The Nigerian Communications Commission (NCC) recently reported that Nigeria’s internet consumption reached 973,455 terabytes in December 2024, a 36.5% increase from the previous year. 

That’s 998.79 million gigabytes of data used in just one month. Despite this surge in demand, the country’s broadband penetration stood at 44.43%, far below the 70% target set in the National Broadband Plan (2020–2025). 

Even more concerning, Nigeria ended 2024 with 164.9 million active telecom subscribers—down from 224.7 million the year before.

Imagine streaming a movie now costing as much as a meal, and staying online feels like a privilege reserved for the wealthy. 

This sharp decline in active subscribers, even with the growing need for internet services, is leading to talks of Nigerians being unable to afford staying connected.

Inflation and currency devaluation have shot the industry’s costs of operations really high, leading to the Nigerian Communications Commission’s (NCC) recent approval of a 50% increase in telecommunications tariffs—the first such hike in over a decade—to help operators manage the high expenses. 

The Need for Inclusive Resilience in the Telecom Sector

This tariff increase has struck conversations across various bodies. The Nigeria Labour Congress (NLC) has labelled the hike as “insensitive” and “unjustifiable,” especially given the current cost-of-living issue. 

While telecom operators argue that the challenges leave them with no choice, consumers are wondering if affordable connectivity is becoming a thing of the past.

Inflation is wearing out disposable income, forcing consumers to prioritise essentials over data and call plans. Businesses, especially SMEs, rely heavily on telecom services, but higher costs threaten their ability to stay competitive.

The rural-urban gap in connectivity may expand, as rural consumers—who already struggle with access—may be priced out of the market.

In the midst of these challenges, the telecom sector must find ways to remain profitable without sidelining lower-income consumers. The key lies in resilient and inclusive strategies that balance affordability, sustainability, and growth.

Strategy 1: Tiered and Flexible Pricing Models

1. The Power of Segmentation in Telecom

To effectively serve a diverse customer base, telecom operators should segment their users into low-income, middle-income, and high-income categories. This segmentation allows for targeted services that meet the specific needs and financial capabilities of each group.

2. Implementing Flexible Pricing Structures

• Pay-as-you-go options: Ideal for price-sensitive users who prefer to control their spending without committing to fixed plans.
• Subscription models: Offer middle-income consumers affordable packages with predictable billing cycles.
• Premium services: Provide high-income users with enhanced features such as high-speed internet and exclusive customer support.

3. Strategy 2: Infrastructure Cost Optimisation Through Public-Private Partnerships (PPP)

1. The High Cost of Expanding Telecom Network Infrastructure

Building and maintaining telecom infrastructure, such as towers and broadband cables, require huge capital investment. Inflation further increases these costs, making it challenging for operators to expand and upgrade their networks.

2. Leveraging PPP to Reduce Financial Stress

Collaborating with government entities and development banks can help telecom operators share the financial risks associated with infrastructure projects. For example, partnerships can be formed to extend network coverage to underserved rural areas, with shared investment and benefits.

In Kenya, the government and private telecom operators have partnered to expand rural connectivity, resulting in increased access to communication services in previously underserved regions.

Initiatives like the National Optic Fibre Backbone Project and partnerships with telecom providers such as Safaricom, Telkom Kenya, and Airtel have helped boost this.

Strategy 3: Digital Transformation and AI-Driven Efficiency

1. How Digital Transformation Can Lower Costs

Leveraging digital tools and automation can simplify operations, reducing the need for manual intervention and lowering operational expenses. For instance, AI-powered network management systems can optimise bandwidth usage and predict maintenance needs, thereby reducing downtime and associated costs.

2. The Impact on End-Users

Customers benefit from faster and more efficient services, such as AI-driven customer support that can handle inquiries promptly. These efficiencies can lead to cost savings for operators, which can be passed on to consumers in the form of more affordable services.

Strategy 4: Expanding Alternative Revenue Streams

1. Moving Beyond Traditional Revenue Models

Relying solely on voice and data services is becoming more and more unsustainable. Diversifying into areas like financial technology (fintech), cloud services, and the Internet of Things (IoT) can open new revenue streams. This is seen in MTN’s transition to a Techco.

2. Monetising Digital Services

• Mobile money and payment solutions: Offer financial services to unbanked populations, generating transaction fees.
• Entertainment bundles: Partner with streaming services to provide bundled offerings, enhancing value for consumers.

MTN’s MoMo, Airtel Money and Safaricom’s M-Pesa are prime examples of telecom operators successfully launching into mobile financial services, greatly contributing to revenue growth.

Strategy 5: Strengthening Local Supply Chains to Mitigate FX Risks

1. The Problem of Foreign Exchange Dependency

Heavy reliance on imported equipment makes telecom operators vulnerable to currency fluctuations, increasing costs unpredictably.

2. Investing in Local Manufacturing and Partnerships

Developing local production capabilities for items like SIM cards and network components can reduce foreign exchange exposure. Partnering with local tech firms can also promote innovation and cost-effective solutions tailored to the local market.

Strategy 6: Data-Driven Decision Making for Telecom Customer Retention

1. The Cost of Customer Churn in an Economic Downturn

Losing customers can be more expensive than retaining existing ones, especially when inflation reduces consumers’ disposable income. High churn rates force telecom companies to spend more on marketing and customer acquisition, which can negatively impact already tight budgets.

2. Leveraging Big Data and Analytics for Personalised Offers

Telecom operators can use customer data analytics to identify usage patterns, predict churn risk, and design personalised retention strategies.

• Usage-based incentives: Offering discounts or data bonuses to customers who frequently recharge can encourage continued engagement.
• Loyalty rewards: Retaining long-term customers through perks such as discounted family plans or exclusive streaming deals.

MTN and Airtel have successfully used data analytics to provide dynamic pricing models, such as location-based discounts and time-sensitive data plans, reducing churn and boosting customer satisfaction.

Strategy 7: Strengthening Regulatory and Industry Collaboration in Telecom

1. The Impact of Government Policies on Telecom Viability

Government policies on taxation, spectrum licensing, and price regulations are important in determining telecom sector stability. The recent 50% tariff hike approved by the Nigerian Communications Commission (NCC) is an example of how policy decisions directly affect consumers and telecom operators.

2. Advocacy for Fair and Sustainable Policies in the Telecom Sector

Telecom companies must engage policymakers and industry regulators in constructive dialogue to ensure that tariff adjustments, tax structures, and regulatory frameworks balance profitability with affordability for consumers.

3. Encouraging Investment-Friendly Policies in the Telecom Sector

• Reducing multiple taxation: Telecom firms should advocate for streamlined tax policies to prevent excessive levies that inflate operational costs.
• Incentives for rural expansion: Government support, such as tax breaks for rural infrastructure projects, can make connectivity more accessible in underserved areas.

Regulatory frameworks can encourage competitive pricing while ensuring telecom operators remain profitable.

Summary of Key Points

Though there are economic pressures like inflation, telecom operators can thrive and ensure inclusive connectivity by implementing seven key strategies:

1. Tiered and flexible pricing models to serve all income groups.
2. Public-private partnerships (PPP) to reduce infrastructure costs.
3. Digital transformation and AI for cost efficiency.
4. Diversifying revenue streams beyond data and voice services.
5. Strengthening local supply chains to reduce foreign exchange risks.
6. Using data-driven strategies to retain customers.
7. Collaborating with regulators to ensure fair pricing policies.

The Lot of Resilient Connectivity

With smart, adaptive strategies, telecom operators can continue to deliver quality services across all economic segments while mitigating the impact of inflation.

The telecom sector must act assertively by adopting innovative pricing, infrastructure investment, and customer-centric solutions. 

Regulators, industry leaders, and consumers must collaborate to ensure that connectivity remains affordable, sustainable, and inclusive—regardless of economic conditions.

Would you agree to participate? What if you knew that your data could be used for other purposes, such as developing new products, services, or policies, without your consent or knowledge? How would you feel about the potential benefits and risks of data sharing in this scenario?

Data sharing is the process of making data available to others for various purposes, such as research, innovation, education, or public service.

Data sharing can have many benefits, such as enhancing scientific discovery, improving social welfare, increasing economic growth, and fostering collaboration.

However, data sharing can also pose many risks, such as violating privacy, enabling discrimination, facilitating exploitation, and undermining trust. T

herefore, data sharing requires careful consideration of the ethical, legal, and social implications of how data is collected, stored, accessed, used, and reused.

The Benefits of Data Sharing in the Era of Big Data and AI

Big data refers to the large volume, variety, and velocity of data that is generated from various sources, such as sensors, devices, platforms, or networks.

AI refers to the use of algorithms and systems that can perform tasks that normally require human intelligence, such as learning, reasoning, or decision-making.

Big data and AI can enable new forms of data analysis and applications that can generate valuable insights and solutions for various problems and challenges.

For example, according to a World Health Organisation (WHO) report, data sharing can enhance scientific discovery by enabling faster and more efficient research collaboration across disciplines, sectors, and regions.

As an example of data sharing for innovation, scientists in South Korea’s Korea Superconducting Tokamak Advanced Research (KSTAR) facility have managed to sustain a nuclear fusion reaction running at temperatures in excess of 100 million°C for 30 seconds for the first time.

This breakthrough could pave the way for clean and unlimited energy sources in the future. As an example of data sharing for education, Khan Academy is a non-profit organisation that provides free online courses and resources for learners of all ages.

By using big data and AI to personalise learning experiences and track progress, Khan Academy has reached over 100 million students worldwide.

As an example of data sharing for public service, the United Nations (UN) has launched a platform called UN Global Pulse that uses big data and AI to monitor and respond to global issues such as poverty, health, climate change, and human rights.

By using real-time data from sources such as social media, mobile phones, satellites, or sensors, UN Global Pulse can provide timely and actionable insights for decision-makers and stakeholders.

The Risks of Data Sharing in the Era of Big Data and AI

However, big data and AI can also create new challenges and threats for data sharing, such as increasing the scale and scope of data collection and use, reducing the transparency and accountability of data processing and outcomes, and amplifying the potential harms and impacts of data misuse and abuse.

For example, according to a report by the International Data Corporation (IDC), the amount of data created and consumed in the world will grow from 59 zettabytes in 2020 to 175 zettabytes in 2025.

This means that more and more data will be collected and used by various actors for various purposes, some of which may not be aligned with the interests or preferences of the data subjects.

Moreover, the complexity and opacity of big data and AI systems may make it difficult or impossible for the data subjects to understand or control how their data is processed or what outcomes are produced.

Furthermore, the misuse or abuse of big data and AI systems may result in serious harms or impacts for the data subjects, such as identity theft, financial loss, psychological distress, social discrimination, or physical harm.

For example, according to a report by the Electronic Frontier Foundation (EFF), there have been many cases of privacy breaches, data leaks, or cyberattacks that have exposed or compromised the personal data of millions of people.

As an example of a privacy breach, Facebook has faced several scandals involving the unauthorised or inappropriate access or use of its users’ data, such as the Cambridge Analytica case involving data manipulation for political purposes.

As an example of a data leak, Equifax, a credit reporting agency, has suffered a massive data breach that affected the personal and financial information of 147 million Americans.

As an example of a cyberattack, the Colonial Pipeline, a major fuel supplier in the US, has been hacked by a ransomware group that demanded a payment of $4.4 million to restore its operations.

The Elements of Balance for Data Sharing in the Era of Big Data and AI

Therefore, it is important to balance the benefits and risks of data sharing in the era of big data and AI. This requires a holistic and multidisciplinary approach that involves various stakeholders, such as data providers, users, regulators, intermediaries, and beneficiaries. Some of the key elements of this approach are:

1. Establishing clear and consistent rules and standards for data sharing that respect the rights and interests of all parties involved. This includes defining the purpose, scope, conditions, and limitations of data sharing; ensuring the quality, security, and integrity of data; protecting the privacy and confidentiality of data subjects; preventing the unauthorised or inappropriate access or use of data; and enforcing the compliance and accountability of data actors.
2. Promoting fair and equitable data-sharing practices and outcomes that balance the costs and benefits of all parties involved. This includes ensuring the consent and participation of data subjects; providing incentives and rewards for data providers; supporting the access and availability of data for users; enhancing the value and utility of data for beneficiaries; and addressing the potential biases or inequalities in data distribution or representation.
3. Fostering a culture of trust and responsibility for data sharing that encourages collaboration and communication among all parties involved. This includes raising awareness and education about the opportunities and challenges of data sharing; engaging in dialogue and consultation with relevant stakeholders; building capacity and competence for data literacy and skills; developing mechanisms for feedback and evaluation; and creating platforms for innovation and learning.

Data sharing is not a simple or straightforward process. It involves multiple dimensions, factors, and trade-offs that need to be carefully weighed and balanced.

Data sharing can have significant benefits for society but also pose serious risks for individuals.

Therefore, we need to be mindful of how we share our data in the era of big data and AI. We need to be informed about our rights and responsibilities as data subjects.

We need to be involved in shaping the rules and standards for data sharing as data providers. We need to be vigilant about the practices and outcomes of data sharing as data users. And we need to be proactive in creating value from our shared data as beneficiaries.

Data sharing is not only a technological challenge, but also an ethical one. It requires us to rethink how we collect, store, access, use, and reuse data in ways that respect the rights and interests of all parties involved, promote fair and equitable practices and outcomes, and foster trust and collaboration among data actors.

How can we achieve this balance? What are the best practices or frameworks that we can adopt or develop? How can we ensure that data sharing serves the common good rather than private interests?

These are some of the questions that we need to address as we navigate the nexus of data sharing and privacy in the era of big data and AI.

*Oluwole Asalu, champions the fusion of data sharing and privacy in the age of Big Data and AI, writes from Lagos, Nigeria,

However, simply collecting data is not enough; the true power lies in leveraging big data analytics to extract actionable insights and drive significant business growth. In order to get informed on the transformative power of data and how harnessing big data analytics can unlock new opportunities and fuel success for businesses, let’s explore these points:

Understanding Big Data Analytics

Before delving into its potential, it’s crucial to understand what big data analytics entails. Big data analytics involves the collection, processing, and analysis of vast amounts of structured and unstructured data to uncover patterns, correlations, and insights that can guide informed decision-making. It encompasses various techniques, such as data mining, predictive modeling, machine learning, and artificial intelligence, to derive actionable intelligence from data.

Uncovering Customer Insights

One of its most significant advantage is its ability to provide businesses with deep customer insights. By analyzing customer behavior, preferences, and purchasing patterns, businesses can gain a comprehensive understanding of their target audience. This knowledge can help tailor marketing strategies, personalize customer experiences, and optimize product offerings, resulting in enhanced customer satisfaction and increased sales.

Improving Operational Efficiency

Big data analytics can also drive improvements in operational efficiency. By analyzing internal data, such as supply chain logistics, production processes, and employee performance, businesses can identify bottlenecks, streamline operations, and optimize resource allocation. This not only reduces costs but also enhances productivity, allowing businesses to achieve operational excellence and gain a competitive edge in the market.

Predictive Analytics for Better Decision-making

The power of this analytics lies in its ability to predict future trends and outcomes. By leveraging predictive analytics models, businesses can make data-driven decisions and anticipate market changes, customer demands, and emerging opportunities. This foresight enables proactive decision-making, minimizes risks, and enables businesses to stay ahead of the competition.

Unlocking Innovation and New Revenue Streams

It can also serve as a catalyst for innovation and the creation of new revenue streams. By analyzing market trends, customer feedback, and external data sources, businesses can identify untapped market segments, emerging trends, and potential areas for expansion. This can drive product innovation, the development of new services, and the exploration of new business models, ultimately leading to growth and increased profitability.

Enhancing Customer Experience

In the age of personalization, customer experience plays a vital role in business success. Big data analytics enables businesses to deliver highly personalized experiences by leveraging customer data. By analyzing customer interactions across various touchpoints, businesses can anticipate individual needs, personalize marketing messages, and offer tailored recommendations. This level of personalization enhances customer satisfaction, fosters loyalty, and drives repeat business.

Conclusion

The power of data and big data analytics cannot be understated in today’s business landscape. By leveraging the wealth of information available, businesses can gain a competitive advantage, fuel growth, and unlock new opportunities. 

From customer insights to operational efficiency, predictive analytics, innovation, and personalized experiences, this kind of data has the potential to transform businesses across industries. Embracing the power and investing in robust data analytics’ capabilities will undoubtedly pave the way for business success in the digital era.

Sometimes it takes just one individual to come up with an innovative new approach that gives your organisation the competitive edge, but more often than not, it requires the collaboration of various different teams and the combination of lots of different data sources.
Most executives agree data-driven operations across lines of business is key to a winning strategy.

Illustrating that point is the 85% increased investment in digital capabilities and 77% increased investment in IT, as reported in the 2022 Gartner CEO and Senior Business Executive Survey.
Giving your employees the ability to access and make sense of their data, whether they sit within technical teams or not, is therefore crucial to your success.

Your data needs to be democratised across the business, although this is often harder than it would seem.

According to New Vantage Partners’ Data and AI Leadership Executive Survey 2022, only 27% of organisations have managed to nail this, with another 19% struggling to establish a data culture.

Through 2025, 80% of organisations seeking to scale digital business will fail because they don’t take a modern approach to data and analytics governance, as stated by Gartner’s State of Data and Analytics Governance.

Unfortunately, modernising tech stacks and migrating to the cloud are not enough to put the right data in the right hands of everyone across the business. Organisations must modernise their governance practices to fully uphold their efforts.

So, what should business leaders think about before they hand over the key to the treasure trove of data to the rest of the organisation? My top six considerations would be:

Take an end-to-end perspective: Successful data governance needs to be implemented from end to end, encompassing your entire data landscape from your data warehouse to your analytics solution.

It’s like any process: if it’s not governed all the way, then you cannot control the end result. On the whole, data governance is about ensuring that the KPIs on which you are basing your business decisions are correct and trusted – having a process in place that ensures secure data is delivered to end-users who have the confidence to use it to make real-time decisions.

Consider the power of synthetic data: Data that is artificially created enables organisations to model innovatively for things that have never happened before while jumping over some of the privacy, copyright and ethical hurdles associated with the real world.

It holds great potential for highly regulated industries like healthcare and financial services. And for anyone questioning its validity, research suggests that models trained on synthetic data can be more accurate than others.

This is why synthetic data is rising in popularity and looks to completely overshadow real data in AI models by 2030.

Automate data delivery: Solutions are now available to help businesses move away from manual data delivery with automation.

This approach enhances control in a number of ways. Automated solutions allow governance to be embedded into the process with rules and policies.

They can also inject bespoke data-quality improvements based on the individual and/or workflow. And when you automate data property identification along the pipeline, you prevent users from seeing what they shouldn’t.

Take a case-based approach to cataloging: Giving more access to data and analytics increases risk with the complexity of managing and securing more users. But democratising access to data is vital for any organisation to reap its true benefits. That’s why establishing a data and analytics catalog will help mitigate the risk.

In terms of the data, everyone within the organisation can see which data is securely available to them in one simplified view, and the IT team knows the catalog is secured by identifying and masking the data on user types and access rights.

From an analytics perspective, the notion of a business glossary and re-usable assets, along with data lineage and impact analysis, provides additional context to the data, thus driving more consistency, understanding and faster, actionable insights.

Data lineage for full visibility: More users also mean more risk of errors. Data lineage gives you the ability to understand and visualise data flows from source to current location. With the ability to discover, track, and correct data process anomalies, businesses can meet data governance goals and lower the cost of regulatory compliance, increase trust and reliance on data across your organization, and improve data analysis and, thereby, business performance.

Take it step-by-step: As with many large-scale, high-risk digital transformation projects, taking a ‘think big, start small and scale fast’ approach to data governance is a sensible one.

Keeping an outside-in perspective is also helpful, particularly if you use self-service analytics.

By this we mean starting your data governance journey by getting an overview of your entire data landscape, identifying which inconsistencies, objectives and errors are most important, and building your efforts from there.

There’s no doubt that every business could benefit from data democratisation, and that appetite to invest in data and analytics continues to grow, with 93% of companies indicating they plan to continue to increase budgets in these areas.

But, rapidly shifting rules and regulations around privacy, as well as the distribution, diversity and dynamics of data, can make it a daunting process.

By taking an end-to-end perspective, a step-by-step approach, considering the power of synthetic data and investing in automated data delivery or case-based cataloging, there’s no reason why even the most risk-averse organisations can’t put the power of data into every single worker’s hands.

Data has become a source of wealth and power. Organisations that have data have a lot of power in their hands.

In the context of conversation today, data has become a key disruptive factor that organisations leverage to gain competitive advantage.

Ola Williams, the Country Manager, Microsoft Nigeria, made further submissions while speaking at Insurance Meets Tech Conference held on Thursday, September 29 2022 in Lagos:

Combined with cloud and innovation, data can actually be a game changer in any business and much more so, in insurance today. 

When we look at leveraging the cloud, cloud drives data innovation and gives organisations the ability to gain a lot of insight that would otherwise not be visible to the ordinary eyes.

When you put your data in an aggregated manner, with the right tools, it would give you detailed reporting of what data signifies.

Taking insurance in a literal context

Insurance is the transfer of risk from one entity to another. That means the insurer collects data from the insured, people that have different products with you. That gives you a lot of insights about the behaviours and preferences of your customers. 

So leveraging big data to see the pattern in which an organisation or individual is gaining claims can help you to customise products and services that are relevant to that organisation.

We can leverage data to:

• Gain competitive advantage;
• Provide differentiated offerings to customers
• Engage customers in a more meaningful way that is relevant to them, etc

With all the possibilities, why is there still very slow adoption of insurance?

There are different factors:

1. Regulatory factor

When we look at regulation from a vicinity point of view, then that limits our ability to drive creativity and innovation.

But when you look at innovation from the point of view of how we ensure that we are taking the right steps, the right resources and the right processes to ensure that the capabilities we have today will be able to leverage them effectively, that is a strategy that organisations need to look at.

How do we continue to innovate even within the guidelines of the regulatory authorities and policies in place?

2. Access to technology 

We see access to technology not only in terms of purchasing power, but also, when technology is deployed within the organisation, that is not the end, we have to look at culture, people and processes.

When we have technology without the right people in place, the right culture to adopt the technology and the right processes within the organisation, then that would reduce the ability for the organisation to leverage technology.

For example, an organisation invests a lot of money into technology and they are not seeing results, the issue is not that the technology is not working but probably and most times, the commensurate culture, processes and procedures to make sure that the technology works and ensure the benefits of the business, are not in alignment.

3. Access to talent 

There’s the need to have the right skillset within the organisation that would help to ensure that technology investment is put to good use.

Talent is not just having brilliant set of people, there’s the need for organisations to be deliberate about skilling and re-skilling because technology also evolves and moves at a high speed.

To see the results of technology, the right talent should be attracted and a proper plan put in place to ensure that talents are skilled regularly to adequately utilise the technology investment within the organisation.

When we talk about regulation, regulators and how much we adopt technology, which do you prefer: a retrofit or disruption?

There’s no magic bullet actually, it’s really different from scenarios and the phase that an organisation is in adopting technology. There are organisations that are just starting, it depends on the trends and the drivers within the industry. 

We see some insurance companies that are investing in insurtech now because they need a place to compete with borne-in-the-cloud competition, and we see insurance companies that are just waiting to see how it goes.

So, for insurance companies that are borne in the cloud, they are just disruptive, that’s a distraction but for organisations that have legacy solutions, legacy customer engagement, policies, procedures and regulations, they need to have a blend of disruptive approach as well as retrofit because you don’t want to destroy what you already have. It’s a journey and we need to take on that journey.

It’s important to partner with organisations like Microsoft because we have what we call industry cloud. Today we are invested heavily in bringing in people with industry expertise into our organisation.

Based on our experience working with multiple organisations within the financial services sector where there’s insurance, banking and others, we have worked with several cloud owners and programs. So it is important to have a partner that will work with you to scale your organization and not bring it down.