Part 1: The importance of MDM
Data volume in the telecommunications sector is growing at an incredible rate and organizations need to find solutions to various data challenges that may arise.
Not only should you expect to encounter challenges in storing data, but also in streamlining the different processes and workflows needed to manage it efficiently. This includes sourcing data, ensuring its quality and uniformity, and providing access to relevant users, among other activities. These can be difficult to achieve with several data sources and systems spread across the organization, which is usually the case.
This is why a consolidated view of data is critical, and that’s where master data management comes in. The goal of MDM is to integrate information into a unified data warehouse and a single source—for a golden record—in order to utilize data optimally. Adopting data governance practices is needed to implement MDM, but you can also take this a step further by automating the process through machine learning.
No shortage of data in telecoms
As the enabler of modern communication through infrastructure and network services, the telecom industry is awash in big data. From network performance to consumption habits, data is constantly being generated and collected by telco organizations.
The COVID-19 crisis has boosted this surge even further. With the increased need for connectivity and digital services, the industry has seen an uptick in usage, consumption, and data. To illustrate: mobile voice call minutes has increased as much as 60% during the pandemic.
Related: From office to home: The new telco landscape in data, analytics & more
Without the proper data strategies however, this wealth of information cannot be fully utilized. Data in its raw, unfiltered form can do more harm than good. In fact, poor quality data costs organizations $14.2 million annually, according to Gartner.
What’s more, with separate data systems spread across an organization, data can be fragmented, duplicated, and inaccurate. Telco organizations usually employ different vendors and sub-contractors to deliver, and the use of different data sources, schemas, and technologies often makes it difficult to ensure data quality. This can lead to costly errors, loss of productivity, and even customer dissatisfaction.
For example, let’s say customer A has just signed up for a mobile plan upgrade. They filled out the needed information on the website, received a confirmation message, and saw the updates on their personal account dashboard. A day later, they receive a call from the telco’s sales department, offering the same upgrade. Customer A is annoyed, and this would not have happened if the sales representative had a golden record—a unified view of that customer’s personal, service, and other information.
Another example is in infrastructure and network management. Operators ensure the smooth operation of networks by analyzing data from different sources such as user devices, routers, switches, and base stations. Without an integrated view of data, conducting a root cause analysis can be challenging and prone to errors. The wrong factors might be identified as the root cause, or KPIs such as packet loss ratio, latency, and traffic load might be inaccurate.
Telcos must strive to adopt data management best practices to ensure the availability, usability, integrity, and security of enterprise data assets. Establish data governance, which consists of the policies and procedures that a data warehouse team and the organization can follow when managing data. And ensure data stewardship, which ensures accountability and responsibility for those policies and procedures.
With these in place, you can take data management a step further by ensuring the uniformity, accuracy, and consistency of your data assets through master data management. With a unified view of data, you can easily get the answers you need, get the most up-to-date information, and confidently make decisions.
Where machine learning comes in
Master data management is a complex discipline that involves a lot of moving parts to ensure success. To create master data assets, there are two basic steps: cleaning the data and matching the data to eliminate duplicates and discrepancies.
Both processes can become very tedious and time-consuming if done manually. In fact, cleaning data usually takes up 80% of a data scientist’s valuable time. And while the traditional data matching approaches like deterministic matching and probabilistic matching do yield quality results, these can still benefit a lot from automation and data modeling—making them more accurate and efficient.
A predictive machine learning algorithm can produce consistent unduplicated data for enterprises, ensuring a consistent and uniform core data set. It can determine if a record is a duplicate and score it based on its accuracy as a match. It can then automatically merge it with existing records, if required.
For example, to ensure network quality, engineers are often tasked to change parameters such as frequency plan, traffic prioritization, or bandwidth. This is often done by manually changing values in specific databases, and a quality check—also manual—is conducted to ensure that there are no database inconsistencies, incomplete data, or entry errors. This entire process can be streamlined, and unwanted dirty data can be avoided with machine learning automation.
This entire process can be streamlined, and unwanted dirty data can be avoided using natural language processing to parse the data and a reliable machine learning pipeline to cleanse the data. The same goes for other external data sources that telco organizations often need to perform analytics: mobile signaling, DPI, M2M, internet application data, streaming data, and so on. To enhance the process automation level for cleaning and managing these data sources, telecoms must shift away from manual intervention.
Must-read: How to build machine learning models in 4 steps
Machine learning involves applying complex mathematical function to analyze bigger, more complex data and deliver faster, more accurate results—even on a very large scale. By inserting machine learning into the mix, you can automate data matching and speed up the master data management process.
Simplify data management with MDM
Say goodbye to disjointed systems and silos. By making master data management, data governance, and data stewardship your prime focus, you can manage information efficiently and effectively. Not only does a single version of the truth facilitate better organizational alignment and interdepartmental collaboration, but you can also expect improved data quality, enhanced scalability, better data compliance, boosts to productivity, and reduced costs.
Read Part 2 here >