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Telecom Operators must reinvigorate their business models beyond connection to fully benefit from the internet of things (IoT), also known as the fourth industrial revolution. The IoT economic opportunity is expected to reach $8 trillion within a decade. The IoT market spans a wide range of industries. This necessitates a broader variety of business partners in the IoT service-delivery ecosystem while assuring sound partner relationship management. Telecoms can flourish in this environment without straying too far from their core capabilities if they use an enabling-platform strategy to create local innovative 5G partner Ecosystems and secure control points in the commercialization of value chains through partner ecosystems management.

Endless Diversity in IoT

The tremendous diversity of application potential is one of the obstacles to IoT expansion. There is a plethora of application sub-sectors, each with many potential uses. Easier access and use of data to revolutionize corporate processes and create new business models is the driving force behind these initiatives. Making the most of the concentration of apps in critical contexts, such as the home, offices, and cities, is one method to turn this difficulty into an opportunity. Clustered applications in 5G partner Ecosystems make it easier to exploit synergies in each location, making interoperable solutions’ value potential more visible. Telcos must participate actively in a diversified ecosystem of technology vendors, vertical-market domain specialists, and channel partners- each having their own KPIs for growth to benefit from the IoT opportunity further up the value chain. Partner lifecycle management is a difficult task given the breadth and depth of IoT prospects and the numerous stakeholders involved.

Why Adopt Partner Ecosystems?

A partner ecosystem management approach allows telcos to extend their companies by adding expert business companions, such as applied analytics and data sourcing, and go-to-market channel partners, in addition to increasing revenues and remaining competitive in the digital age. Telecoms can further deepen their customer relationships by establishing 5G partner Ecosystems of IoT-specific application developers and providing commercialization tools for application mashups, resulting in more business opportunities and lower churn.

Becoming 5G Ready with Partner Ecosystems

According to a new Accenture Strategy analysis, ecosystems have the potential to generate

$100 trillion in value for businesses over the next ten years allows for greater competitive agility, and the telecom industry is especially vulnerable to ecosystem disruption. The telecom industry is rated first in Accenture’s ecosystem capabilities index, which evaluates companies’ capacity to establish thriving ecosystems. In fact, 83 percent of telecom executives said partner lifecycle management and ecosystems are a vital component of their disruptive strategy, as per this research.

Partner Ecosystem Supports more Complex Business Models

As company volumes expand, simply directing ecosystem partners as a manual add-on to existing systems and procedures will not be viable. Long-term, the IoT market’s sheer size necessitates the development of highly automated, simply adaptable, low-cost support systems. Furthermore, as the IoT market evolves due to increased application compatibility and a more powerful partner ecosystem management, orchestration and administration capabilities will become critical to support more sophisticated business models. A multi-party service, for example, with a revenue-sharing model based on resource-usage measurements rather than data consumption. These could include database calls and identity management, connecting one owner to a group of connected devices.

Making the most of the platform

A telco might utilize the platform to sell its products and services to its consumers and markets at first. Telcos will be able to construct new service bundles more readily as the telco-enabled ecosystem grows, merging their products and services with those of other providers and innovators— the telco functions as a tenant-user on its partner ecosystem management platform in both of these cases. The actual strategic value of a digital ecosystem, on the other hand, comes from harnessing the service offerings of a diverse supplier base, using shared orchestration, monetization, and administration tools to offer new service bundles, with all parties benefiting from the platform’s low-cost economics. Over time, the telco’s platform should expand to accommodate as many tenants, creating a market for new service bundles and channels to new market segments.

The Path Forward

A clear and ambitious vision, the right partners, and the ability to think broader and riskier are necessary for ecosystem success. Telecom corporations can take three actions to implement a disruptive 5G partner Ecosystems strategy:

Smarter Decisions: Market plays are at the heart of any ecosystem—disruptive growth opportunities with tremendous income potential. Define the vision, business case, prioritization, and roadmap for each market play. The value proposition that emerges when ecosystem participants combine their functional, technological, and industrial strengths and capabilities might be game-changing.
Choose Partners Wisely: Partners should bring additional capabilities, a collaborative mentality, domain expertise, Customer Relationships, and Data: To assist in bringing the market play to completion. Leaders must detect the level of orchestration from ecosystem partners and their level of involvement in product development after selecting the proper teammates for partner lifecycle management.
Think Out of the Box: The ability of enterprises to adopt an ecosystem “mindset” is critical to ecosystem success. This necessitates embracing new business models, thinking beyond current growth drivers, and exploring for strategic partners and disruptive market plays outside the company’s four walls—and even the industry.

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Sometimes, subscribers’ innate curiosity is to follow up on a missed call even when the number is not recognizable. But this as well can be the start of Wangiri Fraud. The word ”Wangiri” stands for the Japanese term “one ring and cut.” In this fraud, the fraudster places a short call to several customers to leave a missed call notification on the display of customers’ handsets, thus prompting them to call back. When the customer calls back, the call is either routed to an IVR or premium rate service (PRS) number only to realize that they have been charged heftily for the call.

Wangiri Fraud continues to be one of the most prevalent frauds globally. As per the CFCA 2021 fraud loss survey report, it is estimated that telcos are losing close to USD 2.23 billion globally to Wangiri.

Traditional telecom fraud management solutions face several challenges due to the evolving nature of the technology and fraud strategies used by the fraudsters.

Wangiri fraudsters have now started operating on a larger scale. Rather than targeting specific individuals, they use automated dialing machines to work through vast ranges of phone numbers, calling thousands every minute. Recent improvements in automated dialing platforms and the advent of Voice Over IP (VoIP) allow these fraudsters to call thousands of people simultaneously and do so at lower costs than ever before.

Telcos incur both direct and indirect losses from this fraud. As per the RAFM survey by the Risk & Assurance Group (RAG), $0.43 billion was spent on compensating customers for Wangiri fraud. It also impacts the customers adversely, resulting in customer churn due to high customer dissatisfaction from bill shocks and bad customer experience. It also negatively impacts the operator brand image as subscribers complain about the high call rates charged to them without being aware of it.

Some of the major roadblocks in eliminating Wangiri are as follows:

There is no regulation defined on the carrier business, causing the fraudster to take advantage of the vulnerabilities of the carrier network.
The lack of visibility on the end carrier who is terminating the call.

However, the detrimental impact of direct and indirect losses to the operator can be minimized to a large extent by having an advanced and robust fraud management system with real-time action capabilities. This is the need of the hour because the traditional systems in place have been successful only to a minimal extent to curb this menace, due to the following reasons:

Lack of a proactive approach: Operators using the CDR (Call Detail Record) approach may detect Wangiri calls by performing high-level analysis of call detail records. This method may typically concern itself more with the Wangiri response than the initial Wangiri baiting. Also, in some cases, missed calls are not part of the CDRs that gets generated from the switch. They significantly increase the overall CDR volume, making it challenging for the operators to identify Wangiri fraud early in the network.

Lack of timely availability of threat intelligence on Wangiri numbers or origination points: Inability of tools or techniques for automated and near real-time data sharing among various operators can form a hindrance in the attempt for real-time blocking of number ranges and thus reducing fraud run-time.

Evolving fraud techniques: With the advent of new technologies, fraudsters continuously change the number and calling patterns making it difficult for traditional systems to capture them.

The Way Forward

To reduce the impact of Wangiri fraud to a great extent, operators need to adopt a holistic approach towards telecom fraud prevention.It is of paramount importance for telcos to incorporate a solution to monitor traffic at the Signaling level and complement it with AI/ML technology. This will enable proactive detection of Wangiri number or number ranges. The AI-based fraud detection will detect hidden patterns in data for fraudulent activities, which reduces the time-to-detect fraud significantly. It is also essential that the fraud management system in place has the ability for automated data exchange in near real-time of known Wangiri numbers, which would help make quicker decisions. Furthermore, also as a precautionary measure, whenever a subscriber calls back to a high-risk destination upon receiving a missed call, it is recommended to have an IVR system that would inform the subscribers about their called destination. This IVR message would alert the subscribers and enable them to take the appropriate action. Lastly, it is essential that the telcos educate the subscribers of such episodes and the countermeasures, thus preventing them from falling prey to such frauds, thereby enabling a holistic approach towards telecom risk management.

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Even though AI is growing in popularity, many organizations are still unsure how to use this “new” technology. By 2024, half of all AI investments will have been quantified and tied to particular key performance metrics to track ROI. The fact that their business culture does not realize the need for AI is one of the reasons why firms have not progressed in their AI adoption. Other factors are a lack of data and competent personnel and difficulty identifying relevant business cases to democratize AI. Below are the challenges that are a hurdle to AI adoption and how that can be overcome.

1. Lack of skills

The first hurdle is a lack of knowledge. AI will redefine the abilities required to do AI jobs, according to business and IT professionals. For instance, AI is now capable of evaluating X-rays in the same way that human radiologists do. As this technology progresses outside research settings, radiologists’ focus will change to collaborating with other physicians on diagnosis and therapy, treating diseases, performing image-guided medical interventions, and discussing procedures and outcomes with patients with augmented analytics tools.

2. Fear of Unknown

People aren’t fully aware of AI’s advantages and applications in the job. For business and IT leaders, quantifying the advantages of AI projects is a considerable challenge. While specific benefits, such as increased revenue or saved time, have well-defined values, others, such as customer experience, are difficult to describe precisely or evaluate effectively for AI Democratization.

3. Data Quality from AI

The whole data scope or data quality obtained from AI is the third challenge. A large number of data from which enterprises extract knowledge about the optimal response to a scenario is required for successful AI initiatives. Organizations are well aware that AI will fail because of insufficient data or if the circumstance faced differs from previous data. Others understand that the more complicated the situation, the more likely it will not match the AI’s previous data, resulting in AI failures.

4. Unclear goals

Without a clear AI strategy and goals, firms frequently overlook the most important places to begin adopting AI, resulting in lost value and faith that investing in AI is the right decision. While the push for automation and AI is frequently a grassroots effort, the enthusiasm may be misdirected without leadership guidance. When executives pay attention to data and analytics teams, they open up communication channels and learn where AI may add real value. However, it is the job of leadership to ensure that AI adoption is in line with the organization’s overall business goals. A more comprehensive vision will arise from ensuring that AI strategy has roots from the top-down and bottom-up.

5. Silos and Segmentation

When data has an end-to-end path from collection to analysis, insights, and feedback loops, AI adds the most value. Organizational silos obstruct these routes and make it difficult to respond quickly to the information AI provides. Instead of focusing on ways to assist teams in collaborating to operationalize data projects, focus on ways to help them collaborate. The utility and accuracy of AI projects can be improved by building on these foundations using augmented analytics software.

6. Data Labeling

Most of our data were organized or textual just a few years ago. With the Internet of Things (IoT), photos and videos make up a significant portion of the data. There’s nothing wrong with it, and it may appear that there isn’t a problem here, but many machine learning and deep learning systems are taught in a supervised manner, requiring the data to be labeled. The fact that we generate massive volumes of data daily doesn’t help; we’ve reached a point where there aren’t enough people to identify all of the generated data.

7. Explainability

Many “black box” models generate a result, such as a prediction, but do not explain. If the system’s conclusion agrees with what you already know and think is true, you’re unlikely to question it. But what happens if you disagree? You’re curious about how the decision was made. In many cases, simply making a decision is insufficient. For instance, doctors cannot rely solely on the system’s suggestions regarding their patients’ health. It’s much easier to figure out how much we can trust the model when we understand decisions.

8. Bias and Case-Specific decisions

Bias can be caused by a variety of variables, beginning with the method of data collection. If the information is gathered by a survey published in a magazine, we must keep in mind that the responses (data) are confined to those who read the magazine, which is a small social group. We can’t say that the dataset is representative of the entire population in this circumstance. Our intelligence helps us to apply what we’ve learned in one sector to another. Humans can transfer learning from one context to another, similar situation, which is known as transfer of learning. Artificial intelligence is still having trouble transferring its knowledge from one set of conditions to the next.

9. Poor business alignment

Top challenges to AI deployment include a company culture that does not recognize the need for AI and difficulties establishing commercial use cases. To identify AI business cases, managers must have a thorough awareness of AI technology, including its capabilities and limits. A lack of AI expertise may hamper many firms. However, there is another issue here. Some businesses jump on the AI bandwagon with overconfidence and no clear strategy. AI installation necessitates a strategic approach, which includes goals, KPIs, and ROI tracking.

10. Integration challenges

Integrating AI into your existing systems is a more involved process than installing a browser plugin. It’s time to build up the interface and aspects that will address your company’s needs. Some regulations are hard-coded. We must think about data infrastructure, data storage, labeling, and data feeding into the system. Then there’s model training and assessing the usefulness of the produced AI and developing a feedback loop to improve models based on people’s activities continuously and data sampling to limit the quantity of data saved while still generating correct results.

Is your organization facing the challenges of AI adoption? If yes, what are the challenges, and what are you doing to overcome these challenges? Please let us know your thoughts in the comments section.

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Once implemented and configured, network AI technology may automate network capacity planning while considering the organization’s financial and risk appetite. AI can assess many data points in real-time or near-real-time, which is crucial as companies shift to virtualized network overlays across their data centers, cloud environments, and WAN. AI may also be used to analyze network traffic patterns in various ways, allowing businesses to acquire a better understanding of what’s happening on the network and the overall network load. This information is helpful for capacity planning, both short and long-term network capacity planning. An increase in data consumption necessitates a network efficiency-focused approach, with a primary focus on lowering the total cost of ownership. So, how can communication service providers use AI to better plan network capacity and enhance customer experience?

What makes network capacity planning so complex?

Network capacity planning aims to guarantee that enough bandwidth is allocated, allowing network SLA targets like delay, jitter, loss, and availability to be met consistently. It’s a time-consuming, error-prone task with significant cost ramifications or network optimization. Until recently, static, historical, after-the-fact reports were the only way to get the network data needed for insightful capacity planning. Thanks to artificial intelligence, this position is fast changing. New tools such as AI in telecom assist in estimating network needs and in provisioning the difficult task of network capacity. Below are the pointers how AI can help in network capacity planning:

1. AI enhances traditional network capacity planning

IT can drive new and smarter predictive insights to increase network capacity planning accuracy by combining advanced data science and cognitive technology such as AI and machine learning. It enables businesses to liberate data to make more agile decisions, increase operational wisdom, prevent downtime, and improve user experience. AI simulates various performance situations and relates network performance to application performance to identify how different performance conditions affect applications. It uses AI-driven machine learning to improve network performance; a network controller can learn from its previous experiences while improving the network with artificial intelligence in the telecom sector.

2. AI Enables proactive actions

Advanced machine-learning algorithms can provide precise demand estimates for each node in the network and detect intertemporal patterns/trends in network traffic and utilization using large-scale and extremely granular network data as inputs. Improved traffic and demand forecasting will allow for a more accurate estimate of network capacity needs, reducing the need for resource over-provisioning. Organizations can take proactive measures to ensure network performance by detecting and discovering intertemporal trends or changes in network traffic early. Sophisticated predictive models can be paired with optimization or simulation approaches to automatically develop the ideal network topology or structures, the accompanying capacity, and resource plans. These strategies can then be adjusted to the exact performance measures that matter most to the company.

3. From slow and manual to fast, scalable and flexible

In a 1,800-site LTE network, AI use cases in telecom facilitate planning and automate sophisticated data gathering, aggregation, forecasting, dimensioning triggers, and prioritization to reduce real-time planning scenario analysis from weeks to five minutes. The planning strategy phase is the first step in any capacity planning process. It is where network performance, user experience expectations, market segmentation, and business strategy are examined to create the basis of the capacity planning phase’s input. When a more sophisticated approach is not available, spreadsheets are frequently utilized in the capacity planning phase. The pressure is on planning engineers to produce a spreadsheet that performs traffic forecasting and performance prediction using their best knowledge.

4. Cognitive Planning

By combining different planning inputs into the application, operators can significantly examine multiple scenarios to reduce turnaround time. It is possible to partition the network into numerous market areas, define thresholds on more than 20 KPIs and dimensions, and flexibly design spectrum distribution by fully utilizing the flexible input. Cognitive planning enables quick exploration of multiple what-if scenarios that balance different capacity expansion TCO against performance gains, transforms a seasonal activity into something that can be triggered at any time, and is critical in determining the best investment strategy for the future.

5. From NOCs to SOCs

Other products aid CSPs in converting network operation centers (NOCs) to service operation centers (SOCs). A SOC employs analytics and AI to deliver closed-loop automation, whereas a NOC supervises, monitors, and maintains a telecommunications network. CSPs may now detect, diagnose, and recover from service-impacting issues without the need for human intervention.

AI/ML interventions and how it improves network capacity planning

AI assists CSPs in developing self-optimizing networks that maximize network quality based on traffic and service KPI data by area and time zone. These AI apps employ powerful algorithms to identify data trends to detect and anticipate network anomalies and proactively resolve issues before affecting customers. The goal is to use AI/ML to build a Self-Organized Network (SON), which will allow for closed-loop network management with self-planning, self-configuration, self-optimization, and self-healing.

Conduct a network audit by examining key performance indicators (KPIs) for the network.
Automated benchmarking by optimum correlation of network performance KPIs and parameter values.
Maintaining an efficient network by auto-tuning configuration parameters when KPIs exceed a particular threshold.
Antenna setup that is automated to solve coverage issues.

Has your organization adopted AI to improve network capacity planning? If yes, how is your organization doing that? Do let us know in the comment section.

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Big Data is a vast industry that is only growing with each passing year. According to IDC, data created by connected Internet of Things (IoT) devices is expected to expand from 13.6 zettabytes (ZB) in 2019 to 79.4 ZB by 2025. Although Big Data is critical to an organization’s ability to make efficient business choices, most businesses fail to effectively understand the data’s insights. There are two reasons for this: the vast amount of data accessible and most firms still rely on human, bias-prone processes throughout the data value chain. Here’s where augmented analytics can come in handy. Augmented analytics is the future of data and analytics.

An Era of Disruption

Existing business models failed due to COVID-19’s disruption, and as a result, organizations are drowning in big data. There’s a demand for actionable information. Forrester Research estimates that just 0.5 percent of all data gets evaluated and used. Only 12 percent of enterprise data is considered when making business decisions, highlighting the limitations of unlocking the value from data. Even more unnerving, Forrester estimates that expanding data usage for decision-making by just 10% may result in an additional USD 65 million in net income for a typical Fortune 1000 organization. The answer to addressing the issues organizations experience in uncovering insights and realizing the benefits of Big Data could be an augmented analytics platform.

What is Augmented Analytics?

Augmented analytics is the application of enabling technologies like machine learning (ML) and artificial intelligence (AI) to data preparation, insight creation, and explanation in analytics and business intelligence (BI) platforms or AI in telecom. According to Gartner, augmented analytics aids both expert and citizen data scientists by automating various parts of data science, machine learning, and artificial intelligence model development, management, and deployment.

How can telcos benefit from augmented analytics?

While the obstacles that businesses confront are enormous, augmented analytics can help them overcome many of them. Automating data preparation, lowering time to insights, eliminating human analytical bias, and reducing the chance of missing critical insights are just a few of the advantages of augmented analytics at a high level. It also allows less business-savvy people, such as citizen data scientists, to democratize data analytics for those who lack specific training or expertise in data science or analysis for augmented analytics in telecom.

1. Understanding Customer’s Shifting needs

Using augmented analytics, telecom companies can instantly analyze tens of millions of CDRs, identify patterns that may indicate problems, create scalable data visualizations, and use predictive maintenance technologies to reduce dropped calls, poor sound quality, and various other issues that may cause customers to switch providers. It can also be used to create service plans that please clients while also being profitable.

2. Helps Prevent Fraud and Churn

The biggest challenge for telecoms is the high churn rate in the industry, which is estimated to be between 20% and 40% every year. Using churn analysis and churn prediction approaches, providers may better profile their customers and devise a strategy to keep their loyalty by determining who is likely to churn and who might still respond positively to marketing activities. Scams using automated calls or premium rate charges may not constitute a direct threat to telecom companies, but they do have the ability to diminish customer happiness over time. Using AI methods and techniques such as enhanced anomaly detection, fraud is easier to detect.

3. Improves Accuracy and Speed

Traditional BI tools necessitated a lot of IT assistance and required a lot of manual work. The human aspect raises the risk of a mistake in most BI software operational activities, such as cleaning and preparing a massive amount of data, analyzing and processing it, and presenting the results correctly. Robust IT systems with superior Augmented Analytics in telecom at their core can perform tasks with extreme precision and zero errors.

4. Prevents Roadblocks

For organizations to give ultra-fast results, there are various pre-built analytics use cases in the marketing, finance, and technology verticals. Customers can also create AI-powered analytics solutions that are tailored to their specific needs.

5. Predict Network Anomalies

Telecom can boost marketing performance through trend recognition, augmented analytics, and data science. Augmented analytics software can process vast amounts of data, particularly call detail records (CDR), and discover trends to detect and anticipate network anomalies. This function allows companies to notice emerging patterns affecting their operations, such as market shifts or rival activities. It allows marketers to respond more swiftly to rapidly changing market conditions by freeing up time that would otherwise be spent manually investigating trends.

Conclusion

The beauty of augmented analytics is that it can take data from various sources, such as Google Ads, Facebook, Shopify, or any other platform, and apply powerful machine learning algorithms to uncover critical insights that can save money and increase profits. For example, a budget allocation tool can determine which mix of expenditures across several marketing channels will yield the maximum revenue for a given budget. Data science in Telecom accomplishes this by first learning about previous outcomes and recent spending patterns, then creating a regression model for each channel to analyze how spending on each has affected sales over time.

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The role of the citizen data scientist is becoming increasingly enticing to corporate leaders as data-driven developments have influenced nearly every sector within enterprises. A citizen data scientist is a person who builds or generates models that leverage advanced diagnostic analytics or predictive and prescriptive skills but whose significant job role is outside the realm of statistics and analytics.

Who is a Citizen Data Scientist?

Businesses are evolving and realizing that not every data function requires a highly educated data scientist. Only specialists with the appropriate skill set and training are needed to undertake specific tasks in the middle ground. As a result, a new job title has emerged: Citizen Data Scientist. Forget about the data scientist’s important hero role. Citizen data scientists provide a complementary role to expert data scientists. In a world where data outnumber scientists, it’s a crucial role.

Typically, citizen data scientists do not have coding skills but need to develop strong domain expertise to understand the data. They can also build models using drag-and-drop tools, run pre-built data pipelines and models. They do not replace expert data scientists, as they do not have specific advanced data science expertise to do so. But they certainly bring their own business expertise and unique skills.

What Skills do Citizen Data Scientists Possess?

1. Organizational context: The citizen data scientist understands the company’s vision, objectives, and needs, as well as how data may assist them in achieving those goals.

2. Divergent thinking:The ideal citizen data scientist can think beyond the box, creating data models and connections beyond the ordinary employee’s comprehension.

3. Strong analytical skills:As a requirement of the job, a citizen data scientist must be analytical. Part of their job entails being able to undertake quite complex data analysis.

4. Ability to assess information meaningfully: A citizen data scientist must be able to properly examine the data in front of them and draw significant conclusions from it that the typical person could miss.

5. Emphasize business value:To develop into the role of a promotion from their current duties, a citizen data scientist must underline the value of what they are doing in data analysis.

6. Industry adjacency:The best candidates for citizen data scientists work in a subject related to data science, namely one that involves a lot of math and analytical processes. Software developers and engineers may be suitable for the position.

What’s Powering the Rise of Data Scientists?

There are two reasons for the rise of citizen data scientists. First, they’re proving to be a valuable and affordable addition to expert data scientists, who usually are harder to come by and more expensive to hire. Second, data science is becoming more accessible. According to a Gartner post, the usage of new analytics and business intelligence technologies is spreading further throughout the enterprise. Furthermore, solutions like augmented analytics and machine learning-powered tools are assisting employees with data discovery and analytics duties previously reserved for specialist data scientists in data science, AI, and machine learning.

Unleashing the power of citizen data scientists

Here are some tips for empowering citizen data scientists at your company, whether they already work there or you’re just getting started with mainstreaming data analytics, data science, AI, and machine learning practices to a broader range of employees:

Support a data-driven culture:

When it comes to enabling citizen data scientists, it’s necessary to consider people, procedures, and technology, but it’s also crucial to foster a data-driven culture throughout the organization. It will assist more individuals in understanding the function of citizen data scientists and their acceptance and ability to effect change among employees.

Keep an open-door policy:

Citizen data scientists require executive backing and a place for guidance, especially in the early stages of digital transformation. Consider your citizen data scientists as change advocates who must persuade employees from all levels of your organization. They can’t do it by themselves.

Provide the right tools and training:

To get the commercial benefits of data analytics, more individuals must be exposed to the technology. Thanks to today’s analytics and business intelligence solutions, companies may now make great leaps forward in less time. Companies can considerably accelerate their data strategy and scale the benefits of data analytics across the organization when they combine the proper continuing training with them.

Citizen Data Scientists Will Change Workflows

Citizen data science provides freedom to data scientists and analysts, allowing them to focus on more complex initiatives such as coding their algorithms and developing advanced data models. Natural language processing makes the transition from “business person” to “citizen data scientist” easier. Instead of turning their inquiry into a string of keywords or variables, non-technical users can ask what they mean. As a result, people can concentrate on the answer rather than the process. The more accessible, intuitive, and straightforward the process is, the more likely people are to participate and repeat it as needed. Natural language in data science, AI, and machine learning allows for more creativity and curiosity by allowing for more inquiries, follow-up questions, and follow-up questions (traits that every scientist needs).

Are you leveraging Citizen Data Scientists within your organization? If yes, who are they according to you, what are their titles, and what do they do? I’d like to hear your stories in the comment section.

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Augmented analytics is the future of data and analytics. Traditional Business Intelligence systems can no longer handle data sets that have grown in size, complexity, and speed. As a result, a new generation of business intelligence tools, known as Augmented Analytics technology, has emerged. Augmented Analytics in telecom can process enormous amounts of data, including call detail records (CDR) in the telecommunications industry, to uncover patterns, detect, and anticipate network problems using complex machine learning algorithms. It identifies the most valuable accounts based on available data and keeps the company’s database up to date. It addresses the challenge of siloed operations and helps:

Improve Accuracy and Speed

Data scientists, according to Forbes, spend over 80% of their time preparing and cleansing data. However, no matter how many staff are working on data processing and analysis, the time delivery is still less than adequate. The speed of delivery is increased significantly with Augmented Analytics. Requests are processed and analyzed in real-time by an AI-powered system, allowing consumers to receive results in seconds. Traditional BI tools required a lot of IT help and entailed a lot of manual operations. People’s involvement in most BI software operational procedures, such as cleaning and preparing a large amount of data, analyzing and processing it, and presenting the results in a suitable format, clearly increased the danger of a mistake owing to the human factor. Robust IT systems with advanced Augmented Analytics in telecom at its core can conduct jobs with high precision and zero errors.

Reduce Bias

According to Gartner, integrating machine learning and automated service-level management will cut manual data management activities by 45 percent by the end of 2022. Companies can use augmented analytics to liberate their employees from regular data entry. With AI in telecom, they can concentrate on more vital duties as a result. Unsupervised machine learning systems may learn from data without any further technical assistance. It also has fantastic visualization features, allowing users to compare millions of patterns in seconds.

Let us see how augmented analytics in Telecom is a game-changer in marketing.

The beauty of augmented analytics is that it can take data from various sources, such as Google Ads, Facebook, or any other platform, and apply robust machine learning algorithms to reveal crucial insights that can save money and boost the bottom line. A budget allocation feature, for example, can figure out what combination of spending across multiple marketing channels will generate the most income for a given budget. Data science in Telecom does so by learning about historical outcomes and recent expenditure patterns, then building a regression model for each channel to see how spending on each of them has affected revenues over time (going up to several years).

Enhance Campaigns

Through trend recognition, augmented analytics, and data science in telecom can improve the effectiveness of marketing. This feature enables businesses to spot developing patterns that may influence their operations, such as market shifts or competitor activity. It frees up time that would otherwise spend manually investigating trends, allowing marketers to respond more quickly to rapidly changing market conditions.

High-Value Use Cases for Augmented Analytics

1. Providing and Maintaining Good Service

Telecom companies can instantly analyze through tens of millions of CDRs. It recognizes patterns that may point to problems, create scalable data visualizations, and use predictive maintenance technologies to minimize dropped calls, poor sound quality, and other issues. It may cause customers to seek a new provider using machine learning and eventually AI systems.

2. Combating Fraud

While scams involving automated calls or premium rate charges may not pose a direct danger to telecom firms, they do have the potential to reduce customer satisfaction in the long run. The faster telecom companies can spot suspect call or client trends, the more effectively they can prevent fraud (including first-party or true-party fraud). True-party fraud is easier to detect using AI tactics and techniques like improved anomaly detection. Companies that can tell the difference between legitimate credit failures and frauds might concentrate their collection efforts on the instances that are most likely to yield a profit.

3. Churn Prediction

One of the major challenges for telcos is the high churn rate in telecommunications, which is believed to be between 20 and 40 percent per year. Providers may construct better profiles of their consumers and sketch out a strategy to keep their loyalty using churn analysis and churn prediction methodologies, determining who is likely to churn and who might still respond positively to marketing initiatives.

4. Improving Customer Satisfaction

Reduced service calls, expensive for operators, and pulling technicians away from their duties are a vast value gain for telecoms. With Augmented Analytics in telecom, teams can identify unjustified service calls and review technician performance statistics to improve customer service by analyzing vast volumes of data using machine learning techniques.

Is your organization planning to adopt Augmented Analytics? If yes, then what are the challenges your organization is facing? Feel free to share your thoughts in the comments section.

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By 2025, Artificial Intelligence in telecom will be used in 95% of all customer interactions, including live phone and internet conversations, making it impossible for customers to know if they’re talking to a human or bot. Only a few years ago, service providers would mass market to consumers a single offer at a time (or a small number of offers). What we’re seeing now with AI is the potential to sell to much narrower client segments, providing a far better experience for customers. There will be no more wide strokes. As AI takes over the burden, micro-segmentation, custom-made products, and tailored experiences are becoming more prevalent. Here’s how AI and Machine Learning algorithms are changing the telecom’s customer experience.

• Offer Services That Customers Want

With AI/ML in the telecom industry, telecoms can look at their customers’ data, usage patterns, and purchases and identify micro-segments that may not be traditionally apparent. The next step is aligning product offers to these micro-segments, as opposed to having a broad stroke approach. By predicting and marketing new offers to these micro-segments, we increase the chance the consumer will be interested in that offer. There’s a couple of things happening here. Consumers get a better customer experience, getting more of what they want, tailored to them. And the flip side of this is more revenue per user, with the added ability to upsell components consumers might not have known.

• Create Products Faster

Data science in telecom can generate product proposals on its own. By examining the existing product portfolio, customer usage patterns, and customer complaints, data science, AI, and machine learning can assess this data and forecast that, for example, adding another 100 minutes of free voice to this bundle will have a high likelihood of success. It is capable of producing that output on its own. Before launching the freshly generated product, the product management person still validates it and double-checks that everything is in order and can deploy it soon. Furthermore, AI in chatbots can reduce mundane, manual activities to a bare minimum, allowing agents to focus on more complicated jobs and spend more time with those who need it the most. Customers may use AI to make it easier to complain, and it can even engage proactively to prevent complaints.

• Self-Diagnostic Fraud Detection

Fraudulent activities such as theft or phony profiles, unauthorized access, and more can be detected using machine learning algorithms. These algorithms learn what “normal” activity looks like, allowing them to discover anomalies in massive datasets considerably faster than human analysts, allowing them to respond to suspicious activity in near-real time thanks to data science, AI, and machine learning.

• Predictive Maintenance and Improve Network Optimization

Companies can use data-driven insights to monitor equipment, learn from prior data, predict equipment failure, and correct it before it happens. Network optimization is another crucial area where AI can help. Artificial intelligence-powered Self-Organizing Networks (SONs) can help networks adapt and rearrange based on current demands. It’s also useful for creating new networks. AI-enabled networks are more efficient at providing consistent service since they can self-analyze and optimize.

• Real-time Data-supported Decision-making

Telecoms possess enormous amounts of data from customers. With AI and machine learning, telecoms can extract meaningful business insights from this data to make faster and better business decisions. This crunching of the data by AI helps with customer segmentation, customer churn prevention, predict the lifetime value of the customer, product development, improving margins, price optimization, and more. By using AI and real-time decisions, it helps to recognize and understand a customer’s intent through the data they produce. Also, brands can present hyper-personalized, relevant content and offers to customers.

• Omnichannel Marketing

Omnichannel refers to the concept of using all of your channels, both online and offline, to provide your customers with a single, seamless, and personalized shopping experience. When customers interact with your brands through various channels, your underlying IT platform is expected to provide them with relevant and consistent service. It is possible with Artificial Intelligence in telecom. They will provide a recommendation that you will like with a good understanding of the customers’ preferences.

• Natural Language Processing

In the coming days, customers will manage 85% of their communication with the organization without interacting with a human, thanks to NLP. You usually think of chatbots when you hear the word natural language processing (NLP). Chatbots, on the other hand, do not fully leverage AI’s capabilities. Instead, they have AI listen in on client discussions when they escalate to an agent and provide suggested responses, pertinent documents, and, most importantly, prior engagement history. Connect your call center to your CRM so that all customer interactions are tracked in one place. Enabling AI to learn by putting new data into the model is one of the most important tasks for customer service. Agents can source content from the organization and feed it into the knowledge base when data science, AI, and machine learning are unable to answer a question.

What is your organization doing to improve customer experience? Is it using AI/ML for your businesses? If yes, then feel free to share your comments in the section below.

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Artificial intelligence (AI) is poised to play a major role as businesses strive to regain their footing in the wake of the massive human and economic toll caused by the pandemic. The pressure on businesses to use AI was already building prior to the crisis since the technology paid off for early adopters. Many firms are utilizing AI to quickly triage the massive difficulties they confront and define a new route for their employees, customers, and investors in an uncertain, constantly moving landscape, thanks to the COVID-19 crisis. This calls for rapid AI democratization within an organization.

Demystify AI

With recent research focusing on the remarkable (and often unrealistic) uses and consequences of AI democratization, it’s unsurprising that many individuals are wary of its implementation in the workplace. One of the simplest ways to help employees gain a practical grasp of AI is to explain how they can utilize the technology to improve their day-to-day efficiency and effectiveness. While only about 5% of professions are likely to be totally automated, AI and similar technologies will alter the character of many current roles, putting a higher emphasis on tasks that require technological, creative, and critical thinking skills without completely removing human interaction.

Dispel common myths

A data scientist’s understanding of AI is frequently vastly different from a business’s understanding. To democratize AI and your data in a commercial environment, you must debunk some common misconceptions.

1. Data Scientists are Magicians

Artificial intelligence and the democratization of AI are overrated. Companies are launching these AI projects for a variety of reasons, not the least of which is to be the next big thing. Data scientists are regular individuals, and hiring one will not instantly transform your company into an AI-enabled one. Instead, the data scientist can use statistical approaches to uncover patterns using AI’s increased abilities. You get more accurate predictions to help you make better decisions in the future. This isn’t magic at all. Instead, it’s about making targeted, well-informed decisions.

2. It’s hard for a business to apply AI

It’s not simple, but it’s not as difficult as its reputation suggests. Some of the complications arise from the fact that a corporation lacks a specific query or aim, but AI cannot both construct and answer the question. Because AI is advanced problem-solving, defining the correct problem yields the desired outcomes. It’s possible that your AI democratization project is failing because you don’t have the correct problem structure in place. You’ll have more success if your team can identify the proper problem.

3. It needs millions of observations for model creation

Businesses sometimes become overly engrossed in the complexities of projects. When modest efforts aimed to bring customers closer or create better predictions will better serve their business outcomes, they follow the newest, coolest technology.

Know your users

Businesses frequently make broad claims, claiming that drag-and-drop tools have democratized data intake, data cleaning, and data mining. Or they say that by automating the entire machine learning or data science process, they have democratized AI sophisticated statistical and computational model creation. But who is able to access these tools and techniques? Have those users received adequate training, not only in the technology but also in the concepts that govern it?

Provide proper training

Lack of sufficient training in AI creation and deployment could be disastrous, particularly in systems that deal with people’s health or financial well-being. For example, if unskilled or casual users do not grasp the need of separating data into buckets for training, validation, and testing, AI could easily deliver false or unexpected outcomes. If we want to move beyond simply giving access to encouraging the safe use of AI tools, we must first educate casual and power users on the fundamentals of data science and achieve AI democratization.

Encourage usage of existing business intelligence tools

Employees get more comfortable diving into data to test theories as they become more accustomed to using self-service analytics tools. They begin to trust the data more as time goes on, and they become more data-driven. Finally, such mindsets can help employees better prepare for the use of future AI systems and inspire them to think ahead about how AI can help them solve business problems.

Encourage and empower non-tech savvy users to embrace AI

Demand for AI use cases will expand as employees discover how they can utilize AI to address their day-to-day difficulties, and successes will spread awareness among all. This enthusiasm may drive companies to invest more in AI talent and data, leading to additional achievements and elation among even non-tech-aware personnel. The cycle will eventually reach a tipping point, where AI will have a natural “pull” and the entire business will be engaged and involved in democratizing AI.

As the aftershocks from the COVID-19 crisis continue to upset business models that were already facing considerable disruption, getting employees on board and enthused about AI is critical to helping them become a part of the work and preparing them for the changes ahead. This can also start a virtuous cycle that will make it easier to make the larger, transformational changes that will be required to become a successful AI-enabled company for the democratization of AI.

How is your organization is planning to accelerate AI adoption and democratized it across businesses? Let me know your thoughts in the comments section below.

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