our business plan for 2020 – 2025 appendix c – data strategy€¦ · effectively using our data...

Our Business Plan for 2020 – 2025 Appendix C – Data Strategy September 2018

Contents

Our data vision.................................................................................................. 3 1.1. Our data vision .......................................................................................................3 1.2. Our Data Strategy...................................................................................................3 1.3. What does good data management look like?........................................................4 Customers .....................................................................................................................5 Our Business .................................................................................................................6 Our People ....................................................................................................................6 Our Technology .............................................................................................................6 Our Data Objectives.......................................................................................... 8

2.1. Keeping our data fit for purpose .............................................................................8 2.2. Keeping our data secure and safe..........................................................................9 2.3. Keeping our data legal............................................................................................9 2.4. Making our data Accessible..................................................................................10 For our Customers.......................................................................................................10 For our Regulators & External stakeholders ................................................................10 For our Partners...........................................................................................................11 For our People.............................................................................................................11

2.5. Making our data resilient ......................................................................................11 2.6. Keeping our data governed ..................................................................................11 2.7. Mapping data to our outcomes .............................................................................12

Our data principles ......................................................................................... 13 3.1. Our data principles ...............................................................................................13 3.2. Our data lifecycle..................................................................................................13 Stage 1 - Specify data .................................................................................................14 Stage 2 - Collection & storage of data .........................................................................15 Stage 3 - Assess and improve data.............................................................................15 Stage 4 - Analyse data ................................................................................................15 Stage 5 - Share data....................................................................................................16 Stage 6 - Archive and Destroy.....................................................................................16

3.3. Requirements and Governance for the data lifecycle ...........................................17 Our Data Achievements.................................................................................. 19

4.1 Data ownership and collaboration: Open Water ...................................................19 4.2 Information Security..............................................................................................19 4.3 Pushing innovation with social media analytics ....................................................20 4.4 Leveraging the use of data visualisation...............................................................20 4.5 Making Asset Data More Accessible ....................................................................20 5 Our future Initiatives ....................................................................................... 21 5.1 Enterprise Information Management (EIM) and People Training..........................21 5.2 Situational Awareness ..........................................................................................21 5.3 Data Science and Artificial Intelligence (AI) ..........................................................22 5.4 Analytics and reporting tools.................................................................................22 5.5 Developing our analytical “centre of excellence” ..................................................23 6 Summary.......................................................................................................... 24

Our Business Plan for 2020 to 2025 Appendix C of 24

Our data vision 1.1. Our data vision

Data plays a central role in meeting our company objectives and delivering our outcomes. Our ambition is to establish a systematic focus on data and knowledge throughout the business, with a culture of innovation and collaboration that will allow us to achieve and sustain breakthrough service and performance at least cost. We recognise the need to make more use of data to meet customer’s needs, whilst also leveraging the use of innovative technology to achieve this, as highlighted in our innovation strategy. Data Management and Business Intelligence will play a key role in meeting our company vision and customers’ expectations.

When we speak of “Data”, this covers a broad spectrum of both structured and unstructured data, across our multiple information technology systems and business areas; this also extends to data found on physical medium, such as plans, drawings or reports.

This purpose of this appendix is to set out our Data Strategy to provide clarity on how we intend to manage, govern and use data in all its forms to ensure we are maximising our data assets and exploiting their rich information. The strategy provides the guidance on how we intend to become a more data driven organisation across the board.

Our Data Strategy is fundamental to achieving our mission of being the leading community focused water company. Data is the life-blood of the organisation, being generated, validated, stored and used in all our everyday business operations. It is a key asset and pivotal to us achieving our outcomes for the coming future, while providing the means for optimisation and innovation. Our intention is to develop our internal management and practices in AMP7 and beyond around our strategic view of data.

Our aim is to evolve and adapt our culture to be more data conscious, driving quality from the point of capture right through the lifecycle, to support and help in achieving our business objectives. Our people are a critical success factor in achieving the data vision, so we need to plan, train and foster an ethos where data is central to everything we do rather than being just an output of a process or in some cases an afterthought.

Supporting our people on this data journey, we will endeavour to apply proven technologies, learning from our peers in the water industry and other sectors, on how to capitalise on our data assets, as we strive, retain and build our company knowledge.

1.2. Our Data Strategy

A sound corporate level Data Strategy is required to manage the data within our organisation and to use the available information for improving the efficiency of all our business functions.

An effective data strategy ensures:

• A Business wide appreciation for data as a company asset: the ability for all staff, at all levels of the company to understand and appreciate the value which data brings to the company, whilst understanding the need to carefully manage this asset from end to end.

• Adapting our Information Systems for our business : As we become more data driven, we must strengthen our IT-to-business alignment by collaborating more rigorously to define and prioritise data requirements to meet our outcomes;

• Linking Data management to outcomes: Stronger internal alignment, particularly with our Information systems, is a foundation for future-facing enterprise data strategies. Improvements to data and its management should link directly to one or more of our outcomes.

• Collaboration of enterprise data and its strategies: Achieving the new level of business synergy and alignment (as a foundation for our corporate data strategy) demands for


organisational structures that are inherently collaborative for data, such as our data governance boards, data stewardship programs, steering committees, and data management centre of excellence.

• Insight driven decision making: Being able to process, analyse and interpret data at all levels within the company, whilst making sure that findings from data support the delivery of one or more of our outcomes.

• The retention of critical company knowledge and data: It is crucial that we leverage our ability to provide continuity in retention of the company’s knowledge, which must be transferable within the business – ensuring that vital data is properly safeguarded and that information generated (whether in physical or digital format) retains its place with the company and not just its people.

• Coordination among data management teams: Part of the concerted effort of our corporate data strategy involves aligning multiple data management teams that exist within the organisation. These teams need to collaborate and agree upon common standards for defining and modelling key business entities (such as customers, assets, and financials) and how data about these can be improved and shared across information systems. Standards for data and application development should align with stated business goals for data;

• New data strategies for new business practices: We need to improve our response time to operational events so we need fast, frequent data, in near real time via data services. To manage our data requirements, we are investing in new systems, technologies and implementation of best practices.

Our Data Strategy helps us to develop the plan for realising benefits around data problem areas. It helps in effectively using our data for strategic advantage and operational decision making.

Not having a data strategy increases the risk of allowing each person within each department of the organisation to develop their own methods for using and managing the information available. Systematic data management at enterprise level will improve our business decision making, contributing to improvement of business performance and growth, whilst addressing organisational requirements for easily accessible and trusted data to be available for compliance and reporting.

1.3. What does good data management look like?

To achieve our vision, we will aim to govern and manage our data using a defined set of policies and procedures based on agreed standards and best practice. We will develop a consolidated data model and use proven data architecture aligned to data standards. We will strive to manage our data effectively in integrated data repositories collecting data items only once, maintaining them in only one place, and making them available for use in multiple applications across the business and beyond, providing all customers with “a single version of the truth”.

Successful implementation of the Data Strategy will enable us to deliver benefits to customers and the business, broadly by reducing the cost of processing data whilst increasing quality (and thus value) of the data we hold.


Figure 1 – The Data Vision

At the heart of Data vision is the need to fulfil customer’s expectations better through data – we also want to ensure the data vision is passed on through our business & people. Further benefits in other areas include:

Customers

• Improve secure access to master information on asset condition, performance and customer impact, reducing reactive incident response times and minimising interruptions to supply.

• Responding to customer needs in a tailored fashion - utilising customer call information and network performance data, we were able to map the needs of customers at localised level, providing a deep dive view of customer complaints throughout our operational area. Please see case studies for further details.

• Keeping customers informed – We have achieved a 50% increase in the volume of social media followers following the release of our social media application - and improved customer satisfaction scores by over 40% between September 2017 and April 2018. This is a continued trend we will endeavour to maintain. Please see case studies for further details.

• Only through better data insight can we understand and support our vulnerable customers, personalising their experience and meeting their needs.

• Empowering customers to do more with data whilst building trust and transparency. . We will make more use of customer feedback, customer call data and embed feedback into day to day asset


operations. We will also ensure to keep customers informed constantly during major interruptions, as highlighted in our case studies.

Our Business

• At the heart of successful data management lies a robust adherence to controlled processes. This is critical to ensure business continuity, particularly when it comes to knowledge transfer. This will ensure:

• Improved auditability and measurability of our processes and operations, supporting our management processes and regulatory reporting.

• Enhance compliance with regulations, providing the ability to resolve issues quickly and effectively.

• A streamlining of business processes, reducing the waste and cost inefficiencies. Fundamental to process improvements and automation is data quality.

• Improved corporate resilience. Having trusted and accessible data will allow us to adapt to changes and support our people’s knowledge sharing.

• Maturity in data management and quality assurance will allow us to make sure we maximise the value of customers money by making the right investments, thus improving our financial position and increasing customer satisfaction.

• Improvements in data and reporting allow for more assured and informed decision making, reducing risk of failure and potential punitive fines.

Our People

• Clear accountability of data is essential for regulatory compliance. We need to ensure that we keep our company records, controlled documents and asset information in the right shape to deliver a safe working environment.

• Training is key – everybody in the business has specific objectives to fulfil which relate back to our outcomes. Achieving those is only possible by provide on-going development in all aspect of strategic data management.

• Within Wholesale Operations, data is often created, stored, used and updated remotely. It must therefore be accessible anytime, anywhere. Coupled with our planned IT strategy, investment in data management provides the availability of data to our people anytime, anywhere.

• Health and Safety is our priority – a safe working environment starts with full knowledge of asset condition and working procedures, whilst observing health and safety rules which are monitored on a regular basis, through hazard reporting, risk assessments and method statements.

Our Technology

• Securing our data in the most appropriate manner will mitigate security risks, reducing potential punitive penalties from breaches.

• Leveraging the use of innovative technology will become more and more important in the information age – we must have the capability to deploy new methods for managing, processing and storing large data now and in the future.


• Understanding our data, we can make strategic decisions in Information Technology assets ensuring our data is resilient to failures and corruption, thus proving a faster recovery service for business continuity.


Our Data Objectives The realisation of the benefits in the Data Strategy is dependent on the effective alignment of data governance and management combined with technology development and clear articulation of business needs. Our objectives set out the intended end outcomes for our data strategy supported through the applied governance, technology and processes to achieve our data vision.

Figure 2 - Our Data Objectives

2.1. Keeping our data fit for purpose

We are committed to continuously improving the quality of data, making the whole business more efficient by reducing information and knowledge gaps. Our Asset information registers and customer billing registers are key sources of data we rely on to provide our everyday service and therefore their integrity is essential to reduce compliance risk and costs.

Improvement in data quality and its relevance help to:

• Improve overall insights, limiting the “garbage in, garbage out” conundrum.

• Improve trust in our data and information, improving informed decision making.

• Eliminate waste, streamlining processing and reducing errors.

• Reduce costs from extra processing due to inefficient data and gaps.

• Improve customer experience through trusted and correct data.

• Keep our people safe, through accurate information in situation.

Increasing operational efficiency.

• By maximising the use of asset performance data.

• By reducing duplication of information.

Improving customer experience and

perception. • By education with data within the

community • By providing customers with personalised information updates.

Reducing risk of failing to meet our

ODIs. • By responding to operational

incidents quickly. • By making well informed investment

decisions.

Figure 3 - Improving Data Quality


2.2. Keeping our data secure and safe

Data security and the effective use of our information is a strategic priority to us. Through the application of Data Governance, within our overall Data Management programme we will endeavour to apply best practice to assure its data confidentiality, Integrity and Accessibility.

IMB

ISMF DPF DGF

We have embarked on a significant data and information security (InfoSec) programme during AMP6 and continue to invest in it. Over the last 3 years there has been a significant increase in our Information Security

Security Champions

Data protection champions

Data quality & governance champions

Figure 4 - Data Management Structure maturity which was recorded at level 3 in the independent NCC assessment report of early 2017, well above the UK corporate average.

We have been awarded the UK Government’s Cyber Security Essentials+ certification in October 2017 and have continued to build upon the concepts of the certification by beginning the journey to fully align with the Global Information Security Management System Certification ISO27001.

The governance structure is led by the Information Management Board (IMB), a senior executive level board which authorises data and security policy, process, posture and ensure good practice. The IMB is supported by 3 direct working groups:

• The Information Security Management Forum which provides cyber and information security expertise and cyber initiatives for approval by IMB.

• The Data Protection Forum which provides Data Protection expertise and advice on regulatory requirements for approval by IMB.

• The Data Governance Forum which provides data strategic initiatives and proposals for approval by IMB.

Ensuring the confidentiality and integrity of customer and company data is a primary concern of our business - a number of initiatives to protect both physical and digital data are currently in place. We are introducing a company data classification process which, used in conjunction with Office 365 security controls and our deployment of Data Loss Prevention (DLP) systems, will provide us with the ability to monitor and manage data, with an emphasis on Personally Identifiable Information (PII) to fulfil our GDPR commitment. Security testing for vulnerabilities, 3rd party penetration testing and remediation of critical vulnerabilities detected on our external facing interfaces and within our applications respectively are mature established processes. Risk and privacy impact assessments coupled with 3rd party management will improve our risk posture and assist in the alignment with ISO 27001 requirements and GDPR/NIS-D compliance. We have also created and are due to test our internal breach process to ensure our preparedness for incidents and comply with the ICO and NIS-D regulation/directive for breach reporting. We will therefore comply with ICO/NIS-D by effectively reporting breaches to the ICO/NIS-D within the required 72 hours of becoming aware of a breach. The introduction of the monitoring system Darktrace has provided a facility for in depth investigation of our network, server, desktop systems and data flows providing the information security and infrastructure teams with alerts on detected anomalies and in support of our cyber incident management process. This is discussed in more detail within the IT assets section of the wholesale technical appendix. In preparation for GDPR, we have also reviewed contractual agreements with our suppliers and stakeholders with whom we use and share data, all clearly defined within our privacy policy.

2.3. Keeping our data legal

In accordance with new regulation for data and cyber security, we recognise that we are accountable for the security and the correct and lawful processing of data, especially personal data.


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The introduction of the General Data Protection Regulation (GDPR) means we are now exposed to potential fines of up to €20 million (approximately £18 million) or 4% of total worldwide annual turnover, whichever is higher and depending on the breach. We have appointed a Data Protection Officer who is ultimately responsible for our compliance with data protection law. The DPO is supported by an Information Officer plus members of the Legal & Assurance directorate. A Data Protection forum reports to the Information Management Board on all data protection matters. Privacy Impact Assessments are performed on all new projects to mitigate risks around processing of personal data.

Complimenting GDPR is the Network and Information Security Directive (NISD), which potentially has similar punitive penalties for failures. Whereas GDPR’s focus is on the processing of personal data, NISD’s focus is on the security of Information Management Systems within a Critical National Infrastructure (CNI) environment. Our target is to go beyond mere compliance and aim for best practice in data protection as prescribed by the ICO, embedding privacy by design and default throughout our organisation.

2.4. Making our data Accessible

We aim to make our data accessible to authorised employees, partners and customers through secure and robust data services and information systems. Accessibility is key to ensure the usefulness of our data and

Our Customers

Our Regulators

Our People Our Partners (Academia

and Research)

Figure 5 - Data Stakeholders

to proselytise data innovation.

Our data classification policy defines the sensitivity of our information and as such its applicable accessibility to all stakeholders.

We take data very seriously when it comes to sharing it with our stakeholders. Our approach for AMP7 aims to ensure that we apply appropriate protection of the data to meet those requirements. We currently see four types of external stakeholder which we engage with to provide our service and to improve on it: Namely our regulators, the wider public, academia and importantly, customers.

For our Customers

Providing information to customers will support them in understanding their water usage, so they are empowered to reduce their water usage and bills. Through the application of self-service capabilities we can provide information services that support streamlined processing of customer queries improving the overall customer experience. The use of data and insight will also support our goal of providing a personalised customer service and supporting vulnerable customers.

For our Regulators & External stakeholders

Our data strategy is essential to supporting our external asset data vision. We want to develop our engagement with stakeholders (public, third party, academic) where possible, to utilise and engage with our operational and environmental information, providing potential new insights and new ways of working. This is in line with the government’s Open Data charter whilst also meeting Ofwat’s customer data vision as outlined in “Unlocking the value in customer data” report.

Our plan is to be implemented in a phased approach through AMP6 and AMP7, with some of those initiatives shown opposite.

Information sharing with

Local Authorities

Data Hackathons

Opening our Data

Figure 6 – Opening our Data Initiatives


In your area" App

Community Engagement

Open Data Initiative

MyAccount customer

portal

For our Partners

Providing real-world data to our business partners and research organisations has the potential to foster greater insight and drive data innovation, with the potential for step changes in the water industry. Our objective is to improve our data engagement with third parties to improve our business operations, ultimately supporting our business outcomes and customer expectations. Many research projects academic initiatives have been launched thanks to asset performance (leakage, metering, water quality) and environmental information designed to improve out service to customers.

For our People

Our People ensure our business operations are completed in a professional manner, be it in the office or in the field. Access to essential data is critical for our People to do their work in a safe environment.

Through the implementation of information management systems and End User Computing (EUC), we ensure our people have access to the right data, anytime, anywhere and on any device. We are investing further in collaboration and communication systems to support our people’s collaborative working experience.

2.5. Making our data resilient

Our data needs to be as resilient as our organisation. The ability to recover from data corruption and adapt to significant changes in data is part of our data strategy. Over the AMP6 period we have invested in a cloud first strategy to build resilience in our foundational Information Technology. Using cloud services, we have become highly resilient to failures with the ability to recover in rapid time.

We have also made significant strides towards providing a resilient access to asset information remotely, which provides operational resilience, particularly when responding to large incidents such unplanned outages of bursts.

As we invest more in our data technologies our data resilience will mature, especially with the exploitation of emerging technologies and large distributed fail-safe data management and processing solutions.

2.6. Keeping our data governed

Data Governance comprises of a group of people with a common objective (improve the use of data within the organisation), developing rules, processes, procedures and standards to be followed within the enterprise for improving the organisation’s operational efficiency. Data Governance ensures maintenance of data quality, appropriate use of data and available resources and standardised management and mastering of data within the organisation. Accountability and ownership of data is the best practice within any data strategy. We aim to implement Data Governance as part of our Data Strategy to help guide our people on how best to use the data within our Company. Our Governance starts from the top, driven through our governance board and forums right down to our end users.

As part of our Data Governance, a programme of learning will be undertaken to educate our people on great data use and promote a cultural step change toward good data practices and acceptable use. The diagram below depicts our hierarchy of data users and ownership.


perf

orm

ance

dat

a.

-

-

’The Senior Information Risk Owner (SIRO) is the company s key representative and overall owner of data management risk and is the chair of our IMB

Senior Information Risk Owner

Information Asset

Owners

Data Stewards

and Officers

Data Entry Clerks

Data stewards and officers are individuals who have specialist knowledge and understanding of datasets and reports. They have responsibility for monitoring data quality and ensuring our data are fit for purpose.

Information Asset Owners (IAOs) are EMT members and senior managers who have overall accountability for management of data within a specific business function. They are budget holders for data remediation.

Our End users, responsible for data capture at source. Responsible for ensuring accurately and adequately captured data at the point of creation.

Figure 7 - Our Data Users

2.7. Mapping data to our outcomes

The following table shows the data objectives and how they map to our outcomes and expectations. Achieving the below objectives allows us to reduce risk and further improve our internal business efficiency.

Wat

er q

ualit

y an

d as

set

Leak

age,

cons

umpt

ion

&en

viro

nmen

tal d

ata.

* We employ smart technology to monitor our leakage levels. * Collect accurate PCC data to plan supply and demand at a localised level. * Monitor aquifer and river levels data to minimise customer impact and protect the environment.

Mak

ing

the

mos

t out

of a

llda

ta fo

r cus

tom

ers. .* Using accurate asset

criticality and condition data to make the right investments . * Move to a more personalised service for customers, delivering more information and data on service level. * Simplify access to our tools and services (Billing, network updates, water quality, new developments)

Anal

yse

serv

ice

deliv

ery,

asse

t hea

lth a

nd c

ondi

tion. * Create insight from

data to help reduce disruptions and improve operational performance. * Enable our control vision * providing real time information to customers on disruption minimise unplanned outages . * We monitor pressure and burst data to make the right investments at least cost.

Supplying highquality water you

can trust

Making sure you have enough water, whileleaving more water inthe environment

Providing a greatservice that you

value

Minimising disruption to you

and your community

* We manage our asset performance and track water quality data from source to tap. * We use water quality data to plan our acitivities and meet our CRI and Mean zonal compliance commitments. * We provide customers with easily accessible and transparent data on our water quality.

Figure 8 - Mapping Data to Outcomes

Improvements in our data management is largely incentivised by these core pillars; improving operational efficiency and expenditure, reducing risk associated with failing to meet our regulatory objective and upholding the company’s perception to customers and stakeholders (who recognise our ability to manage and safeguard critical data, whilst maximising its use to serve them best). This approach is critical to help us map key areas for improvement, such as realising Opex savings in energy use, reducing our response to network incidents or increasing the transparency and the volume of information we provide to customers. In order to achieve our individual performance commitments, we intend to follow the approach given in the mapping above – where data quality will be critical and collaboration will be crucial.


Our data principles 3.1. Our data principles

The management of data has many similarities with our core activities, data needs to be captured, treated, stored and distributed. Like water, data is a valuable resource and should be managed as a utility rather than a luxury. To support this, our data strategy is underpinned by six core pillars, helping guide our approach to data.

Completeness

•We collect and hold data that is relevant to the business, both current and historic

•We ensure a complete set of data is available for each record and every instance, it can be used to answer every reasonable stakeholder question

Consistency

•We ensure data is consistent in its definition, stewardship and interpretation, structure, rules, format and value at any time, place and version of this information being used

Figure 9 - Key Data Pillars

3.2. Our data lifecycle

Correctness

•We ensure the data is correct in all details and is a true record of the instance it represents

•We measure and monitor the level of accuracy

•We comply with the organisational standards for data update timescales

Security

•We maintain adequate data access and control to ensure users have what they need

•We comply with legal requirements and best practice data storage rules

Accessibility

•We can easily access data when required and share it to enable collaborative decision-making

Uniqueness

•We take reasonable steps to ensure all items are unique with no duplication of data

•We keep business-critical data as unique version ensuring there is a singlesource of the truth

Our core principles are to be managed and maintained through good data practices and strengthened through an iterative data lifecycle. We are committed to delivering a robust lifecycle approach to data, which links right back to our vision and objectives.

The data lifecycle allows us to gain trust of our data through right oversight throughout its life. This will allow us to optimise our data’s usefulness, improving our information while minimising the potential for error. Finally archiving or disposing the data at the end of its useful life, will ensure we are compliant with applicable legislation and will reduce the consumption of valuable IT resources.

The data lifecycle will become an important process within our Data Management programme especially with the expected future data explosion from Big Data and the on-going development of the Internet of Things (IoT) that will become prevalent within our industry over the coming years.


https://searchdatamanagement.techtarget.com/definition/big-data

https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT

-

Specify

Archive and

Destory

Share

Analyse

Assess

Collect & Store

High Quality Water

Minimise Disruptions

Fulfil our AMP7 commitments.

Enough Water

Value for Money service

Meet regulatoryrequirements

Comply with bestpractice standards

Becoming the leadingcommunity focus water company

Operational, Customers, Financial, Asset,Regulatory, People, Technology & IT

Requirements & Governance

Our Drivers

Our Vision

Our Objectives

Our Insight

Figure 10 - Data lifecycle

Our approach to data is iterative and cyclical, whereby we need to continue and grow with the data, each cycle brings a new dimension and insight.

In practice, it is not uncommon for a data stage needing to go backwards to the previous stage, typically this is completed through the Governance processes or realignment of business requirements, which essentially act as a review process and request for further definition and clarity. In effect, the data lifecycle can have mini cycles within a few stages before moving on to the following stage.

Stage 1 - Specify data

One of the key principles of good data management lies in specifying what data we should keep and to what standard, to conduct our day to day operations of the business.

What ?

•What data do we seek to collect ?

•What format is the data required in ?

• Can we quantify the extent of the required data ?

Why ?

•Is there a regulatory/statutory or legislative driver?

• What is the customer benefit?

•How does it link to our performance commitments ?

How ?

•Are we clear on the methodology for data collection ?

• Has a similar exercise been done in past ?

Figure 11 - Specify data process

When collecting new data, whether they are collected via machine automation or through human input, we endeavour to ensure we collect the appropriate metadata and information properties which are needed. Metadata is ‘data about data’, its characteristics that allow effective and efficient referencing. A data object’s metadata can be thought of as a ‘fact file’ about it. This may include meaning, relationships to


other data, origin, usage, format etc. Metadata is fundamental to successful data governance: it acts as a reference point to promote common understanding, describing what it means, specifying any relevant standards and clarifying how it is used within the organisation.

The above process is already embedded as part of our “privacy by design” approach to data protection, where we privilege the need to protect our personal data with the utmost care and privacy, particularly when delivering new digital solutions likely to yield data and information as input or output.

Stage 2 - Collection & storage of data

Data collection occurs early in the lifecycle based on the specified data in stage one. This data could be a raw data, file, image, or in document form. The information is typically entered or uploaded into an application and accessible to certain roles within the organisational hierarchy on whatever devices offer an access point to the proprietary system.

At this stage, it is important for those conducting data entry to ensure that the data they input or receive is as accurate as possible, this will limit the data quality issues downstream.

As part of a solutions delivery process, the data storage mechanism and its associated security, availability and recoverability is designed. Typically, this is implemented through a traditional database management system onto physical disk, although larger distributed data management solutions are changing this pattern. However, this design process provides valuable knowledge and accountability as part of our governance process ensuring continued business operations through strong architectural processes.

Stage 3 - Assess and improve data

Data quality is an essential element of any data strategy as it demonstrates the effectiveness of the data strategy and underpins the trust in our data inputs and outputs.

The “assess and improve” stage supports data quality by providing the “checks and balances” to our data, ensuring what we’ve specified in stage 1 matches what we’ve collected in stage 2. This stage provides the assurance that our data aligns to our requirements and governance controls, providing a measurable view of our data.

Any identified gaps in accuracy or completeness will be addressed, as this critical to the analysis phase. For instance, burst records maybe missing information on pipe characteristics and thus may need to be inferred. This stage provides the assurance that our data aligns to our requirements and governance controls, providing a measurable view of our data.

It is a necessity that we trust our data, and only though continually data quality validation can we gain the measures by which we have confidence on our data. Without this trust, the next stages of the data lifecycle cannot be trusted and as such, our data strategy would be ineffective.

As noted, this stage uses the information generated from stages 1 & 2 to validate our data, however, it also looks to provide the mechanism and guidelines to correct and continuously improve our data. Where data quality issues are found this should be highlighted back through the Requirements and Governance process and readdressed through stages 1 or 2.

Stage 4 - Analyse data

The Analyse stage supports the use of data to generate information and provide insight. Typically categorised under the Business Intelligence (BI) banner, this includes the reporting, dashboarding and analytical processing of our data into information and knowledge. Simply put, how we use our data to be informed.

Underpinning our analyse data stage is our hereditary need to optimise our “totex” management of the asset portfolio, whilst delivering great customer service. The techniques and services used during this stage of the data lifecycle have an inherent impact on our efficiency as a business from an Opex and Capex perspective.


We use a multitude of tools and data analysis techniques to manage information within our respective areas of the business. Much of this is generated thanks to common statistical techniques and modelling tools which provide the best level of information management possible.

However, we also rely heavily of ad-hoc analysis of structured data, through business intelligence platform services for reporting and analytics dashboarding. These tools enable us to explore the data in more detail utilising descriptive statistics techniques (mean, mode, median, and frequencies) to understand current trends, with more qualitative data analysis, particularly when examining unwanted contacts from customers.

All these systems and techniques support our regulatory submissions (WRMP, Business Plan, Annual Financial Reports) but the majority of our data analysis is carried out to support our business operations.

Stage 5 - Share data

This stage of the data lifecycle relates to making data and information derived from the previous stages available to our people, partners and customers in a secure and robust manner.

One of our goals is to support our regulators view which aims to:

• Develop a collaborative network to share best practice and develop ideas;

• Champion innovative uses of data in the water sector;

• Use data to improve customer service and support to those who need it most.

• Improve operational resilience;

• Be more transparent, which in turn drives accountability and legitimacy building trust;

• Develop materials and support to help sell the benefits of data sharing internally.

To get the most value from our data and drive different behaviours, it is key to keep our people informed. Through the application of technology, we will be able to share our information to support our operational teams, so they can work as efficiently as possible in a safe working environment. During AMP6 we have invested heavily in our communication and collaborative systems, enabling the means to effectively deliver information anytime, anywhere on any device.

To support the government’s Open Data charter and Ofwat’s customer data vision as outlined in “Unlocking the value in customer data” report, our vision is to develop our engagement with stakeholders (public, third party, academic), utilising our operational and environmental information to educate and develop new techniques and understanding.

Our aim is to help customers navigate to the information they need, whilst also facilitating the collection of the data we need from customers (for example, the new MyAccount platform will enable customers to update their personal occupancy and billing details online, therefore facilitating and enhancing the customer experience). To support customers and provide an inclusive service for all, we will build on our self-service capabilities to provide richer information in a secure manner. We see this as critical to empowering the customer to effectively manage their water use.

Stage 6 - Archive and Destroy

Stage 6 of the data lifecycle supports our responsibility to ensure our data and information is managed and destroyed in accordance with legislations and customer expectations.

We adopt a robust data archiving and destruction policy which ensures that retired devices and media have their contents securely removed, destroyed, or overwritten and physical documents are destroyed appropriate to their security classification.


3.3. Requirements and Governance for the data lifecycle

The Requirements and Governance stage supports our data lifecycle, providing the business “wants” and “needs” for data and the policies, procedures and assurance that data and information is being governed and managed in an appropriate manner.

The requirements and governance should be validated at each stage of the lifecycle and provides the mechanism to revert to previous stages in an iterative process where further understanding may be required.

Requirements

Requirements are essential to great data management. Requirements set the expected outcomes required within the data’s lifecycle and provide understanding to each stage to ensure what is being delivered meets our expectations.

Governance

Data governance is an essential part of our overall data strategy and provides the roles, policies, procedures and guidance to ensure accountability and ownership of our data assets. Data governance provides the means to measure and support the delivery the Data Management programme in a sustainable manner.

During AMP6, supporting our Information Security programmes we have developed a series of user forums supported by a centralised Information Management Board (IMB). These forums will work to provide the necessary assurance and governance to our Data Strategy. The diagram below depicts our governance organisational hierarchy. Although not explicitly stated in the title, the Requirements and Governance stages should also validate that our data conforms to our information security standards taking a Confidentiality, Integrity and Availability (CIA) approach.

Info

rmat

ion

Man

agem

ent

Boar

d (IM

B)

Information SecurityManagement Forum Securty Champions

Data Protection Forum Data Protection Champions

Data Governance Forum

Data Quality and Governance Champions

Figure 12 - Governance Boards

Although not explicitly stated in the title, the Requirements and Governance stages should also validate that our data conforms to our information security standards taking a Confidentiality, Integrity and Availability (CIA) approach.

Confidentiality


Confidentiality is the ability to hide information from those people unauthorised to view it. It is perhaps the most obvious aspect of the CIA triad when it comes to security; but correspondingly, it is also the one which is attacked most often. Cryptography and Encryption methods are an example of an attempt to ensure confidentiality of data transferred from one computer to another.

Integrity

The ability to ensure that data is an accurate and unchanged representation of the original secure information. One type of security attack is to intercept some important data and make changes to it before sending it on to the intended receiver.

Availability

It is important to ensure that the information concerned is readily accessible to the authorised viewer at all times. Some types of security attack attempt to deny access to the appropriate user, either for the sake of inconveniencing them, or because there is some secondary effect. For example, by breaking the web site for a particular search engine, a rival may become more popular.


Our Data Achievements 4.1 Data ownership and collaboration: Open Water

Clear accountability of data is as essential as health and safety compliance - through a programme of education and awareness on data ownership, we were able to improve the open water process readiness and achieve the tight deadline for market opening. Our process during and after market reform, means that we are now top of the league table for water wholesalers. We run a set of data quality reports using tools such as Talend, to actively manage the accuracy and completeness of data. Some of the core reporting tools which we use are:

• MDS tool (Market Dataset Synchronisation) • Data divergence tool (Measuring the accuracy of properties and SPID details against CMOS) • Audit and compliance tool

These reports are monitored on a regular basis to ensure we can follow up on data changes in our register. Any issues which cause significant risk are dealt with by the wholesale team, data quality team and IT. We have also developed a data cleansing dependency model which maps and identifies the people and data sources affected by changes to non-household properties or meter attributes; this means that changes to the data in our system triggers a notification to stakeholders who are responsible for the document updates. This can be explored further in our case studies.

4.2 Information Security

Cyber and data protection training courses through a corporate Learning Management System (LMS) have been introduced. A full program of work to be compliant with GDPR and NISD have been identified and prioritised. A number of initiatives are being introduced and we will continue to meet the regulations requirements and fulfil the Information Security needs through its alignment with ISO27001.

Looking after customer data is our primary concern. Initiatives to protect both physical and digital data are currently in place. We are currently reviewing the introduction of a data classification system in conjunction with Office 365 security controls and a Data Loss Prevention (DLP) system to monitor and manage all data, including personally identifiable information.

Security testing for vulnerabilities and 3rd party penetration testing and remediation of critical and external facing interfaces are established processes. Risk and privacy impact assessments and 3rd party management is being further improved as part of our alignment with ISO27001 and GDPR requirements. We have reviewed our contractual agreements with our suppliers and stakeholders whom we use and share data with as clearly stated as part of our privacy policy1.

We have also reviewed our internal breach process to ensure we duly prepare for incidents and comply with the ICO regulation on breach reporting; therefore, we ensure to report breaches to the ICO within 72 of becoming aware of the breach.

The introduction of the monitoring system Darktrace combined with inbuilt machine learning has provided a facility for in depth investigation view of our IT network, server, desktop systems and the data flows providing security and infrastructure teams with alerts on detected anomalies to support our cyber incident management processes.

1 https://www.affinitywater.co.uk/privacy-notice.aspx


https://www.affinitywater.co.uk/privacy-notice.aspx

4.3 Pushing innovation with social media analytics

Social media analytics is fast becoming a focal point in the industry – it helps us to identify sentiment and gives a granular picture of the sort of issues (leakage, discolouration, bursts) which affect customers on a regular basis, interacting on often very localised issues. By using our web analytics and Facebook sentiment data, we successfully mapped and identified web traffic during incidents at postcode district level, which helped us to keep live updates and messages online via our website. This was hugely beneficial to customers during our major outages in 2017 (Baldock, Letchworth) where we understood that page views to our “in your area” app went up exponentially in those areas during incidents. This insight helps us to plan our communication better – in areas where social media feedback is limited, we try to identify usage of our SMS service (which allows customers to be contacted on updates to disruptions) in order tailor our communication approach. This approach supported our customer satisfaction and social media feedback as outlined in our case studies.

By making more use of customer calls data and analysing social media traffic during incidents we were able to pro-actively support customers with a good knowledge of issues before they call us, thus helping us to improve our customer satisfaction score by 40%. This can be explored further in our case studies on social media and contact centre analytics.

4.4 Leveraging the use of data visualisation

Visualising complex data to represent network performance is a must – through the use of our Navig-8 tool, we provide mastered single source data on network performance and asset maintenance, helping end users to visualise and extract data whilst also being able to view it spatially. The data requires minimal manipulation and thus simplifies the ETL process and enables rapid updates of the data. In addition to this, the tool allows us to visualise asset performance at various spatial levels, from a macro level company view down to localised performance issues which tend to affect individual parts of our network (such as DMAs)This approach has greatly influenced the outcome of pressure management and leakage targeting schemes, which are supported by the insight generated from the data representation. In addition to this, the Navig-8 suite of dashboards enables end users to save, export and print outputs with document meta data, which facilitates the auditing of the data source – meaning we can ensure confidence and rigour in the governance of the information when sharing, one of our core data principles.

4.5 Making Asset Data More Accessible

Responding to operational incidents is greatly affected by our ability to access asset performance data and documentation; thanks to our cloud based document management system, we are able to provide up to date documentation via a secure platform which can be accessed from mobile devices – this provides our operational staff with field access to the latest version of our asset documentation in a structured and organised manner. As part of this process we have transferred over 60000 documents onto our new platform, which allows users to navigate through a simple frontend interface to view asset documentation on the go. The platform also provides up-to-date document analytics which enable document controllers to manage their respective documents in accordance to their periodic review dates and overall validity. This is a significant step towards achieving our goal for ISO 55001 compliance.


5 Our future Initiatives 5.1 Enterprise Information Management (EIM) and People Training.

Our Enterprise Information Management (EIM) Programme is the delivery vehicle for our Data Management vision and initiatives set out within this document. EIM’s key aim to provide a structured approach to the delivery of our strategy, through best practice programme, project, architectural and development processes, to ensure value to the organisation and its customers.

Through EIM we aim to support the delivery of the Data Strategy by:

• Education, through our standard learning processes and tools to embed our Data Strategy and processes throughout the organisation.

• Data Knowledge, through strong architectural processes and knowledge sharing, proving the collateral to our communities to understand our data and its use.

• Data Governance, through delivery of tools and processes to support our communities in their data roles and responsibilities

• Data Security, through great Data Governance combined with Information security processes

• Data Innovation, through knowledge, we aim to implement pilot projects and initiatives to implement data services and technologies and improve insights

• Compliance, through Data Knowledge and Governance combined with a risk-based approach to understand how we comply with legislation and regulation.

To support the delivery of our EIM programme, we ensure to deliver robust training to our staff in order to develop our data management skills in relation to our strategic objective, including cyber security, data protection, data ownership, data process management and analysis of data. This journey is on-going in AMP6 and will continue in AMP7 as we ready our people to be more data centric in thinking, a radical shift in culture. Training will also enable us to continue our compliance with legislative and regulatory standards, whilst pushing ISO compliance in key areas such as data quality (ISO8000:150) or cyber security (ISO27001).

5.2 Situational Awareness

One of the biggest challenges and opportunities we currently face is the management and of growing volume of data which is being generated, from such devices as network loggers, smart metering technology or network contacts. With the development of further Internet of Things (IoT) devices, the potential for data growth over the coming years will be unprecedented.

We have developed our Situational Awareness solution which provides a single plain of glass view of our production and network telemetry data, enriched with our operational information, customer contact and combined with geospatial data layers. The aim of this solution is to understand our operational events through telemetry data in the context of the environment around them, in “situation”. This solution supports our Control Vision programme which is designed to increase our operational awareness and streamline processes to achieve our demanding outcomes, particularly in leakage, bursts, unplanned outage and water quality commitments.

In AMP7 we aim to invest and develop this solution further, exploiting proven technologies such as predictive analytics and Machine Learning in efforts to move to more real-time operations and event management, reducing the amount of manual intervention.


5.3 Data Science and Artificial Intelligence (AI)

Artificial Intelligence and its associated subsets such as Machine Learning and Deep Learning are set to have mainstream adoption over the coming years. Software vendors are starting to exploit these technologies and embed them in the information system products to provide insight and real-time event processing at source. The Data Science practice is also making use of and maturing these techniques and as such will become a must have for future Business Intelligence and operations.

Through the adoption of AI, we see potential in supporting both our business operational communities and customers. Complemented with predictive analytics and automation we aim to deliver a more cost-effective service through informed decision making.

Today we have employed Machine Learning as part of our Darktrace implementation. Darktrace is a security monitoring solution that is actively tracking our IT infrastructure usage patterns, using machine learning to spot anomalies and suspicious events, making our Information security operational team aware to investigate potential issues.

Moving forward we anticipate the use of AI within our Situational Awareness application, will support our shift to more real-time event processing, thus reducing our Time to Action and Mean Time To Repair (MTTR).

5.4 Analytics and reporting tools

We are committed to fully migrate to a cloud first strategy by the end of AMP6, which will enable a better working environment and provide a more scalable infrastructure. As we move into the next AMP, the expectation of leveraging data to make more informed business decisions is ever evolving and increasing in expectation. With various initiatives planned to tackle data quality issues as well as consolidation of legacy systems, the expectation is that with improved data mastering, the Business Intelligence layer will in turn improve in quality.

A well-formed governance framework is therefore required in AMP7 with the expectation of leveraging augmented and embedded analytics with machine learning for example, generating insights on increasingly vast amounts of data.

Qlik as well as Business Objects are Business Intelligence platforms currently being used and invested in. Both within AMP6 and AMP7 we’ll see further focus on the Qlik products, particularly the usage of the self-service visualisation product; QlikSense. Qlik’s associative model allows users to probe various data associations across one or more data sources. With its ability to dynamically calculate analytics and speed in which it identifies & connects associations, it provides a highly valuable tool for senior leadership to identify Wholesale or Retail trends, issues or areas of opportunity.

Qlikview is already extensively used as part of the Navig-8 tool, a key asset management suite of dashboards used for service delivery mapping (SDM).

Further data management tooling investment is ear-marked for AMP7. These platform capabilities are for accessing, integrating, transforming and loading data into self-contained performance engines, with the ability to index data and manage data loads and refresh scheduling. Talend (shown here on the right) is our current Extract, Transform & Load (ETL) choice of tooling and examples of its usage in AMP7 include the integration of key asset systems such as Hi-Affinity, GIS, Trace, Pioneer and AMIS. Such endeavours will assist our company to eliminate inconsistencies through automation of manual interfaces between key systems.

Figure 13 - Navig-8


As part of the Talend Data Management platform we also have data quality processing and stewardship functionality to build upon our data initiatives for improved Data Quality. This tool will become a value asset in our data governance processes.

Figure 14 - Talend Data Management Platform

5.5 Developing our analytical “centre of excellence”

Sharing our capabilities within the business is as important as the service we provide to customers: it enables us to make best use of resources whilst developing a collaborative view of best practice and data analysis in general.

This is why we are working with key data specialists across the business to strengthen our data lifecycle management, focussing on sharing analysis techniques, data processing skills, document safekeeping and sharing valuable insight and knowledge which already exists within the business. To this end we encourage our people who play key roles in data management and are involved in core business processes to engage with the centre of excellence.

This group will be critical to help us deliver a successful outcome in AMP7 for Information management, ensuring we review our reporting priorities, work to provide technical data training where required and share important issues which may have cross – department impact.


6 Summary Our data strategy fundamentally underpins the delivery of our outcomes and performance commitments. It is vital to the future of our business, but also customers, that we approach data with a strategic view and build on the efforts of the already achieved initiatives from across the business. In AMP7 we plan to ramp up our data management initiatives to support this strategy – this will be a critical success factor to the delivery of our plan for customers.

In short, we will ensure that we keep our data

• Fit for purpose

• Secure and safe

• Legal

Whilst making it accessible to our people, customers, regulators, and our partners.

We are committed to delivering a robust lifecycle approach to data and using innovative information technology to deliver our company vision and objectives. This will be achieved through the synergy of our people, processes, data and our information systems, which will work in unison for the successful delivery of our strategy.

The case studies follow as an annex to this document.


PR19 Data Strategy Case Studies

Our Data Lifecycle in Motion with examples of best practice data use.

Each of the case studies

demonstrate the value added by

applying the data lifecycle to

business as usual data.

2

-

-

Our Data Lifecycle in Action See how data driven decision making supports our service to customers.

• Each case study looks at how we have applied our

data lifecycle approach to meet our strategic

objectives for customers.

• They provide a link between data based planning in

key areas of the business to strategic outcomes,

which is critical to ensure the business plan

promises are met.

Specify:

Address data requirements to meet

our business and customer objectives.

Share:

Develop appropriate solutions for collection

of data.

Collect & Store:

Develop the right solutions to get best

quality data.

Analyse:

Appropriate techniques and modelling tools to

meet data requirements.

Assess & Improve:

Data Quality Reporting and monitoring Quantifying risk.

Archive & Destroy:

a systematic approach to ensure all redundant data is either archived or destroyed safely.

Becoming the leading

community focus water company

Enough Water

High Quality Water

Minimise Disruptions

Value for Money service

Fulfil our AMP7

commitments.

Meet regulatory

requirements

Comply with

best practice

standards

Operational, Customers, Financial, Asset,

Regulatory, People, Technology & IT

Specify

Archive and

Destroy

Share

Analyse

Assess

Collect & Store

Requirements

& Governance

3

-

Using web data, social media and customer analytics To provide a bespoke customer service and improve customer satisfaction scores by 40%.

Background During the SIM wave of 2017, we received poor

feedback and low customer satisfaction scores in three

areas:

• Leakage Issues

• Defective Stop taps

• Pressure Issues

To understand performance (particularly in relation

to SLA’s) we needed data on all jobs raised and

their overall response times. We also required

information on the causes and location of issues.

We also required information on website page

views and social media info to understand when

people looked at our website.

Solution We decided to start using our page views data, social

media sentiment and call analytics information to

better understand the concerns of customers. This

data has been compiled into a dashboard which

provides page views, call volume trends and

geospatial distribution of inbound customer calls. The

proactive team analyse all customer call data to

reduce unnecessary technician visits.

We also deployed our IVR service to give

customers the choice to call us back if required,

without losing their space in the queue, reducing

wait times by 20 minutes.

Benefits Thanks to strong collaboration, the deployment of

the IVR service and the use of the analytical

dashboard, we achieved some encouraging results

within the first year;

• 16% reduction in very bad C-sat scores.

• A significant reduction in erroneous

technician visits, thus improving customer

feedback!

• An improvement in customer satisfaction

scores of 40% between June 2017 and April

2018.

4

Customer call data, web page views, social media and work management

data.

Data stored in google analytics, twitter /

Facebook API and Hi Affinity.

Adding location information and call

duration data to populate dashboard.

We found that our gatekeeping process reduced erroneous

technician visits by 60%.

The dashboard is shared between customer

relations and the service desk team to continuously

improve our service.

“ ” –

Delivering real time data updates during interruptions Driving positive customer feedback whilst reducing unwanted contact.

Background We have developed our “in your area” mapping application to give customers an overview of bursts,

leaks and general planned and unplanned works within

their local area. The system was substantially

revamped in 2017 to provide up to date information.

We seeked to use streetworks data and social

media sentiment analysis to reduce our negative

feedback during a major outages. The below

analysis show social media sentiment recorded

between January and June 2017 – only 11% of

customers felt positive about our service.

Neutral 66.80%

Negative 22.10%

Positive 11.10%

Solution We developed a new strategy focused on analysing

our social media data and page views. We analysed

the relationship between social media activity and

page views, whilst also analysing this against network

incidents (bursts, DG3 interruptions to supply). The

data was mapped to identify page views within certain

locations, related to incidents and its relation to

bursts.

On the 1st of August 2017, we saw 6900 page

views on our “in your area” webpage, 50% of which came from Letchwork, Baldock and Hitchin.

These areas were affected by a large interruption.

Benefits We found that customers were appreciative of the

up to date information and were happy that:

• We could keep them informed on when the

water would be back on.

• Interruptions were updated regularly on the

“in your area” web page.

• We provide active video and image content

on the state of repairs via twitter.

Overall we managed to improve positive social

media sentiment by 16% (July to December 2017).

We also toppled 100k page views in 2017 on the in

your area app.

Neutral

Negative

Positive

56.70%

15.70%

27.60%

5

Social Media Data (Facebook and Twitter), In your Area page views

, Bursts and DG3 Incident data.

Twitter & Facebook API, Google Analytics, Bursts and DG3 data stored in

Maximo and our Register.

Location information was added to the page views and social media content to understand source of

the data.

Page views go up exponentially during major

interruptions 25* Increase on 1st of August

2017.

Data was used to target tweets and social media

comments in Baldock area, whilst also using

page views data to help plan bottled water.

Using network data to understand pressure issues And improve customer knowledge of low pressure symptoms.

Background We currently report pressure under the DG2 metric

(number of properties receiving less than 15m in the

main for longer than 15 minutes on more than 5

occasions in a year) which is reported through Discover

Water which compares results across all water

companies. Currently there are around 2000 properties

on the DG2 register. AWL is an outlier against other

companies.

The service level is within the main (15m) and not at

the customer property, hence the requirement to

distinguish customer/network side. We decided to

look at our network data to understand actual

mains pressure and head loss issues against

customer calls for low pressure.

Solution The calls from the analysis were plotted against the

low pressures identified through loggers and any that

do not correlate are investigated further and included

in the measure. We also analysed customer calls

against actual communication pipe failures. Below is

an example for June 2017.

In-depth customer interviews found that in low

pressure areas, customers have little

understanding of the causes of low pressure, and

whether it is the responsibility of the water

company or the customer.

Benefits The analysis provided us the certainty that

customers are receiving the right level of service

and thus reducing the volume of disputes and

unwanted contact. It is also helping us to shape our

communication strategy, serving more information to

customers.

£1.25

Million

Committed

to reduce

DG2.

Part of the information will include awareness

for plumbers to ensure that they are installing

the correct diameter internal pipework to

support the lowest possible pressure received

as advised by us. A capital budget of £1.25m

was approved for investments required to

increase the pressure to properties on the DG2

register and thus the number of DG2 properties.

6

We required network pressure data from our loggers, customer calls related to poor pressure and customer feedback.

Logger data was collected from our network telemetry system

(Waternet), customer contacts came from our

billing system.

We added value to the customer data by carrying

our interviews with affected customers to better understand their awareness of the issue.

Over 60% of customers interviewed lacked

awareness of the root cause of their specific

pressure issue.

More information shared via our website and on the phone to aid customers in

diagnosing cause of pressure issue within their

home.

“ “

Segmentation of QOS calls by Postcode Districts Developing a spatial understanding of customer issues to provide them the right support.

Background We serve over 3.5 Million customers in the south east

of England – this equates to close to 200 postcode

districts, all with varying needs and service level issues.

We were interested in broadening our understanding of

customer issues by creating a “portrait” of our postcode districts.

Customer calls data

Local Operation

al data

Postcode district

Segments

We also wanted to develop a spatial understanding

of those issues, to link service delivery

to our customer feedback.

Solution We decided to undertake a spatial segmentation of

postcode districts by grouping quality of service

(QOS) calls by code type (over 30 codes) received

within each postcode district since 1999. We used the

number calls per 1000 population as a standardised

measure across all postcodes.

The initial analysis produced 3 high level

segments and revealed some interesting splits

between our urban areas and suburban/rural

postcodes, especially with water quality issues.

Benefits The analysis provides us with a much needed

insight into customer issues spatially . We have over

95% coverage of our customer based in terms of

Qos calls, which means that we can see the issues

which customers experience throughout the year.

Segment B: 67%

Segment C: 2%

Segment A: 35%

We plan to develop this segmentation further by

applying the QOS data at various

geographical levels-this will allow us to link

asset performance and works management data

with customer calls, to make sure we have

useful insight on customers to support them

when contacting us, supporting C-Mex.

7

We needed customer calls information with the

location of those postcode districts. We also required

network data.

The data is collected via our service desk, who

collate information and is stored in our Billing System (Hi Affinity)

Data was cleansed and normalised to compare

like for like call volume per capita.

Three core groups identified which

differentiate themselves.

Results stored as a spatial dataset and tabular list of

segments – the methodology is replicable

and re-usable.

––

Improving data management in Open Water Our Wholesale service desk is now top of the wholesalers' league table.

Background As Part of the market reform for commercial customers

which came into vigor in April 2017, Affinity Water

embarked on a data quality and governance journey

designed to meet the requirements set by the Market

Code, Operational code and service agreement; this

ensures that we meet the compliance requirements set

by MOSL.

Data quality is therefore important - inaccurate

records and poor information management

processes contribute to increased expenditure and

poor retailer feedback.

Solution We formed a coherent working group between

various functions of the business (wholesale

operations, IT and asset management) to facilitate the

updates and improvements in data quality. Tasks of

the group are to;

• Chair data resolution/improvement team.

• Maintain rules for data quality (standards and

tolerance)

• Work with data owners and stewards on data

quality improvements (with WOSD)

• Produce and update processes for investigation

and resolution of data quality issues.

Active measurement of our SLA and meter

exchanges has ensured we pro-actively manage

our compliance and fulfill customer requirements

as swiftly as possible.

Benefits Over the course of the last financial year (Yr3)

operational performance standards (OPS), which

sets service level agreements (SLA) for a number of

activities (for example a meter exchange should be

completed within 22 business days.) Went from

95.81% to 99.7%, putting us in top spot within the

industry overall.

IT Service and Delivery

Business Intelligence WO Service Desk

8

We need to ensure that the data held on CMOS and in our systems is of a high quality to drive settlements and SLAs ensuring it mirrors the current

state of the market.

Information is stored in the CMOS database and our

system divergence of data in monitored to enable us to

minimise manual data cleansing.

The working group is able to monitor and assess data

quality on SPID, address point and database divergence to ensure we maintain a high

standard in data quality

operational performance standards (OPS), which sets

service level agreements (SLA) Went from 95.81% to 99.7%, putting us in top spot

within the industry overall.

The performance data on meter exchanges, supply pipe changes and retailer changes

is shared amongst the 3 parties in the working group to

continue our great service.

Improving Information Security with Cyber Essentials Certification to the UK Government Cyber Essentials Plus Standard.

Background As part of the InfoSec maturity model assessment

completed in Dec 2016 by and independent partner

NCC is was agreed that AWL required a more effective

formal structured approach within its InfoSec function.

This would allow the team to concentrate efforts and

resources on core security functions, formalise its

future cyber strategy and deliver mature security

processes to the business.

Solution A decision was made to initially certify to the Cyber

Essentials certification and then upgrade to Cyber

Essentials Plus. An initial gap analysis was performed

to review our compliance with the standards to

identify short falls and weaknesses as input into a

focused improvement plan. This included

improvements to:

– Vulnerability Management & Remediation.

– Active Directory (AD) Access Control.

– Network, Server and Desktop build hardening.

– Malware and AV detection.

Certification to the standard results in yearly

audits from our external partner to ensure

compliance with the standards clauses. This

assists the team in ensuring that Information

Security processes are fit for purpose.

Benefits Since the implementation of the standard we have

removed all critical and high criteria vulnerabilities

from our external interfaces. We have also reduced

internal critical vulnerabilities by 90%. Our AD has

been cleansed and the data is trusted acting as the

single point of truth for access control mechanisms.

The Information Security team has been imbedded

within the improvements process for our networked

equipment builds which along with systems

monitoring tools provide early warning of potential

data loss and better control of network anomalies

through improved alerting. We have improved our

AV, Malware and proxy protection provision and

have seen a 20% decline in malware penetrating

through to the user decreasing potential risk of user

induced infections. Certification to the Cyber

Essentials Plus program was granted.

9

We needed to ensure that the Information Security team adhered to Government standards to fulfil

our regulatory and Customer requirements for data protection.

After an initial gap analysis the team identified weaknesses and

potential non compliance and instigated a change plan.

The plan included areas of improvement and a project was

formulated to meet the standard.

Improvements to our security function highlighted by the standard enabled us to meet the standards

clauses.

A third party audit against the standard was conducted and the

certification was granted.

our business plan for 2020 – 2025 appendix c – data strategy€¦ · effectively using our data...

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