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“Look Daddy! I cleaned my room”.  It was a job well done.  But I knew that despite her efforts, by the next day my daughter’s room would go back to its “normal” state: clothes and toys all over the place.  I wanted to explain to her the second law of thermodynamics and the need to continuously put in work to prevent a system from going into chaos.  I thought better and celebrated the current state of entropy in this system, albeit short lived.

There are many such similarities we can draw with managing data in modern business.  There is a constant need to put in the effort to maintain an ordered state with organisational data.  But how can data management become an asset management function in your business?  I explore one of the latest tools in data compliance, cleansing and integration.

  1. The 7 Laws of Data

In their seminal paper “Measuring The Value Of Information: An Asset Valuation Approach[1], Daniel Moody and Peter Walsh recognised seven laws of information when they evaluated information as an asset.

Their seven laws were titled:

  • First Law: Information Is (Infinitely) Shareable
  • Second Law: The Value of Information Increases With Use
  • Third Law: Information is Perishable
  • Fourth Law: The Value of Information Increases With Accuracy
  • Fifth Law: The Value of Information Increases When Combined With Other Information
  • Sixth Law: More Is Not Necessarily Better
  • Seventh Law: Information is not Depletable

From here I conveniently link information back to data, with data being the commodity with which we have developed a much more intuitive feeling about its location, size, movement and order.

You see, without understanding these basic aspects associated with data, it is unlikely that your organisation will be able to extract full value from it.  Without order, data will not become useful information nor will it digitise the wisdom with which you run your business.

  1. Data is Fluid

Just like a body of water, data is fluid.  Although fluid, it can also become stagnant.  You see, if data is not flowing to and from (and within) your organisation, your data is stagnant and of little use.  It might become contaminated.  Diseased.  As per Moody and Walsh’s Third Law: Perishable.

Data needs to move between people.  Not all of it – just the right amount at the right time so that a person can make decisions and take action.  Once this flow is optimised, the true value of data is revealed.

In order to manage the flow of water, we need to know its location, how it will move and how much will move.  Then we can design a system to manage this flow.  The same for data.  Yet, we find that most organisations avoid an integrated asset management approach for data and rather remain responsive to how data accumulates and is used (or abused) within the business.  This comes with a constant set of surprises.  Surely we won’t build a dam wall without an understanding of the body of water and the flow that we need to manage? So when management does not have this level of understanding about their data, it should not come as a surprise that their failure to invest is met with various breaches: cybercrime, data theft, misappropriation of data and other non-compliant activities.

We thus need to organise data (dam wall, flow system and measurement system), but contain it in such a way that it remains fluid and fresh (control system, release system and purification system).

  1. Data Quality

Water can contain various impurities and contaminants that, despite its abundance on earth, render only a small volume useful to us.  Again, similar is true with data.  Organisations have built large bodies of data, referred to as data lakes, where systems push and pull to channel, process, and replicate data before sending it to the desired destination.  This can get messy;  it is easier to build a bigger dam than to manage all the various water sources and contaminant sources.  So some built bigger data lakes.  Bigger lakes become cumbersome, difficult and expensive to manage when contamination sets in.  The second law of thermodynamics requires ever more effort to prevent the state of chaos becoming unmanageable.

What can we do to manage data quality? Well, we need to understand what we have, where it is and what we do with it before we can start cleaning the data.  Many organisations are in a constant process of developing “master data”, although really all they are doing is building bigger lakes.  Not much is done to improve the quality of data (creating, managing and disposing of unwanted data) and few are removing/changing the processes and activities that reduce the quality of data.  ICT managers are under constant pressure to clean up with constant pressure on budgets.

Fortunately, better tools are now available to filter the contaminants and improve the system holistically.  Of course, the whole organisation must own this objective.

  1. Data Compliance

So what are these contaminants?  No, we are not referring to crashed hard drives or overwritten files.  The issue here is much more fundamental.  You see, for decades we have been building, pushing and pulling data, oblivious to who has it, gets it and how they will use it.  Our ignorance had disastrous consequences, in particular in relation to user privacy and content abuse.  The fundamental issues are therefore that of data governance and data compliance.

In recent years strict new laws have come into effect, most notably the General Data Protection Regulation (GDPR 2016/679), a regulation in EU law on data protection and privacy in the European Union and the European Economic Area.  GDPR also addresses the transfer of personal data outside the EU and EEA areas, therefore it impacts everyone that conducts business with entities in this region.  Similar, but with distinct differences, we have the Protection Of Personal Information Act, Act No.  4 of 2013, in South Africa.  Although it has been around for some time, 1 July 2020 is the effective date for POPIA.  After the POPIA  effective date there is a 12-month grace period, therefore the actual compliance deadline is 1 July 2021.  If you are not attending to data compliance in your business right now… well, let’s say you have been warned!

It is not the purpose of this article to elaborate on data governance and compliance regulations, but rather to demonstrate how you can look at data beyond just a compliance activity that will usually only enjoy ever diminishing Opex budgets.  We suggest that you focus on data as an asset and see the compliance element as an added bonus following from your excellent management approach.

  1. Extracting Data Value

Once you treat data as an asset, you will find that you direct the right energy and commitment towards extracting value.  Knowing what you have, how it moves into and out of your organisation and critically what value is derived within your business, will equip an organisation with the capability to treat its data like PP&E on the balance sheet.  There are various models for valuing your data, yet most align with the principle of consumption.  Back to our water analogy: data is valuable where and when consumed.  Fortunately, like water, we can treat our data and use it again and again. Moody and Walsh’s Seventh law.

By looking into the source, location and use (flow) of data, and linking these to value drivers within your business, you will extract its full value.  You need to start with knowing what you have and what is out of place.

  1. Mining Data

It is clear that your data governance strategy must include methods for finding, evaluating and extracting data before attempts can be made to improve and unlock ultimate value from your data.

Utilising modern systems such as dataBelt® by AiM, organisations will be able to take a leap in data compliance, cleansing and integration.  dataBelt®’s Keras Tensorflow machine learning (ML) and optical character reading (OCR) tools are used to discover images, audio and video files stored in any accessible location throughout the company’s data infrastructure.  dataBelt® is employed to locate and classify files based on the analysis of textual information and image types.  Non-compliant data can then be flagged for further attention.  A number of models are used to train dataBelt® so that image, audio and video identification can be done quickly and accurately.  A dashboard provides management with constant oversight of how data compliance ebbs and flows throughout the organisation.

Having this level of oversight of your data compliance does not only help you to meet compliance deadlines but sets the foundation for strengthening data value optimisation.  If you know what you have, who has it, and how it moves, you can set KPIs to improve the value of your data asset.

So, how have you set your budget for data management in 2020/21?  Costs for compliance activities or costs for deriving value from a fast-flowing asset? There is a better way…


Johan Louw is the founder of Aguru Business Solutions.  We empower clients in industrial processing and manufacturing industries with differentiating strategies to plan and deliver automation and digitalisation solutions that drive efficiencies and improve asset value. 

Aguru Business Solutions is the authorised representative in Southern Africa of dataBelt® – AiM’s Data Governance and GDPR Cloud Software Platform. 


Aguru Business Solutions







[1] https://www.researchgate.net/profile/Faris_Alshubiri/post/How_to_determine_information_asset_value/attachment/59d6278679197b8077985d05/AS%3A326144877449217%401454770408208/download/1000.pdf

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