Why do some CSPs still struggle with utilizing the potential of data they have? Over the past few years, the phrase big data has moved from being a fancy buzzword into daily routine for most companies, CSPs included. Nowadays CSPs are actively using technology like Hadoop or another big data platforms in their infrastructure.
Let’s have a closer look at how big data topics look like in a CSP’s world. In general, there are 2 main approaches to the use of big data.
Top-down approach is when we start with a already defined problem and big data is essential to finding the solution. This approach is mostly represented by advanced BI analytics and is not sufficiently utilizing all of the big data benefits. It reflects the CSPs operations and issues related to core lines of business.
Bottom-up approach is mostly about proofs of concept, thinking out of the box, validation and testing of ideas. This approach is strictly linked with internal change of strategy, and change of behavior within telco organizations. Telco companies that can adapt their internal organizational structure to this new way of thinking are more likely to be successful in the big data game and gain subsequent benefits. In big corporations which CSPs are, this is at present one of the biggest challenges.
If we look closer at the challenges we can point out the following:
- Unclear governance and roadmap
- Lack of resource allocation
- Lack of relevant people (data scientists, influencers and so called bilingual people – those who understand both IT and business well)
- Missing agile approach and space for employees to work on innovative ideas
Of course there are significant differences between CSPs and while we know about success stories in the big data domain, many CSPs are still not getting benefits from the data they collect and store.
Due to the complexity and focus of the daily routine of CSPs, it will be quite difficult and time consuming to adapt their internal environment. CSPs are primary focusing on using big data to solve their usual business issues, internal use cases mostly related to network investments optimization and planning, sell, upsell and churn predictions, and from time to time we can also see a few cases of boosting customer experience.
Points mentioned above are categorized as internal use cases. On another hand we have external use cases. Data rich companies, such as CSPs, have big databases of customer information ready for monetization. They also have several years worth of consents for storage, aggregation, and commercial use of customer data. However, based on our knowledge, external use cases, or external monetization is often overlooked by CSPs despite the fact this area can be a significant stream of new revenue, a way to differentiate and a means to improve internal processes & insight as well.
Here are several tips on how to successfully proceed in big data monetization:
- Define stakeholders and key contributors within the company
- Prepare and validate short term and mid-term strategy
- Define list of use cases (max 10) and a roadmap and make sure the use-cases have a clear business foundation
- Include both internal and external use cases
- Identify key external contributors that can help you to fulfill certain use cases in your strategy
External big data contributors include Instarea and are able to provide ready made and industry proven solutions for external big data monetization. The solutions can be customized to fit the specific company and they can be delivered in a time frame ranging from only a few months with ROI within the 1 year. We also feel building internal capabilities is important and thus see our cooperation with CSPs more as a partnership & knowledge transfer, rather than a pure delivery.