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A Presentation Delivered to the Senior Management Board of the NWDA on Data Stewardship and the Impact of Data-Driven Evidence-based PolicyMaking at Business Link Northwest
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“How Evidence can influence policy and the role of Business Link in
the North West”
A Presentation to Business Link Northwest
by
Ged Mirfin
Definition“Data-driven decision-making is using data to better understand
what is actually going on rather than what is assumed to be going
on”
The Curse of Bad Data•Data is now far easier & cheaper to gather, store, analyze and disseminate than ever before
•Unfortunately this means that it is much easier to collect data that is inaccurate, out-of-date or at worst simply wrong
•Results in a lack of trust of data by end-users
•Decision-makers are thus forced trust to their own intuition.
•Tendency to rely on the infallibility of their own “judgemental opinion” because it is simple & convenient.
•Results in a “Traditionally it has always been done that way mentality”.
•No one is able to prove conclusively otherwise or show policymakers they are wrong.
•The result – badly formulated and more poorly applied policy!
•Works fine in a benign economic environment. In a harsher climate when assumptions are being fundamentally challenged it is much more difficult to defend the indefensible and to find a solution when you are sometimes forced to justify not only your very existence but the funding rationale!
•Need for Hard evidence: high quality validated quantitative data
•Is why Policymakers have turned to a data-driven approach
Governing by Numbers: Data-driven evidence based policymaking: what is it?
•Collection and analysis of data to spotlight problem areas and potential solutions – data capture & accessibility!
•Development of quantifiable measures & indices to assess policy performance and draw comparisons across similar circumstances, geographical boundaries or peer groups so “best practice” can be identified & widened – segmentation & benchmarking!
•Public dissemination of data and metrics/indices to assist in policy formulation, policy making and ultimately in assessing the impact of policy and policy performance – should it be done? has it been done? and has it been done well?
Data Driven Evidence-based Policy Formulation
Aim: Facilitate change from Anecdotal &
Judgmental to Evidence-based
Policy formulation
Evidence-influenced
Evidence-based
Opinion-basedEvidence-influencedB
LN
W D
ata
War
eho
use
Policy Making Environment
Experian
Northwest
Business
Database
Political Inputs
Source: Morgan Stanley as at 30 June 2006
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CRM 3 – Powerful Engine. Huge Development
Potential. Vanilla Version no
Customisation.
Data Import into CRM - manually fills the system with very
poor quality & aged data from legacy
systems.
Poor attempt to address data quality
issue via “Data Cleanse” carried
out by Third Party results in the overwriting of
current data with even older data.
This affected CRM system and hampered
operational performance
Building of Data Warehouse to separate and report on new data delivers
highly functional web accessed
granular reporting capability
Lack of clean up-to-date prospect
marketing data begins to impact on
BLNW’s ability to meet
penetration targets
Arrival of Experian NBD with access to Yell, Thomson, Commercial, & Origins Mosaic
gives BLNW access to a rich
data source which it can be segmented to target specific
customer details in a scientific
manner
The development
of a Web Accessed Reporting Dashboard
with the ability to represent data via GIS
Mapping software and
the development
of a Marketing Data
Warehouse taking BLNW Reporting & Analytical
Capabilities to the Next Level.
BLNWs Capability Levels:
How Data has affected this
BL
NW
Cap
ability
Experian’s NBD
-Business Link had 100k company records in its database.
-The companies had been assisted by Business Link over the last 20 months
-The data captured was used to satisfy contract outputs and as such was very specific
-We needed to increase the data set both in terms of size and data richness
-Experian profiled Business Link data and found 536k businesses (both Ltd. And unincorporated) at location in its National Business database for the Northwest.
-Business Link acquired the data from Experian. Each Experian record had in excess of 180 data characteristics (appends)
-The extra data records allowed a significant level of analytics to be done. The data had access to classification systems (YELL Thompson) and allowed for detailed segmentation
-One of the Primary data attributes was “Risk Scores and Financial Performance data”
100k
536k
Data Attributes•Data is “real time” - updated monthly and in case Commercial Risk Data the plan is fortnightly with Weekly Alerts for Businesses experiencing a serious worsening in their performance
•Is the first (b2b) business profiling system in the public sector
•Offers real time intelligence to support our efforts to address the current market conditions
•Data is very granular and can be segmented to very specific levels
•Key data segmentation is geographic (down to postcode) and sectoral (RES, SIC group, Yell classification code, Thompson directory classification code)
Benefits to Key StakeholdersMarketing
Build increased
penetration amongst
service users through
improved segmentation and targeting
Operations
Improve take-up of intensive
assists for Broker Team by increasing
lead generation
Executive
Meeting of Strategic
Priorities: To be recognized as the leader on regional business
intelligence and playing a vital role in informing business
support policy making
Cluster Orgs.
Make definitive pronouncements
about the effectiveness of BLNW services delivered to the NW Business Community including
Membership & Cluster Orgs,
Local Councils, Politicians &
Opinion Leaders
“Advanced Customer
Segmentation”
“Vastly Improved
Lead Quality”
“Delivering Strategic
Priorities”
“Sharing of Key Data across the
Region”
NWDA
Provision of relevant and up-to-date
information on emerging business
trends allowing the NWDA to
service requests from Government,
Political Parties & Lobbying
Orgs
“One Version of the Truth”
The Business Support EnvironmentINTERNAL EXTERNAL
Data becoming more relevant for decision making bodies
Internal
Sector specialists and cluster management teams
First interaction with third party data consumers and political oganisations
Internal
Geographically dispersed bodies require data to confirm Business support activities or to quantify the impact of future plans and policies
Business Support Community Partners look to BLNW for Data & Analysis as their first point of call
Sector
Dependency building
Internal
Local hierarchy of business support functions demand input to decision making and assessment of economic impact.
Sector Geography
Allows Business Support Community Partners to
Engage with Decision Makers on an Evidence-Based Basis
The User Base is Being Significantly Widened
Internal
Demand for joined up information sources and “one version of the truth” among all business support organisations.
Tie together regional strategy and delivery with a system of quantifiable evidence based results
The BPI (“Business Performance Index”: A Consolidation of Business Intelligence
Sector Geography Local Govt.
The Response to companies in the North West
adversely affected by the current economic downturn
The BPI: Identifying High Risk Businesses Risk Category Description
Maximum risk High value of unsatisfied CCJs, accounts overdue, start-up business with adverse data, proprietor with adverse data or maiden accounts show loss
High risk Large company with weak balance sheet, medium sized firm with very weak balance sheet, combination of above average risk features, start-up with adverse trading
Above average
Large company with very weak balance sheet, medium to small firms with (high levels of credit search, payment difficulty, weak balance sheets), start-up firm without adverse information
The BPI Portal: The Hub of the Action for Response Framework
• Experian/BLNW – Business Performance Index Structured Intelligence that can be immediately disseminated to partners
• Business Link to provide region-wide data-pool and reporting at Regional, Sub-regional and Local levels
CHAMBERS OF COMMERCE
SUB-REGIONAL PARTNERS
LOCAL AUTHORITIES
CLUSTERS & TRADE
ASSOCIATIONS NWDA
BUSINESS LINK NW
TUC / UNIONS
GOVERNMENT OFFICE NW
JOB CENTRE PLUS
HR1 to BERR
BUSINESS LINK DATA WAREHOUSE
RECORD BY COMPANYCompany Name
Registered NumberCompany Address
Local Authority & WardSector
Turnover & GVA EstimateNo. of Perm employees
No. of Jobs at Risk
>20 Redundancies within 90 days
Figure 1 Data Capture
ESTABLISH RAPID RESPONSE TEAM
DEVELOP STRATEGY/POLICY FOR SUPPORTING COMPANIES IN CRISIS
COMMUNICATE STRATEGY/POLICY TO PARTNERS (JCP, LSC, BLNW, TRADE
ASSOCIATIONS) & INTERNAL PARTNERS
The Action for Response Hub
The Power of the BPI: Project Rapier – Liverpool Vision’s Objective of Spending £10M to Save 40 Businesses specifically
in the Retail Sector by End Q1 2009
• How do you Identify a……………………..
Liverpool (13,704)
Employs 50 or more (207)
Central Ward (66)
In Retail Sector (4)
Deteriorating Payment Profile (1)
•…Company that is at least of Above Average Risk
•Which is based in Liverpool
•Which Employs 50 or more Employees at Site
•Specifically in the Central Ward
•Which is in the Retail Sector
•Whose payment profile is deteriorating
This information is based on data provided by Experian. The data has been subject to further analysis by Business Link North West.
£729.71 £48,309.18 £151,515.15
£2.5M £10M
And that Business was?• Demonstrates that the
Liverpool Vision’s approach needs revising
• Also demonstrates that coordinated action by Local Authorities has the ability to provide financial assistance to some of the big High Street Retail Chains if they so wished
• Does this suggest a return to the days of municipal administrations with Local Authorities maintaining part ownership of key civic businesses?
Data Driven Evidence-Based: BLNW Making an Impact
Decision Making Based on Intuition, Judgemental Opinion or Tradition
Data Driven Evidence-based Decision Making
Disjointed programmes and policy initiativesJoined-Up programmes based on highly focussed
targeted strategies to address identified need based on documented evidence
Budgetary decisions based on prior practice and historic priorities
Budget allocations to programmes based on data-informed needs
Spending allocations based on volume of voices of special interests and eligibility criteria of existing regimes
Spending allocations based on market failure gaps as indicated by the data
Generic reports to all stakeholders based on historic aggregate data inappropriate for policymaking at a a micro-
economic level
Detailed reporting on a range of indices to relevant stakeholders on a regularised basis - weekly,
fortnightly, monthly, quarterly, half yearly based on agreed service level agreements
Goal-setting by board members, administrators, project managers with special treatment given to pet projects and
initiatives or the current fads of the day.
Goal setting based on accurate estimates of the financial consequences of proposed policy options allowing for prioritisation thus helping to predict the
impact of policy options to stakeholders in a “winners and losers” format
Death by committee: Undue focus on ensuring that money is spent and that it is seen to be spent
Highly focussed report-back and monitoring forums which ensure that not only is money spent well but also that the impact of spending can be tracked and
measured