CFO Insights 2020 September

MASTER DATA MANAGEMENT - Your Digital Journey Starts here!

CFO Insights is a monthly publication to deliver an easily digestible and regular stream of perspectives on the challenges confronting CFOs. This time, we describe the digital transformation journey in this era and transition to an insight-driven organization.

Why the digital transformation journey matters even more now?

In the past eight months, the world of business and finance has seen a dramatic shift as organizations scramble to accelerate their digital transformation agenda. Many organizations are now looking at the precipice, faced with critical decisions that dictate the sustainability of their organization’s ability to thrive in a new normal. As always, CFOs continue to be assailed on multiple fronts having to manage the financial actions of their company, track cash flow, and monitor the performance of their finance operations. There is even greater reliance today, for the finance organization to stay one-step ahead of competitors, to navigate increased regulatory and compliance scrutiny, and also guide their organizations through the current global economic crisis. As executive leadership across organizations begin to ramp down forecasts, production, and sales due to economic uncertainty. It is natural that many organizations are turning data analytics, as part of their greater digital transformation initiatives, to develop the capabilities to help with operations optimization and generating profitability.

Throughout the past decade, leading edge companies have invested significant capital in setting up their capabilities and infrastructure to handle immeasurable amounts of data. Today, approximately over 2.5 quintillion bytes of data is consumed globally on average a day. CFOs and leaders of organizations should be asking themselves important questions on whether or not they have positioned their organizations to be able to capitalize on the data.

Much of that success is highly dependent on organization’s ability to consume data, to quickly analyze data, and accurately generate insights for their company, product or customers. With the myriad of available tools, technology, digital repositories and techniques at the touch of the fingertip, the finance leader of the digital age has to be ready to serve as the steward at the helm guiding their organization through the transition to an insights-driven and action-oriented company.

What does it mean to transition to an insight-driven organization?

When the words data, analytics, insights pops up in conversations today; the immediate thought on many people’s mind is on topics like driving insights with analytics tools by either reducing costs, enabling quick decision making, or increasing profitability. To transition to an insights-driven organization, CFOs should focus on bridging the gap between strategic and operational decision-making with analytics by partnering with the leadership from IT, Business, Compliance, and Data Owners.

A smart CFO is able to arm themselves with an impressive array of technology and tools to help with comprehending data models, visualizing data trends, building proactive monitoring, and leveraging analytics to drive corrective actions and process improvement across the organization. The finance organization should look to position itself as a centralized command center, at the heartbeat of an organization, presiding over finance master data which allows CFOs and controllers to have a tight pulse on the vital signs of their business.

Armed with data insights, trends, and monitoring of KPIs, finance functions can exercise more-centralized control of operational business decision-making, answering questions such as:

  1. What price point should be used for this customer on this day?
  2. Which investment at risk should be terminated? 
  3. What inventory products should be pulled forward or out of the supply chain?

With the transition to an insights-driven organization, CFOs could leverage this operating model to focus on three critical areas for their organization:

Reduction of costs. Organizations can leverage data analysis to drill down to the root cause of the issues to identify opportunity for process improvement, to understand breakdown of costs, and pinpoint logistics handling errors that could potentially reduce the cost in supply chain, optimize workforce planning, and to eliminate redundant efforts in storing and analyzing diverse sets of data.

Faster and better decision making. With the advent of big data, organizations can tap into unlimited sources of data. Big data can consolidate and collate market events across geographies to be captured in real-time via unstructured sources such as news, research, graphs, audio, visuals, and social media. With the speed of data storage and processing, combined with the ability to analyze new sources of data and the visibility provided by dashboard visualizations, organizations are able to focus on key topics that require them to analyze root cause or changes immediately – and make decisions based on what they have learned.

Profitable products and services. Organizations with multiple touchpoints can apply big data and analytics to develop a single but holistic view of customers, deep dive into understanding customer sentiments, and simultaneously track and record customer experience focusing on key interactions along the product and service lifecycle. Analytics can also be utilized for analyzing product profitability, identifying customer behavior profiles, and monitoring spending habits to drive both customer and business value.

How can organizations embark on this journey?

In a recent survey collected by Deloitte, it was identified that one of the key challenges that organizations face with deployment of analytic solutions lie with data management. One of the most cliché terms in consulting has always been around the common phrase ‘garbage in and garbage out.’ Yet despite all of the times that you as a leader may have either heard this term or used this term yourself; how many organizations have actually doubled down and focused on the foundation of their data quality standards, maintenance practices, and governing policies?

Source: Deloitte Analytics Survey 2019

Road to success starts with master data management

When it comes setting up master data management, organizations of all sizes, shapes, and backgrounds all struggle with having an active and hands on approach with providing governance and maintenance of the data. Mature, industry-leading organizations often have clearly defined processes and built-in controls for creation, maintenance and monitoring of master data. In addition to clearly defined processes, these organizations often also have a centralized unit for governance and oversight of master data standards, managing change communication, and engaging with key business partners across the functions.

When it comes to data strategy, mature data driven organizations have clear plans for how the data will be stored whether on premise or in the cloud, how the data will be cleaned-up and de-duplicated, and most importantly of all, what they want to utilize their data for. Executive leadership should adopt a new mindset when treating and handling their organization’s data. Data should be treated like a core asset to the business, as a foundation that helps drive the engine for digital success. The following are best practices that CFOs or drivers of the master data management agenda should take into consideration.

  1. Collaborate across the silos. Identify data business owners across critical business functions that are responsible and accountable for the data. These data business owners would be responsible for data quality, management, maintenance, and analytics. Organizations that underestimate the complexity and intricacies of master data management can be easily frustrated when they are not getting the right traction and derived value from their analytics models. Thus an alliance across functions can help to mitigate these concerns, to navigate internal politics, and to steer the organization towards a joint strategic vision for how they want to use the data. 
  2. Define clear hierarchies and definitions used for finance master domains. Creation of a clear policy or Chart of Accounts should be a fundamental step in active master data management; which helps to dictate corporate processes, ownership, and maintenance policy for finance master data. Additionally, documentation like master data taxonomy and documented rules for creation and maintenance standards are required to ensure that critical master data attributes are aligned across the organization. 
  3. Clear view of downstream impacts. As organizations grow in size and complexity, master data also grows in size and complexity. Smart leaders have to anticipate and plan for these changes, and have the appropriate mechanisms in place to ensure that changes to the master data will be reflected and downstream impacts to financial reports, management reporting, and systems are planned for and taken into consideration.
  4. Develop centralized command model. Assess the existing financial and digital ecosystem, and develop a target operating model for an insight-driven function. As part of this process, a vision for the Finance and its role, and the approach to data governance and advanced analytics as well as management relevant processes has to be defined. 
  5. Define the blueprint for success. A roadmap towards the to-be vision helps prioritize transformation activities and effectively implement necessary big data analytics tool kit and drive the future of Finance. When planning for technology stack, organizations needs to consider the end to end process needs from application, to registration, to monitoring, to maintenance, to governance, to testing, and to storage and retention. 
  6.  Identify quicks wins. It is critical for organizations to begin formulating a plan and strategy to cleanse master data as soon as possible. Executive leadership that invest into master data management often want to see benefits of their investments quickly to confirm their decisions. Quick wins can range from identifying transaction and master data to be archived to reduce database sizes and improve system performance. Other quick wins could potentially be active sweeps of master data for incorrect registration such as blanks, incorrect characters, duplicates, and missing data.

With organizations ever more pressed to accelerate their digital transformation agendas, organizations looking to become an insights driven organization should first spend some time assessing the current state of their processes, capabilities, and enabling technologies.

While the road to digital transformation can be long and arduous, it is important for leaders to plan accordingly and ensure that the foundation for data management is established first before hastily rushing into introducing new technology architecture into the ecosystem.

While the transition to insight-driven finance organization may result in short-term setbacks with technology implementation mistakes or steep learning curves, the perceived business benefits and competitive advantages easily outweigh these roadblocks. By defining a roadmap, starting small, learning from mistakes and carefully managing change, your analytics initiative can become a cornerstone of your business finance strategy.


If you would like more information on this article, please contact Andrew Chang (

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