Big Data Analytics
THE Bottom Line
Linx has undertaken multiple Competitive Intelligence (CI) projects in the Big Data Analytics space, focussing on pricing models for Big Data Analytics (BDA) platforms demanded by CSPs and supplied by other vendors in this space. We have extensively interviewed key vendors who provide products and solutions in the BDA space, as well as CSPs on the demand side of this market, in order to understand the licensing criteria and use cases being deployed by CSPs globally for their BDA deployments.
For all of our projects, Linx relies on in-depth, double-blinded interviews with key executives at leading and niche players in the industry including vendors, service providers, customers and systems integrators / channel partners. Every study we complete is based on specific and detailed interview protocols established together with our clients that drive toward producing actionable intelligence.
What does our research show?
The leading global CSPs that we spoke to identified a good diversity of BDA use case requirements, both in our interviews and online surveys. In terms of survey results, Customer Segmentation, Policy on Device Implementations, and Order to Activation Optimization received the largest number of responses (14%). These were followed by Cross Sell / Upsell of Products and Services (12%) and mobile display advertising (12%). Social Network Analytics for Marketing, Direct Marketing Campaigns, Fraud Management, Personal Data Regulation and Compliance each received 10% responses in terms of CSP`s BDA application use cases. This diversity of use case requirements also came out in our interviews with various CSP executives. Our interviewee at Orange, for example, stated that Orange is currently evaluating over 100 use cases that they are looking to implement across their network.
In our view, another important use case for BDA platforms is how it can act as an enabler to CSPs that are wanting to sell their anonymized and aggregated data to their enterprise clients in various verticals. For example, our interview with an executive at Telefonica Digital showed that its BDA platform uses anonymized and aggregated data from Telefonica's networks, including machine-to-machine (M2M) data, to develop a range of products and services that it sells to its customers with a price range of €50,000 - €300,000 annually. In terms of the business model, Telefonica in Spain is selling network-based information on their mobile users to the transportation and retail sectors. They provide three types of products to customers: Aggregated and anonymized information, Analytical support, and Licensed software. We found this business model also at other CSPs we interviewed.
In terms of software pricing, the vast majority of respondents (88%) to our on-line surveys said that software is paid for by CSPs through either an up-front fixed license price (62%) or a fixed license price spread out over a
2 – 5 year period (25%). It is, therefore, clear that CSPs are currently paying for their BDA software through a traditional software pricing model.
In terms of licensing criteria for software, only 13% of CSP respondents said that software licensing was on a `per subscriber` basis. 45% of respondents said that software licensing was on a throughput basis (presumably a perpetual license) while another 32% of respondents identified a `right to use` model.
These points are broadly in line with our interviews with CSPs and vendors where cloud-based analytics solutions seem to be important options for CSPs in addition to the traditional perpetual software pricing model. We note that as a response to one of our on-line survey questions, 21 out of 24 respondents (87.5%) said that their companies deploy both Hadoop-based and RDBMS-based data warehouse platforms, confirming this high proportion of CSPs that are paying for their software using a right to use model.
In terms of pricing models for data science and administrative services, CSPs are using a variety of pricing models. The largest number of responses (about 30%) was for hiring internal staff to perform data science and administrative services, followed by Time & Material-based fees (30%) and fixed fees where the scope of the project is well-defined (25%).
This is broadly in line with our interviews where CSP executives have highlighted that while most data science services are performed internally, Time & Material and fixed fees for well defined projects where also common pricing models for their BDA projects.
One nuance that is a key pain point for CSPs in the BDA space emerged from several interviews we have conducted. CSPs interviewed stated that they would rather hire data science and administrative staff internally than outsource these functions to external players because they are not confident about analytics vendors` capabilities in understanding telco-specific data.
Generally, hardware project budgets appear to be larger in size than software budgets for BDA at CSPs. For BDA Hardware, 54% of respondents in a recent survey we conducted said that the average budget size for their hardware spending was in the $1 Million - $5 million range and another 25% of respondents said that the size of their hardware budgets was in the $5 Million - $10 Million range. This can be compared to BDA software budgets, where 71% of respondents said that the average budget size for their software spending was in the
$1 Million - $5 million range.
Budgetary allocations for Data Science Services and Administrative Services were identical. For each of these, 46% of respondents we have surveyed said that their budget allocation was in the $1 million - $5 million range and another 42% said that their budget allocation was under $1 million for these two service categories.
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