True, many are achieving relative success by implementing the technology to support strategic activities. Key examples include delivering contextual customer engagements, providing next best action (NBA) and next best offer (NBO) recommendations to service reps and driving intelligent virtual assistants, among others. Yet, a key problem remains in the data itself, which is limited and siloed – meaning they are only relevant for specific use cases.

The issue is that as long as data is siloed, the insights cannot be leveraged for the benefit of the entire organization. And, as long as the data sets are limited, so will be the insights that can be extracted from them.

Taking a centralized approach

“Many CSPs have struggled to get expected benefits from their AI projects, with many of proof-of-concepts failing to be put into full production.” (“More advanced telcos are using Centralized Intelligence to deliver the full promise of AI,” Analysys Mason, 2020)

To deliver the full benefits of AI to the entire organization, service providers need to move away from the siloed approach and shift to a more holistic and centralized approach (“AI on top”) that utilizes the data and insights generated by each individual department and line of business.

A Centralized Intelligence primer

“AI on top,” otherwise known as Centralized Intelligence (CI) entails:

• Consolidating data from across the organization, systems, and applications
• Making insights available and equally accessible to anyone in the organization
• Driving governance processes for data and insights, ensuring privacy and security.

Such an approach brings many benefits, including higher utilization of data by drawing from larger, more consistent data sets; insights that can be shared and leveraged by multiple departments and LoBs; and the elimination of effort wasted on replicated insights.

That’s why it’s no surprise that in the recent survey report by Analysys Mason sponsored by Amdocs, nearly 60% of service providers considered the implementation of CI to be a high priority. Additional motivators for prioritizing CI include the need for automation (50%) and the need to fully support the operational automation required for running 5G processes efficiently.

The blueprint for a successful CI implementation

As with any strategy, the approach towards implementing CI must be properly thought out and planned. Here’s our blueprint for maximizing its success:

All roads lead back to data

The most important pillar upon which a successful CI implementation rests is – data. Good quality data is the key to driving high-quality and timely insights. According to Analysys Mason’s report, the key to ensuring the quality of data is as follows: “Tools that provide this should be telecoms-aware, where possible, to support telecoms data sets and use cases to help reduce effort and provide a template of ideas for a CSP’s specific needs.”

Next: the data layer

Once you have your data right, the next step is to build a centralized data layer that will be a subset of any central data lake that is available.

Management support & project leadership

Two more critical components to the success of a CI implementation project include securing support from management and ensuring robust project management leadership to mitigate data ownership and flow, and to ensure overall delivery effectiveness and efficiency.

Change management

But it’s not just about delivery – it’s also about cultural change. Whenever we break down silos in an organization, there is always a need to align and enlighten, so that every relevant stakeholder will understand the new benefits that are available to them and how to make sure they don’t miss out.

Got skills?

The final piece of the puzzle is to have enough AI-proficient data experts on hand to execute CI implementation and ongoing operation. Though, according to Analysys Mason, the early adopters of CI have cited that not having enough trained staff is probably their most significant challenge.

The right partner

This is where the right partner comes in. That is, to accelerate the implementation of CI and to expedite the delivery of its benefits, service providers who are adopting CI should turn to a partner who has the requisite AI and data skill sets, as well as telecoms-specific industry expertise and tool set for addressing the unique use cases and AI and data needs of communications and media service providers.

Want to see how centralized intelligence can also help your organization? I invite you to reach out to me directly to learn more.