X

An Oracle blog about Internet Of Things

Unleash the Wealth of your Data and Thrive

Guest Author

By: Andrea Canessa, Global Director, Communications Industry Solutions

Today’s communication service providers (CSPs) are rich in data—certainly the most valuable commodity in the Digital Age. Yet few have been successful at capitalizing on the data they have at their disposal. Meanwhile, over-the-top (OTT) providers have built lucrative business models on data monetization. A recent survey by Analysys Mason put this disconnect in stark terms: 89% of CSPs are pursuing or planning to pursue data monetization, but just 24% are finding it commercially significant. A significant minority, 11%, do not believe they can compete with OTTs at all.

The survey also uncovered several impediments to data monetization. The primary hurdle CSPs cite are privacy regulations such as the EU’s General Data Protection Regulation (GDPR), but they’re also challenged by a lack of technical skills and sales knowledge as well as the cost of systems needed to implement.

 

Challenges to Data Monetization

There are numerous ways to monetize customer data, but they boil down to selling the raw data to marketers and advertisers or selling customer insights derived from that data.

While packaging and selling raw customer data is the most straightforward, immediate, and potentially lucrative way to monetize it, CSPs have traditionally shied away from this approach. This is certainly understandable, given their long history in a highly regulated environment. Data security, privacy, and transparency regulations are looming ever larger, with jurisdictions enacting regulations like Europe’s game-changing GDPR. Given that OTT players successfully sell such data, and other regulated industries such as finance are also finding success, it’s reasonable to assume that this challenge, while not insignificant, is largely cultural and historical.

To avoid the regulatory issues involved in selling raw customer data, CSPs have attempted to derive insight from aggregated customer data and either utilize it internally (to improve customer service and experience, cut costs, and reduce fraud, for example) or sell it to third parties. While this approach largely circumvents regulatory obstacles, it adds a host of others—not the least of which is the value of the insights up for sale. Here’s a closer look at the challenges CSPs face when attempting to monetize data insights:

The talent drought: Creating insights requires data scientists, which are in high demand—making them difficult to recruit and costly to retain. Furthermore, data analysis is not typically part of a CSP’s core competency, so building it out would require significant structural and even cultural adaptation.

Static modeling: When CSPs do manage to create data models to drive insight, they are typically static, resulting in predefined models into which the data must fit. That may have been adequate with a limited volume of data, but these days, such limits simply don’t exist. Data is getting bigger and bigger, and static models—even if they are accurate—become quickly outdated. The advent of machine learning (ML) means that the model can be derived from unseen patterns in the data, rather than the other way around. These new models are dynamic, so they continue to yield valuable insight.

Sales structure: Just as CSPs are poorly adapted to deriving valuable insights from their data, they are equally unequipped to sell it. Doing so would require building a separate sales organization from scratch.

Organizational siloes: CSPs are among the largest corporations in the world, and departments such as IT and marketing have solidified into independent fiefdoms over the years. The CTO focuses on the network and the CMO focuses on customers, often with little interaction beyond what is necessary for business as usual.

Data siloes: As a result of these organizational barriers, network data and customer data don’t mix. The problem? Aggregating data multiplies its value, as it allows you to correlate it to derive insights. Adding in external data, such as social media, offers the potential for even greater insight. But if CSPs can’t even aggregate their own internal data, enriching it with external data is superfluous.

 

 

The Solution: Finding the Right Technology Partner

CSPs looking to monetize their data don’t have to go it alone. The Analysys Mason study concludes by recommending that CSPs seek partnerships with technology vendors that have the competence and technology to automate insight discovery and provide capabilities such as the deployment of a data monetization platform (DMP). Your partner should be able to not only upload and anonymize relevant data from any part of your organization, but also enrich it with multiple external sources. It should be equipped to sell the data while complying with data privacy regulations globally to provide a turnkey revenue-sharing solution that precludes the cost of building your own data science and sales organizations.

The large lakes of data owned by CSPs represents a tremendous opportunity as well as significant challenges that range from data privacy regulation to the lack of appropriate skills and technology. Oracle's Data Driven Innovations empowers CSPs to extract significant value from the largely untapped lakes of customer, device, and network data. With Data Driven Innovations, CSPs have one unified platform to aggregate, analyze, and activate data so they can create new revenue streams, differentiate services, gain efficiency, and power their digital transformation.

 

Be the first to comment

Comments ( 0 )
Please enter your name.Please provide a valid email address.Please enter a comment.CAPTCHA challenge response provided was incorrect. Please try again.