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Tag Archives: Analytics

The 6 worst pitfalls of not having an Analytics Maturity Assessment in place

Analytics, General, Telecom Insights & Analytics

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Most organizations use analytics to improve their operations to enhance business performance and growth. However, through our experience of working with telecom organisations across various geographies, we have witnessed that analytics in many cases is done in an ad-hoc manner rather than in a planned phase. This results in organisations not witnessing the kind of return they were expecting from their analytics investments. An Analytics Maturity Assessment (AMA henceforth) helps in identifying such pitfalls and avoiding them. Few of the pitfalls that AMA helps in identifying are:

  1. Data Issues & trust: The root of any analytics project is the Data element. It is important that the end user does not have any form of data trust issues when it comes to their data. Proper data capturing, storing, infrastructure design, validation etc. are important dimensions which AMA investigates with proper checks and balances based on industry standards.

 

  1. Lack of Clarity: The first step to having clean data comes from having a good understanding of the data. There are basic graphs, queries, questions that every data analysis requires at the start of any project. For companies to be truly data driven, a Data Analysis pack consisting of the above elements and beyond is necessary. AMA checks these points to help figure out whether the first cut is robust enough. This understanding, the results of such a pack, is essential not only for the analytics team but helps business users as well gather lots of useful insights.

 

  1. Lack of Business Understanding: Analytics is a means to an end and not an end in itself. The intention is to help the organization improve on its top line, bottom line and operational efficiency. This is not possible unless the analytics team has a good understanding of the business as well the business team clearly understands what analytics can bring to the table. Synergy between the two is needed for implementable outcomes. The analytics solution should answer the questions that the end user wants to know. AMA helps in avoiding this pitfall or checks the status quo by having multiple checklists such as SMEs, trainings, meetings, presentations on this dimension.

 

  1. Improper Implementation: After what is to be done is clear, how it is done is essential for effective implementation. Ad hoc project work and repetition of same mistakes are cost centers for organizations. There must be standard practices for project implementation.

 

  1. Information Asymmetry: Having to constantly reinvent the wheel is another cost factor which takes the essential time of resources. This issue is usual the result of teams working in silos and not functioning as a larger team. Often similar analytics projects are undertaken in different verticals such as say finance and operations. This is another grey area which AMA helps in identifying and avoiding.

 

  1. Missing dollar accountability: Many organizations do not have long term vision in place. In these cases, analytics becomes good to have but it remains just a cost center. The vision and purpose of analytics needs to should come from the top and be clearly identified and communicated. Each project’s expected outcomes should be predefined and once implemented, its ROI calculated. This is essential for the integration of analytics into the DNA of the organization for it to be able to make a string contribution to the growth story.

Conclusion:

It is essential for every organizations to take stock of the situation after certain intervals. It is even more important when trying to inculcate something very different and new within the organisation to bring about a mindset/cultural change. Most of organizations are investing in analytics but find using it effectively difficult. AMA helps in streamlining this change. It helps in asking pertinent questions and figuring out the change methodology. As noted above, there are quite a few pitfalls of not having an AMA for an organization, and not having an AMA in place can be expensive considering the associated risks.

To understand more on how you can adopt an AMA within your organisation, view a recent webinar we had conducted on the topic.

Click here to view the webinar

The growing interest in big data technologies for Fraud Management & Revenue Assurance

Fraud Management, Revenue Assurance

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The telecommunications sector is no stranger to big data. As a premier vendor for Revenue Assurance and Fraud Management solutions, Subex is no stranger to big data too. Traditionally, our RA & FM applications process billions of transactions every single day. The past few years have been particularly demanding – with the proliferation of 4G, LTE and the upcoming 5G, data has exploded and continues to. Change seems to be the only constant.

The bid management team, I’m part of, is a witness to the change. We are at the helm of affairs in serving our customers needs that come via an RFP.  Over the past year, we are witnessing a key ask in the RFP —

“Does your tool comply with big data technologies like Hadoop”?

The Trend

Request for proposals (RFXs), are one of the modes, through which Subex periodically receives the customer requirements. A key component in Subex RA & FM solution is the database. For many years, RDBMS like Oracle, MYSQL, Vertica, have been the preferred choice for telcos. There is a new trend now – Hadoop.

Telcos are now considering big data technologies like Hadoop. Looking into the trend of RFPs over the past years, gives an understanding of the future. The requirement for big data technologies have gone up two times in the past 2 years.

GROWTING TREND IN BIG DATA ASK

GROWTING TREND

A further drill-down of the requirements is also a testimony to the changing face of RA & FM functions. Few of the key requirements taking shape are:

  • Data lake: Data lake is becoming de-facto ask. With the explosion of data volumes and data types (structured and un-structured), telcos are increasingly preferring to set-up a common data lake to feed multiple downstream systems
  • Data quality: Given the importance telcos are now placing for the quality of data, expectation is to procure systems that provide high level of data integrity and sanctity of the underlying data
  • Analytics & Machine Learning: Clearly, RA & FM functions are now including analytics, insights and pattern recognitions to their repertoire of tools at disposal to combat fraud and leakages. The RFPs are now focusing beyond transaction monitoring and entering the realm of business intelligence and machine learning

Subex Big Data Solution

Recognising the importance of big data and its’ relevance to the RA & FM teams, Subex started offering Hadoop as a platform of choice a few years back. Today, we are hosts to many live implementations around the world, processing billions of transactions every single day.

Subex RA & FM solutions with Hadoop as a technology, delivers value on the 4-fronts:

  • Volume: Capability to handle large volumes of data loads and deliver at scale
  • Velocity: Forget the days of “T+n” monitoring. Real-time assurance is becoming the norm
  • Variety: Gleam insights from the good old structured to semi-structured and un-structured data, using traditional rule engines and new age ML & AI capabilities
  • Veracity: Highest quality of output with high integrity

Big data is empowering RA & FM teams with big possibilities. To learn how you can benefit, talk to us.

The Road to Being Data-Driven Starts with Knowing Where You Are

Analytics, General, Telecom Insights & Analytics

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The telecom world is changing, and organisations are challenged to not only grow, but to merely stay relevant.  To cope up with the fast-changing environment, organizations need to be at a certain level of maturity where the decision-making process needs to move from gut feelings to number & reasoning-based practices. The mandate here is clear: Organisations need to become data-driven or risk being left behind. However, the reality is bleak considering that nearly 100% of enterprises want to become more data-driven, but only less than a third have accomplished that goal. The starting point to being data-driven starts with being able to assess where one stands, and which is the best way to move ahead to a certain objective, a notion which continues to remain challenging for organisations.

Almost all organisations have ventured into the woods of data analytics with a purpose to make a sense and get the best out of the huge volumes of data they have. Sometimes they ask what others (either in the same industry or across industries and academia) are doing and how they can replicate it, while at times they ask what new can be done that no one else has. To analogize the entire data analytics practice to the workings of an engine, an engine can be only as good as the sum of its parts. The parts must fit in well, the oiling mechanisms should help in friction reduction, the oil supply has to be timely and of course, the sparks need to be perfect. As many say, data is indeed the new oil and data analytics is fast becoming the engine (in this case, of growth).

Fine tuning this engine is the need of the hour. Assessing where we stand, which directions we can move towards and where would that lead us to, what would be the best enabler to move in each direction, and how to go about doing it, is mission critical. This, in itself, is an optimization problem where maximizing returns and minimizing costs given the multiple constraints is challenging.

Transformation is important, but to ensure true competitive advantage, organizations must transform themselves in a planned phase. The approach to analytics cannot be stochastic as it would result in more troubles than benefits. Organizations must traverse one stage at a time. Not only defining those stages is the need of the hour but also is knowing what steps one needs to take at a point of time to move from one stage to another. In this scenario, an Analytics Maturity Assessment becomes imperative.

We have been working with customers across the globe in helping them assess their Analytics Maturity, to define the business objectives and carve out an Analytics roadmap towards said objectives, through our Subex Analytics Maturity Models. Subex Analytics Maturity Models defines those stages and the steps between those stages, through an assessment across People, Process, Technology and most importantly Data.

To know more about why an Analytics Maturity Assessment is important, and how it can help your organisation, Schedule a Demo with us and our Subject Matter Experts will get in touch with you.

Addressing the Trust Gap. It is Possible

Analytics, Telecom Insights & Analytics

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In our previous blog, we spoke about how in today’s world of rapid and constant change it has become ever so important to make the most of real-time inflow of data. Data is the new oil – and like oil, data needs a refinery before it is used across business use cases. To quickly take a step back, this trend always reminds me of the comic Tintin and The Land of Black Gold – “Boom! … One day your car goes Boom!”. The plot revolves around car engines exploding because of faulty petrol at its source. Similarly, if data is not clean at the source, your business decisions are bound to go “BOOM”!

‘TinTin: Land of Black Gold’ by Hergé

 

Analytics has been commoditized today, with the entry of open source tools and technologies. However, there is a significant trust gap when it comes to the consumption of analytics. How much do you trust your data? How significant is the output of analytics in the organisations board meeting? While in our last blog we looked deeply into the trust gap and its roots, at the end of the day, we need to realise that analytics is just an application of Math-Technology-Business on data. So, if we believe in Mathematics, have faith in the technological revolution and are confident of our business intuition, there is no reason for analytics not to be considered as the most critical function – all that remains is refining the oil, i.e., data.

We at Subex, recognize and respect this trust gap. We also believe an analytics strategy should build around the golden triangle – People, Process, and Technology.  The first and most critical step would be to have analytics done in a democratized manner. Everyone in the organization, from the C-Level, to the Department Head level, to the Analyst level should be armed to be data-driven. The involvement of machine should not undermine the trustworthiness, nor should it lead to a decrease in human involvement; instead, you should leverage the best of both human and machine intelligence to improve products, enhance the quality of service (QoS) and derive more returns from your investments.

Does Human Intelligence + Machine Intelligence = Trust?

Taking the Human Intelligence + Machine Intelligence philosophy into account, we have come up with a concept known as Subex ACT (Analytics Centre of Trust), designed to bridge the Trust gap by covering the end-end cycle of Data-Insights-Decisions (D.I.D.). Let us take a quick look at the three pillars of our ACT program:

  1. Defining a Strategy

Before starting the analytics journey, it is imperative to assess the following

  1. What is the analytical maturity of the organization?
  2. What are my objectives from the analytical program vis-à-vis the business vision
  3. Do I have a roadmap in place?

The main Objectives of this process are:

  • Setting up the goals for the organization: The Strategy can help deliver competitive advantage, create incremental revenue opportunities, and reduce costs.
  • Assessing your analytics maturity vis-à-vis your goals: Understand where you are in terms of your analytics maturity and identify the target maturity which will help you reach the goals defined in step 1
  • Plan for the Transition: Understand how you will transition from your current maturity level to the desired maturity level and ensure the process is time-bound, tangible and step-wise. What we recommend is that you identify tangible use cases, such as churn, and move the analytics maturity of addressing churn from, say, 3 to 4. Once that is completed, define another use case and increase the maturity to address that similarly
  1. Setting up an Information Infrastructure

Post defining the analytical strategy it is imperative we have the right set of tools to handle the task at hand. The tools which organisations need today need to be the following:

  • Agile: Create the ability to address the problem statements based on the requirements
  • Scalable: Should be able to handle massive volumes and different types of data
  • Reliable: The information that is generated by the system needs to be trustworthy
  • Real-Time: For quick and accurate decision making the reports should be in real-time
  • API Integration: The tools should be compatible with API-based integration
  • User-friendly: Consumption of the reports/data should be easy to use
  • Secure: The tool should be compliant with security guidelines
  • Self-Serviceable: Accessible UI enabling the end user to self-generate reports

Such an Information Infrastructure should offer a self-service reporting environment wherein each stakeholder gets the access to the tools to analyze and act upon the information. This will not only reduce the time gap in execution but raise the operational efficiency to a new level. As the model evolves into an Analytics Centre of Trust (ACT), the transformation journey becomes smooth.

  • Analytical Driven Business Outcomes

The final piece of the Analytical Framework, is clearly towards the analytical output. For too long, organisations have set up analytics practices with a mandate towards delivering on analytics outcomes. Subex is of the firm belief that the key to analytics is to attain business outcomes while ensuring insights are available across all audience levels in a democratized fashion which is easily understandable.

Conclusion

The world of digital technologies is open for Telcos to build new business opportunities as well as excel on the existing ones. It’s time to identify the gaps in your analytics strategy and develop an ACT that helps you climb the ladder faster. As an organization is preparing to capture the active markets, your analytics goals must focus on using information as a strategic asset to generate revenue, improve operational efficiency and provide best-in-class customer service.

In our next blog, we will cover how Subex ACT helps CSPs regarding addressing these three pillars and how it helps bring Agility, an Analytics to Business mindset and Democratization through Consumable Outcomes to your organisation.

This blog has been co-authored with Sandeep Banga. 

The Analytics Trust Gap. It Is Very Real

Analytics

So, you are looking to start an Analytics Program within your organisation. You have the tools ready, you have the resources designated for the task, and you have all the required process you need in place to run a robust Analytics program. You expect to see a massive revenue growth within a year; however, after the passage of 365 days, the outcomes are well short of your expectations. At this point, you have a set of questions to ask yourself:

  • Where could I have gone wrong?
  • My organisation has a massive volume of data. Was the quality of my data not up to mark?
  • I had all the tools in place, with Artificial Intelligence automating all the processes. Where did I fall short?
  • I have received data from multiple sources? Is this causing the shortfall?
  • Have I ensured that the data residing in my data lakes are free from breaches and attacks?

All these questions which arise could ultimately lead you to lose faith in your analytics program.

Over the past several years, we have seen how data analytics has evolved from the simple exploratory level to the current predictive level. With Business Intelligence (BI) playing the pivotal role in decision making, organizations are seeking the power of advanced analytics and machine intelligence to attain agility and competitive differentiation. Telcos, which own the most significant share of customer data among all the industries, are in the best position to leverage them to achieve higher levels of maturity. However, a recent KPMG report reveals a paradox that despite the huge investments in data analytics, organizations are not able to build value around it due to lack of trust. According to the report, only 35% of decision-makers have a high level of trust in their own organization’s analytics, and 25% admit that they either have limited trust or active distrust in their analytics. Moreover, only 10% said they excel in managing the quality of data and analytics, and 13% said they excel in the privacy and ethical use of data and analytics

What Causes the Trust Gap?

The above findings come as no surprise considering the growing complexity associated with handling the data originating from disparate sources. Many studies now indicate that the once 4 Vs to describe key aspects of data (Volume, Velocity, Variety, and Velocity) has now grown to 10 (to include Variability, Veracity, Validity, Vulnerability, Volatility, and Visualization).

But besides the growing complexity of data, there are multiple other aspects which are leading to the trust gap, some of which are captured below.

Quality- a top concern

As the data grows more complex, analysis can be challenging. Poor data quality or incompetent analysis can lead to disaster. As Gartner puts it, “As organizations accelerate their digital business efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance.”

Working with false or incomplete data could result in uninformed and biased decisions, which could prove harmful to the overall business. Gartner has also estimated that poor data quality can lead to an average of $15 million per year in losses.

Can we trust machines?

In today’s machine-controlled analytics landscape, building trust becomes even more challenging. The advent of artificial intelligence (AI), coupled with the advancements in machine learning (ML), has opened a plethora of opportunities in data analytics. We have seen many horror stories wherein placing complete faith in AI without human intervention has had disastrous consequences.

Integration of disparate data

Considering that organisations do not have a single source of truth when it comes to the data they gather, data integration is another major roadblock, resulting in poor execution and sometimes complete failure of analytics implementation. Organizations which are slow in their transformation journey confront challenges in integrating data of different formats. They lack the skills, training, and tools to build a centralized access and control policy. Historically, the self-service concept is appealing but has often fallen short of expectations due to barriers faced at multiple levels – technology, people, and process.

Security-an everlasting concern

There is no respite from data breaches and misuse. The fact that fraudsters are making headway by exploiting advanced techniques escalates the concerns.  The thin line between the security breach and the reputation of an organization brings the transformation to a halt.

These are but a few reasons to why a trust gap is being created, but they are very real. Should it remain, the trust gap will lead to severe implications in terms of competitive advantage, operational efficiency, and growth. The inability to rise as a data-driven organization means that they also lag mature organizations considering data-driven organisations witness 23x Customer Acquisition, 6x Customer Retention, and 19x Better Profitability (according to McKinsey). And non-data driven companies miss out on all these benefits.

It is clear – The time has now come to bridge the trust gap! The only question that now remains is, how? Stay tuned to our blog for the answer.

Actionable predictive analytics: overcoming the analysis paralysis

Analytics, Capacity Management, Network Capacity, Network Management

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Why standard forecasting analytics models fail to deliver in today’s world of complex digital networks and why telcos need a domain-specific analytics solution.

“Your analytical dashboards and visualizations look good, but I prefer actionable reports and insights”, said the deputy CEO of a Southeast Asia-based telecom service provider during one of our meetings last year. This was not just one odd instance. We have heard this many times in the past year from other CSP executives. There are many domain-agnostic AI/ML based analytics solution providers in the market, but what telcos really want is an analytical solution which provides end-to-end domain-specific actionable insights. Forecasting traffic or pointing out anomalies is one thing, but how to incorporate those recommendations into capacity planning? What is the root-cause for that anomaly so that it could be prevented in future? Instead of getting lost in analysis paralysis amidst thousands of fancy statistical metrics; a simpler, actionable and reliable predictive analytics solution is the need of the hour.

With the right mix of domain knowledge and analytics advantage, centered around the actual requirements of the network planners; Subex has come up with the concept of actionable predictive analytics. Network planners should be enabled for efficient, reliable and cost-effective capacity planning. Hence, here the focus is more on what matters to the telco network teams, i.e. the business values such as capex optimization, network performance improvement, customer experience enhancement and operational efficiency; rather than on underlying analytical components such as configured models or feature engineering.

Here are two of the most important aspects about Subex’s approach to predictive analytics which are different from the traditional forecasting models –

Multi-variate analysis

Unlike the traditional forecasting systems which predict the future trends for a metric based on the historical pattern of that given metric, in multivariate approach, the system understands the lagging or leading effect on the given KPI from other KPIs. With this, the telco can predict, in near real-time, what is going to happen in the future and adopt appropriate measures to prevent capacity issues. A multi-variate, self-learning forecasting model which runs on the in-house machine learning platform is complemented by domain-specific configurations and expertise which is equally essential for intelligent forecasting.

The figures below compare a multivariate model scenario that considers the lagging effect of KPI1 (e.g. customer complaints) on KPI2 (e.g. capacity utilization) with that of a traditional model that does not give such insights. In the first case, the operator does not get accurate results as yielded in the second case because there is a direct relation between traffic and customer complaints. For example, if there was an aberrant increase in traffic, the operator can take that fact into consideration for accurate prediction of future customer complaints.

One more use-case could be accurately predicting the time to capacity exhaust for a site if one of the neighboring sites is planned for decommissioning soon. In this case, with the help of geo-spatial analytics, the additional load on the given site due to decommissioning of the neighboring site would also be considered for calculating time to capacity exhaust.

capacity exhaust

Domain Specific Insights

Be it wireless or hybrid fiber-coaxial networks, even an accurate capacity forecast is incomplete without the required domain-specific insights. Without a proper root cause analysis for a network element exhausting soon (in terms of capacity), the network planners won’t be able to make the right decision about its proactive mitigation.

These are some questions to consider when developing your network augment action plan:

  • How many customers will be impacted when a given network element hits a capacity exhaustion threshold?
  • Will prioritizing the given candidate for capacity augment above other options result in the best customer experience improvement and maximized ROI?
  • What is the reason for this capacity exhaust? Is it because of seasonality, periodicity or cyclicity? Is it an anomaly due to some one-off event?
  • Will new Capex be required to address the capacity bottleneck, or are there alternatives to new spending?

Some of the insights that could be useful for the planners leveraging predictive analytics for capacity planning and management are shown below –

capacity planning

Predictive Analytics

Apart from the above two key differentiators, some other important aspects for a pragmatic, accurate and reliable predictive analytics solution are scalability and flexibility.

Multi-variate forecast models need to run thousands of simulations across the network to identify the correct correlated metrics for accurate predictions.  Such models need to be configurable, flexible and easy-to-understand for non-data scientists.

Are Traditional Data Warehouse Challenges Affecting Your Business?

Analytics

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With data emerging as the new currency for businesses, data warehousing demands a new approach in dealing with the challenges. As Gartner puts it, poor data warehousing practices “undermine the organization’s digital initiatives, weaken their competitive standing and sow customer distrust.” Telecom operators are among the most affected by data warehousing challenges as they handle billions of customer data generated from multiple sources like files and probes (SS7, SIP, SIGTRAN), as well as the massive volume of data streamed from social media platforms.

As the volume, velocity, variety, and veracity of data generated continue to grow; the traditional data warehouse approach flounders while managing and analyzing the data. With conventional data warehouse analytics offerings, answering even seemingly simple questions such as “Who are my ten best customers?” could take up to 5-10 days. Even after the team figures out the right criteria, compiling and analyzing the data could again be a time-consuming process. As the questions grow complex, the burden only grows further.

While in-memory databases have helped alleviate the problem to some extent by providing better performance but with rigid data models, it makes data analytics workloads more and more compute-bound. Even it is well understood that traditional data warehouse approaches have become arduous due to the IT dependency and upfront data modeling. As a result, the time to value grows longer, and the outcome materializes only when the business can start to use the reports and insights provided by the data warehouse. Since this approach is rigid, it may also call for data modeling changes, which will further delay the process execution.

Working with data always carried an inherent risk that false or incomplete data could lead to uninformed or even misinformed decisions. Telcos have a winning edge as they are the custodian of largest repository of customer data in the world. Therefore, businesses can no longer ignore these challenges as new business opportunities are emerging around data usage. Look at the sheer size of the data generated during a typical business day, an operator serving 80 million mobile subscribers generates around 20 billion Detail Records (xCDRs) daily.

Worldwide several Telcos have identified opportunities around data monetization – both internal and external. The growth of the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML) has largely contributed to this upswing. Telcos’ growing engagement with content providers, IoT companies, VAS companies and others prove they are striking it right. At this juncture, it becomes crucial for telecom companies to revise their data strategy around modern big data analytics tools.

Working with poor-quality data can also bring damage to the existing business, especially concerning customer value. As you know, customer preferences are evolving, so ensuring customer satisfaction and loyalty mostly relies on how quickly you address their issues. Big data and real-time analytics gain relevance in this context.

Hence, a robust big data platform is no more a luxury but a business imperative! Stay tuned to know more about Subex’s approach in handling the complexities around a traditional DWH and data quality issues.

For more information on how Subex is helping Telcos address gaps in their analytics approach through an end-to-end framework, attend a webinar we are hosting with Telecoms.com entitled, Bridging the Analytics ‘Trust Gap’ Within Telcos.

Register yourself here: https://bit.ly/2NzFXJF

The Trifecta Effect for Telco Analytics –Anomaly Detection

Analytics, General, Telecom Insights & Analytics

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Subex recently participated in the Monetising Big Data in Telecoms World Summit 2018, Singapore, where we demonstrated our expertise of 25+ years in the Telecom domain handling data at a massive scale. We did this by presenting on the topic: The Trifecta Effect for Telco Analytics –Anomaly Detection

To take a step back, Subex has partnered with 250+ telcos across 100 countries. We have been handling big data and have an understanding of the business of telcos working in different contexts, demographics and geographies, and at different stages of their growth. We have been leveraging analytics for 10+ years in the assurance portfolio, and have made a foray into analytics across all domains in the telecom sector. While starting on this journey, we did a survey across many operators and what came out was unexpected albeit not exactly surprising, based on our experience.

We saw that, while the telecom domain today is at the forefront of innovation, the industry can be considered as a relative laggard in adopting analytics, when compared with other industries. Around 60-70% of the executives still lack relevant data for decision making. To top it all, where analytics is being used, around 60% of the organizations are still not very confident about their analytics insights.

Based on our market research and after multiple interviews we believe that the reason for these above problems can be classified along the following categories:

  1. Poor ROI
  2. Multiple and Complex Dashboards
  3. Long Development Cycles
  4. Data quality issues
  5. Short Supply of Data Scientists
  6. Lack of Agility

To cater to these problems, we at Subex have designed a way to address these issues. Our solution, ROC Insights stands on three pillars which we call the Trifecta of Analytics: Agility, Cost & Consumption. Subex does this by

  • Delivering insights in less than 8 weeks
  • Following a pure OPEX model which takes care of the cost
  • Most importantly ROC Insights simplifies consumption of analytics insights tremendously through the use of storyboards

At the Monetising Big Data in Telecoms World Summit 2018, we explain how the Trifecta can be leveraged by using a simple example of Anomaly Analytics. For a telecom, with massive volumes of data, it is more difficult to detect anomalies in data than finding a needle in a haystack. In case of the latter we know that we are looking for a needle. In this case, we don’t even know what exactly one is looking for. However, it is extremely important for a telco to manage anomalies to mitigate risk and to prevent missing out on opportunities.

There is a very fine line between the definition of an anomaly and outlier. Rather the distinction is rather fuzzy as anomalies and outliers are intersecting but certainly not subsets. Take for instance, a queen bee in a bee hive. She is an outlier but not an anomaly. Anomalies usually remain undetected. They are unknown problems with unknown solutions. Our work detects, curates and qualifies these problems to move it from the unknown-unknown realm to the known-known realm by breaking it into parts, analyzing them using various ML/DL algorithms and as well doing causal analysis to find the root factors.

We talk of two examples where we work with Telcos to solve their anomaly problems using advanced analytics. In the first case, our solution of anomaly detection required modification as the client was based out of East Africa with pockets of high population density in vast open areas. We developed anomaly for cell sites using algorithms such as time series, manifold learning, LSTM etc. We did anomaly detection for different KPIs such as call duration, data and customer latching. This was further qualified along the dimensions of 2G/3G/4G, on-net/offnet and so on. Anomaly analysis generally suffers from the problem of ‘too many’ and difficulty in prioritization. Our solution gives revenue numbers to the anomaly and prioritizes them. Most importantly it looks also at the long term business impact of high value customers, churn etc. and also considers the factors whether load balancing is done by nearby cell sites and the customers were really impacted or not.

Our second use case was regarding our work for a client based out of N. America. They were having difficulty in detecting lost handset on time. This was causing them huge monetary loses. With the help of algorithms like probabilistic graphical model, Markov chain etc. we created algorithm to detect whether a handset was stolen or not. Our algorithm helped in improving the client’s solution 9 times in detecting the algorithm. As well in 80% of the cases, the instances were detected within 24 hours, thereby improving the customer’s bottom line.

All in all, the presentation provided for a good opportunity for attendees to understand how Subex is working with big data, and how ROC Insights can help telcos by pinpointing upon a very specific problem. At large, we enjoyed presenting at the event and meeting with telcos from across the APAC region. Hope to see you there next year!

Why Analytics is the Answer for the Modern Day CFO?

Analytics, Telecom Insights & Analytics

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As Gordon Gekko from the movie Wall Street (1987), rightly said, “The most valuable commodity I know of is information.” Fortunately for telecom operators and their Chief Financial Officers, they possess no dearth of this ‘valuable commodity’, which they can leverage through telecom analytics

Find out how CFOs can leverage their data through telecom analytics, by gener to make better decisions to drive growth and mitigate risks by viewing the recording of the webinar on ‘Don’t Get Left Behind – a CFO Guide to Leveraging Advanced Analytics’, which took place on December 14th .

But let’s take a step back for a moment.

The telecom world as we know it is evolving, and with it, the role of the CFOs has also been undergoing a drastic change. His role is no longer confined to be solely focused on past performance, on the numbers, and on financial reporting, but the mandate seems almost universally to have been exceeded, with the CFO needing to also provide information about where the business is going and how quickly it is getting there. [1]

The CFOs involvement in corporate strategy has also become an integral part of the job, with CFOs now having the ability and the mandate to contribute directly to the direction of the business as well as reviewing and reporting on its performance1. This all means that today’s CFOs need to be more strategic and need to ensure that there is better alignment with strategic business imperatives and this requirement puts the CFO at the focal point for not just financial reporting but also managerial reporting, along with his core objective of maintain a strong and healthy balance sheet.

But, as we know, the dynamic nature of the telecom environment places multiple hurdles in the face of the modern day CFO, which include, but are not restricted to the following:

  • CFOs today need to ensure that they are able to increase margin and earnings performance
    • It is a well-known fact that ARPUs have been steadily declining in every region of the world, and coupled with slow revenue growth is leading to a steady erosion of margins since 2010 in most regions[2]. In the midst of these challenges, the mandate of CFOs to increase margins and earnings performances in becoming increasingly critical, and difficult.
  • Organisational attempts at growing revenues are being deflated by errors and leakage
    • Addressing Revenue leakages are a major concern for telecom operators[3], and is quickly becoming a CFO mandate considering currently most RA teams ultimately report to the CFO[4]. Considering that revenue leakages have a direct impact on revenue growth, it is now the role of the CFO to take a proactive stance in addressing any errors and leakages.
  • Assessing risks and developing measure to prevent security breaches
    • Like Revenue Leakages, security breaches and telecom fraud can cost operators heavily, and is an obstacle in the way of ensuring CFOs maintain a strong balance sheet. $38.1 Billion (USD) was lost to fraud in 2015, and though the number is decreasing YoY, telcos are still feeling the pinch of losing cash to fraud, and the task to resolve this lies with the CFO.
  • Increasing Capital Expenses during a period of decreasing revenues
    • A recent survey that was conducted by TMForum led by Subex revealed the following findings:
      • 1 in 3 operators do not measure returns on CAPEX investment
      • 77% of the respondents believed that inadequate asset utilization leads to increase in costs
      • 55% of the respondents believed that network planning is based on guesses
      • 64% believed that capex planning is driven by technology and not business objectives

Moreover Global CSP revenues declined by 5.3% for the year ended March 2016, while capex increased, pushing up capital expenses (capex/revenues) to 19.8% for the year.[5]

  • Increasing competition, even from OTT players
    • Which according to Ovum, is expected to cost Telcos a total combined $386 billion between 2012 and 2018
  • Responding to the volatility and velocity of change
    • The signs are that revenues from traditional services will plateau over the next 10 years. Indeed, income from traditional communications services is anticipated by some analysts to decline by 50% from current levels by 2025. This means that CSPs need to embrace the digital revolution, and can no longer remain as dumb pipes but need to be seen as smart pipes by offering digital services and be seen as DSPs or even LSPs (Lifestyle Service Providers)

And the challenges don’t just end there! Today CFOs need to spend more time and effort managing the future rather than dwelling in the past, and hence need to take an even closer look at data analytics to connect the dots and to predict the future. To their advantage, telecom CFOs possess unprecedented quantities of data, from multiple sources including customer data and network data, and can leverage this data through the power of telecom analytics.

If leveraged in the right way, by applying advanced analytics, telecom CFOs will be able to address the challenges they are facing, and achieve business outcomes that align with their agenda, through the generation of actionable telecom insights. CFOs will possess the power to have a 360 degree view of their business context and identify and even predict issues, opportunities and threats proactively, and will help them address them before internal audits. For these reasons, it has now become the mandate of the CFO to drive analytics for both strategic and operational decision-making.

By generating Telecom Insights, an Advanced Analytics Solution can help CFOs to meet the increasing expectations placed on their changing roles by enabling them to:

  • Proactively predict and direct resources to counter risks and leverage opportunities
  • Reduce uncertainty by predicting disruptive changes and respond and adapt to create growth opportunities
  • Predict revenue leakages and fraud to proactively address risks
  • Predict redundancies and reallocate budgets to reduce and control costs
  • Increase impact of pricing and promotion decisions through optimization

Advanced Telecom Analytics has the scope of helping CFOs of telecom operators meet business objectives drastically, and we have even witnessed, or rather helped a Tier 1 CSP, based in North America save costs by purely helping them resolve disputes. Through the generation of telecom insights, the partnership helped the CSP improve their hit ratio of predicting and addressing disputes to 9x, which in turn helped them save up to a few million dollars. Thus is the power of Advanced Telecom Analytics.

To find out more about how CFOs can leverage telecom analytics for revenue maximization and risk mitigation, view the recording of the webinar on ‘Don’t Get Left Behind – a CFO Guide to Leveraging Advanced Analytics’, on December 14th.

[1] https://www.ey.com/gl/en/issues/managing-finance/the-dna-of-the-cfo—perspectives-on-the-evolving-role—the-cfo-s-contribution

[2] https://www.strategyanalytics.com/strategy-analytics/news/strategy-analytics-press-releases/strategy-analytics-press-release/2015/01/23/global-trends-for-mobile-operators-show-stagnant-revenues-and-declining-margins#.WD-iJeZ9600

[3] https://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublications/global-revenue-assurance-survey/Documents/global-revenue-assurance-survey.pdf

[4] https://www.kpmg.com/Global/en/IssuesAndInsights/ArticlesPublications/global-revenue-assurance-survey/Documents/global-revenue-assurance-survey.pdf

[5] https://www.ovum.com/research/communications-service-provider-csp-revenue-capex-tracker-1q16/

IoT Settlements – Leverage the world of opportunity

General, Partner Settlement

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The Scenario

Of late IoT has gained a lot of attention and every operator is at least thinking of leveraging this latest platform to offer innovative products, but the big question is how?

Many operators are relying on their vendors to come up with IoT use cases, the challenge here is that even the Vendors are still in process of deep diving as IoT is still a niche market.

One of the major factor that makes IoT an unknown area is the lack of visible use cases to be seen in our day to day life, though the developed counties have made significant progress, developing world is yet to embrace the “Smart” systems. With lot of emphasis on Smart cities, IoT business is here to stay.

When it comes to IoT Settlements, both Telcos and Vendors should start thinking out of the traditional wholesale approach. Telcos are looking for vendors who can support them with their traditional as well as new business areas with the centralized solution.

The Concept

Understanding “Internet of Things” concept is very simple, it is a network of “Things”. Things are physical objects that can be added to a network, have sensors and can be controlled using software. These things can be as common as day to day devices like a Fridge, a Car, a thermostat and so on. The purpose of connecting ‘things’ is to have a centralized access to their features and to control enormous data they are capable of producing.

One very obvious fact that can be identified from IoT is to have a medium for keeping things connected. This creates an immense opportunity for telecom operators to provide medium for supporting the connected items. According to Gartner by 2020, the Internet of Things will grow to 26 billion units installed which excludes connected PCs and smartphones. This will add $1.9 trillion to the global economy. Intel estimates 31 billion devices to be connected by 2020. According to Cisco by 2020, 50 billion devices will be connected, whereas Morgan Stanley feels that the number is much higher and it can go up to 75 billion.  The good news is, there is a substantial growth opportunity for everyone, right from smart device manufacturers to the smart service providers everyone can get their share of business from IoT.

The Process

Let us consider an example of a smart home. There are multiple interconnected devices which are installed for security, entertainment, utility etc. These are all connected to a centralized hub, which in turn is connected to the IoT platform. IoT platform consumes the data generated by these smart devices for insights and to make sense of this huge data.

To establish this network of devices connected to hub and IoT Platform, internet is needed. This gap is filled by the Telcos. So bringing in smartness requires lot of partners to work together. Let us enrich above example to get more clear understanding of the multi partner involvement.

A  leading furniture retailer has introduced a new Smart Home Solution, where consumer can install smart devices such as TV, Fridge, Air conditioner, Washing Machine, Radio, lighting solution, thermostat and security solution. Finally these devices are connected to hub to have a centralized control of the devices.

A smart Hub ensures all smart devices speak the same language, this enables user to remotely control the devices even if the user is far away from the home. To bring in more intelligence the data gets transmitted from smart devices to the IoT Platform. The IoT platform analyzes the data, apply rules and makes devices more smart based on the usage patterns.

Finally the most important piece of this setup is facilitated by a Telecom operator to ensure internet connectivity for all the devices to communicate. Telecom operators can also bundle voice and SMS services along with data to take actions based on the defined rules. E.g. in case there is a security breach, device can initiate a call & SMS to the owner and insurance company to inform this breach.

So in this particular eco system, we have seen multiple partners working together to establish a Smart Home solution.  Similarly there are multi partner IoT use cases for Smart Car Fleet, Smart Healthcare, Smart Grids, etc. In all the IoT use cases irrespective of the catered domain, Telecom Operators and IoT platform vendors will always play a significant role, directly or indirectly they will contribute to billions of dollars in the IoT economy.

For a Telco, providing backbone is not the only important thing in IoT space. With the complex partnership models, Partner Management, Billing and Settlements are other crucial activities that will result in the Cost and Revenue identification.

The Solution – Partner Management, Billing & Settlement

With the cut throat competition and reducing margins in the traditional Wholesale business, operators are adding new dimensions to their business with immense revenue generation capabilities of Internet of Things.

A new age partner Settlement solution cannot limit its functionalities to just traditional business models. There is a requirement for settlement solutions to be more agile in accepting and delivering new business requirements with short time to market. If we talk about IoT for a Telco, now partners are not limited to Voice or Content providers, rather the list is getting much diversified with partners coming in from various domains like health care, agriculture, utility, etc.

For a smart home solution, a telecom operator can provide IoT backbone to a furniture retailer , where the Telco will ensure internet connectivity and will enable IoT platform in collaboration with a cloud computing platform. Here the furniture retailer becomes the Telecom operator’s customer and the cloud platform provider is Telco’s vendor.

Partner Management & Settlement solution deployed at Telco should take care of partner (Customer & Vendor) lifecycle management, easy on-boarding, business transparency along with IoT billing & settlements. Partner Settlement solution should also be capable of managing plethora of Meta data that will be provided by the IoT Platform for billing and Partner analysis. The volume of IoT data can be much more compared to traditional usage data.

The system should also be capable of providing innovative products and should do billing accordingly. Key point here is to have personalized plans created based on the business need. Some of the products that can be offered as a part of IoT platform are:

  1. Flat Rating – Flat rates for data, call and SMS for each units.
  2. Fixed Charges – Fixed product price for unlimited data, voice & SMS
  3. One time & Recurring Charges – Product to support one time and recurring charge capabilities
  4. Device Based – Charge based on number of devices connected
  5. Slab & Tired based – Data, Voice and SMS to be defined, rates varies based on the slabs & Tiers
  6. Pay-as-you-go – Charge only based on the usage, deduction from Prepaid Balance
  7. Cross Domain Products – IoT clubbed with content or other interactive services

IoT billing & settlement is not just limited to the Telco and their direct partners, it has to be extended to the associated MVNOs in from of Billing as a service. There can be a multi-level partner involvement as well, say based on the movie genre analysis information available in meta data generated by a Smart TV, a latest movie can be suggested for subscription through an entertainment company , and hence the entertainment company can become 2nd level Advert/Content partner.

IoT is still evolving, there can be many aspect that are yet to be explored. This is the right time for Telcos, platform and equipment vendors to start investing in IoT to stay ahead of the competition.

Infographic :

partner-settlement-solution

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