BI and Decision SupportWhat is it and how can it be

used in modern Recruitment?

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What is B.I.? Business intelligence (BI) refers to

computer-based techniques used in spotting, digging-out, and analysing Business Data – typically for the purposes of reporting, data mining, business performance management, benchmarking and statistical and predictive analytics.

(Wikipedia)

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Why do we need it?“People want access to business information regardless of where it lives.”

“The next quantum leap in productivity will come from the use of IT systems that analyse structured and unstructured data... It will contribute to an improvement in all aspects of business operations.”

GartnerdaXtra

B.I. uses technology and applications to analyse mostly internal and mostly structured

data and business processes

Candidates

Clients Vacancies

Contacts

Placements

Leads

RMS

Corporate Email

Accounting and Finance applications

File Shares

Marketing?(online, off-

line, the website)

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Competitive Intelligence is done by gathering, analysing and disseminating mostly external

information and data, structured or unstructured

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B.I. + C.I. = Decision Support System

In-house Data:

• Candidates• Clients• Contacts• Jobs• Financials• Sales-related• Documents

“Cloud” Data:

• Candidates• Organisations• People• Jobs• Financials• Background• News

DSS

Analyses ReportingForecasts

and Predictions

Data Mining

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According to Gartner... Because of lack of information, processes, and tools,

through 2012, more than 35% of the top 5,000 global companies will regularly fail to make insightful decisions about significant changes in their business and markets.

By 2012, business units will control at least 40% of the total budget for business intelligence.

By 2012, 33% of analytic applications applied to business processes will be delivered through application and data mashups.

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Information and application mashup dashboard (Person)

Person

Works as PM at XYZ Ltd

(CRM, LinkedIn)

Currently Travelling in

Europe (Facebook)

Maybe looking around

(Monster)

Has hired from us before on 20% margin (CRM, Sage)XYZ Ltd. is a

profitable business

(Companies House)

XYZ Ltd invests in ABC Ltd. (Recruiter

magazine, PRWeb)

XYZ Ltd is hiring

(LinkedIn, Jobserve, xyz.com)

Visited our website 3

times (Google Analytics)

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Information and application mashup dashboard (Company)

Company

Contact information, addresses

(CRM, Web)

P&L Accs, Directors

(Companies House)

Share Price, Corporate and Industry news

(websites, Feeds)

Job openings (Monster, TJ,

XYZ.com)

Hired from us before on 20% (CRM, Sage)

Candidates applied to us: Hilary Smith

(CRM)

55 staff, names+roles

(CRM, LinkedIn,

Zoominfo)

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Practical Challenges

• Time-consuming/InaccurateData Retrieval

• Manual/Expensive/Time-consumingData Entry

• Inconsistent/Poor/InaccurateData Quality

• Multiple disparate SourcesData Availability

• Limited Tools, No analysisData Reporting

Do you trust your data?

Good decisions can only be made with fact-based Business Intelligence tools, harvesting reliable data consistently from both local and external sources, and strong searching, reporting and analytic tools to run against the data.

What data is good data?NO Data is BAD

BAD Data is BAD

Unverified Data is BAD

Verified Data is GOOD

A major new independent report based on detailed feedback from 2,665 respondents reveals that data quality is the most common problem in BI deployments

Information Access maturity

1 - Information retrival in silos

2 - Structuring Information

3 - Unifying In-formation

4 - Optimising Information

1

2

3

4Implementing federated search over multiple sources and advanced Enterprise Search

Implementing Analytic Applications to deliver Unified Information Access for shared management information and real-time analytics

Process Improvement

Busi

ness

Crit

ical

ity

Implementing information feeds from targeted web, news and industry sources

Implementing CRM, database Search and basic B.I. for data analytics

Implementing information Parsing, tracking and linking

Data Sources Data Aggregation, Mashup and Unification

Analytical Engine

Sample tools and technologies

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Quick check-list before you start• Evaluate current technology before investing in

new – many existing systems are simply under-utilised

• Ensure your data is up to scratch – there is nothing to analyse or report on if the data is poor or unavailable

• Tender a number of vendors for additional services and decide based on quality over cost

• Factor in support and development costs when considering bespoke or open sourcedaXtra

Sample reports Preferred Vendor reports – how many of the

published vacancies have been filled Consultant KPIs – have all assigned

requirements been filled, if not then Why Marketing Reports – Advertising cost vs

Placement revenue, website analytics New Business – Companies that your

candidates have worked for that are not on your database

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Questions and suggestions

?daXtra