It used to be said that human resources are an organisation’s greatest asset. Today, some argue that data is number one. It doesn’t matter which camp you fall into, says Jeremy Cowan, it’s time to blend the two at TM Forum’s 3-day event Digital Transformation World, held in Copenhagen, Denmark from September 20-22.
DTW SPOTLIGHT Whether you’re a service provider, solution vendor or enabler, at Digital Transformation World you can expand your knowledge of Data & AIOps (artificial intelligence operations), enable new partnerships in the Growth Summit, and get your technology fix at the Tech Summit.
Over three days, A-List speakers will share their expertise on how to better manage your data, starting with presentations on eliminating service provider silos, and enabling scale.
This is not just an event for data analysts, it has plenty for anyone in a commercial or managerial role. All 10 of the world’s largest operators will be there, say the organisers, and you can use the DTW event app to manage your time, connect and meet like-minded delegates.
Broad agenda
On Day One, Tuesday September 20, McKinsey & Co. will walk you through the process of unlocking value in your data, from first insights to monetisation. Here, you can find out more about eliminating data silos and enabling scale.
Also see our COO in the Spotlight interview (below) with Amit Sanyal, EVP & chief operating officer for Growth Marketing Solutions at Comviva.
One of the highlights on Wednesday Sept 21st will be a session led by Amy Cameron, principal analyst at STL Partners with commentary from Ahmad Latif Ali, associate vice president, IDC. The session’s speakers are:
Michael Bell, Group GM: BI and Analytics, MTN, talking on Embedding AI across an organisation for real world impact: Where to start?
Shailesh Kumar, chief data scientist, CoE AI/ML, Jio: Emergent intelligence: The next frontier of AI for complex environments.
Velu Sinha, partner, Bain & Company: Building an ethical framework for AI.
Thursday, Sept 22nd features a Masterclass on a new era of data governance hosted by Aaron Boasman-Patel, vice president of AI, customer experience and data at TM Forum, and Wim Stoop, senior director, Product Marketing at Cloudera. As they point out, in recent years data governance has become more vital, driving automation and innovation in telecommunications, while navigating challenging topics of privacy and security. At the same time, migrating infrastructures from on-prem to public cloud challenges the data architect and enterprise architect alike.
Other key speakers: There is not enough room here to list all the agenda items, so you may want to check out https://dtw. tmforum.org/ for details of presentations from speakers such as:
- Aayush Bhatnagar, SVP, Reliance Jio
- Antonietta Mastroianni, chief digital & IT officer of Proximus
- Charles Molapisi, CEO, MTN South Africa
- Cornelia Schaurecker, global group director, AI & Big Data, Vodafone Group
- Dan Thygesen, SVP, Wholesale & Platform Operations, T-Mobile USA
- Elsa Chen, chief customer officer, CityFibre
- Hesham Fahmy, chief development officer, Telus
- Laurent Leboucher, group CTO & SVP, Orange
- Monika Gullin, CTO & EVP, Nuuday
- Peter Leukert, Global CIO, Deutsche Telekom
- Vikram Sinha, CEO, Indosat Ooredoo Hutchison
- Yessie Yosetya, chief strategic transformation & IT officer, PT XL Axiata
COO in the Spotlight
Ahead of DTW we asked Amit Sanyal, EVP & chief operating officer for Growth Marketing Solutions at Comviva about the challenges for service providers planning digital transformations.
Amit, service providers (CSPs, DSPs, etc.) have faced challenges with data silos for years. How can they avoid repeating silo problems in next gen data-enabled services?
Amit Sanyal: Data is a critical business asset today. Service providers are sitting on a data gold mine. It’s a struggle to utilise data stored in silos across units to form a single unified customer view for utilisation across business lines.
To solve the entrenched problem of silos, service providers need to incorporate a data-driven mind-set throughout the organisation. The end goal is to better understand customers, evolve the business model and drive new revenue models. This calls for a top-down approach along with a combination of change management while keeping in mind the far-reaching potential of data.
How does the industry prevent artificial intelligence (AI) projects failing as we scale up enterprise data management? By better integration processes? More agile workflows? Enhanced sharing of relevant data within teams? Continuous AI governance? Or something else?
Amit Sanyal: The goal for all organisations engaged on AI projects is to achieve the promise of AI, which will enable organisations to stay competitive in the near future. Some of the foremost reasons why AI projects fail is a balance of Data Viability, Business ROI (return on investment) and Implementation Capabilities.
Data Viability includes consolidation of relevant data across touchpoints and driving meaningful insights that contribute business needs at hand. Business ROI requires a planned methodology for defining a problem statement and measurement criteria. Implementation Capabilities are a capability analysis that includes timely build and execution of AI/ ML (machine learning) models, along with the right fit on the consumer lifecycle.
Agile workflows and enhanced sharing of relevant data simplify the way projects operate. Continuous AI governance keeps a check on our progress against the set project objectives.
Are digital twins already enhancing enterprise transformations? Can you give any examples?
Amit Sanyal: In today’s world, advance level digital twins have AI-enabled systems. These are equipped with AI/ ML algorithms that are trained based on collected data. Such systems can quickly detect abnormal behaviour and initiate corrective action.
The network twin showcases a telecom use-case utilising digital twins. This presents an area with a high consequences of downtime and network failure. The twin allows modelling of the existing network infrastructure, and predicts failure points during extreme high usage scenarios such as natural disasters and power outages. This twin is also useful because it can incorporate everything from weather pattern to location of street obstacles, to deduce the impact on signal strength and thus overall network quality.
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