Recommend: The data management value chain

This article outlines some of the opportunities that are emerging for accountancy professionals in this critical value chain. The profession’s paradigm is broadening as the stewardship responsibilities go beyond just financial information. As you may agree, it is now imperative that finance professionals, managers and leaders align to ‘business’ more effectively and use their expertise in the management of non-financial information, decision-making and value creation.

DATA MANAGEMENT AND THE ACCOUNTANCY PROFESSION

• Value chains are evolving from the ‘traditional’ industrial (creation of products and services) to more digital value chains that create outcomes (for example data or evidence-based strategy or artificial intelligence solutions that perform routine decision-making).
• Accountants are familiar with structured, particularly financial data (including the analysis of such data). This is now evolving to include unstructured and non-financial datasets from different sources to provide quality insights needed for decision-making.
• Accountants are uniquely positioned to meet the challenges of disruption and ongoing changes into a data-driven world – and support organisations and governments.
• By mastering the data management value chain, accountants can secure a strong and vibrant future for themselves in the digital economy and can shepherd organisations across sectors and industries to adapt and adopt relevant technologies.
• Digital data is both a commodity that can be bought and sold and an asset that can be used to generate powerful insights for key business decisions.
• The legal, regulatory, and ethical environment is still unclear, and this lack of clarity impacts digital transformation and results in lost opportunities. Regulatory standards have not kept pace with the emergence of this valuable new asset.
• Accountancy toolkits need to evolve to address the volume, velocity, variety, and veracity of data and to be able to leverage its value.

THE DATA MANAGEMENT VALUE CHAIN

EMERGING ROLES AND RESPONSIBILITIES
While some of the terms used to describe the following functions and responsibilities in the data management value chain may seem to place them outside the expertise of chartered accountants (CAs) and associate general accountant accountants (AGAs), most can be effectively filled by those with a professional accountant’s experience and training.

As is usually the case, some contexts and organisations may require specialised or advanced skills, knowledge, or expertise for the fulfilment of some of the responsibilities in the functions of the value chain. A competent professional should still be able to contribute to these complex environments through for example coordinating efforts, providing leadership or strategic advisory.

Data gathering
Data engineer − Ensures that data used has integrity and is clean and reliable.
While the expertise of a data engineer may be required at times, accountants may complete aspects of the function as follows:

• Cleaning – Algorithms to ensure data is usable
  o Checks and balances to ensure the integrity of data (valid, accurate and complete − VAC)
  o Adequate reviews and approvals, duplicates are removed, blank fields followed up, etc
• Collection – Instruments or processes used to collect data
  o Mapping data from sources to repositories that can be used, including reviews, transformation, harmonisation, and the final output (Input > Processing > Output)
  o Consideration of automated processes (compared to non-automated, and the process of automating)
• Grading – Systems to assess the quality of the data collected
  o Fit-for-purpose assessments that determine whether data is sufficient (suitable quantities at the point of decision-making) and appropriate (reliable and relevant)
  o Identifying and communicating any limitations and risks associated with the data and, more importantly, making recommendations on and managing these
  o Manage the varying reliability requirements for data, communicate with decision-makers regarding reliability (for example options analysis would require less reliable data versus the continue or stop operations decision point which would require greater reliability)
  o Process management: Data-based decision-making, including the management of the impact of subsequent data or events on decisions

Adapting current and future roles/responsibilities

• Audit and assurance
  o Expand the role to include non-financial data and information
• Internal control
  o Implementation and testing of internal controls for data management (data collection, cleansing and grading)
• Fit for purpose assessments
  o Test datasets to ensure they meet the criteria for different decision points
  o Understanding, communicating, and managing limitations and assumptions
• Disclosures and communication
  o Communicate (to decision-makers or stakeholders) relating dataset appropriateness for decision-making, including any limitations

Data sharing
Data controller − Similar to the existing controllership role (stewardship of financial and physical resources), the function focuses on the stewardship of data resources (enhancing the value of data, through its protection, curation, and appropriate use)

• Internal use − Who should have access to the data
  o Stewardship over financial data versus overall data resources (the overlap in roles and/or natural evolution to broaden the traditional responsibility – may depend on specific organisational dynamics)
  o Data protection (access policies, rights, etc), valuation and controlled use, compliance with laws and requirements
  o Stewardship is an enabling function and not necessarily ownership. It will ensure that the data owners protect, curate, share and use the data according to the necessary constraints (law, regulation, policies, etc)
• External use – Proving the origins and validity of data
  o As the monetisation and commercialisation of data grows, requirements for certification, verification or assurance are expected to be developed
  o Certification will be required to prove the source and integrity (validity, accuracy and perhaps completeness) of data (provenance or lineage)
• Jurisdictional issues – Legal changes impacting the use of data/insights
  o The significant opportunities come with legal and ethical challenges. It is important to align with societal values and expectations, which are complex
  o Encourage and promote an ethics-based culture around data management and use, in line with the SAICA Code of Professional Conduct
  o Facilitate and ensure compliance with the requirements of the Protection of Personal Information Act (POPI Act)

Adapting current and future roles/responsibilities

• Data protection and jurisdictional boundaries
  o Appropriate access and other controls to ensure data is protected (including data origins)
  o Stewardship responsibilities (including controlled or monitored use for intended purposes)
  o Fit-for-purpose assessment and provenance or lineage related certification

Data insights
Data scientist − Analyse and interpret complex data to develop insights to support decision-making and deliver business value.

• Measurement – Algorithms to ensure data is usable
  o There is a need for new approaches and different thinking around data reliability. While financial analysis and reporting data tend to be reliable and certain, non-financial data can be very unstructured and uncertain
  o This uncertainty needs to be understood, assessed and incorporated into models. Serious considerations may need to be made regarding data reliability, costs, time and money to collect better data for better insights and/or decisions
  o Assessment of the suitability of data for use in a particular model (including conversion into a common structure or format)
  o Simple to complex data conversions and the use of assumptions and professional judgement for the data to reach a state where it can be used
  o Analysis of data appropriateness for the detailed analysis to be conducted and insights produced
  o Data may need to be sourced externally. This data may require adjustment (which would need to be communicated) and a fit-for-purpose assessment
• Modelling – Instruments or processes used to collect, clarify, and analyse data
  o Build, coordinate, supervise or lead the building of models that use data to develop insights to inform decision-making, from simple spreadsheets to complicated artificial intelligence (AI) solutions that include other techniques
  o Contribute to, coordinate, or lead interdisciplinary teams of experts that are developing models and insights
  o Identify and document key controls in models. Test and monitor the ongoing alignment and suitability of controls
  o The shift from periodic to real-time reporting will make complicated problems more complex as policies, procedures, models and assurance providers will need to enhance trust in the data, insights and resultantly decisions in real-time
  o The move towards real-time reporting will transform the accounting and assurance methodologies and standards
• Insights – Systems to assess the quality of the data collected
  o Understand the insights generated through models and the data that underpins the model
  o Assurance for decision-makers over data used in models and whether it is fit-for-purpose and communicate any limitations and assumptions
  o The ability or potential to sell insights and data will open new business models and evolve products, services and the whole organisation

Adapting current and future roles/responsibilities

• Credible measurement activities underpin models
  o Ensuring that data is fit for purpose
  o Assumptions are clear and reasonable and that the controls used in the models are developed, document, tested, and are being monitored
• Artificial intelligence (AI)
  o Model’s insights are strategically aligned and remain so, particularly with self-learning models
  o Ensuring tactical alignment to policies and other jurisdictional boundaries
• Chief data officer
  o Integrate different perspectives and leverage role as a strategic business advisor to bring multi-faceted solutions and create value
  o Establish credibility through competence in data management to earn a c-level or senior management role
• Commercialisation and assurance
  o Attest on appropriate curation of datasets to support analysis.
  o Confirm reliability, relevance, and verifiability of external data
  o Conduct risks/rewards analysis (on possible sale), assess if a market for the sale of insights exists
  o Provide finance function support to the business on related transactions (costing, selling prices, valuations, reporting, etc)

Communication

Strategic advisor − Frames, analyses and explains complex business issues within a local, national, or global context based on the strengths and limitations of the data, and on the assumptions and models that underpin derived insights.

A good strategic advisor is a strong storyteller who focuses on relevance and value creation through insights. They recognise that there is a flood of data and that the actual data and technicalities are not what matters most, but rather their ability to decipher the insights, make recommendations and influence decisions.

• Internal context – Context/limits of insights for decision-making
  o The exponential increase in the amount of data available is making decision-making more challenging rather than easier
  o Accountancy professional responsibilities are also becoming complex in the process, with possibilities for bias, distraction, lack of competence and tendencies to trust automated systems and favour their outcomes
  o The IFAC Code of Ethics is being updated to promote the role and mindset expected from accountancy professionals
  o Professionals need to simplify decision-making by developing superior and effective communication skills that declutter technical data and outline key limitations and assumptions
  o A deeper understanding of how decisions are made is necessary to make a more meaningful contribution that goes beyond financial considerations
• External clients – Can the lineage and validity of insights be proven?
  o Adequate documentation to ensure the lineage is known, especially where insights are to be sold
  o This will include documentation and testing of controls and ongoing monitoring of the integrity of insights (especially for self-learning models)
  o Certification (which may be complex) will be required for data and insights being sold (for example data origination and allowed uses for insights sold)
• External stakeholders – Value of data/insights to the organisation
  o Stakeholders trust the profession for our competence and ethical behaviour, which will be relied on in the data management value chain
  o Also, our commitment to act in the public interest and create sustainable value (including measuring an organisation’s success at creating and protecting value)
  o Professionals will be trusted in various measurement activities in the sale or disclosure of data and insights
  o Play a broader role in developing insights to support strategic decision-making or for external use

Adapting current and future roles/responsibilities

• Strategic contribution in the digital economy
  o Contextualise data insights related to non-financial information (incorporating financial as appropriate)
  o Communicate, in a simplified manner, relevant aspects of the value chain to different stakeholders
  o Identify, document, test and monitor internal controls associated with artificial intelligence solutions and provide lineage for insights
  o Discuss the integrity of the data and reasonableness of any assumptions underpinning the analysis (model) which yielded insights

BUSINESS INTELLIGENCE
As responsible leaders involved in various ways in the value creation processes of organisations, SAICA members have a significant role to play in organisational business intelligence (BI). BI combines business analytics, data mining, data visualisation, data tools and infrastructure, and best practices to help organisations to make more better data-driven decisions.

Source
The information contained in this article has been sourced primarily from the International Federation of Accountants (IFAC).

 

Author
Msizi Gwala, Project Director: Enabling Competencies