REACHING YOUR KEY BUSINESS OBJECTIVES WITH BIG DATA

By Alan Royal

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MY CO-SPEAKER

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From the 1990’s companies have been seeking to bring together internal and external data sources in order to gain unique competitive “insight” other competitors did not have.

Problems with customer “insight” initiatives included a lack of external data sources which could be matched against internal data sources which would result in unique competitive advantage.

In the subsequent 20 years external data sources have exploded based upon companies selling internet based data of which social media is one of the key examples.

This explosion of data which can be used by companies to create competitive advantage when combined by Data Scientists is known as “Big Data”

THE JOURNEY TOWARD BIG DATA

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BIG DATA DEFINED

Big Data has been referred to as a meaningless marketing tool to sell hardware and data base software by those who have not taken the time to understand what Big Data is and the fact that Big Data Facilities actually exist.

Specifically, Big Data can be simply defined as data compiled from hundreds of sources, most often associated with an industry, available for usage by companies in order to improve the competitive advantage of their companies.

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COMMON BIG DATA VALUE POINTS TO COMPANIES

Predictive Big Data is often used in order to provide predictive insight regarding how a customer is likely to behave in the future which would not be known otherwise.

Defensive Big Data can often be used to predict a customers pending adverse activity such as terminating their relationship with the company based upon certain predictive criteria.

Strategic Big Data can often predict insight to what products and services a company needs to invest in, in order to be more competitive in their future market and acquire more customers.

Legal Compliance Big Data can often be used in order to identify unique customer attributes which are high risk to perform business transactions which would violate legal or compliance regulations.

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COMMON BIG DATA VALUE POINTS TO COMPANIES

Tactical Big Data can often provide insights how a company may better understand their businesses and associated trends in order to be able to modify their business operations in order to save operating costs.

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COMMON BIG DATA VALUE POINTS TO COMPANIES

Simulation Big Data allows for “Simulating” actions by companies to gain insight to the value, or lack there of, which might be generated from a specific action or set of actions.

Correlation Big Data can be leveraged to identify correlations that are common between specific customer groups. This provides a basis for better customer management.

Data Gaps Big Data can identify specific data collection gaps of their customers such that, if captured would enhance the ability of a company to further leverage value from their customers.

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USE CASE

IBM undertook a ground-breaking project with the government of China in order to create a “Smart Electric Grid” where by which the electric utility could receive data over the same infrastructure previously only used for power transmission.

This project allowed for electric utilities to acquire customer consumption data without having to physically read meters. It also allowed for the better planning for expansion of service capabilities as well as detect suspicious usage patterns which could be investigated real time before utilities were potentially forced to absorb unauthorized usage.

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BIG DATA THE LEADING EDGETechnology today allows for, on a limited basis, capabilities only dreamed about five years ago.

You enter a retail establishments where you regularly do business. As you are entering this establishment cameras capture your facial signature, which identifies you, and provides a sales associate your purchasing history via a transmitter to the sales associates speaker in their ear.

You are then greeted by the sales associate who is fully informed of your purchasing history and is able to not only assist you in a repeat purchase, but also through the use of predictive analytics, is able to offer new products which might be of interest based upon predictive analytics.

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SUMMARY

As demonstrated Big Data can play a material role across all the value drivers identified in this presentation. The key, however, as in most cases is highly experienced Data Scientists providing their unique skillset to match the right internal data with supplemental data from the “Big Data Repositories” which are matched to achieve the desired business benefit outcomes.

BIG DATA CHALLENGES

By Alan Royal

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BIG DATA INITIATIVE COMMENCEMENT

Justification For Starting A Big Data Initiative Appropriate Bid Data Governance Structure Scope Of The Big Data Initiative Budgeting Of A Big Data Initiative Justification And Creation Of Smart Grid Technology Resources To Execute A Big Data Initiative Agreement Of Desired Outcomes Creating Of A Value Driven Big Data Initiative Business

Case Big Data Project Prioritization Big Data Delivery Milestones

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BIG DATA INTERNAL CULTURAL CHALLENGES

Business Unit Data Sharing What Data Is Used To Drive Which Business Decisions Credibility Of Data Outcome Reliability Hiring Of Cultural Acceptable Data Scientists Recognition Of Top Management Of The Big Data Value

Proposition Delivering Big Data Analytics In A Presentable Form Company Areas For Big Data Analytic Focus Action Programs Resulting From Big Data Analytic

Outcomes

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BIG DATA DELIVERY CHALLENGES

Appropriate Governance Steering Committee Appropriate Delivery Methodology Appropriate Big Data Analytic Tools Acquiring Relevant Internal Data For Use In Analytics Poor Internal Data Quality Acquiring The Appropriately Skilled Analytic

Employees Acquiring The Appropriate Big Data Analytic Data

Sets Generating Analytical Solution Sets Which Add

Material Business Value

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SPECIFIC ANALYTIC CHALLENGES

Data Complexity Data Volumes Performance Necessary Skills Data Velocity Project Cost

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BIG DATA VISUALIZATION CHALLENGES

Meet The Need For Speed Understanding The Data Addressing Data Quality Displaying Meaningful Results Dealing With Outliers

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SUMMARY

Big Data value generation within a company often requires not only significant financial investment, but also business sponsorship of a quite foreign data processing paradigm. Thus, as the old saying reflects, value generated is in direct proportuion to the energy and investment expended. Undertaking a Big Data initiative without full enterprise support is a sure cause of project failure.