisation of corporate datasets

Proactive data collection points (Apps, embedded devices, etc)

Open data sources

o o o

+Business savvy analytics professionals.

Deeper understanding of business context and business datasets

Translation of critical insights into messages (stories) that can be effectively (visually) communicated to executives/boards

Free-thinking, creative abilities to combine and blend datasets to uncover previously unknown facts. Approach a problem without a "target in mind" Translate insights into positive outcomes for business

Differentiation through smarter algorithms and models, blending of disparate datasets (both structured and unstructured) to gain deeper insights

Protection of IP becomes more important as the playing field becomes more level

+Understand and address security concerns

Cloud-based issues data sovereignty geographic diversity for DR

Validate and cross-check predictive models ability to 'walk' business users through the logic visually

Transparency and auditable algorithms regulatory scrutiny on financial services

Data privacy and protection routines data masking, tokenise, encryption, etc.

+Summary Increased adoption of "advanced analytics" by

business users

Data collection, preparation, blending capability outside IT

Analysts developing storyboards to present insights to execs

Visualisations, discovery and exploration of disparate datasets

Increasing numbers of datasets for enrichment

Addressing security concerns with commercial value of data

Deeper business understandingSelf Service Analytics

Collect > Prepare > Blend > AnalyseDeeper, richer, data lakes

INSIGHTS