Before We Talk About Data, Let’s Talk About HOW We Talk About Data

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How do you feel about everyone you speak to having the same understanding of the definitions of words we use in everyday conversations? Take the word Asset for example. How many different ways can it be interpreted? A test of this would be to approach someone you know from finance and ask them for their definition of an Asset and then do the same thing with your plant manager. If the first statement holds true, there will have been two very different answers to your question. The point is definitions are important understanding them in a common way is super important. The application of the definition within our work tasks is one of the fundamentals of data management and governance. Thus if the definition is not applied in a consistent fashion then we all know what happens next!

The following definitions are included as relevant to the discussion of asset information management. They are formulated from objective sources in an attempt to remove subjectivity but provide guidance in your daily conversations.

Information Need – an insight that is necessary or required in order to solve problems, to manage risks, and to achieve goals and objectives (ISO 27000:2014)

Information Integration – the merging of information from heterogeneous sources with differing conceptual, contextual and typographical representations (Wikipedia)

Data is defined:

1) information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful (Merriam-Webster)

2) the quantities, characters, or symbols on which operations are performed by a computer which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media (Oxford Dictionaries)

Data Management – Administrative process by which the required data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users. (

Information – the communication or reception of knowledge or intelligence (Webster’s Dictionary)

Information Management – concerns a cycle of organizational activity: the acquisition of information from one or more sources, the custodianship and the distribution of that information to those who need it, and its ultimate disposition through archiving or deletion (Wikipedia).

Asset Information – is a combination of data about physical assets used to inform decisions about how they are managed (The Institute of Asset Management)

Asset Management – enables an organization to examine the need for, and performance of, assets and asset systems at different levels. Additionally, it enables the application of analytical approaches towards managing an asset over the different stages of its life cycle (ISO 55000 – Asset management – Overview, principles and terminology)

Asset Management Capabilities – include processes, resources, competences and technologies to enable the effective and efficient development and delivery of asset management plans and asset life activities, and their continual improvement (ISO 55000 – Asset management – Overview, principles and terminology)

These words and their definitions will be the template for our blogs going forward. A standard language that is accurate and agreed to will only lead to better understanding and revelation. Look for our next installment soon.

Norm Poynter is a widely known author and consultant around all things data, data integrity and data administration. Norm is facilitating his popular hands-on SAP Master Data Workshop in Houston on May 11 & 12. Don’t miss out!

Connect with Norm on LinkedIn and follow him on Twitter.

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Norm Poynter
Norm Poynter is a management consultant, specializing in SAP PM along with integration to engineering and plant process systems. For the past 17 years he has been involved in continuous improvement, best practices, plant turnarounds, re-engineering, process improvements, process safety and operations excellence development & implementation projects. Norm has held technical and functional roles with Agrium, NOVA Chemicals, DuPont, Canexus, Fortis. Marathon Oil, Suncor, and Nexen.

Today, Norm focuses on Operations Excellence Management Systems along side the creation of sustainable communities of practice. Key passions for Norm are enabling standardization, consistency and productivity through better use grass root technologies like SAP.

One thought on “Before We Talk About Data, Let’s Talk About HOW We Talk About Data

  • Firstly, congratulations to Norm for his insightful & challenging kick-off to the topic of “..HOW We Talk About Data”.

    Norm correctly observes that we must be disciplined in our language and understanding to facilitate effective and accurate discussion of the systems and processes that utilize our “Data”. As a thought experiment, imagine your General Practitioner trying to explain to your surgeon about a repair to your radial collateral ligament of the elbow using terms like “funny bone” – pretty obviously, taxonomy matters.

    As Norm notes, the Merriam Webster site defines “data” as “facts or information …”. We shouldn’t read too much into the interchangeability implied by MW, the context of their definition it is fairly wide.

    Wikipedia takes it a step further: “Data, information, knowledge and wisdom are closely related concepts, but each has its own role in relation to the other”. So we learn that within the context of each other, the terms are not interchangeable.

    US Astrophysicist, Clifford Stoll is widely quoted as saying, “Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom.” Here the terms are clearly not interchangeable. The context of the quote was probably scientific and the distinctions are vitally important in that context.

    Norm’s context is “the discussion of asset information management”. What might be the distinctions that count in that context? We toss around terms like Master Data, Technical Information, Equipment Data, but do these terms really mean what we think they mean and what might be the impact of misconstrued understanding?

    In a paper for an ISO 55000 conference, I argued that sources we traditionally call “Technical Information” (parts books, instructional manuals, drawings, etc) are closer to Stoll’s “data” – of no immediate use without investment of time and energy to discover the embedded “information”.

    To avoid becoming unnecessarily abstruse, let’s look at a practical example: a manually operated valve of a model that can be used in multiple applications. Any given physical might be made up of, say, 18 unique parts, but the generic parts list, covering all possible applications, might include 150 unique parts. The generic list might include five different “Shaft Seal” options and ten “Disc” options. Consider the situation where the same model of valve is used on a cooling water circuit and on a circuit carrying hot or corrosive fluid. It is clear to see how identification of the correct shaft seal elevates in its importance.

    If the generic parts list forms part of the Technical Information supplied by the vendor, then some time and energy is justified to pick the right parts for a given application – particularly a hazardous application.

    OK, let’s say we invested time and energy and extracted some “information” in the form of a part number for the correct seal for a hazardous application – what might we do with that information? We might have it catalogued and possibly added to a kitting list or BOM. Then we’d class it as “Master Data”.

    This is not an exercise in semantics. The first thing to note here is that by capturing the information out of the data into a reusable form, we’ve obviated the need to invest more time and energy to extract the same information from the same data for each event – find once, use many. Now our whole system is marginally more efficient and more accurate.

    In a business environment with, let’s say 800,000 line items of catalogued spares, that’s 800,000 opportunities for adding marginal efficiency and accuracy.

    The next natural avenue of enquiry is to look at “Master Data” – a perennial Top 3 item of concern for asset management conferences year after year – and ask whether the things we operate on as “Master Data” are in fact “Data” or “Master”. Can a catalogue item (Material Master, Stock Code, SKU, etc) that defines a dozer blade edge protector as interchangeable with an excavator lift cylinder be “Master Data”? Certainly this genuine case was an error but it informs us about the governance system that mines information out of data and then puts that information to use to drive our “asset information management”.

    Norm has challenged us to be disciplined in our understanding and use of terms that define our systems and processes. From that platform, I have offered some thoughts on context and consequences. I look forward to some interesting follow on contributions.

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