Advanced Analytics & Data Governance
Data & analytics (D&A) promises players in the telecommunications segment far-reaching competitive advantages
Data & analytics (D&A) promises players in the telecommunications segment far-reaching competitive advantages
For telecommunications providers and other players in the telecommunications segment, the era of artificial intelligence (AI) has long since dawned. Whether optimizing customer service, analyzing mobile user data or visualizing use cases such as self-organized networks, AI initiatives have become an integral part of every telecom provider’s strategy. The ambition to generate business value through data-driven methods and transform entire business areas is already being driven forward, and revenue growth, customer value, innovation and risk management are key value propositions.
Telecommunications providers face a paradox: while mobile devices, network data, and customer interactions generate more and more data to understand customers better, the market and the general business environment are becoming increasingly complex and volatile.
We believe competitive advantage is generated where technology, people, and business expertise meet. To unleash the potential of big data, D&A solutions must be deeply woven into processes and ways of working. With years of project and industry experience, our project teams use a combination of business know-how and technical expertise to guide your company on its successful journey to becoming a data-driven company.
Our long-term cooperation with our clients in the communications, media and entertainment sectors shows that data & analytics projects often struggle with three typical issues.
There are two typical process models for data-driven initiatives. Often, D&A projects are managed by the IT department, which usually lacks the necessary business know-how. As a result, these projects often focus too much on technical aspects and neglect the added value. However, if pure business departments steer AI projects, there is often an underestimation of the technical complexity with devastating consequences for project planning. Therefore, an interdisciplinary project team and the involvement of “translators” are elementary for project success.
According to a Gartner study, only 15% of data & analytics strategies use concrete success metrics – with the result that projects often fail to deliver the hoped-for benefits. Defining clear KPIs is usually due to the difficulty of measuring the concrete and isolated benefits of data & analytics initiatives. However, linking to ROI or similar KPIs should be a central prerequisite for a project launch and an appropriate buy-in by executives. Relevant KPIs should be defined at the start of the project.
Another fallacy is the attempt to transfer classic waterfall planning to the area of data & analytics. Data-driven projects – especially in AI – carry a high degree of uncertainty and dynamism due to their inherently more experimental nature, which is often in direct contrast to management’s desire for planning security and defined long-term milestones. In such a volatile project environment, agile methods that include regular feedback loops with the relevant stakeholders and can quickly take countermeasures due to changing framework conditions have proven their worth. At the same time, this enables a close link with the needs of the end-users.
Our long-term project experience shows that at the beginning of data-driven projects, the benefit for a company must be clearly defined. For this purpose, we have developed a value-based process model based on our experience, which is centered on the idea of always approaching data-driven projects from the perspective of business benefits. A detailed data analysis should only take place after defining concrete questions or possibilities for decision support.
Based on strategic objectives, we first identify promising use cases in collaboration with you and subsequently evaluate feasibility and business value. Finally, we define a target picture with the relevant stakeholders, for example, in the form of a user front end. The implementation would include phases of data analysis, model development and evaluation of the generated added value.
Whether it’s an AI strategy workshop to identify use cases, developing a proof of concept for a cross-departmental dashboard or end-to-end implementation, our experts support you all along the way on your AI journey and help you bridge the gap between AI strategies and day-to-day operations. We bundle our D&A offerings as part of five service offerings – but we’re also happy to advise you on individual requests at any time. Please feel free to contact us!
