Finance Data Strategy

Artificial Intelligence (AI) is a much talked about topic and will continue to be talked about, where corporates try and to put it into practice. But wait… what needs to be done well before this?

For AI to work properly, the data needs to be cleansed, it needs to have the characteristics of an unbiased representation of what the organisation intends to use it for. Putting on my auditing cap, it needs to be accurate, complete, relevant and secure. Moreover, using the principles of mathematics it also needs to be a representation of the issue that the organisation wants to solve, with ample data (N>30) needed to cover all eventualities. Lastly, high-quality, clean and supported in a centralised (one-source of truth) data lake or warehouse. Why?

Otherwise, the AI algorithm will use past data to try and predict a possible future outcome, based on data that is biased, flawed, unrepresentative and limited.

Coincidentally, whilst at a client office last month I overheard few people talking and saying, “what should we do in order first, AI, ERP/ systems, consolidation, what about modelling?.”  This leads me swiftly onto the crux of this article… read on.

Finance data strategy

Finance data strategy is about how to leverage data to achieve the desired outcomes of finance transformation while accommodating the unique challenges and constraints in your existing organisation. The development of a finance data strategy at the beginning of a project helps brings awareness to the importance of data and provides a foundation to begin making design decisions. It also provides a framework on how the organisation intends to address key design decisions, who needs to be included and what principles will guide the decision-making process.

As a precursor, it is important to note that the finance data strategy should be revisited again after completing initial development sessions. As the finance transformation kicks off, education and design workshops will reveal new learnings and requirements that need continual refinement, adjustment - evolution. A meaningful finance data strategy should serve as a NorthStar (shining needle) throughout the transformation, helping workstreams stay honest, on track, get new team members up to speed and serve as a tool to help resolve gridlocked discussions.

Key finance data strategy considerations

Every heading below has unique challenges and constraints, requiring awareness and alignment during the design thinking stages.

Enterprise information model

An enterprise information model (EIM) provides the foundation on which an organisation’s business processes and reporting will be built. It transforms a generic ERP package into a specific structure to fit the needs of your organisation. The ideal EIM should be robust, purposefully built and tailored to the business model while still allowing flexibility for new product launches, reorganisations and acquisitions. A complex, layered and well-defined EIM should provide the business with flexible reporting, harmonised transactional processing, agility and expanded views of transactions and profitability.

Getting to a flexible, multidimensional finance data model will require high-quality inputs from all applicable stakeholder groups. These inputs are obtained by conducting multiple design cycles to align with business process requirements, rationalise master data values, visualise design decisions and assess impact on internal and external reporting. This iterative approach transforms the data model design with each cycle  stage, becoming more complex and closer to business reality whilst being validated by the different stakeholder groups throughout the business.

Each iteration of design should be integrated with real-time reconciliation, mapping and transformation rules for stakeholders to understand the impacts of decisions. Architecting the EIM involves tackling question around, the how to initiate: profit and cost centres; need the Global COA to break-out revenue for IFRS 8 segmental reporting; can the primary statements be remapped for the new IFRS 18 requirements. This will help establish a high-level design for each of the individual finance data elements, including naming conventions, number ranges, creation of a primary hierarchy, relationships and validation rules between data elements, fields, data imports and mapping logic from existing source systems.

Data governance model

Establishing an effective data governance model requires solving for three critical components: people, process and technology.

• People: What are the roles and responsibilities of the data governance team, including business owners, data stewards, technical architects, and steering committees?

• Process: How is data created, provisioned, stored, consolidated and shared? This includes policies, procedures, guiding principles and metrics for tracking governance.

• Technology: What systems and data architecture are required for successful data governance?

A transparent and enforced governance structure should enable consistency across finance data elements, prevent redundancies and encompass feedback from all relevant stakeholders prior to creating new or changing existing data values. Governance will provide the business with unique, clearly defined, well-named values for all data objects that make an end user’s life easier.

Artificial Intelligence (AI)

To introduce AI into your business, it is crucial to review and evaluate existing processes to determine which existing processes can and should be automated to free up time for employees to focus on higher-value tasks. It is important to hold discussions to identify the processes that are repetitive and tedious and those that can be carried out with automated methods, whilst ensuring the master data principles looked at first.

Sure, AI can automate day-to-day transactions- depends on what needs to be done. However, organisations should continue to iteratively evaluate their processes so that AI can be implemented to maximise process efficiency across the business.

Key considerations:

When developing your approach to finance data strategy, the EIM and data governance, there are several key takeaways to keep in mind.

• Build for the future: Protect your finance transformation from old ways of thinking. Consider how your ERP will need to change over time, during M&A activity or reorganisations. Introduction of AI¹.

  
  

• Reporting requirements: Consider whether your EIM can support all financial, management, local statutory and tax reporting (MTD) requirements¹.

  
  

• Select the right team: Include Subject matter experts¹, relevant stakeholder groups, including corporate accounting, external reporting, FP&A, business finance partners, local accounting, tax and treasury.

  
  

• Keep it clean: ERP vendor have their own finance architecture that should be used as intended to maximize future flexibility. Take a fresh start, leveraging leading practices¹.

  
  

• Integration: Consider whether any upstream systems are capable of supporting data requirements and downstream systems are ready to receive financial information in a new format.

After defining a finance data strategy, including an EIM supported with the new data governance should entail defining a reporting strategy to ensure your ERP system can support decision-making and regulatory compliance. To do this a finance vision is needed. ¹ Thereafter AI should be looked into.

Contact Kothari Partners for a free confidential discussion on how we can help with your challenges.

Dee Singh Kothari is a senior partner in Kothari Partners

¹ At Kothari Partners, our approach is to help our clients understand their current situation, identify the value and decide on the scope, vision and set of strategies for what they could achieve for their business. We help plan their implementation and support them and deliver the solution/ change needed, so it is properly and permanently embedded in their organisation.

We aim to help past and future clients by delivering high-quality work to their organisation, generate real efficiencies and free up time to support better business decisions.

For a confidential discussion please free to contact us, via our corporate website: https://www.KothariPartners.com