The data asset: What’s it really worth?

All data has intrinsic worth, but its real value lies in how an organisation uses it, and whether it is fit for purpose when needed.

By , Director of Knowledge Integration Dynamics (KID) and represents the ICT services arm of the Thesele Group.
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Data is being widely described as the ‘new gold’ or the ‘new oil’, and the most valuable asset enterprises have. In many respects, this is true. But while the forward-looking enterprise depends heavily on its , putting a rand value to this data remains a challenge.

By its very nature, data has at its core informative, instructional and locational properties that make it the “glue” within any ontology or existence of anything.

It is fundamental for all business, institutional, governmental, household and public processes, systems or exchanges to operate, whether manually or automatically. Data enables , learning, design, knowledge, information transfer, actions or execution in all spheres of life.

As an enterprise asset, the type of data most frequently valued is franchised (aka “monetised”) data – that which has been processed to produce outcomes like metrics and insights that are important and valuable to the company and down-stream consumers for regulating and operating the business.

Pricing models exist to put a rand value to this data based on factors such as acquisition, and processing time, data volume, the number of inputs it had and the importance of the decisions it can inform.

Data enables communication, learning, design, knowledge, information transfer, actions or execution in all spheres of life.

However, most data valuation models are subjective and based on criteria that are not always standardised across industry sectors and disciplines; and in many cases, data is not even recognised as an asset for accounting, strategy and other purposes.

Where data is recognised as an asset with monetary value, valuations could be made based on the cost of managing and provisioning data, the volumes available for data discovery or analytical consumption purposes, trigger actioning dependencies, the data’s importance for decision-making, enabling or learning and knowledge transfer capability, and even the distance the data is transmitted.

Even data ‘placeholders’ or capacity can be priced and sold to enable communication before usage as evidenced with the telcos and data vendors of the world.

Although most of these data pricing models in existence are regulated by communications and government authorities, these models are by no means altogether perfect, and the perceived value and pricing criteria of these data ‘placeholders’ may differ from one community to another, with the entire model subject to the risk of monopolisation.

The value assigned to the data may vary depending on the sector the organisation operates in. For example, in the financial sector, the most valuable data is likely fashioned around bottom line or profit; for others, the key focus might be data relating to sales (revenue) or expenses. Data such as this may well be valued as an asset during the sale of a business, and due to its importance to the enterprise, it might even be insured against loss.

But what about the data relating to enterprise intellectual property (IP) – its algorithms, models and methods? Or its data currently in transit without context, or its historic data, which may not be in use currently, but could become vital for trend modelling. It is harder to put a rand value to IP and data which has not been used in years but has the potential to improve the business at some point in future.

Maintaining and increasing the value of data assets

All data has value, but without context and effective data management, it cannot contribute its full value to the outcomes of whoever is using that data.

It could be argued that analytics models can use weightings to overcome inconsistencies and gaps in data; however, the ideal is to not have the inconsistencies or gaps at all. To achieve this, organisations need to retain and properly manage quality data that has been verified, validated, cleansed, integrated and reconciled.

Effective governance should also be in place, to ensure and direct data management practices, with tried and tested rules, controls and architectures to assure commonality in practices/processes and sustained data quality and avoid the dreaded data ‘spaghetti junction’.

Even when quality data is available and well-managed, however, the value of this data remains subjective and theoretical – particularly when its future importance is not known yet.

Historic data, for many simply volumes in costly storage, is very important for those in the financial sector, for example, serving as irrefutable evidence when analysing the full lifecycle and long-term behaviour of customers, and building predictions informed by this.

Outside of the business world, data indicators going back millions of years are vital for fields such as astronomy, archaeology or geology.

Therefore, no matter what the current accepted data valuation models are, the answer to the question: ‘what is data worth?’ is that quality, well managed data is potentially priceless.

AI for enterprise: Get the basics right before making the move

Machine Learning and Artificial Intelligence (AI) are certainly the way of the future, offering enterprises faster, more accurate and more efficient way of automating processes than have ever before, writes Chris Pallikarides, General Manager, ITBusiness. Gartner reports that AI adoption in organisations around the world has tripled over the past year, with 37% of organisations having deployed AI – or about to do so. By 2021, Gartner expects AI augmentation to create $2.9 trillion in business value and 6.2 billion hours of worker productivity globally.

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In South Africa, Machine Learning and AI have been talking points for many years. However, the practical implementation and application of these technologies has not quite caught up with the rest of the world. Mid-size and large local enterprises are looking to Machine Learning and AI to streamline operations, support strategic and personnel planning and gain insights such as whether certain products are performing.

While many companies are talking the talk, they often seem to forget the fundamentals – crucially:  knowing what data they have and where it is sitting. Without the Data Engineering piece of the puzzle in place, Machine Learning and AI cannot deliver on expectations.

Data Quality is a big issue in many South African organisations, and most of them are aware of the problem. Amid exponential growth in data volumes, companies have lost control over data sources and standards; they lack effective data governance and stringent controls. On top of this, while most want to optimize their data use, possibly even monetising it, many have not formulated a clear strategy for doing so.

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Therefore, while Machine Learning and AI should definitely be on the roadmap for every South African organisation, attention has to be given to the fundamentals – including strategic planning, data quality and data governance first.

Addressing underlying issues could take as little as a few weeks, or up to several years, depending on budget, the size of the business, the amount of data involved, the technologies in use and the skills available. A data governance exercise alone could take 36 months to implement. But these are necessary processes. Embarking on an AI project before addressing underlying data quality issues could result in flawed outputs, unexpected additional costs or delays in project delivery.

In addition to assuring data quality, organisations need a clear vision on how they intend to use their data in future. If, for example, they hope to monetise their data, then in early planning, they might work backwards, considering the type of data they have, its potential value, and models for monetising it, while also taking into account the regulations around data privacy.

Once organisations have clear roadmaps, all the necessary data and they know they can trust it, making strategic decisions become a much easier task; and once the right people, processes and technologies are in place, the whole discussion becomes streamlined.

ITBusiness has long been a Data Warehouse and BI specialist in the South African market, and with that extensive knowledge and skill we assist customers to collect their data, store it intelligently, and maximise insights gained from the data collected. As the environment evolves, our approach has evolved accordingly, and we now recommend starting consultations from the outset when a customer has a data requirement.

By carrying out a full maturity assessment and gap analysis covering people, processes and technology, and by getting the basics right first, we find that data projects – and those intended to support later AI projects – are more successful and more likely to deliver the expected outcomes.

Getting to grips with data for CECL and risk forecasting best practice

Organisations have the data they need, but they must move to proper data governance and management to align with global risk forecasting models

By Mervyn Mooi, director at Knowledge Integration Dynamics (KID)CECL_1200

The Current Expected Credit Loss (CECL) model, the new Financial Accounting Standards Board (FASB) standard for estimating credit losses on financial instruments, is to be implemented from next year for publicly traded companies and from 2023 for private companies. This new model governs the recognition and measurement of credit losses for loans and debt securities. Because CECL (and compliances such as BCBS239) requires organisations to measure credit exposures and expected credit losses across the life of a loan, there are concerns that it could require more data and more careful data modelling.

As organisations around the world prepare to implement CECL, many say they may not have enough data – or the right quality data – to effectively comply. Three years ago, Moody’s research in the US found that banks foresaw challenges in terms of the data they had available to comply, and an Abrigo Lender Survey  earlier this year found that almost one in six respondents are unsure whether they have the quantity and quality of data necessary to estimate losses under the CECL standard.

But they are likely wrong.

Most major financial institutions have all the historical and current data they need to forecast losses accurately and with confidence within the CECL model. The CECL model in effect underpins best practice in reporting, loss forecasting and production of balance sheets and income statements, and therefore banks – and all businesses – should already have the foundations in place to align with the CECL model.

The challenge they may face, however, is unravelling their own data ‘spaghetti junctions’ to consolidate and report on the appropriate data.

To estimate potential losses on credit given to its customers, credit providers (such as the banks) are fully reliant on their data collation processes and storage of such relevant data which has been qualified (verified, validated, cleansed, integrated, reconciled) i.e. quality/trusted data. The reality is that such processes are often disjointed and overlapped/duplicated with differing logic which results in “many truths”. Some data is traditionally hidden across departments for internal competitive reasons, which could also complicate forecasting.

Many organisations also implement manual interventions in the collation and preparation of the data, which introduces more risk with regards to data quality. Furthermore, periodic reports may be excluding transactions in transit (in the process of being committed by the payees / lenders) or those in suspense, which will skew loss totals (i.e. overstate the losses). In many instances loss deviations are used in reports to compensate and reconcile the numbers.

In line with CECL and BCBS239 compliance, organisations have to show how losses were calculated and which governance processes were followed that approved these figures, to ultimately reveal its data lineage and prove governance.

In most organisations, the necessary data exists.

Many also have the necessary risk models and forecasting expertise in place. The problem in preparing for CECL lies in disparities in the understanding of their data, and the overall management of their data.In many cases, traditional data management evolved without proper controls in place, and over time data management was not formalised or organised, which could skew the trustworthiness of data. Where data management practices have not been able to keep up with changes, organisations have tended to skip the set, traditional frameworks and standards, which is the cause of data chaos.

To unravel the spaghetti junction and prepare the quality data needed for accurate loss and risk forecasting, organisations benefit from centralised stores of quality data and full data tracking (or lineage) and reporting capability. Enterprise-wide sharing of data supports best practice governance, compliance and risk management, but also allows organisations to better understand their market and identify growth opportunities through cross-selling and up-selling.

Enterprises also have to organise their data in a formalised fashion – and this the objective of data governance. The advent of data governance in the past few years provides all the guidelines necessary for data best practice, and uniform data management covering the rules, policies and standards to be applied to data throughout its life-cycles is the execution of this, setting in place all the foundations an organisation needs to align with CECL or any future best practice reporting or forecasting models to emerge in future.

 

 

Metadata management is a science

Meta analytics is the new model for enabling complete data and process oversight.

By Mervyn Mooi, Director of Knowledge Integration Dynamics (KID) and represents the ICT services arm of the Thesele Group.

Data governance is crucial, and is embedded in most well-run enterprises. But most organisations are not yet maximising the full potential of their management and governance measures, in that they are not yet linking data management to governance to compliance.

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Data management differs from governance. Data management refers to planning, building and running capabilities. Governance relates to monitoring, evaluating and directing enablers, with creation through assuring efficiencies using governance “place-holders” or gates, the latter which are entrenched in system or project management life-cycles.

Governance monitors, ensures and directs data management practices not only in the execution of processes and business activities, but also needs to help achieve efficiencies; eg, in project management and system development life-cycles.

Moving to the next level

Most governance happens at a purely technical and operational level, but to elevate governance to support high-level compliance, organisations need to link rules, regulations, policies and guidelines to the actual processes and people at operational level. Compliance is set to become ever-more challenging as organisations deal with growing volumes of data across an expanded landscape of processes.

Compliance is set to become ever-more challenging as organisations deal with growing volumes of data across an expanded landscape of processes.

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I advocate that governance not only be addressed at technical/operational (data management) levels, but should also be linked to compliance which carries risk and drives the organisation’s strategy. Major South African enterprises are starting to realise that linking governance to compliance could support the audit process and deliver multiple business benefits at the same time.

Recently, I highlighted how data stewards were stepping up their focus on mapping governance, risk management and compliance rules to actual processes, looking to the management of meta data to provide audit trails and evidence of compliance.

Traditionally, these audit trails have been hard to come by. Auditors – many of them with a limited technical background – had to assess reams of documents and request interviews with IT to track the linkages from legislation and guidelines to actual processes. In most cases, the processes linked to are purely technical in nature.

From a regulatory compliance point of view, traditional models do not provide direct links to a particular clause in legislation or best practice guidelines, illustrating the location and management of data, including where it resides, who uses it and how, in light of the requirements of the clause or legislation. Auditors, however, need enterprises to prove lineage and articulate governance in the context of compliance.

Establishing the linkages

While enterprises typically say they are aware they could potentially link data management to governance to compliance, most do not undertake such exercises, possibly because they don’t have a mandate to do so, because they believe the tools to enable this are complex and costly, or simply because they believe the process will be too time-consuming.

Using sound methodology, this once-off exercise can take as little as two to three hours to map a process to legislation or guidelines. In the typical organisation, with around 1 000 processes, it could take less than a year to map all of them.

The organisation then gains the ability to track the processes without having to rely on elaborate business process management tools, and capture it all in Excel, store the information on any relational database and get insights: Where are the propensities, affinities, gaps and manual processes, and more importantly, what accords are they mapped to?

Mapping data is stored with timestamps and current version indicators, so if a process changes over time, or a rule, control or validation has changed, this information will be captured, indicating when it happened and where it was initiated. At the press of a button, the organisation is then able to demonstrate the exact lineage, drill down to any process within the system, and indicate where the concentration of effort lies, and where rules, conditions and checks are done within processes.

Additionally, it can attach risk weights at process level or accord level, helping shape strategy and gauge strategy execution.

Not only does this mapping give enterprises clear linkages between policies or regulations and processes, it also gives sudden new visibility into inefficiencies, the people and divisions involved in each process and more, so helping to enhance efficiencies and supporting overall organisational strategy.

With governance and compliance mandatory, it’s high time organisations moved to support governance and compliance evidence, and make the auditing process simpler and more effective.

Thesele takes 40% stake in Knowledge Integration Dynamics (Pty) Ltd. (KID)

Thesele Group (Thesele) has bought a 40% stake in Knowledge Integration Dynamics (KID), marking the investment holding company’s first foray into the ICT space, and making KID South Africa’s largest black-owned focused data management solutions company.

The multi-million rand deal, which came into effect last month, makes KID a majority black-owned entity with a Level 4 BBBEE rating, with several of its subsidiaries now 100% black-owned as well as BBBEE Level 1 rated companies.

KID co-founder and MD Aubrey van Aswegen says the investment marks the start of a new growth phase for the data management specialists. “KID has grown fairly organically over its 20-year history, but we are now approaching a point where fuelling the same pace of growth will demand a more aggressive expansion phase and possibly strategic acquisitions. Our new partnership with Thesele Group will support this growth strategy,” he says.

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Thesele, founded in 2005 by Sello Moloko and Thabo Leeuw, has a diverse investment portfolio across financial services, logistics, manufacturing and automotive industries. Thesele recently announced its acquisition of a 35% stake in South African water and wastewater solutions provider Talbot & Talbot. The KID acquisition is in line with Thesele’s long-term investment approach in existing and emerging growth sectors, says Thesele Executive Director Oliver Petersen.

Van Aswegen says KID had been in the market for a suitable BEE partner for some time. “We were looking for a suitable investor to not only improve our scorecard, but to play an active role in business development for us and bolster our growth aspirations,” he says. Thesele’s track record, networks and reputation in the investment community, along with its ethical approach to business, aligned with KID’s own culture and business model. The partnership will not be a ’passive’ one, he says. Thesele will work closely with KID to support mutually beneficial growth.

For Thesele, the investment in KID leverages several synergies, including the fact that “both entities have long operated in the financial services sector,” says Petersen. “Both groups also have the view that data and data management is a key growth area, with a wide range of opportunities in areas such as big data, the Internet of Things, automation, robotics and Artificial Intelligence.”

“This is a key milestone – not only for KID as a company, but also for its stakeholders, including staff and customers,” Van Aswegen says. “It will facilitate growth for us, and we look forward to Thesele growing their exposure to the ICT space using KID as the platform.”

About Thesele Group

https://www.thesele.co.za/pages/about-us

 

 

Meta Analytics (MA) – the new model for enabling complete data and process oversight

The new science of Meta Analytics has been formalised to enable broad oversight of data and processes, with key objectives of supporting governance thereof, proving compliance, achieving alignment and leveraging efficiencies.

By Mervyn Mooi, director at Knowledge Integration Dynamics (KID)

As governance and compliance becomes an increasingly top of mind issue for data stewards and their enterprises alike, the challenge of mapping governance, risk management and compliance (GRC) rules to actual the data and processes has come to the fore.

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Where once, companies tended to focus on the content – the data itself – rather than its containers – the metadata – management of metadata is now becoming a key focus. Metadata, covering factors such as data and process context, design and specifications, execution information might accurately be described as the ‘information about data’. Metadata Management has become a science in itself. However, in South Africa, systems analysts, database administrators or systems administrators still tend to interrogate metadata at a fairly basic and technical level for operational purposes.

Mapping for GRC

Linking metadata to GRC rules, the latter which are abstracted or prescribed from the organisation’s PPSGs (Policies, Principles, Procedures, Standards, Regulations and Guidelines) has become increasingly important, since it allows for the mapping of metadata directly to organisational capabilities, services, processes, data objects, work-flows, service/business units and individuals. In doing so, it provides a clear view of the business and operational architectural landscapes and data life-cycles. Furthermore, the mappings link in confirmatory communiques and audit trails, as evidence of compliance, action or conformance to the rules (PPSGs).

Typically, the processes and data within computer application systems are designed, built and mapped based on functional and information requirements, often not considering vertical lineage to business processes, work-flows or services, mapping to PPSG (GRC) rules, inclusion of risk management factors and linking to architectural model and capabilities. These are usually managed separately by a different competency team and set of tools e.g. Business Process Management or Data Modelling tools. The operational processes that result from this situation are often disjointed, manual and non-aligned to the PPSGs. Evidence is there of mappings being done for audit purpose, but on a small-scale, ad-hoc basis the practice of which is not sustained and usually do not link directly to the business and operational roles of individuals on the ground tasked with operating in alignment with particular PPSGs.

When called on to produce evidence of compliance or conformance to the PPSGs, business units, departments and IT must often rush to map processes and surface execution evidence of the rules on data thus to prove compliance e.g. with POPI, FICA, other legislation or internal standards. This process is time consuming and challenging, and even though the department can produce mapping and recon reports, they can seldom show exactly which actions were taken where, to align with which GRC rules and the risk of not applying these.

The evolution of MA

GRC mapper tools tend to have limited capabilities – simply linking an accord, condition or service. Moving beyond these rudimentary capabilities is becoming increasingly important as businesses depend more heavily on the quality of their data and processing economy, and GRC compliance / conformance becomes crucial.

In recent years, KID has evolved solutions and methodologies that encompass a “marriage” (convergence) between governance and metadata management, enabling the proving of compliance and delivering a complete oversight of the business and operational landscapes.

Formalizing the solution under the banner Meta Analytics (MA), we can now link respective metadata to all applicable compliancies.

The mapping process can be a lengthy one, but fortunately, it’s a once-off exercise with updates thereafter as the landscapes change / improve. The process identifies the PPSGs in scope and steps thereof – the steps constitute RCCSs (rules, conditions, checks, controls, constraints, technical standards) or actions, as it should be applied in the lineage of the services and/or system processes and roles involved. The RCCSs are plotted against a data management life-cycle (DMLC) for the data being processed and linked to organisational capabilities.  This gives cross-sectional views of the RCCSs against PPSGs, Services, Processes, DMLCs and architectural components e.g. data models.

The advantages of MA

The MA methodology gives enterprises insights of gaps or dispositions within the landscape, to data life-cycles, how GRC (PPSG) rules are articulated and where, where processes are duplicated / overlapped, who are involved in which processes, when rules are executed (actions) and enables risk analysis.  All of this to support efficiencies (e.g. where services or processes could be merged) and prove compliance.

MA shows the affinities of roles and activities, indicates automated or manual processes – it also allows enterprises to attach risk weights to PPSGs, services or individual processes, and attach processes to competency teams or architectural capabilities.  When linked to actual metadata – like system execution logs, e-mail communiques or signed documents – MA delivers evidence that all necessary steps (rules) are being executed.

From a data governance point of view, data stewards and analysts will use MA to determine at any point in time who is using what data and in which processes; or they could gain insights such as the last execution date of a process or the date of the last confirmation email. This allows for the surfacing of many gaps in compliance and support for the identification of risks associated with not applying rules in line with the guidelines. It also allows for the identification of dispositions and mavericks, and supports investigations.

With MA, ad-hoc mapping on demand becomes a thing of the past: if a Chief Data Officer wanted to see a synopsis of all automated and manual processes or gauge compliance risk, they could use these mappings to view the environment in a single step. MA enables data stewards or business unit managers to interrogate exactly what happens to their customer data during its life-cycle.  MA supports back-end efficiency, enhanced customer experience, averts compliance risk and – of course – enables proactive oversight of the entire environment. As a welcome by-product, MA also surfaces gaps and dispositions, or misalignments between the operations and business environments, which is good for efficiency and complimentary to change and project management.

MA brings a new approach to giving oversight for compliance and for surfacing gaps and inefficiencies – not just at a technical level, but also at a business level. It helps enhance both data and processes and delivers better data for better business.

 

Expand data horizons for greater analytics value

Attempting to find purposeful insights in data could be a futile exercise unless you look beyond the siloes.

With the mainstreaming of advanced data analytics technologies, companies today can risk becoming too dependent on the outputs they receive from the analytics tools, which could serve biased results unless solid data analytics models are applied to the way in which the data is interrogated.

While data is your friend, and the only valid way for organisations to strategise based on fact, data analytics tools can only deliver the outputs they have been asked for. If the pool of data being analysed is too limited, or there is no end objective or purpose for using the results after the scientific methods have been applied to the data, then the whole exercise is virtually futile.

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It is seldom enough to drill down into a limited data repository and base broad strategic decisions on the findings. In effect, this would be like a novelty manufacturer assessing only the pre-festive season sales and concluding that Christmas trees are a perennial best-seller. Common sense tells us this will not be the case, and that Christmas trees won’t sell at all in January. But in the case of more complex products and services, trends and markets are not as easy to predict. This is where analytics comes in. Crucially, analytics must look beyond specific domain insights and seek a broader view for a more objective insight.

Comparisons and correlations

A factory may deploy analytics to determine which products to focus on to increase profits, for example. But where the questioning is too narrow, the results will not support strategic growth goals. The company must qualify and complement the questioning with comparatives. It is not enough to assess which products are the biggest sellers – the factory also needs to determine what products are manufactured at the lowest cost, and which deliver the highest return. By bringing together more components and correlating the data on the lowest cost products, highest return products and top sellers, the factory is positioned to make better strategic decisions.

In South Africa, many companies do not approach analytics in this way. They have a set of specific insights they want, and once they find them, they stop there. In this siloed approach, the results are not correlated against a broader pool of data for more objective outcomes. This may be due in part to factors such as the time and cost required for ongoing comparison and correlation, but it is also due to a lack of maturity in the market.

In mature organisations, data sciences are applied to all possible angles/queues and information resources to produce insights to monetise or franchise the data.  It is not just a case of finding unknown trends and insights – the discovery has to be purposeful as well.

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