Group Meeting June 3 2020 Caroline Gabriel NBED title of report in - - PowerPoint PPT Presentation

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TowerXchange Data Working Group Meeting June 3 2020

Caroline Gabriel

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An ambitious programme based on inputs from all stakeholders Last year – process of identifying key priorities and next steps This meeting will kick off some important work items including stakeholder survey, taxonomy Ongoing collaboration and discussion between group meetings Analysys Mason will help facilitate this, leveraging deep experience working with the tower and MNO sectors

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Recap: ap: the e workin king g group object jectiv ives es

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Key Requ quireme ment nt Approac aches hes Understand towerco requirements better, and their appetite for new solutions Survey of towercos – their data gaps, their key requirements Support towerco requirements document Improve CxO understanding of reason to invest in data management, to CFO level

• White paper – risks of not knowing (valuations,

safety etc), impact on opex

• ROI/business case analysis based on the case

studies and vendor inputs, relate to different aspects of data, scenarios

• Benchmarking exercise

Come to common view of best practice and communicate this

• Database of case studies, aggregate best

practice

• Comparisons with other industries

Make it easier for towercos to take steps to better data practice

• Build clear taxonomy of products and services

available

• Clear roadmap with practical steps from Excel

to AI

• Common data parameters for tower data -

template

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Summa mmary y of key issues sues and suggest gested ed actions

• ns for the WG in 2020

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• Top line survey – 48 respondents
• Towercos and MNO tower divisions
• 27% are using no data systems

except Excel or similar

• Data management is strategically

important but there are barriers

• Usage is highest in core functions
• Some use cases are regional e.g.

power

• Towercos starting to think about

automation and optimisation, not just efficiency

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Where e are data a managemen agement t syst stems ems being g used d most st activ ively? ely?

67 62 56 52 45 27 22 18 12 10 20 30 40 50 60 70 80

% of towercos

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Who is r responsible for data strate tegy?

How impor

• rtant

tant is an improved ed data a managemen agement t strat rategy egy to your business iness goals? ls?

33 33 30 30 17 17 18 18 2

Critical Very important Important Moderately important Unimportant

5 28 28 17 17 26 26 24 24

Chief data officer CIO CTO CFO Business unit managers Nobody

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What are the prima imary y objec ecti tives es of your r data managemen agement t strat trategy egy (ple lease ase rank k top 2)?

34 34 30 30 30 30 28 28 20 20 20 20 18 18 12 12 8

5 10 15 20 25 30 35 40 Reduce TCO Understand asset value better Maximise revenue and monetisation Progress to predictive management of assets Support acquisition and divestment strategy Optimise revenue collection Support better strategic decision making Improve negotiating position with tenants and landlords Prepare for extending portfolio to new site types

% placing in top 2

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Only 26% have a holistic approach

• This is because of
• no coordinated leadership
• fragmented supply chain
• business unit responsibility

Other reasons?

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How do you curren rently tly collect, ect, analyse yse and manage age data?

26 26 28 28 22 22 28 28

In a holistic away across most business processes Separately for each business process For selected business processes only On a very limited basis

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78 78 75 75 56 56 52 52 52 52 40 40 38 38 35 35

10 20 30 40 50 60 70 80 90 Big data analytics Predictive maintenance Visual analytics AI-enhanced analytics GIS and location mapping IoT/sensor-based data collection Augmented reality/digital twin Real time data collection and analysis

% of towercos

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Which technolo hnologi gies es do you curren rently tly use, or plan to use within hin 3 years? s?

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40 40 36 36 32 32 22 22 22 22 20 20 18 18 10 10

5 10 15 20 25 30 35 40 45 Cost Available solutions do not meet our needs ROI is unclear Supply chain is fragmented, no catch- all solution Current methods are adequate for our purposes Lack of skills and expertise Integrating data from many asset types is too complex Lack of senior management support

• r leadership

% placing in top 2

• Suppliers can help to lower the barriers
• Many comments relate to transparent pricing and better alignment with requirements

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What are the prima imary y barrier riers to init itiat iating ing or enhanci ncing ng a data managemen agement t strat rategy egy (please ease rank k top 2)?

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• Set up email/discussion group to update and share ideas
• Finalise taxonomy and populate database
• Outline of white paper/ROI document - approve
• Work on structure of database of case studies and best practice,

template for case studies – derive from wider group

• Work on more detailed questionnaire for in-depth survey

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Next xt steps eps

Conta ntact ct det etails ails

New Delhi Tel: +91 124 4501860 newdelhi@analysysmason.com Milan an Tel: +39 02 76 31 88 34 milan@analysysmason.com Manc ncheste ter Tel: +44 (0)161 877 7808 manchester@analysysmason.com Cambridge Tel: +44 (0)1223 460600 cambridge@analysysmason.com Dubai Tel: +971 (0)4 446 7473 dubai@analysysmason.com Dublin Tel: +353 (0)1 602 4755 dublin@analysysmason.com Madrid Tel: +34 91 399 5016 madrid@analysysmason.com Paris Tel: +33 (0)1 72 71 96 96 paris@analysysmason.com Sing ngapore Tel: +65 6493 6038 singapore@analysysmason.com Bosto ton Tel: +1 202 331 3080 boston@analysysmason.com Hong Kong ng Tel: +852 3669 7090 hongkong@analysysmason.com London Tel: +44 (0)20 7395 9000 london@analysysmason.com Oslo Tel: +47 920 49 000

• slo@analysysmason.com

Stockh kholm Tel: +46 709 211 719 stockholm@analysysmason.com

0)1223 460866

@AnalysysMason linkedin.com/company/analysys-mason youtube.com/AnalysysMason analysysmason.com/RSS Kolkat kata Tel: +91 33 4003 3093 kolkata@analysysmason.com New York Tel: +1 212 944 5100 newyork@analysysmason.com

Carolin

• line Gabri

riel

Princ ncip ipal al Analy lyst, Wirele less carolin line.ga gabrie iel@ l@ana analy lysysmason.c

• n.com
• m

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• Define simple way to collect data on equipment and update year to year, even with

unskilled contractors.

• Define core requirements for the analytics mechanisms to apply intelligence to that data

and support business decisions

• Identify short and long term solutions and their likely ROI – establish blueprints for

towercos which want to move quickly or cautiously (e.g. there may be interim steps to improve the case for a chief data officer)

• Communicate with the vendor community about what the towercos need (e.g. pricing

mechanisms, solution complexity, support)

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Additi tional

• nal ideas

as for medium dium term rm consi nside dera rati tion

• n