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How CSPs are Taking Advantage of Big Data Ovum Ovum TMT intelligence | ovum.informa.com Adaora Okeleke Senior Analyst
How CSPs are Taking
Advantage of Big Data
OvumOvumTMT intelligence |
ovum.informa.com
Adaora OkelekeSenior Analyst
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Investment in big data is a high priority for CSPs. Current business challenges and priorities are driving CSPs to identify how they can better exploit their data assets. When asked to rank big data as a priority on a scale of 1–10 (with 1 being least relevant and 10 being most relevant), more than half of our survey respondents selected a high-ranking score of between 8–10, implying that CSPs see big data as being instrumental to the execution of overall company strategies.
Big data encourages and enables correlation that was never possible before, and is providing insights that were never exposed to decision-makers before. CSPs are looking to big data to support business decisions that will drive strategies in key areas such as revenue growth, customer experience and customer intimacy, network and cost optimization, security, and fraud management. CSPs need to utilize the vast volumes of data being generated by their networks, operations, and customers which when analyzed provide insights on new revenue opportunities, and new ways to deepen relationships with customers. The ongoing deployment of SDN/NFV within the CSP network is expected to generate more network data and therefore drive more focus on big data. As the state of virtual network functions (VNFs) change, the amount of network traffic generated, results in a rapid increase in
network data. To achieve the network agility promised by SDN/NFV, CSPs will require big data to inform the SDN controllers on actions required to deliver consistently high-quality services to subscribers.
Delivering personalized customer experience remains a business challenge for CSPs
Consistently offering quality customer experience requires CSPs to deliver services targeted at the actual needs of their customers. Service personalization demands that CSPs have a deep understanding of who their customers are and know exactly what they want or what their intentions are at every point along each customer’s journey. Unfortunately, CSPs still find it difficult to deliver personalized customer experience.
Indeed, Ovum’s 2016/17 ICT Enterprise Insights survey of 476 C-level telecoms executives indicates that creating a personalized customer experience was CSPs’ top business challenge in 2017 (see Figure 2).
One of the key challenges for CSPs is that their customers now expect them to deliver the same type of experience
they obtain from digital service providers (DSPs) such as Amazon, Facebook, and Google. Amazon collects and analyzes data relating to every customer’s purchase history and uses the insights to provide uniform and personalized recommendations to customers each time they log onto its e-commerce website or applications. As CSP customers are now used to this type of experience, when they call up a CSPs contact center they expect that the care agents have an idea of why they are calling and are ready to provide recommendations tailored to their needs.
Fulfilling customers’ desire for personalized service delivery should not be a challenge for CSPs given the level of information they already have on their customers such as customer spend patterns, online activities, and social networks. However, it is critical that customer consent is obtained before these data sets are used to ensure customer privacy is not violated.
The difficulty for CSPs is that the ever-expanding customer engagement channels (and systems supporting these channels) tend to operate in silos. Until the centralization of these data sets is achieved, it will prove difficult to build robust profiles and offer their customers the personalization they crave.
Sixty-five per cent of respondents have set up an organizational structure to manage big data programs
Key insights coming from our survey indicates that most CSPs are running big data programs. Amongour survey respondents, 65% indicated that they have a formal big data programs. These programsconsist of multiple projects which in most cases run concurrently and are expected to run between 5–10 years. Examples of CSPs with big data organizations include AT&T, Axiata Group, NTT DoCoMo, Telefonica, and Verizon.
The key drivers of these big data programs often come from the marketing departments with almost50% of respondents indicating that the marketing department leads these big data programs. The marketing department now sees that the correlation of data from multiple departments provides valuable insights.
The creation of the role of a Chief Data Officer (CDO) is also becoming a trend in the telecoms industry. As big data begins to play a more strategic role within the CSP, the CDO becomes critical in governing the use and exploitation of CSPs’ enterprise data resources and in sharpening the direction of CSPs big data initiatives.
Over the last three years, big data has moved from hype, to investments, to actual implementation. To investigate current trends related to big data, Ovum conducted a survey to investigate how communications service providers (CSPs) have developed their big data programs. It reveals that 60% of CSPs see big data as critical to their business strategies. However, the results also suggest that there are plenty of challenges for CSPs to overcome if they really want to take advantage of big data.
Big data remains critical to CSPs’ operations
Creating a personalized customer experience
Launching digital services
Applying big data analytics to business processes
Transforming enterprise operations
Launching cloud services
Moving to Agile business processes
Adopting (public) cloud delivery models
Moving to a virtualized environment (using SDN and
Supporting omnichannel services
0%
4 (Very important) 1 (Not important)3 2
100%90%80%70%60%50%40%30%20%10%
Figure 2: Top business challenges for CSPs
Sample size: 476
Question: How important are the following business challenges?
Vertical: Telecoms. Telecoms provider type: All. Country: All. Enterprise size: All.
Source: Ovum’s 2016/17 ICT Enterprise Insights survey
Big data investments are dominated by internal use cases
Sixty-five per cent of respondents highlighted that big data investments have been focused on internal use cases (those focused on improving internal business operations such as marketing, sales, customer care, network operations, security, etc.)
Pursuing these internal use cases presents opportunities for quicker returns and allows CSPs to exploit their data resources while not violating privacy regulations. For example, to get back to growth, CSPs are adopting big data to find new ways of retaining their customers. Most CSPs are utilizing their big data investments to identify how customer behavior, and consumption patterns and preferences – in combination with network-related information – would help drive better and more efficient ways to exploit assets and deliver better operational performance.
External use cases are still important, but CSPs are treading with caution
While most respondents indicated that they have deployed more internal uses of big data than external use cases, more than a third indicated that they have deployed an equal proportion of internal and external use cases. By external use cases, we refer to the external monetization use cases where CSPs seek to use the insights they have from the network and their customers to support enterprises across several verticals such as financial services, security, and mobile advertising.
Geolocation analytics based on information provided from mobile data can be considered the most sort-after information from brands across industries. For example,
banks in the financial services market can use geolocation insights pulled out of a customer’s calling patterns or base station information to identify customers’ actual locations. In the Internet of Things (IoT) depending on the functions of the IoT devices, CSPs can also pull and analyze the IoT-related data, and obtain insights on the device and its immediate environment, as well as the users’ life style.
According to the survey participants, the biggest opportunity derives from digital advertising, given the pervasive use of mobile devices and how consumers are spending more time on their mobile devices than on PCs (desktops or laptops).
Digital advertising as a type of advertising service has become a key focus for top-tier CSPs such as Telefonica, SingTel, and Verizon, with other CSPs closely investigating the opportunity. Brands see value in using CSP data to enrich their digital advertising campaigns.
The core areas of focus for CSP big data programs
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Data remaining in silos is a top challenge for CSPs
CSPs encounter multiple challenges when executing big data programs. However, one in particular stands out. One in every two of our survey respondents indicated that data remaining in silos is the biggest challenge they face when executing their big data strategies. The data silo challenge creates the core that leads to the challenges that CSPs face with security, privacy concerns, and data quality.
Challenges faced by CSPs deploying big data
CSPs must deal with a lot of legacy systems which have no provision to integrate with other systems. Pulling data out of these systems and integrating them with big data platforms is very difficult, time consuming, and expensive. The data silo challenge also creates a data governance issue as different data repositories have differing policies that relate to access and storage. Without a centralized data governance structure, executing any big data program will be difficult, if not impossible, to achieve.
The siloed nature of the CSP organization results in duplication of costs with respect to how big datadeployments are being performed. Some organizations such as marketing and customer service/care-run disparate big data initiatives which are limited to the
confines of the department. This approach leads to additional costs as well as duplication of efforts. The benefit of big data is the varied levels of insights that merged data sets obtained across all organizations can bring to the entire business. Thus, data silos stifle every big data effort.
Security and privacy concerns limit the extent to which CSPs can use big data
As CSPs exploit their big data assets, CSPs are careful not to cannibalize existing services and customer relationships by infringing on customer privacy or breaking any data protection rules.
Data remaining in silos
Security/privacy concerns
Lack of qualified staff
Access to quality data
Legacy systems
Effort required to cleanse data
Not understanding the potential that big data offers
Senior management buy-in
Budget
50%0% 10% 20% 30% 40%
Figure 9: Data silos remain a challenge for CSPs’ big data strategiesBiggest challenges when executing your big data strategies
Proportion of respondents (%)Source: Ovum’s 2017 Big Data survey
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While the data breach issues related to CSPs such as TalkTalk and Three Mobile relate to cyber hacking incidences, the impact of these issues reflects how critical it is for CSPs to protect customer data and adhere to industry regulations. TalkTalk, for example, is reported to have lost more than 100,000 customers following the cyberattack that took place in 2016.
Lack of qualified staff
CSPs do not have all the skills and qualified expertise required to take full advantage of their big data assets. Given the strong focus on internal use cases for big data, more investment has gone into developing internal expertise to support these use cases. However, more effort will need to go into building out expertise required to serve the external use cases.
Access to quality data
Insights drawn from big data are as good as the quality of the data that is used to generate them. Most of the big data generated is either not standardized, is full of errors, or does not include all of the information required to run the analytics models and algorithms, or in most cases are duplicated records.
Taking a strategic approach will help counter big data challenges
Several CSPs have adopted different approaches to resolve their big data challenges, the most common of which is proving the value of big data. Almost half of our respondents indicated that they have taken this approach.
When looking to improve its fortunes after experiencing strong competition from its peers, T-Mobile US set up a growth initiative focused on exploiting its data assets. The CSP understood that to deliver its customers’ needs, it had to consolidate customer data that sits across the organization. T-Mobile started building a single data lake for the organization and then used it as a bartering chip to get cross-departmental buy in; the other departments were encouraged to store their data in the lake, if in return they provided the business with access to a small portion of it. T-Mobile got a single view of the business which enabled the company to launch its Un-carrier program, based on customer insights.
CSPs also need to extend their big data partner ecosystem to include companies with data science skills and industry specific expertise. Verizon and AT&T are engaging with vendors that have helped to upscale their big data initiatives. Both CSPs are also leveraging universities and the academia to get the best big data talent to drive their big data programs.
In addition to setting up a formal structure to drive big data programs, CSPs have indicated that they plan to increase current investments in these programs. About half of the respondents indicated that they would increase investments in big data by 20–50%, with 24% indicating that they would increase investment by more than 50%.
More than half of CSPs prioritize investment in predictive analytics and Artificial Intelligence (AI)
Although technologies like AI are not necessarily new to the CSP industry, the demand being placed on these tools is evolving. For example, AT&T has claimed to be using AI for more than 10 years; however, the kind of AI capabilities adopted were more reactive than proactive. Customer requirements such as on-demand services which require real-time service provisioning and activation capabilities are driving CSPs to seek out AI capabilities that can automate existing operations.
CSPs plan to increase current investment in big data programs
Big data remains critical to CSPs’ overall company strategies as it generates insights that solve the industry’s key business challenges. Right now, investment in big data programs are being driven by internal use cases, but external use cases are also important to the industry.
However, CSPs continue to face challenges with their big data programs. A lot of data remains in silos, there are security and privacy concerns, as well as inadequate skills and poor data quality.
Despite these challenges, CSPs plan to grow investments in big data with investments going into the development of robust big data infrastructure, and analytics tools such as predictive and AI tools.
To really take advantage of big data, CSPs need to develop a well-structured framework (big data organization) that governs the data, technology, and processes which will define the success of big data initiatives.
Moreover, while there are many competing opportunities to be explored with big data, to maximize both value and visibility from the start, it is important to stay focused on solving the most immediate business challenges, backed by close monitoring of performance following deployment.
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In summary
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