Data, Policing & the Public Interest Chief Data Office / Denis - PowerPoint PPT Presentation

Data, Policing & the Public Interest Chief Data Office / Denis Hamill / February 2020

Policing drivers for data management Share relevant data quickly Risk assessments allowing for Compliance with GDPR, Data with partner agencies. early intervention before point of Protection Act, Freedom of crisis; prevent harm; keeping Identify synergies. Health Information, National Records of communities & vulnerable people in Justice. Academic Scotland safe research Stay Compliant Public Safety Partnerships & Wellbeing Real time “intel cells”; tailored Accurate data ensure resources Minimise data breaches, crime prevention for communities; deployed efficiently; Enable and data loss; protect data analysis of nominals and efficient missing persons relationships; Link crimes; Predict investigations; Trusted data offenders. entered once, accessed easily Prevent & Stay Secure Save Time Detect Crime Access to linked, trusted data to Reduce effort required to Accurate data on existing access situational risk and threat capture and consume data; re- services will inform new future level usable assets; realise project services savings Prepared Save Money Officer Safety for future Our aim is to put data at the heart of decision making to deliver a more effective and efficient policing service 3

“Data is an Asset” and must be managed as such Data is now widely recognised as an asset for almost every company. The ability to quickly acquire data, process it, analyse it, to gain actionable business insight, will become a business differentiator. Like every asset, data has a lifecycle, and to manage data you must manage the data lifecycle. Identify Acquire Store & Share Use Retire Single Source Partnerships Operations Partners of Truth Smart Assets Data Standards Data Models Business Predictive Intelligence Analytics Destroy Archive Channels Social Cyber Data Quality (Mobile, Phone, Email) Business value is only achieved at Paper the stage. However, all previous steps have a cost and must be External (e.g. managed to ensure value can be Weather) extracted from data. “Data refinery” 5

Store & Identify Acquire Use Retire Share What data and where is it? We need to be able to answer the fundamental question of “do we know what data we have, and where it is?” What data does Police Scotland have? - Common Business Language: • Business understanding of what data Police Scotland holds Key Re-usable Artefacts: • Agreed business definitions compiled into glossary Business Outcomes: • improve usability of data Where is the data stored? - Data Mapping to Systems: • remove ambiguities • “Common business language” mapped to data in physical Common business • reduce data preparation Data Model & language systems creating a “data map”, or data dictionary & reconciliation issues Data Flows • reduce project time • data affects resource by up How can I find this info? - Fully discoverable from a central repository: to 20% (Gartner) • foundation for compliance • This “data map” is stored in a common repository • Data Dictionaries Anyone should be able to find/discover where our data is Online Data Catalog of Key Systems Strategic Intent – Establish a central “Data Catalogue” which will be searchable by all business to ensure consistency of data definitions and system data lineage 6

Police Scotland – Common Data Model

Store & Identify Acquire Use Retire Share How good is your data quality? To improve data quality, we must first measure it, i.e. “once we measure, we can then manage & improve” Quantify Identify Measure Remediate Business Impact Root Causes Data Quality Data Fix % populated Operational • People DQ controls % conforming to std Reputational • Process Training % valid values Financial • Technology Enrich data Compliance • Data Business Outcomes Improved operations; reduced cost due to efficiency savings; improved compliance Strategic Intent – Establish a Data Quality Mgt process , which will measure the quality of critical data elements and manage any Data Quality issues to resolution. 7

Store & Identify Acquire Use Retire Share A standard approach to managing critical data We need to identify Police Scotland’s Critical Data Elements (CDE’s ), and define data standards for those CDE’s and apply those standards into our solutions. Data Standards are typically the main root cause of poor data quality. Police Scotland “certified data” Apply Data Identify Critical Create Data Standards at Projects Enforce Data Data Elements Standards for “point of entry” BAU Quality controls and “data (CDE’s) each CDE movement” Certified for Data Quality Data Governance • What is a Data Standard? – The desired data validation rules for CDE’s, e.g. data type, length, format, allowable values • What does it make easier? – Data standards applied at the point of “capture” and “movement” act as a data control, which ensures the quality of data Strategic Intent – Define data standards for all critical data elements, and ensure those standards are applied to our key authoritative source systems 8

Store & Identify Acquire Use Retire Share Delivering Data to the Business Where do I go, to get Where can I run my Where are my trusted sources of data? the data I need? analytics from? Lack of trusted nominal data 60-75% of restricts analyst time operational taken by data processes preparations Faster, easy Single Source of Foundation for access to data Truth Predictive Policing for analytics (e.g. Golden Nominal) Strategic Intent – Strategic Intent – Strategic Intent – Establish trusted Establish “force - wide” Establish Predictive source of Nominals Analytics Platform Policing capability 9

Store & Identify Acquire Use Retire Share Data Literacy – “Do you speak Data?” Data literacy is the ability to read, write and communicate data in context; deriving meaningful information from data Find & collect By 2020, 50% of organizations will lack sufficient data literacy relevant skills data Data Location Data literacy skills include the following abilities: • Data Location – ability to find and collect relevant data Define questions to Understand • Data Comprehension – ability to understand what the data improve what the data practice using represents represents data Data • Data Interpretation – ability to understand what the data DATA Question Comprehension means Posing LITERACY • Decision-Making – ability to make decision to address problems identified by data • Question Posing – ability to define question on how to improve practice/processes using data Make Understand decisions what the data based on data “ teach data as a second language to enable means Decision- Data data- driven organisation” Making Interpretation 14

Emerging Focus Areas Data Sharing Data Ethics Data Foundations/Quality Balance between what we have the Information Sharing Agreements Single common data language “right to do” and what is the “right Academic Research Data standards for critical data thing to do” Health in Justice programme Trusted source of nominals Data Ethics Steering Group Scottish Government Enabler for increased analytics Align with NPCC & Centre for Data Enable partners to access data that and data sharing Ethics and Innovation (CDEI) is complete and accurate, unless Enabler for single crime system Align to National Advisory Panels & there is legitimate need to withhold SG New Data Technologies Group

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