Rapid Analytics A visual, live approach to requirements gathering - - PowerPoint PPT Presentation

A visual, live approach to requirements gathering and business analytic development Mark Marinelli, VP of Product Management

Rapid Analytics

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Agenda

• Why Do Traditional Analytics Projects Fail?
• What Is Rapid Analytics?
• Demonstration
• Rapid Analytics Opportunities In The

Business

• Conclusion
• Q&A

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We give enterprises a new, agile way to analyze, optimize and control their data and processes.

• 15+ years in operation
• Thousands of users at 80+ customers
• Billions of dollars in customer value created
• Major offices in USA, UK, Australia
• 120+ employees

Lavastorm’s software makes business analysts heroes by giving them the power of a programmer to:

• Rapidly unify disparate data
• Easily construct complex analytics
• Effectively deliver actionable insights and results

CUSTOMERS (partial list) PARTNERS AWARDS

Lavastorm Analytics at a Glance

The Problem …

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70-80% of Corporate BI Projects Fail

The Main Cause of Failure – An Understanding of the Real Requirements

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Requirements Application

Sources of Failure

• Poor communication between IT and the business
• Failure to ask the right questions
• Failure to think about the real needs of the business

Traditional Approach and Technology for BI Thrives Where Requirements Are Slow to Change

Providing

• Reports and

dashboards with answers to pre- determined questions

• Analysis of highly

modeled and cleansed data

• Very limited, pre-

determined drill paths

• r very complex

query tools for technologists

• IT development and

governance

Traditional BI Uses Relational DBs

Cubes Requirements Design Implementation Verification Maintenance

Best applied where:

• Stability Reigns

Data sources & requirements change little

• Data is well known, well

controlled

• Technologists answer most,

if not all, the questions

• IT owns all of the source

data

and

But the BI/Analytic Needs of Businesses Have Changed

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Business Compete on Analytics Customer Expectations

Speed, Specialization

Big Data

Volume, Variety, Velocity

More Complexity

More data, decision makers, change (ex: N-tier Supply Chains, Fractured Value Chains) Speed, Accuracy, Visibility

Rapid Analytics Is A New Approach Requiring Different Technologies

Enabled By New Technologies A New Methodology Emphasis on rapid prototypes, not traditional specifications Emphasis on responsiveness and reactions, not fortune telling or time- consuming over-engineering Requires new technology that enables agile methodology – choices include self-service BI, data discovery, mashup options Data-driven, not schema-driven, models and analytics Ability to handle complex, disparate, dissimilar data structures Easier UI for end user self-service Emphasis on direct business end user interactions rather than using documentation or working through IT liaisons

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ETL BI 5GL

Lavastorm Analytics Engine Overview

Agile Extract, Transform, & Load

• Merge any data (e.g. RDBMS, Excel, ERP / CRM,

XML, binary, etc.) – no code required

• Big Data ready
• Profile, inspect, cleanse, and transform sources

5th Generation Language

• Process modeling
• Not traditional programming
• Rapid visual modeling using pre-built “nodes”
• Reusable, sharable components / models

Business Intelligence/Analytics

• Self-service control for discovery/ad hoc analysis
• No IT involvement
• Data visualizations
• Automated continuous monitoring/auditing

Rapid Analytics Driven By a Unique Combination of Capabilities

Demonstration

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Problem Description

• Telecommunications operator
• Goal: Every customer is billed for their services

– Analysis necessary to determine where unbilled customers exist, and

• ptimally why their records are not accurate
• Challenges:

– Diverse data - multiple acquisitions have produced a mix of multiple billing systems and a heterogeneous network – None of the relevant data sources are warehoused, but extracts are produced for other purposes – No visibility into the problem, so no business case to consolidate data sources into the EDW – Small team will research and correct the errant account records, so cannot waste effort on false positives

What Did We See In the Demo

• Incremental, discovery-based analysis

– Real-time, collaborative requirements->implementation->validation process using intuitive visual tools

• Accurate answers in seconds

– Business rules are tested with live data, with validations and improvements applied in real time – Rapid, low-cost application of hypotheses encourages the search for the exceptions and edge cases which often account for process infirmities

• Rapid data acquisition

– Initial data sets obtained from raw sources without the need for intermediaries, structured schema, or warehousing

• Self-sufficient data federation and analysis

– Ability to manipulate and prepare data sets within the tool fosters independence from traditional data brokers – Tools for testing data integrity avoid pollution of these satellite data sets

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The Benefits of An Agile Approach – Examples from the Field

• New Data Sources

400 hours invested with no results using traditional means, but 3 days to a working application with Lavastorm

• Communications Service Provider
• Greater Responsiveness

“Sometimes I can get the information I need while I’m in the middle of an IM with somebody, or, if they’re in the meeting, I may have the information before the meeting is over…it almost makes people a little shocked that we can get the answer that quickly.”

• Tom Tannehill, Lead Analyst, CenturyLink
• More Visibility

Went from ignoring information because no business case to warehouse the data to uncovering tens of millions of dollars of lost revenue

• Consumer packaged goods company

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Research, Design Manufacture Procure Market, Sell, Distribute Support, Deploy

Opportunities for Rapid Analytics Are All Around In Departmental and Corporate Boundaries

Hire, Manage Finance Agile Agile Agile Agile Agile Agile 3rd Parties, Partners, Competitors Customers Agile Agile

A Rapid Analytics Solution Can Sit Alongside & Complement Your Traditional BI Infrastructure

Process Data / BI Infrastructure Cubes DWs

• Ent. Apps

Marts Dashboards, Reports

• Data discovery
• Continuous analytics
• Rapid prototyping
• Data integration and

management

• Visualizations

Individual Data

Controls Data Data Data

What Does It Really Mean?

• User experience

– Analytic applications, not reports – All relevant data is joined and in one place – Real-time updates are possible – Changes are expected – Designed for business users, no IT degree required

• IT approach

– Data foundation build through light mapping, not heavy modeling – Work with data “as is”, no cleansing required – Collaborate with the business throughout All enabled by new technology that is complementary to traditional BI Rapid Analytics is an entirely different experience for IT and information consumers Opportunities for Rapid Analytics are all around and across the company – where speed matters

Thank You, Next Steps

Contact Me Mark Marinelli Lavastorm Analytics +1 617-345-5422 mmarinelli@lavastorm.com Follow Us www.lavastorm.com

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