How does ‘Big Data in Telecom’ look like? Isn’t Telecom inundated with tons of scattered data? In the age of Internet of Things, any business where the results of one action would affect the subsequent action can apply Big Data and reap benefits.
The means of data transmission has been evolving endlessly from analog to digital in the last two centuries. As Communications Service Providers (CSP) market is growing, as smart phones and mobile internet are no longer a novelty, it is important for the telecom businesses to respond in real-time to customer behavioral changes to have a competitive edge. Before investigating ways to monetize data in Telecom businesses, lets us first understand the dimensions of data and its uncertainty.
Volume of the data defines the mass quantities of the information. Most of the organizations are still unclear about how ‘big’ the data should be for Big Data practices in their business. There is no textbook answer for that. What seems like ‘High Volume’ today might demand higher volume tomorrow. Data comes in different shapes and types depending on its source, structure, and generation method.
Variety of data manages the complexity including structured, unstructured, and semi-structured data types. The data can be sourced from internal sources such as sensors, forms, log files etc., or external sources such as social collaborations, partner data, etc.
Velocity of data is the quality of speed at which the data is being created, accessed, processed, and analyzed for insights. This dimension of data contributes to the real-time-ness of BI applications like fraud detection, instant marketing, etc.
Veracity of data indicates quality and certainty which is often compared to the volume of the data. Not every data point is inherently clean and ready to use. Yet, embracing such data and applying advanced mathematics and optimization techniques is the beauty of Big Data in Telecom.
Value of data is probably one of the most underrated property. It is this element of data that helps businesses make investment decisions to support the data. There are various Data valuation methods and assessments are available. We shall discuss them in detail in future.
Understanding these characteristics of data is the 101 of Big Data in Telecom, in fact in any industry vertical.
Telecom businesses have a unique advantage with customer data. They have access to the subscriber’s demographics, preferences, application usage information, and a lot of others.
Just having right means of modeling and aggregating this data can not only transform the core business but also establish a BI ecosystem for the partner community. Data Analytics as a Service (DAaaS) is a high yielding yet rarely tapped on opportunity to expand business for the Telecom providers.
Here is a broad outlook of end-to-end Data Life Cycle depicting stages from ingesting data from various sources, processing it through hybrid infrastructure, generating value out of it and applying it to business use cases. Just like any other business vertical, the objective behind applying Big Data in Telecom is to gain a 360° view of the customer. The entire data flow is protected with an added layer of data security, governance, and access control systems.
Kicking off your Big Data Journey
The post-pandamic world is going to depend on digital communications more than ever. It is a golden opportunity for every Telecom provider especially for the Communications Service Providers. Big data in Telecom is evolving gradually and it is an opportunity for the traditional players to take advantage of the decades old data, digitize it, correlate the trends with the new data, and compete with the digital-born ones.
Adoption of Big Data in telecom can start from wherever you are standing right now. While working on some of our strategic and long-term projects, we introduced data analytics wherever we identified an opportunity, and seen dramatic changes in the business outcomes.
Based on our learnings, this is the most proven and practical approach to initiate Big Data practices.
Educate – Expand your knowledge about competitors, technology trends in the industry, most critical use cases in your own business, and commonly faced challenges.
Explore – This requires more attention from the CIOs or tech leaders of the business. The front runners of the data transformation plan should team up with potential technology partners and draft a blue print.
Engage – Create a practical roadmap and present it to the board to get a complete buy-in and confirm an active business leader sponsorship. This is the cue for POCs and pilot projects. Analyze the existing in-house talents and identify the need of upskilling or expanding teams.
Execute – Use the momentum of pilot project’s success to apply the learned lessons on a bigger business use case. Identify the business processes that can benefit from Data transformation and draft detailed execution plans for each.
Embrace – Document the quantifyable outcomes and compare the results with expectations every now and then. Initiate org-wide communications and spread awareness about the big changes and prepare the workforce for it.
Evolve – Establish a Center of Excellence to assess the trending big data tools and see if any of them can accelerate your data transformation journey. Strengthen your data/information governance, privacy, and security practices. Work on resilience.
Why is ‘now’ better time than ever for Big Data in Telecom?
The rise of smart phones, 5G, OTT platforms, and device connectivity technologies has shapeshifted the positioning of Big Data in Telecom industry. Although there are undeniable challenges like Data heterogeneity, siloed sources, extra-large arrays, and processing complexities; exploring business use cases and opportunities of big data in Telecom can improve the customer insight which directly impacts the quality and diversity of service given to the users and customer experience. This in turn impacts both your top-line and bottom-line.
If we look at the growth of Big Data in Telecom over the last 5 years,
AT&T started their Data science journey to better understand their customer needs and now the American telecom giant is investing millions of dollars every year to build millions of lines of code to support their research and development of AI-based network technologies and the corresponding data ecosystem. They are leveraging IoT and monitoring their devices in real time to fix problems remotely before even the customer notices it. VP of AT&T’s Big Data division also said in a recent interview that the customer service call durations has been cut down to half since they eliminated unnecessary steps of customer identification and verification.
Deutsche Telekom, the largest CSP provider of Europe and 5th largest world-wide went a step ahead and created a range of big data products and services and successfully created a parallel business line and monetizing the decades of data. Telekom says not every business needs to have a full-blown data center to be able to analyze data. Their solutions that are built with code-to-data paradigm apparently saves bandwidth and transmission costs.
Telecom industry is tapping into data sciences and started benefitting from it. According to a global forecast conducted by Markets and Markets, worldwide big data market is going to witness a tremendous growth and would value $ 229.4 billion by 2025 which was $ 138.9 billion in 2020, at 10.6% CAGR. If you are into telecommunications and still didn’t start monetizing data, you might soon end up having to pay for your own customer insights.
Here are a few proven outcomes after applying Big Data in Telecom
Emergence of 5G networks, IoT adoption, demand for real-time threat detection mechanisms, and hyper personalization efforts of marketers need more actionable insights. Did you know World Economic Forum estimated that 463 exabytes of data will be created each day globally by the end of 2025. One Exabyte has 18 zeroes. Yes, that’s huge! As we mentioned earlier, Telecom businesses have an advantage of huge volumes of data with rapid flows. On the contrary to popular opinion, too much volume and velocity of data is also a challenge. But not if your journey of Big Data in Telecom started with a clear understanding of status-quo and scalable strategy.
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