How Batelco Automated its Data Quality Monitoring Using More than 150 Performance Metrics


Learn how Kamran Bader, Director of Data and Analytics at Batelco, utilises data and analytics to transform the organisation’s digital strategy.

Datatechvibe spoke to Kamran Bader, Director – Data & Analytics at Batelco, Bahrain, about understanding the data value chain to transform it into a meaningful resource for the business. 

At Batelco, the leading integrated communications provider in the Kingdom of Bahrain, Bader leads the Data and Analytics team responsible for creating business value from its extensive data assets. The team oversees the entire data value chain, from data quality and governance to developing machine learning models, which are then integrated into their customer value management and engagement programmes.

The function requires collaboration with diverse teams across the organisation to ensure value is added at every data lifecycle stage. For example, in partnership with the technology teams, Bader’s resources focus on building and maintaining a robust data platform utilising cloud and on-premises solutions. This encompasses capabilities such as Data Lakehouse, Data Orchestration, and Real-time Event Processing. With the business teams, the work is centred around enabling data-driven intelligent automation to achieve key business objectives. It includes implementing propensity modelling to enhance customer engagement and reduce attrition, employing real-time analytics for decision automation and faster service delivery, and utilising location intelligence for real-time geo-marketing.

“It is imperative that our business stakeholders understand the value generated by effectively utilising data capabilities. In today’s data-driven world, data literacy is a crucial skill, and it is our responsibility as data leaders to promote and increase data literacy within our organisations,” says Bader.

Excerpts from the interview;

How is Batelco utilising data and analytics to gain insights into customer behaviour and preferences, and how has this impacted product development?

Data is a key component of our digital strategy. We have realised that data capabilities are not enough to extract value from this resource. Just like crude oil needs to be refined, and the supply chain needs to be developed to monetise it, data also needs a value chain to transform it into a valuable resource. At the fundamental level, the data value chain consists of three stages: data, discovery and deployment. Data capabilities are aimed at extracting, storing, and processing data. Discovery capabilities are focused on generating actionable insights and intelligence from data. Deployment capabilities are aimed at automating processes and embedding intelligence in these processes to achieve business objectives. We are developing competencies in all three areas simultaneously to realise the benefits of data, not only for the organisation but also for our customers and partners.

Let’s take a use case to demonstrate how competencies in all three value chain stages are needed to create hyper-personalised real-time marketing capabilities. 

Consider a Mobile App user who has repeatedly viewed a mobile phone on the app without adding it to their shopping cart. Once this customer passes near our retail shop, we should be able to push notifications in real-time to this customer with a personalised offer for the phone. This offer should be based on an analysis of the customer’s past purchases and payment behaviour to maximise the relevance and probability of accepting the offer. To enable this use case, our data capabilities need to go beyond structured data to include semi-structured and unstructured data. Logs from Mobile Apps are a rich source of customer behaviour data and must be stored and processed. Our discovery capabilities need to be enhanced to include machine-learning models which can identify relevant behavioural triggers for each customer. Deployment capabilities like real-time location-based campaigns are required to activate the intelligence extracted from the data and discovery systems.

What advice would you give data leaders to ensure the quality of data across the organisation, and how to measure the effectiveness of these efforts?

Poor data quality can lead to inaccurate decision-making, missed opportunities, and wasted resources. Data leaders must, therefore, give high priority to data quality measurement and improvement capabilities. Data quality requires a robust data governance framework which defines data policies, standards, and procedures. The challenge is aligning all stakeholders involved in the data lifecycle on this framework. To build consensus, you should start by highlighting the implications of poor data quality and demonstrate the business impacts. Once awareness has been created, the next step is to assign responsibility by including data quality KPIs in the performance objectives of relevant stakeholders. 

To sustain data quality initiatives, it is important that data quality measurement is automated and is not dependent on manual collection, analysis, and reporting of KPIs. An automated process will ensure unbiased reporting of data quality KPIs and effective tracking of data quality improvement initiatives. As an example, we implemented a data quality monitoring solution last year that measures data quality along four dimensions: completeness, duplication, accuracy, and consistency. Over 150 data quality performance metrics are monitored daily, and an overall DQ score is calculated for the company. This score can be broken down into domains and DQ targets assigned to each domain owner. This monitoring system enables us to track progress as well as measure the effectiveness of data quality improvement initiatives.

Tell us how data leaders can measure the success of new initiatives like with Batelco’s Digital Shop, for example. 

Data leaders can measure the success of new initiatives by establishing clear goals and key performance indicators that align with the organisation’s business objectives. Business goals may include an increase in revenue, improved customer satisfaction, or reduced costs. Once goals are defined, data leaders should identify KPIs that help measure progress. KPIs may include conversion rates, retention rates, incremental revenue etc.

It is important to establish a baseline before implementing new initiatives. This will provide the starting point for measuring progress and determining the impact of the new initiatives. As we monitor progress, we should make sure that we are open to changes and adjustments to the initiatives and are not rigidly following a plan irrespective of business impacts.

Our digital initiatives are linked to goals and objectives established by our digital transformation strategy. The digital shop not only results in reduced operating costs for the company but also enable us to provide personalised services which are more relevant and convenient for our customers. We have a well-defined set of KPIs that track our digital initiatives’ performance.

What do you look for in enterprise technology partners?

The data and AI landscape is complex, with technology companies competing in some areas and collaborating with the same companies in other areas. There is a significant drive towards data and AI technology convergence. Therefore, alliances and technology partnerships are becoming critical components of an organisation’s data strategy.

Our approach is to partner with technology providers who can help us achieve business impact with their technology. Our emphasis is not on deploying the latest state-of-the-art technology. Instead, we seek solutions with a proven track record that can help us quickly realise value from technology investments. It is common in enterprise technology projects that there is a big gap between what is presented during a sales pitch and what gets implemented. It is vital that we can rely on our technology partners to deliver what has been promised. The most important criteria for us when working with technology partners are trust and open communication.

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