ARCHIVE FOR THE ‘data-science’ CATEGORY

Kris Oldland, Editor-in-Chief, Field Service News hosts with Gopinathan Krishnaswami, Senior General Manager, Global Head, Infrastructure Alliances at Tata Consultancy Services as his guest as the two dive into the importance of data in field service including how much data is too much data and the importance of Machine Learning in getting actual insight out of the deluge of data you may be drowning in. 

If there is one industry that should be leveraging data in every way possible, it’s telecommunications. The telecommunications industry services billions of people each day, generating massive amounts of data. Though not many telecom companies are leveraging this data, the introduction of data science, machine learning, and artificial intelligence in this industry are inevitable.

A study by McKinsey, Telcos: The Untapped Promise of Big Data, based on a survey of leaders from 273 telecom organizations, found that most companies had not yet seriously leveraged the data at their disposal to increase profits. And only 30 per cent say they have already made investments in big data.

So while there is certainly debate within telecom companies about whether the return on investment is worthwhile, there is no doubt that data science, machine learning (ML), and artificial intelligence (AI) are inevitable when it comes to the industry’s future. Those that figure out how to leverage these techniques and technologies will thrive; those that don’t will be left behind.

By using data science, machine learning, and artificial intelligence strategies, telecommunication companies can improve four areas of their services.

The importance of data science, ML, and AI to the telecom industry will likely present itself in these four areas in particular, which this paper will take a look at individually:

 

1. Troubleshooting:

One of the major challenges for telecom providers is being able to guarantee quality service to subscribers. Analyzing call detail records (CDR) generated by subscribers at any given moment of the day is key to troubleshooting. However, CDRs are challenging to work with because the volume of data gets massive and unwieldy quickly. For example, the largest telecommunication companies can collect six billion CDRs per day.

With data science, machine learning (ML), and artificial intelligence (AI), companies can instantaneously parse through millions of CDRs in real-time, identify patterns, create scalable data visualizations, and predict future problems.

 

2. Fraud Detection: 

Verizon estimated in 2014 that fraud costs the telecom industry upwards of $4 billion a year. However, the faster that telecom companies analyze large amounts of data, the better off they are in identifying suspicious call patterns that correlate with fraudulent activity.

Cutting-edge ML and AI strategies like advanced anomaly detection make it much easier for telecommunication companies to identify “true party” fraud quickly.

 

3. Marketing:

The high churn rate in telecommunications, estimated at between 20-40% annually, is the greatest challenge for telecom companies. Telecommunication companies can use data to build better profiles of customers, figure out how to best win their loyalty (in the most scalable and automated way), and adequately allocate a marketing budget. With improved data architecture, they are able to harvest and store a greater diversity of data that provide insights into each customer such as demographics, location, devices used, the frequency of purchases, and usage patterns. By combining data from other sources like social media, they can have a stronger understanding of their customers.

Using machine learning gives a more accurate picture of which channels are most responsible for customer conversions for better ad buying as well.

 

4. Customer Experience: 

Telecommunication companies can enhance their services by analyzing the millions of customer complaints they get every year to figure out which types of improvements will have the greatest impact on customer satisfaction and thereby increase customer retention. They can also leverage data at a larger and more automated scale to gain insights into the performance of their technicians.

The more that telecommunication companies can analyze data on customer calls, the more they can begin to recognize which types of problems are most likely to lead to unwarranted “truck rolls” and put in place measures to prevent those calls. Given the number of calls and the depth of analysis required, this necessarily dictates a machine learning approach - more specifically, a deep learning approach. Because analyzing the calls themselves means dealing with lots of unstructured data, it’s the perfect place to expand into ML and deep learning for big gains.

 

The future of data in the telecom industry

Data science is already a big part of the telecommunications industry, and as big data tools become more available and sophisticated, data science, ML, and AI will all continue to grow in this space.

In the coming years, companies that succeed will be those that figure out how to best use the massive number of data points that are flowing both through their network and around it to reduce labor costs, develop better technology and, to better understand what the seven billion potential customers around the world want to do with their smartphones and computers.

To learn more, download the whitepaper White Paper: Top 4 Growth Areas of Machine Learning in Telecommunications.

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Mashed up by machine learning? Dumbfounded by data science? Agnostic about AI? Nick Frank, Managing Consultant, Si2 Partners doesn’t promise to the provide all the answers, but he can offer some crucial insight into the management process on turning your field service data into profits...

Recently I have been working with Data Scientist Eric Topham co-founder of The Data Analysis Bureau, to understand why many company leaders are struggling to turn data into profits. Eric solves data problems. He is the professional who will understand if it is a Data Science or a Data Analytics challenge and then deliver the appropriate math-based algorithms.

Data Science is about discovering new patterns in data in order to make predictions and take real-time action. The mathematical technologies used in this process are dynamic and self-learning, sometimes being grouped under the ‘Artificial Intelligence’ label. In Field Service, the types of data problems addressed by these technologies might include scheduling or predictive maintenance.

Data Analytics deals with historical and more ‘static’ data, where the desire is to test ideas or hypothesis, understand relationships and develop insights into historical patterns.Data Analytics deals with historical and more ‘static’ data, where the desire is to test ideas or hypothesis, understand relationships and develop insights into historical patterns. Here techniques such as statistical modelling, data mining and visualization are used to gain results. Common examples you might recognize are knowledge management or performance reporting.

Data problem solvers such as Eric will tell you that the hardest part of his job is not developing the data solution, it is defining the problem to be solved in terms of reducing costs or increasing revenues or hopefully both.

The companies who can to articulate their business problem in terms of money and performance, make it much easier for his team to create the mathematical models to answer the questions posed.

One of the ways of defining the business problem is to use value mapping tools, such as the Value Iceberg described in February’s issue of Field Service news “Don’t be caught in the Emperor’s new clothes. First focus on the customer”.

These help companies articulate not only the direct benefits to the customer, but more importantly the hidden value of their product or service, such as improved material through-put, lower energy costs or reduced risk.

A good example would be a manufacturer of air conditioning systems who targets facility managers for whom 30% of the building’s running costs is energy. This company targets their products and services to reduce their energy by 10%, enabling a very compelling sales argument.

However, the vast majority are far blander and generally fall into three broad categories:

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• Bland USPers: Ask people about their value and they will trot out a predictable unique selling point(USP) such as 24/7 spare parts delivery. The question is do they know what this means to the customer and price accordingly.
• The Easy and Obvious: Many can tell you what their customers tell them, but not much more! Do you hear phrases such as. ‘My customer needs fast and right-first-time resolution!’. What does this really mean to the customer in terms of money and performance?
• Know, but cannot say: Then there is also a significant proportion who intuitively know their customers, but struggle to move themselves beyond the immediate need. They need help to articulate how they make their customers more profitable.

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If the key to monetizing the data is to never separate the business problem from the data problem, how should companies approach this challenge. Many lack the confidence to take the journey due to the intimidating jargon and fast pace of change.

This high-level roadmap is our attempt to demystify the process by breaking it down into 5 key common-sense steps:

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1. Define the business problem: Whether it’s internal service operations or new services, a value mapping exercise such as the Value Iceberg is the essential start point. But do not just look at the customer. Look at the end to end industry supply chain and in particular the data hand-offs between the different actors in the supply chain. We discussed this more in our 2016 Field Service news article ‘ 5 patterns to discovering new data-driven service revenues’.
2. Solution and data needs: Identify the solutions you might offer, the critical data you need and how you will collect it. In their rush to create data services solutions, many companies jump to this step first without a clear view of the business problem. The result can be developing IoT platforms with no revenue stream or data they cannot analyse.
3. Define data problem: Formulate and scope the problem. Then scope and design the solution. Here matching internal capabilities matched with external expert partners is often the key to success.
4. Implement & evaluate: Start with a manageable pilot, revisit the business problem and ensure the solution is able to add the value you desire.
5. Scale Up: When successful, you are ready to scale up across your organization

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If data is particularly relevant to growing your field service business, then you can reach me @ nick.frank@si2partners.com

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