Saudi Telecom Company’s data governance journey

This is the first of a two part article mapping Saudi Telecom Company's path to effective, compliant data governance across the organization. In the age of digitization, data is the new gold. Indeed by 2030, data collection and analysis will become basis of all future service offerings and business models. Thus, Saudi Telecom Company (STC) created its corporate analytics and data (CAD) sector in 2016 to help meet its objective of introducing global best practices for data governance and management in the digital era. As shown in figure 1 below, the core areas of focus for CAD are data management and advanced analytics focused on both commercial and operational purposes. STC will use effective data management to build strong customer insights, to be able to offer more personalized solutions. Advanced analytics will meanwhile help to improve the quality and the efficiency of the company's ICT services.

Why data management and data governance?

Data management along with data protection is very much essential in the interest of protecting STC data and its assets. Knowing how rapidly data production and usage grows in STC, the company required a comprehensive and dedicated program. Initially, with data management being a vast topic, STC developed its own framework in early 2019 adopting the best practices from DAMA to cater to its specific needs, with a special emphasis on data protection. The approach evolved from passive and reactive data governance – where the focus was on addressing urgent data issues such as quality and discrepancies, to proactive data governance – addressing issues/problems before they occur by implementing policies and controls, data ownership and building other preventative, proactive governance. These defined controls and rules enabled and drove change in other key areas as shown in figure 2 below.

Challenges

STC identified and addressed the following challenges as effectively as possible through effective program management and foreseeing the challenges proactively:

• Low awareness of the program to all stakeholders (internal and external)
• Program direction
• Scope
• Selecting the right tools – STC didn’t have mature tools for implementing data, so the company turned to vendors
• Establishing an organizational hierarchy and accountability within the program

STC data governance pillars

STC’s pillars of data governance are people, processes and technologies (see pillar descriptions below illustration). The company has employed best practices as recommended in the DAMA framework, with the data governance program addressing all pillars for a successful transformation (see figure 3 below).

Since STC launched the data governance program, its CAD department has excelled in converting the organization into a data driven company by providing better insights on: customers behaviors, business performance, operational performance, and quality of service, making STC much more competitive in the regional market, with a positive impact on different areas of revenue, risks and other factor (see figure 4 below).

1. People: To introduce the right leadership, skills and cultural changes allowing STC to embrace a new way of managing data, with greater fact/insight-based decision making, and a strong attention to data quality and insight reliability.
2. Processes: To introduce the organizational and procedural changes in the way data is managed.
3. Technologies: Adopting tools to automate and accelerate data governance and data quality

STC data governance Journey timeline

The journey began 2016 with a vision for STC to follow the global industry data management best practices available, and continued as per the timeline below: 2016 STC created the Corporate Analytics and Data (CAD) sector, with the objective to introduce global best practices regarding the governance and management of data in the digital era. The program focused initially on deploying a compelling data governance practice in STC. 2017 It was the year of the “early wins”. CAD leveraged an interim ‘reactive’ model (as previously mentioned) to identify and address a number of data discrepancies (i.e. code inconsistencies causing incorrect reporting) fixing anything causing immediate financial and business liabilities 2018 CAD launched a new data governance model and data management platform to activate the data governance processes such as assignment of data owners, stewards and custodians, workflows for data quality, data access management etc. A new virtual organization model – the data governance operating model - was introduced, assigning accountability of data is assigned to specific business data owners (such as the CFO for financial data), supported by the relevant subject matter expertwhile IT continued to have the data custodian role (data related process management and coordination in the systems). CAD provided an overall governance for this new virtual organization model. 2019 CAD plans to roll-out all the other DAMA data governance capabilities/disciplines (Master Data Management, Meta-Data Management, etc.) fully supported by an automation platform that leverages the major data analytics technologies (Collibra, Microstrategy, etc.).

STC’s Data Governance Model

The most crucial part of STC’s data governance program was developing the data governance model. As there were no existing models, and all future data management activities were related to the required model, much consideration taken in referencing to best practices and proven adoption cases around the world. The organizational hierarchy of STC’s data governance model is shown in figure 5 below.

CAD is continuing its journey with focused goals and clear direction by adopting the best practices and following its pillars of data governance excellence (people, processes, technology). CAD now seeks to: The eventual goal is to see positive business change by defining data workflows and requirements through data governance, and STC can do this only by advocating a culture of data governance throughout the organization, empowering staff to embrace these values and act accordingly.

1. Adopt innovative solutions for data governance and quality that leverage automation and artificial intelligence (AI). This is to reduce time-consuming data processing, and use data for more value adding activities.
2. Extend the data governance concept to “analytics governance”; both are key competitive assets in the current and future digital landscape
3. Create an ‘Analytics Center of Excellence’ involving CAD, business units and IT, to incubate design-thinking activity for data, advanced analytics and artificial intelligence use cases, and evaluate their expected return