Data Analytics Tools

What is Data Analytics?

• Analytics is not a technology – it’s a CONCEPT

• It refers to the use of certain technologies, skill sets and processes for the exploration, evaluation and investigation of business operations

• It is the use of raw data to produce insights or conclusions that can be acted upon

• The practice of Analytics make extensive use of data, statistical and quantitative analysis, confirmatory data analysis, as well as data management

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Capabilities of Data Analytics

Hindsight

• What happened and why?

Insight

• Where is the problem and what action needs to be taken to solve it?

Foresight

• What will happen if these trends continue?

3

Facts Understanding Knowledge

Data Analytics: What is It?

Traditional Business

Intelligence

Increasing business valueTransactional

An

alyt

ical

Mat

uri

ty

Strategic

• Data integrity & quality

• Basic employee lists & extracts

• Understanding business

parameters

• Visualizing transactions

• Integrated analytics

• Multiple sources of data

• Forecasting and predicting

future outcomes

• Modelling and

understanding correlations

and causalities

• Simulate and

experiment with

possible scenarios

• Optimize efficiency

and resource usage

What is happening?

Why is it happening?

What might be happening?

Basic KPI Reporting

Visual data exploration

Segmentation

PredictiveModelling

Simulation &Optimization

• Understand groups

and outliers

• Discover and target

opportunities

Data analytics can be broadly categorized into a three layered process

5

ETL (Extract, Transform, Load) AnalyzePresent

ETL (Extract, Transform & Load) Process

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Example of ETL layer Analytics Tool

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What do Consultants need to know about ETL layer

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To be honest, not much!

Unless you are a system design consultant, chances are you will never have to deal directly with an ETL Tool. In most cases, clients will already have their ETL setup and will just provide you with the data.

Key Learning points:

• Know the language. Next time your client says their ETL system is not robust, you know what they are talking about

• Ask them about the data format- it is important to understand the format in which data is extracted from the ETL system. Is it text based, Is it excel, is it already in tabular format, what additional transformations are required, etc.

A few new ETL Tools (Will add, maybe)

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Analysis process

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Example of Analysis layer Analytics Tool

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Factors to consider while choosing Analysis Analytic tool

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• Data size and format

• In memory vs. HD storage

• Usage Complexity vs. available talent

• Cost

• Ad-hoc vs. continuous usage

• Integration with ETL & Presentation layer

Comparison of some most used Analysis Tools

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Comparison points based on previous slide

Sample Scenario1- In house analytics for limited data size and ad-hoc use

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Sample Scenario 2- Continuous analytics for a client for large sized data

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Analysis Tools to look out for

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The Presentation Layer

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Example of Presentation Layer Analytics Tool

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Factors to consider while choosing Presentation tool

Visual analysis Information delivery

Used by analysts Used by operational workers

Test hypotheses Predefined subjects and metrics

User driven interaction Restricted interaction

Linked charts show multiple data aspects Individual familiar charts

Smaller learning curve Build from prepackaged components

Preferable for smaller teams Push reports to the enterprise

Visualization focused vendors Part of an enterprise BI stack

Used for exploration Used for monitoring

Some statistical and manipulation capabilities Analysis outside the tool

Requires data exploration skills Requires developers, SQL skills

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Exploratory Analysis BI & Dashboard Reporting

Choosing the appropriate tool requires considering the purpose of the visuals, the users who consume the data and integration with advanced analytical components. These considerations can be mapped to the spectrum attributes to target the tool search process.

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Where do we place each visualization tool?

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AnalyticVisualization

DashboardReporting

BI Platforms Spotfire

QlikView

MicroStrategy

Tableau

Exploratory Analysis BI & Dashboard Reporting

• The spectrum region where QlikView lies overlaps each category, since it also supports dashboards

• Tableau’s strength is analytic visualization, while its BI functionality is more limited

Sample Scenario 1 with Visualization example for Tableau

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Sample Scenario 2 with Visualization example for QlikView

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Hybrid Tools to look out for

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Best practices

• Know what you want : Have a detailed program enlisting all the procedures

• Get the data format right: Provide list of key fields, layout

• Agree on a source document: A financial report, MIS report, System control totals – screen shots should

be determined before hand to check for completeness of data

• Extract Data much before: Check what happens when huge data is extracted. Truncation,

Inconsistent/Incorrect values

• Convert data into useable format: Have your tools ready to convert data into a format that can be

analyzed. Check for data consistency (related/paired fields) and data accuracy (data integrity of key fields)

before starting your analysis

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If you don’t have right data;

You don’t have right answers

Challenges

•Clients: Why is data required? What do I get in return?

•System: Getting entire data becomes a challenge due to slow systemperformance and/or lack of knowledge on data extraction techniques

•Format of Data: Data files get extracted in a format which cannot beanalyzed. E.g. Report files in text, PDF files, Excel files (these too are notalways the best)

•Volume of Data: Data files are provided in multiple files with differentformat and layouts; incomplete files; truncated data

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Challenges (contd.)

•How would you know if your data is:

• Complete (nothing was missed in transition – client system to data file to your system)

• Accurate (Represents what you want to test – has requisite key fields with the values you expect)

• Consistent (Paired/Related Fields have values that makes sense – is consistent with business logic)

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Know your needs Get the Right Data

Analytics – a journey and not a destination

• Start where you are: Assess your current capabilities and get a clear picture of gaps

• Know which questions matter most to your industry, strategy and priorities

• Accelerate insights through automation. Automate delivery of the information – to management, operational and analytical processes

• Engage and visualise: Output must deliver insights people need – in whatever forms required – to make fact based decisions

• Develop a fact driven culture. Embed analytics capabilities into decision making processes matching your approach with your style

• Practice “right fit” analytics: Match statistical and analytics techniques to the job at hand

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Conclusion