The Anatomy of a Data Function – Part III

Part I Part II Part III

Sepia's Anatomy

This is the third and final part of my review of the anatomy of a Data Function, Part I may be viewed here and Part II here.

In the first article, I introduced the following Data Function organogram:

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

and went on to cover each of Data Strategy, Analytics & Insight and Data Operations & Technology. In Part II, I discussed the two remaining Data Function areas of Data Architecture and Data Management. In this final article, I wanted to cover the Related Areas that appear on the right of the above diagram. This naturally segues into talking about the practicalities of establishing a Data Function and highlighting some problems to be avoided or managed.

As in Parts I and II, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Related Areas

Related Areas

I have outlined some of the key areas with which the Data Function will work. This is not intended to be a comprehensive list and indeed the boxes may be different in different organisations. Regardless of the departments that appear here, the general approach will however be similar. I won’t go through each function in great detail here. There are some obvious points to make however. The first is an overall one that clearly a collaborative approach is mandatory. While there are undeniably some police-like attributes of any Data Function, it would be best if these were carried out by friendly community policemen or women, not paramilitaries.

So rather more:

Community Police

and rather less:

Not quite so Community Police

Data Privacy and Information Security

Though strongly related, these areas do not generally fall under the Data Function. Indeed some legislation requires that they are separate functions. Data Privacy and Information Security are related, but also distinct from each other. Definitions are as follows:

[Data Privacy] pertains to data held by organisations about individuals (customers, counterparties etc.) and specifically to data that can be used to identify people (personally identifiable data), or is sensitive in nature, such as medical records, financial transactions and so on. There is a legal obligation to safeguard such information and many regulations around how it can be used and how long it can be retained. Often the storage and use of such data requires explicit consent from the person involved.

Data and Analytics Dictionary entry: Data Privacy

Information Security consists of the steps that are necessary to make sure that any data or information, particularly sensitive information (trade secrets, financial information, intellectual property, employee details, customer and supplier details and so on), is protected from unauthorised access or use. Threats to be guarded against would include everything from intentional industrial espionage, to ad hoc hacking, to employees releasing or selling company information. The practice of Information Security also applies to the (nowadays typical) situation where some elements of internal information is made available via the internet. There is a need here to ensure that only those people who are authenticated to access such information can do so.

Data and Analytics Dictionary entry: Information Security

Digital

Digital is not a box that would have necessarily have appeared on this chart 15, or even 10, years ago. However, nowadays this is often an important (and large) department in many organisations. Digital departments leverage data heavily; both what they gather themselves and and data drawn from other parts of the organisation. This can be to show customers their transactions, to guide next best actions, or to suggest potentially useful products or services. Given this, collaboration with the Data Function should be particularly strong.

Change Management

There are some specific points to make with respect to Change collaboration. One dimension of this was covered in Part II. Looking at things the other way round, as well as being a regular department, with what are laughingly referred to as “business as usual” responsibilities [1], the Data Function will also drive a number of projects and programmes. Depending on how this is approached in an organisation, this means either that the Data Function will need its own Project Managers etc., or to have such allocated from Change. This means that interactions with Change are bidirectional, which may be particularly challenging.

For some reason, Change departments have often ended up holding the purse strings for all projects and programmes (perhaps a less than ideal outcome), so a Data Function looking to get its own work done may run counter to this (see also the second section of this article).

IT

While the role of IT is perhaps narrower nowadays than historically [2], they are deeply involved in the world of data and the infrastructure that supports its movement around the organisation. This means that the Data Function needs to pay particular attention to its relationship with IT.

Embedded Analytics Teams

A wholly centralised approach to delivering Analytics is neither feasible, nor desirable. I generally recommend hybrid arrangements with a strong centralised group and affiliated analytical resource embedded in business teams. In some organisations such people may be part of the Data Function, or have a dotted line into it. In others the connection may be less formal. Whatever the arrangements, the best result would be if embedded analytical staff viewed themselves as part of a broader analytical and data community, which can share tips, work to standards and leverage each other’s work.

Data Stewards

Data Stewards are a concept that arises from a requirement to embed Data Governance policies and processes. Data Function Governance staff and Data Architects both need to work closely with Data Stewards. A definition is as follows:

This is a concept that arises out of Data Governance. It recognises that accountability for things like data quality, metadata and the implementation of data policies needs to be devolved to business departments and often locations. A Data Steward is the person within a particular part of an organisation who is responsible for ensuring that their data is fit for purpose and that their area adheres to data policies and guidelines.

Data and Analytics Dictionary entry: Data Steward


End User Computing

There are several good reasons for engaging with this area. First, the various EUCs that have been developed will embody some element (unsatisfied elsewhere) of requirements for the processing and or distribution of data; these needs probably need to be met. Second, EUCs can present significant risks to organisations (as well as delivering significant benefits) and ameliorating these (while hopefully retaining the benefits) should be on the list of any Data Function. Third, the people who have built EUCs tend to be knowledgeable about an organisation’s data, the sort of people who can be useful sources of information and also potential allies.

[End User Computing] is a term used to cover systems developed by people other than an organisation’s IT department or an approved commercial software vendor. It may be that such software is developed and maintained by a small group of people within a department, but more typically a single person will have created and cares for the code. EUCs may be written in mainstream languages such as Java, C++ or Python, but are frequently instead Excel- or Access-based, leveraging their shared macro/scripting language, VBA (for Visual Basic for Applications). While related to Microsoft Visual Basic (the precursor to .NET), VBA is not a stand-alone language and can only run within a Microsoft Office application, such as Excel.

Data and Analytics Dictionary entry: End User Computing (EUC)

Third Party Providers

Often such organisations may be contracted through the IT function; however the Data Function may also hire its own consultants / service providers. In either case, the Data Function will need to pay similar attention to external groups as it does to internal service providers.
 
 
Building a Data Function for the Practical Man [3]

Flag Planting for the Practical Man

When I published Part I of this trilogy, many people were kind enough to say that they found reading it helpful. However, some of the same people went on to ask for some practical advice on how to go about setting up such a Data Function and – in particular – how to navigate the inevitable political hurdles. While I don’t believe in recipes for success that are guaranteed to work in all circumstances, the second section of this article will cover three selected high-level themes that I think are helpful to bear in mind at the start of a Data Function journey. Here I am assuming that you are the leader of the nascent Data Function and it is your accountability to build the team while adding demonstrable business value [4].

Starting Small

It is a truth universally acknowledged, that a Leader newly in possession of a Data Function, must be in want of some staff [5]. However seldom will such a person be furnished with a budget and headcount commensurate with the task at hand; at least in the early days. Often instead, the mission, should you choose to accept it, is to begin to make a difference in the Data World with a skeleton crew at best [6]. Well no one can work miracles and so it is a question of judgement where to apply scarce resource.

My view is that this is best applied in shining a light on the existing data landscape, but in two ways. First, at the Analytics end of the spectrum, looking to unearth novel findings from an organisation’s data; the sort of task you give to a capable Data Scientist with some background in the industry sector they are operating in. Second, at the Governance end of the spectrum, documenting failures in existing data processing and reporting; in particular any that could expose the organisation to specific and tangible risks. In B2C organisations, an obvious place to look is in customer data. In B2B ones instead you can look at transactions with counterparties, or in the preparation of data for external reports, either Financial or Regulatory. Here the ideal person is a competent Data Analyst with some knowledge of the existing data landscape, in particular the compromises that have to be made to work with it.

In both cases, the objective is to tell the organisation things it does not know. Positively, a glimmer of what nuggets its data holds and the impact this could have. Negatively, examples of where a poor data landscape leads to legal, regulatory, or reputational risks.

These activities can add value early on and increase demand for more of this type of work. The first investigation can lead to the creation of a Data Science team, the second to the establishment of regular Data Audits and people to run these.

A corollary here is a point that I ceaselessly make, data exploitation and data control are two sides of the same coin. By making progress in areas that are at least superficially at antipodal locations within a Data Function, the connective tissue between them becomes more apparent.

BAU or Project?

There is a pernicious opinion held by an awful lot of people which goes as follows.

  1. We have issues with our data, its quality, completeness and fitness for purpose.
  2. We do not do a good enough job of leveraging our data to guide decision making.
  3. Therefore we need a data project / programme to sort this out once and for all.
  4. Where is the telephone number of the Change Director?

Well there is some logic to the above and setting up a data project (more likely programme) is a helpful thing to do. However, this is necessary, but not sufficient [7]. Let’s think of a comparison?

  1. We need to ensure that our Financial and Management accounts are sound.
  2. It would be helpful if business leaders had good Financial reports to help them understand the state of their business.
  3. Therefore we need a Finance project / programme to sort this out once and for all.
  4. Where is the telephone number of the Change Director?

Most CFOs would view the above as their responsibility. They have an entire function focussed on such matters. Of course they may want to run some Finance projects and Change will help with this, but a Finance Department is an ongoing necessity.

To pick another example one that illustrates just how quickly the make-up of organisations can change, just replace the word “Finance” with “Risk” in the above and “CFO” with “CRO”. While programmes may be helpful to improve either Risk or Finance, they do not run the Risk or Finance functions, the designated officers do and they have a complement of staff to assist them. It is exactly the same with data. Data programmes will enhance your use of data or control of it, but they will not ensure the day-to-day management and leverage of data in your organisation. Running “data” is the responsibility of the designated officer [8] and they should have a complement of staff to assist them as well.

The Data Function is a “business as usual” [9] function. Conveying this fact to a range of stakeholders is going to be one of the first challenges. It may be that the couple of examples I cite above can provide some ammunition for this task.

Demolishing Demoralising Demarcations

With Data Functions and their leaders both being relative emergent phenomena [10], the separation of duties between them and other areas of a business that also deal with data can be less than clear. Scanning down the Related Areas column of the overall Data Function chart, three entities stand out who may feel that they have a strong role to play in data matters: Digital, Change Management and IT.

Of course each is correct and collaboration is the best way forward. However, human nature is not always do benign and I have several times seen jockeying for position between Data, Digital, Change and IT. Route A to resolving this is of course having clarity as to everyone’s roles and a lead Executive (normally a CEO or COO) who ensures that people play nicely with each other. Back in the real world, it will be down to the leaders in each of these areas to forge some sort of consensus about who does what and why. It is probably best to realise this upfront, rather than wasting time and effort lobbying Executives to rule on things they probably have no intention of ruling on.

Nascent Data Function leaders should be aware that there will be a tendency for other teams to carve out what might be seen as the sexier elements of Data work; this can almost seem logical when – for example – a Digital team already has a full complement of web analytics staff; surely it is just a matter of pointing these at other internal data sets, right?

If we assume that the Data Function is the last of the above mentioned departments to form, then “zero sum game” thinking would dictate that whatever is accretive to the Data Function is deleterious to existing data staff in other departments. Perhaps a good place to start in combatting this mind-set is to first acknowledge it and second to take steps to allay people’s fears. It may well make sense for some staff to gravitate to the Data Function, but only if there is a compelling logic and only if all parties agree. Offering the leaders of other departments joint decision-making on such sensitive issues can be a good confidence-building step.

Setting out explicitly to help colleagues in other departments, where feasible to do so, can make very good sense and begin the necessary work of building bridges. As with most areas of human endeavour, forging good relationships and working towards the common good are both the right thing to do and put the Data Function leader in a good place as and when more contentious discussions arise.

To make this concrete, when people in another function appear to be stepping on the toes of the Data Function, instead of reacting with outrage, it may be preferable to embrace and fully understand the work that is being done. It may even make sense to support such work, even if the ultimate view is to do things a bit differently. Insisting on organisational purity and a “my way, or the highway” attitude to data matters are both steps towards a failed Data Function. Instead, engage, listen, support and – maybe over time – seek to nudge things towards your desired state.
 
 
Closing Thoughts

That's All Folks

So we have reached the end of our anatomical journey. While maybe the information contained in these three articles would pale into insignificance compared to an actual course in human anatomy, we have nevertheless covered five main work-areas within a Data Function, splitting these down into nineteen sub-areas and cataloguing eight functions with which collaboration will be key in driving success. I have also typed over 8,000 words to convey my ideas. For those who have read all of them, thank you for your perseverance; I hope that the effort has been worthwhile and that you found some of my opinions thought-provoking.

I would also like to thank the various people who have provided positive feedback on this series via LinkedIn and Facebook. Your comments were particularly influential in shaping this final chapter.

So what are the main takeaways? Well first the word collaboration has cropped up a lot and – because data is so pervasive in organisations – the need to collaborate with a wide variety of people and departments is strong. Second, extending the human anatomy analogy, while each human shares a certain basic layout (upright, bipedal, two arms, etc.), there is considerable variation within the basic parameters. The same goes for the organogram of a Data Function that I have presented at the beginning of each of these articles. The boxes may be rearranged in some organisations, some may not sit in the Data Function in others, the amount of people allocated to each work-area will vary enormously. As with human anatomy, grasping the overall shape is more important than focussing on the inevitable variations between different people.

Third, a central concept is of course that a Data Function is necessary, not just a series of data-centric projects. Even if it starts small, some dedicated resource will be necessary and it would probably be foolish to embark on a data journey without at least a skeleton crew. Fourth, in such straitened circumstances, it is important to point early and clearly to the value of data, both in reducing potentially expensive risks and in driving insights that can save money, boost market share or improve products or services. If the budget is limited, attend to these two things first.

A fifth and final thought is how little these three articles have focussed on technology. Hadoop clusters, data visualisation suites and data governance tools all have their place, but the success or failure of data-centric work tends to pivot on more human and process considerations. This theme of technology being the least important part of data work is one I have come back to time and time again over the nine years that this blog has been published. This observation remains as true today as back in 2008.
 

Part I Part II Part III

 
Notes

 
[1]
 
BAU should in general be filed along with other mythical creatures such as Unicorns, Bigfoot, The Kraken and The Loch Ness Monster.
 
[2]
 
Not least because of the rise of Data Functions, Digital Teams and stand-alone Change Organisations.
 
[3]
 
A title borrowed from J E Thompson’s Calculus for the Practical Man; a tome read by the young Richard Feynman in childhood. Today “Calculus for the Practical Person” might be a more inclusive title.
 
[4]
 
Also known as “pulling yourself up by your bootstraps”.
 
[5]
 
I seem to be channelling JA a lot at present – see A truth universally acknowledged….
 
[6]
 
Indeed I have stated on this particular journey with just myself for company on no fewer than for occasions (these three 1, 2, 3, plus at Bupa).
 
[7]
 
Once a Mathematician, always a Mathematician.
 
[8]
 
See Alphabet Soup for some ideas about what he or she might be called.
 
[9]
 
See note 1.
 
[10]
 
Despite early high-profile CDOs beginning to appear at the turn of the millennium – Joe Bugajski was appointed VP and Chief Data Officer at Visa International in 2001 (Wikipedia).

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

The Anatomy of a Data Function – Part II

Part I Part II Part III

Sepia's Anatomy

This is the second part of my review of the anatomy of a Data Function, the artfully named Part I may be viewed here. As seems to happen all too often to me, this series will now extend to having a Part III, which may be viewed here.

In the first article, I introduced the following Data Function organogram:

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

and went on to cover each of Data Strategy, Analytics & Insight and Data Operations & Technology. In Part II, I will consider the two remaining Data Function areas of Data Architecture and Data Management. Covering Related Areas, and presenting some thoughts on how to go about setting up a Data Function and the pitfalls to be faced along the way, together form the third and final part of this trilogy.

As in Part I, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Data Architecture

Data Architecture

To be somewhat self-referential, this area acts a a cornerstone for the rest of the Data Function. While sometimes non-Data architects can seem to inhabit a loftier plane than most mere mortals, Data Architects (who definitively must be part of the Data Function and none of the Business, Enterprise or Solutions Architecture groups) tend to be more practical sorts with actual hands-on technical skills. Perhaps instead of the title “Architect”, “Structural Engineer” would be more appropriate. When a Data Architect draws a diagram with connected boxes, he or she generally understands how the connections work and could probably take a fair stab at implementing the linkages themselves. The other denizens of this area, such as Data Business Analysts, are also essentially pragmatic people, focused on real business outcomes. Data Architecture is a non-theoretical discipline and here I present some of the real-world activities that its members are often engaged in.

Change Portfolio Engagement

One of the most important services that a good Data Function can perform is to act as a moderator for the otherwise deleterious impact that uncontrolled (and uncoordinated) Change portfolios can have on even the best of data landscapes [1]. As I mention in another article:

Over the last decade or so, the delivery of technological change has evolved to the point where many streams of parallel work are run independently of each other with each receiving very close management scrutiny in order to ensure delivery on-time and on-budget. It should be recognised that some of this shift in modus operandi has been as a result of IT departments running projects that have spiralled out of control, or where delivery has been significantly delayed or compromised. The gimlet-like focus of Change on delivery “come Hell or High-water” represents the pendulum swinging to the other extreme.

What this shift in approach means in practice is that – as is often the case – when things go wrong or take longer than anticipated, areas of work are de-scoped to secure delivery dates. In my experience, 9 times out of 10 one of the things that gets thrown out is data-related work; be that not bothering to develop reporting on top of new systems, not integrating new data into existing repositories, not complying with data standards, or not implementing master data management.

As well as the danger of skipping necessary data related work, if some data-related work is actually undertaken, then corners may be cut to meet deadlines and budgets. It is not atypical for instance that a Change Programme, while adding their new capabilities to interfaces or ETL, compromises or overwrites existing functionality. This can mean that data-centric code is in a worse state after a Change Programme than before. My roadworks anecdote begins to feel all too apt a metaphor to employ.

Looking more broadly at Change Programmes, even without the curse of de-scopes, their focus is seldom data and the expertise of Change staff is not often in data matters. Because of this, such work can indeed seem to be analogous to continually digging up the same stretch of road for different purposes, combined with patching things up again in a manner that can sometimes be barely adequate. Extending our metaphor, the result of Change that is not controlled from a data point of view can be a landscape with lumps, bumps and pot-holes. Maybe the sewer was re-laid on time and to budget, but the road has been trashed in the process. Perhaps a new system was shoe-horned in to production, but rendered elements of an Analytical Repository useless in the process.

Excerpted from: Bumps in the Road

A primary responsibility of a properly constituted Data Function is to lean hard against the prevailing winds of Change in order to protect existing data capabilities that would otherwise likely be blown away [2]. Given the gargantuan size of most current Change teams, it makes sense to have at least a reasonable amount of Data Function resource applied to this area. Hopefully early interventions in projects and programmes can mitigate any potentially adverse impacts and perhaps even lead to Change being accretive to data landscapes, as it really ought to be.

The best approach, as with most human endeavours is a collaborative one, with Data Function staff (probably Data Architects) getting involved in new Change projects and programmes at an early stage and shaping them to be positive from a Data dimension. However, there also needs to be teeth in the process; on occasion the Data Function must be able to prevent work that would cause true damage from going ahead; hopefully powers that are used more in breach than observance.

Data Modelling

It is in this area that the practical bent of Data Architects and Data Business Analysts is seen very clearly. Data modelling mirrors the realities of systems and databases the way that Theoretical Physicists use Mathematics to model the Natural World [3]. In both cases, while there may be a degree of abstraction, the end purpose is to achieve something more concrete. A definition is as follows:

[Data Modelling is] the process of examining data sets (e.g. the database underpinning a system) in order to understand how they are structured, the relationships between their various parts and the business entities and transactions they represent. While system data will have a specific Physical Data Model (the tables it contains and their linkages), Data Modelling may instead look to create a higher-level and more abstract set of pseudo-tables, which would be easier to relate to for non-technical staff and would more closely map to business terms and activities; this is known as a Conceptual Data Model. Sitting somewhere between the two may be found Logical Data Models. There are several specific documents produced by such work, one of the most common being an Entity-Relationship diagram, e.g. a sales order has a customer and one or more line items, each of which has a product.

Data and Analytics Dictionary entry: Data Modelling

Data Business Analysis

Another critical role. In my long experience of both setting up Data Functions and running Data Programmes, having good Data Business Analysts on board is often the difference between success and failure. I cannot stress enough how important this role is.

Data Business Analysts are neither regular Business Analysts, nor just Data Analysts, but rather a combination of the best of both. They do have all the requirement gathering skills of the best BAs, but complement these with Data Modelling abilities, always seeking to translate new requirements into expanded or refined Data Models. Also the way that they approach business requirements will be very specific. The optimal way to do this is by teasing out (and they collating and categorising) business questions and then determining the information needed to answer these. A good Data Business Analyst will also have strong Data Analysis skills, being able to work with unfamiliar and lightly-documented datasets to discern meaning and link this to business concepts. A definition is as follows:

A person who has extensive understanding of both business processes and the data necessary to support these. A Business Analyst is expert at discerning what people need to do. A Data Analyst is adept at working with datasets and extracting meaning from them. A Data Business Analyst can work equally happily in both worlds at the same time. When they talk to people about their requirements for information, they are simultaneously updating mental models of the data necessary to meet these needs. When they are considering how lightly-documented datasets hang together, they constantly have in mind the business purpose to which such resources may be bent.

Data and Analytics Dictionary entry: Data Business Analyst

 
 
Data Management

Data Management

Again, it is worth noting that I have probably defined this area more narrowly than many. It could be argued that it should encompass the work I have under Data Architecture and maybe much of what is under Data Operations & Technology. The actual hierarchy is likely to be driven by factors like the nature of the organisation and the seniority of Managers in the Data Function. For good or ill, I have focussed Data Management more on the care and feeding of Data Assets in my recommended set-up. A definition is as follows:

The day-to-day management of data within an organisation, which encompasses areas such as Data Architecture, Data Quality, Data Governance (normally on behalf of a Data Governance Committee) and often some elements of data provision and / or regular reporting. The objective is to appropriately manage the lifecycle of data throughout the entire organisation, which both ensures the reliability of data and enables it to become a valuable and strategic asset.

In some organisations, Data Management and Analytics are part of the same organisation, in others they are separate but work closely together to achieve shared objectives.

Data and Analytics Dictionary entry: Data Management

Data Governance

There is a clear link here with some of the Data Architecture activities, particularly the Change Portfolio Engagement work-area. Governance should represent the strategic management of the data component of Change (i.e. most of Change), day-to-day collaboration would sit more in the Data Architecture area.

The management processes and policies necessary to ensure that data captured or generated within a company is of an appropriate standard to use, represents actual business facts and has its integrity preserved when transferred to repositories (e.g. Data Lakes and / or Data Warehouses, General Ledgers etc.), especially when this transfer involves aggregation or merging of different data sets. The activities that Data Governance has oversight of include the operation of and changes to Systems of Record and the activities of Data Management and Analytics departments (which may be merged into one unit, or discrete but with close collaboration).

Data Governance has a strategic role, often involving senior management. Day-to-day tasks supporting Data Governance are often carried out by a Data Management team.

Data and Analytics Dictionary entry: Data Governance

Data Definitions & Metadata

This is a relatively straightforward area to conceptualise. Rigorous and consistent definitions of master data and calculated data are indispensable in all aspects of how a Data Function operates and how an organisation both leverages and protects its data. Focusing on Metadata, a definition would be as follows:

[Metadata is] data about data. So descriptions of what appears in fields, how these relate to other fields and what concepts bigger constructs like Tables embody. This helps people unfamiliar with a dataset to understand how it hangs together and is good practice in the same way that documentation of any other type of code is good practice. Metadata can be used to support some elements of Data Discovery by less technical people. It is also invaluable when there is a need for Data Migration.

Data and Analytics Dictionary entry: Metadata

 
Data Audit

One of the challenges in driving Data Quality improvements in organisations is actually highlighting the problems and their impacts. Often poor Data Quality is a hidden cost, spread across many people taking longer to do their jobs than is necessary, or specific instances where interactions with business counterparties (including customers) are compromised. Organisations obviously cope – at least in general – with these issues, but they are a drag on efficiency and, in extremis, can lead to incidents which can cause significant financial loss and/or reputational damage. A way to make such problems more explicit is via a regular Data Audit, a review of data in source systems and as it travels through various data repositories. This would include some assessment of the completeness and overall quality of data, highlighting areas of particular concern. So one component might include the percentage of active records which suffer from a significant data quality issue.

It is important that any such issues are categorised. Are they the result of less than perfect data entry procedures, which could be tightened up? Are they due to deficient validation in transactional systems, where this could be improved and there may be a role for Master Data Management? Are data interfaces between systems to blame, where these need to be reengineered or potentially replaced? Are there architectural issues with systems or repositories, which will require remedial work to address?

This information needs to be rolled up and presented in an accessible manner so that those responsible for systems and processes can understand where issues lie. Data Audits, even if partially automated, take time and effort, so it may be appropriate to carry them out quarterly. In this case, it is valuable to understand how the situation is changing over time and also to track the – hopefully positive – impact of any remedial action. Experienced Data Analysts with a good appreciation of how business is conducted in the organisation are the type of resource best suited to Data Audit work.

Data Quality

Much that needs to be said here is covered in the previous section about Data Audit. Data Quality can be defined as follows:

The characteristics of data that cover how accurately and completely it mirrors real world events and thereby how much reliance can be placed on it for the purpose of generating information and insight. Enhancing Data Quality should be a primary objective of Data Management teams.

Data and Analytics Dictionary entry: Data Quality

A Data Quality team, which would work closely with Data Audit colleagues, would be focussed on helping to drive improvements. The details of such work are covered in an earlier article, from which the following is excerpted:

There are a number of elements that combine to improve the quality of data:

  1. Improve how the data is entered
  2. Make sure your interfaces aren’t the problem
  3. Check how the data is entered / interfaced
  4. Don’t suppress bad data in your BI

As with any strategy, it is ideal to have the support of all four pillars. However, I have seen greater and quicker improvements through the fourth element than with any of the others.

Excerpted from: Using BI to drive improvements in data quality

Master Data Management

There is some overlap here with Data Definitions & Metadata as mentioned above. Master Data Management has also been mentioned here in the context of Data Quality initiatives. However this specialist area tends to demand dedicated staff. A definition is as follows:

Master Data Management is the term used to both describe the set of process by which Master Data is created, changed and deleted in an organisation and also the technological tools that can facilitate these processes. There is a strong relation here to Data Governance, an area which also encompasses broader objectives. The aim of MDM is to ensure that the creation of business transactions results in valid data, which can then be leveraged confidently to create Information.

Many of the difficulties in MDM arise from items of Master Data that can change over time; for example when one counterparty is acquired by another, or an organisational structure is changed (maybe creating new departments and consolidating old ones). The challenges here include, how to report historical transactions that are tagged with Master Data that has now changed.

Data and Analytics Dictionary entry: Master Data Management

 
 
At this point, we have covered all of the work-areas within our idealised Data Function. In the third and final piece, we will consider the right-hand column of Related Areas, ones that a Data Function must collaborate with. Having covered these, the trilogy will close by offering some thoughts on the challenges of setting up a Data Function and how these may be overcome.
 

Part I Part II Part III

 
Notes

 
[1]
 
I am old enough to recall a time before Change portfolios, I can recall no organisation in which I have worked over the last 20 years in which Change portfolios have had a positive impact on data assets; maybe I have just been unlucky, but it begins to feel more like a fundamental Physical Law.
 
[2]
 
I have clearly been writing about hurricanes too much recently!
 
[3]
 
As is seen, for example in, the Introduction to my [as yet unfinished] book on the role of Group Theory in Theoretical Physics, Glimpses of Symmetry.

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

The Anatomy of a Data Function – Part I

Part I Part II Part III

Back in Alphabet Soup, I presented a diagram covering what I think are good and bad approaches to organising Analytics and Data Management. I wanted to offer an expanded view [1] of the good organisation chart and to talk a bit about each of its components. Originally, I planned to address these objectives across two articles. As happens to me all too frequently, the piece has now expanded to become three parts. The second may be read here, and the third here.

Let’s leap right in and look at my suggested chart:

The Anatomy of a Data Function

Larger PDF version (opens in a new tab)

I appreciate that the above is a lot of boxes! I can feel Finance and HR staff reaching for their FTE calculators as I write. A few things to note:

  1. I have avoided the temptation to add the titles of executives, managers or team leaders. Alphabet Soup itself pointed out how tough it can be to wrestle with the nomenclature. Instead I have just focussed on areas of work.
     
  2. The term “work areas” is intentional. In larger organisations, there may be teams or individuals corresponding to each box. In smaller ones Data Function staff will wear many hats and several work areas may be covered by one person.
     
  3. In some places, a number of work areas that I have tagged as Data Function ones may be performed in other parts of the organisation, though it is to be hoped with collaboration and coordination.

Having dealt with these caveats, let’s provide some colour on each of these progressing from top to bottom and left to right. In this first article we will consider the Data Strategy, Analytics & Insight and Data Operations & Technology areas. The second part will cover the remaining elements of Data Architecture and Data Management. The final article, considers Related Areas before also covering some of the challenges that may be faced in setting up a Data Function.

In what follows, unless otherwise stated, text indented as a quotation is excerpted from the Data and Analytics Dictionary.
 
 
Data Strategy

Data Strategy

A clear strategy is obviously most important to establish in the early days of a Data Function. Indeed a Data Strategy may well call for the creation of a Data Function where none currently exists. For anyone interested in this process, I recommend my series of three articles on this subject [2]. However a Data Strategy is not something carved in stone, it will need to be revisited and adapted (maybe significantly) as circumstances change (e.g. after an acquisition, a change in market conditions or potentially due to the emergence of some new technology). There is thus a need for ongoing work in this area. However, as demand for strategic work will tend to be lumpy, I suggest amalgamating Data Strategy with the following two sub-areas.

Data Comms & Education

Elsewhere on this site, I have highlighted the need for effective communication, education and assiduous follow-up in data programmes [3]. Education on data matters does not stop when a data quality drive is successfully completed, or when a new set of analytical capabilities are introduced, this is a need for an ongoing commitment here. Activities falling into this work area include: publishing regular data newsletters and infographics, designing and helping to deliver training programmes, providing follow-up and support to aid the embedded used of new capabilities or to ingrain new behaviours.

Relationship Management

There is a need for all Data Function staff to establish and maintain good working relations with any colleagues they come into contact with, regardless of their level or influence. However, the nature of, generally hierarchical, organisations is that it is often prudent to pay special attention to more senior staff, or to the type of person (common in many companies) who may not be that senior, but whose opinion is influential. In aggregate these two groups of people are often described as stakeholders. Providing regular updates to stakeholders and ensuring both that they are comfortable with Data Function work and that this is aligned with their priorities can be invaluable [4]. Having senior, business-savvy Data Function people available to do this work is the most likely path to success.
 
 
Analytics & Insight

Analytics & Insight

Broadly speaking the Analytics area and its sub-areas are focussed more on one-off analyses rather that the recurrent production of information [5], the latter being more the preserve of the Data Operations & Technology area. There is also more of a statistical flavour to the work carried out here.

[Analytics relates to] deriving insights from data which are generally beyond the purpose for which the data was originally captured – to be contrasted with Information which relates to the meaning inherent in data (i.e. the reason that it was captured in the first place). Analytics often employ advanced statistical techniques (logistic regression, multivariate regression, time series analysis etc.) to derive meaning from data.

Data and Analytics Dictionary entry: Analytics

Data Science

I have Data Science as a sub-area of analytics, as with most terminology used in the data arena and most organisational units that exist in Data Functions, some people might argue that I have this the wrong way round and that Data Science should be preeminent. Reconciling different points of view is not my objective here, I think most people will agree that both work areas should be covered. This comment pertains to many other parts of this article. Here is a definition of the area (or rather the people who populate it):

[Data Scientists are people who are] au fait with exploiting data in many formats from Flat Files to Data Warehouses to Data Lakes. Such individuals possess equal abilities in the data technologies (such as Big Data) and how to derive benefit from these via statistical modelling. Data Scientists are often lapsed actual scientists.

Data and Analytics Dictionary entry: Data Scientist

Data Visualisation

There is an overlap here with both the Data Science team within the Analytics & Insight area and the Business Intelligence team in the Data Operations & Technology area. Many of the outputs of a good Data Function will include graphs, charts and other such exhibits. However, here would be located the real specialists, the people who would set standards for the presentation of visual data across the Data Function and be the most able in leveraging visualisation tools. A definition of Data Visualisation is as follows:

Techniques – such as graphs – for presenting complex information in a manner in which it can be more easily digested by human observers. Based on the concept that a picture paints a thousand words (or a dozen Excel sheets).

Data and Analytics Dictionary entry: Data Visualisation

Predictive Analytics

Gartner refer to four types of Analytics: descriptive, diagnostic, predictive and prescriptive analytics. In an article I referred to these as:

  • What happened?
  • Why did it happen?
  • What is going to happen next?
  • What should we be doing?

Data and Analytics Dictionary entry: Analytics

Predictive analytics is that element of the Analytics function that aims to predict the future, “What is going to happen next?” in the above list. This can be as simple as extrapolating data based on a trend line, or can involve more sophisticated techniques such as Time Series Analysis. As with most elements of the Data Function, there is overlap between Predictive Analytics and both Data Science and Business Intelligence.

“Skunkworks”

As with Data Strategy, state-of-the-art in Analytics & Insight will continue to evolve. This part of the Data Function will aim to keep current with the latest developments and to try out new techniques and new technologies that may later be adopted more widely by Data Function colleagues. The “skunkworks” team would be staffed by capable programmers / data scientists / statisticians.
 
 
Data Operations & Technology

Data Operations & Technology

It could be reasonably argued that this area is part of Data Management; I probably would not object too strongly to this suggestion. However, there are some benefits to considering it separately. This is the most IT-like of the areas considered here. It recognises that data technology (being it the Hadoop suite, Data Warehouse technology, or combinations of both) is different to many other forms of technology and needs its own specialists to focus on it. It is likely that the staff in this area will also collaborate closely with IT (see the final work area in Part II) or, in some cases, supervise work carried out by IT. As well as directly creating data capabilities, Data Operations & Technology staff would be active in the day-to-day running of these; again in collaboration with colleagues from both inside and outside of the Data Function.

Business Intelligence

There is no ISO definition, but I use this term as a catch-all to describe the transformation of raw data into information that can be disseminated to business people to support decision-making.

Data and Analytics Dictionary entry: Business Intelligence

This sub-area focusses on the relatively mature task of providing Business Intelligence solutions to organisations and working with IT to support and maintain these. Good BI tools work best on a sound underlying information architecture and so there would need to also be close collaboration with Data Infrastructure staff within Data Operations & Technology as well as colleagues from Data Architecture and also Analytics & Insight.

Regular Reporting

If BI provides interactive capabilities to support decision making, Regular Reporting is about the provision of specific key reports to relevant parties on a periodic basis; daily, weekly, monthly etc. These may be burst out to people’s e-mail accounts, provided at some central location, or both. While this an area that is ideally automated, there will still be significant need for human monitoring and to support the inevitable changes.

Data Service

One of the things that any part of a Data Function will find itself doing on a very regular basis is crafting ad hoc data extracts for other departments, e.g. Marketing, Risk & Compliance etc. Sometimes such a need will be on an ongoing basis and a web-service or some other Data Integration mechanism will need to be set up. Rather than having this be something that is supported out of the general running costs of the Data Function, it makes sense to have a specific unit whose role is to fulfil these needs. Even so, there may be a need for queuing and prioritisation of requests

Data Infrastructure

This relates to the physical architecture of the data landscape (for various flavours of logical architectures, see Data Architecture in Part II). While some of the tasks here may be carried out by (or in collaboration with) IT, the Data Infrastructure team will be expert at the care and feeding of Hadoop and related technologies and have experience in the fine-tuning of Data Warehouses and Data Marts.

SWAT Team

While (as both mentioned above and also covered in Part III this article) some of the heavy lifting in data matters will be carried out by an organisation’s IT team and / or its external partners, the process for getting things done in this way can be slow, tortuous and expensive [6]. It is important that a Data Function has its own capability to make at least minor technological changes, or to build and deploy helpful data facilities without having to engage with the overall bureaucracy. The SWAT Team will have a small number of very capable and business-knowledgeable programmers, capable of quickly generating robust and functional code.
 
 
The second part of this piece picks up where I have left off here and first consider Data Architecture.
 

Part I Part II Part III

 
Notes

 
[1]
 
I have added some functions that were absent in the previous one, mostly as they were not central to the points I was making in the previous article.
 
[2]
 
My trilogy on Formatting a Data / Information Strategy has the following parts:

  1. Part I – General Strategy
  2. Part II – Situational Analysis
  3. Part III – Completing the Strategy
 
[3]
 
While this theme runs through most of my writing, it is most explicitly referenced in the following three articles:

  1. Marketing Change
  2. Education and Cultural Transformation
  3. Sustaining Cultural Change
 
[4]
 
It should be noted that the relationship management described here is not the same as a Project Manager covering progress against plan. This is more of a two way conversation to ensure that the Data Function remains cognisant of stakeholder needs
 
[5]
 
Though of course sometimes one-off analyses have value on an ongoing basis and so need to be productionised. In such cases the Analytics & Insight team would work with the Data Operations & Technology team to achieve this.
 
[6]
 
No citation needed.

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

Hurricanes and Data Visualisation: Part II(b) – Ooops!

Ooops!

The first half of my planned thoughts on Hurricanes and Data Visualisation, Rainbow’s Gravity and was published earlier back in September. Part two, Map Reading, joined it this month. In between, the first hurricane-centric article acquired an addendum, The Mona Lisa. With this post, the same has happened to the second article. Apparently you can’t keep a good hurricane story down.
 
 
One of our Hurricanes is missing

When I started writing about Hurricanes back in September of this year, it was in the aftermath of Harvey and Irma, both of which were safely far away from my native United Kingdom. Little did I think that in closing this mini-series Hurricane Ophelia (or at least the remnants of it) would be heading for these shores; I hope this is coincidence and not karma for me criticising the US National Weather Service’s diagrams!

As we batten down here, an odd occurrence was brought to my attention by Bill McKibben (@billmckibben), someone I connected with while working on this set of articles. Here is what he tweeted:

Ooops!

I am sure that inhabitants of both the Shetland Islands and the East Midlands will be breathing sighs of relief!

Clearly both the northward and eastward extent of Ophelia was outside of the scope of either the underlying model or the mapping software. A useful reminder to data professionals to ensure we set the boundaries of both modelling and visualisation work appropriately.

As an aside, this image is another for the Hall of Infamy, relying as it does on the less than helpful rainbow palette we critiqued all the way back in the first article.

I’ll hope to be writing again soon – hurricanes allowing!
 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

Hurricanes and Data Visualisation: Part II – Map Reading

Confusing map reading

This is the second of two articles whose genesis was the nexus of hurricanes and data visualisation. The first article was, Part I – Rainbow’s Gravity [1].
 
 
Introduction

In the first article in this mini-series we looked at alternative approaches to colour and how these could inform or mislead in data visualisations relating to weather events. In particular we discussed drawbacks of using a rainbow palette in such visualisations and some alternatives. Here we move into much more serious territory, how best to inform the public about what a specific hurricane will do next and the risks that it poses. It would not be an exaggeration to say that sometimes this area may be a matter of life and death. As with rainbow-coloured maps of weather events, some aspects of how the estimated future course of hurricanes are communicated and understood leave much to be desired.

Hurricane Irma cone
Source: US National Weather Service (NWS)

The above diagram is called a the cone of uncertainty of a hurricane. Cone of uncertainty sounds like an odd term. What does it mean? Let’s start by offering a historical perspective on hurricane modelling.
 
 
Paleomodelling

Well like any other type of weather prediction, determining the future direction and speed of a hurricane is not an exact science [2]. In the earlier days of hurricane modelling, Meteorologists used to employ statistical models, which were built based on detailed information about previous hurricanes, took as input many data points about the history of a current hurricane’s evolution and provided as output a prediction of what it could do in coming days.

Black Box

There were a variety of statistical models, but the output of them was split into two types when used for hurricane prediction.

Type A and Type B
A) A prediction of the next location of the hurricane was made. This included an average error (in km) based on the inaccuracy of previous hurricane predictions. A percentage of this, say 80%, gave a “circle of uncertainty”, x km around the prediction.

B) Many predictions were generated and plotted; each had a location and an probability associated with it. The centroid of these (adjusted by the probability associated with each location) was calculated and used as the central prediction (cf. A). A circle was drawn (y km from the centroid) so that a percentage of the predictions fell within this. Above, there are 100 estimates and the chosen percentage is 80%; so 80 points lie within the red “circle of uncertainty”.

Type A

First, the model could have generated a single prediction (the centre of the hurricane will be at 32.3078° N, 64.7505° W tomorrow) and supplemented this with an error measure. The error measure would have been based on historical hurricane data and related to how far out prior predictions had been on average; this measure would have been in kilometres. It would have been typical to employ some fraction of the error measure to define a “circle of uncertainty” around the central prediction; 80% in the example directly above (compared to two thirds in the NWS exhibit at the start of the article).

Type B

Second, the model could have generated a large number of mini-predictions, each of which would have had a probability associated with it (e.g. the first two estimates of location could be that the centre of the hurricane is at 32.3078° N, 64.7505° W with a 5% chance, or a mile away at 32.3223° N, 64.7505° W with a 2% chance and so on). In general if you had picked the “centre of gravity” of the second type of output, it would have been analogous to the single prediction of the first type of output [3]. The spread of point predictions in the second method would have also been analogous to the error measure of the first. Drawing a circle around the centroid would have captured a percentage of the mini-predictions, once more 80% in the example immediately above and two thirds in the NWS chart, generating another “circle of uncertainty”.
 
 
Here comes the Science

Fluid Dynamics

That was then of course, nowadays the statistical element of hurricane models is less significant. With increased processing power and the ability to store and manipulate vast amounts of data, most hurricane models instead rely upon scientific models; let’s call this Type C.

Type C

As the air is a fluid [4], its behaviour falls into the area of study known as fluid dynamics. If we treat the atmosphere as being viscous, then the appropriate equation governing fluid dynamics is the Navier-Stokes equation, which is itself derived from the Cauchy Momentum equation:

\displaystyle\frac{\partial}{\partial t}(\rho \boldsymbol{u}) + \nabla \cdot (\rho \boldsymbol{u}\otimes \boldsymbol{u})=-\nabla\cdot p\boldsymbol{I}+\nabla\cdot\boldsymbol{\tau} + \rho\boldsymbol{g}

If viscosity is taken as zero (as a simplification), instead the Euler equations apply:

\displaystyle\left\{\begin{array}{lr}\displaystyle\frac{\partial\boldsymbol{u}}{\partial t} + \nabla \cdot (\boldsymbol{u}\otimes \boldsymbol{u} + w\boldsymbol{I}) = \boldsymbol{g} \\ \\ \nabla \cdot \boldsymbol{u}= 0\end{array}\right.

The reader may be glad to know that I don’t propose to talk about any of the above equations any further.

3D Grid

To get back to the model, in general the atmosphere will be split into a three dimensional grid (the atmosphere has height as well). The current temperature, pressure, moisture content etc. are fed in (or sometimes interpolated) at each point and equations such as the ones above are used to determine the evolution of fluid flow at a given grid element. Of course – as is typical in such situations – approximations of the equations are used and there is some flexibility over which approximations to employ. Also, there may be uncertainty about the input parameters, so statistics does not disappear entirely. Leaving this to one side, how the atmospheric conditions change over time at each grid point rolls up to provide a predictive basis for what a hurricane will do next.

Although the methods are very different, the output of these scientific models will be pretty similar, qualitatively, to the Type A statistical model above. In particular, uncertainty will be delineated based on how well the model performed on previous occasions. For example, what was the average difference between prediction and fact after 6 hours, 12 hours and so on. Again, the uncertainty will have similar characteristics to that of Type A above.
 
 
A Section about Conics

An advanced statistical approach

In all of the cases discussed above, we have a central prediction (which may be an average of several predictions as per Type B) and a circular distribution around this indicating uncertainty. Let’s consider how these predictions might change as we move into the future.

If today is Monday, then there will be some uncertainty about what the hurricane does on Tuesday. For Wednesday, the uncertainty will be greater than for Tuesday (the “circle of uncertainty” will have grown) and so on. With the Type A and Type C outputs, the error measure will increase with time. With the Type B output, if the model spits out 100 possible locations for the hurricane on a specific day (complete with the likelihood of each of these occurring), then these will be fairly close together on Tuesday and further apart on Wednesday. In all cases, uncertainty about the location of the becomes smeared out over time, resulting in a larger area where it is likely to be located and a bigger “circle of uncertainty”.

This is where the circles of uncertainty combine to become a cone of uncertainty. For the same example, on each day, the meteorologists will plot the central prediction for the hurricane’s location and then draw a circle centered on this which captures the uncertainty of the prediction. For the same reason as stated above, the size of the circle will (in general) increase with time; Wednesday’s circle will be bigger than Tuesday’s. Also each day’s central prediction will be in a different place from the previous day’s as the hurricane moves along. Joining up all of these circles gives us the cone of uncertainty [5].

If the central predictions imply that a hurricane is moving with constant speed and direction, then its cone of uncertainty would look something like this:

Cone of uncertainty

In this diagram, broadly speaking, on each day, there is a 67% probability that the centre of the hurricane will be found within the relevant circle that makes up the cone of uncertainty. We will explore the implications of the underlined phrase in the next section.

Of course hurricanes don’t move in a single direction at an unvarying pace (see the actual NWS exhibit above as opposed to my idealised rendition), so part of the purpose of the cone of uncertainty diagram is to elucidate this.
 
 
The Central Issue

So hopefully the intent of the NWS chart at the beginning of this article is now clearer. What is the problem with it? Well I’ll go back to the words I highlighted couple of paragraphs back:

There is a 67% probability that the centre of the hurricane will be found within the relevant circle that makes up the cone of uncertainty

So the cone helps us with where the centre of the hurricane may be. A reasonable question is, what about the rest of the hurricane?

For ease of reference, here is the NWS exhibit again:

Hurricane Irma cone

Let’s first of all pause to work out how big some of the NWS “circles of uncertainty” are. To do this we can note that the grid lines (though not labelled) are clearly at 5° intervals. The distance between two lines of latitude (ones drawn parallel to the equator) that are 1° apart from each other is a relatively consistent number; approximately 111 km [6]. This means that the lines of latitude on the page are around 555 km apart. Using this as a reference, the “circle of uncertainty” labelled “8 PM Sat” has a diameter of about 420 km (260 miles).

Let’s now consider how big Hurricane Irma was [7].

Size of Irma
Source: RMS

Aside: I’d be remiss if I didn’t point out here that RMS have selected what seems to me to be a pretty good colour palette in the chart above.

Well there is no defined sharp edge of a hurricane, rather the speed of winds tails off as may be seen in the above diagram. In order to get some sense of the size of Irma, I’ll use the dashed line in the chart that indicates where wind speeds drop below that classified as a tropical storm (65 kmph or 40 mph [8]). This area is not uniform, but measures around 580 km (360 miles) wide.

Misplaced hurricane
A) The size of the hurricane is greater than the size of the “circle of uncertainty”, the former extends 80 km beyond the circumference of the latter in all directions.

B) The “circle of uncertainty” captures the area within which the hurricane’s centre is likely to fall. But this includes cases where the centre of the hurricane is on the circumference of the “circle of uncertainty”. This means the the furthermost edge of the hurricane could be up to 290 km outside of the “circle of uncertainty”.

There are two issues here, which are illustrated in the above diagram.

Issue A

Irma was actually bigger [9] than at least some of the “circles of uncertainty”. A cursory glance at the NWS exhibit would probably give the sense that the cone of uncertainty represents the extent of the storm, it doesn’t. In our example, Irma extends 80 km beyond the “circle of uncertainty” we measured above. If you thought you were safe because you were 50 km from the edge of the cone, then this was probably an erroneous conclusion.

Issue B

Even more pernicious, because each “circle of uncertainty” provides an area within which the centre of the hurricane could be situated, this includes cases where the centre of the hurricane sits on the circumference of the “circle of uncertainty”. This, together with the size of the storm, means that someone 290 km from the edge of the “circle of uncertainty” could suffer 65 kmph (40 mph) winds. Again, based on the diagram, if you felt that you were guaranteed to be OK if you were 250 km away from the edge of the cone, you could get a nasty surprise.

Hurricane Season

These are not academic distinctions, the real danger that hurricane cones were misinterpreted led the NWS to start labelling their charts with “This cone DOES NOT REPRESENT THE SIZE OF THE STORM!![10].

Even Florida senator Marco Rubio got in on the act, tweeting:

Rubio tweet

When you need a politician help you avoid misinterpreting a data visualisation, you know that there is something amiss.
 
 
In Summary

Could do better

The last thing I want to do is to appear critical of the men and women of the US National Weather Service. I’m sure that they do a fine job. If anything, the issues we have been dissecting here demonstrate that even highly expert people with a strong motivation to communicate clearly can still find it tough to select the right visual metaphor for a data visualisation; particularly when there is a diverse audience consuming the results. It also doesn’t help that there are many degrees of uncertainty here: where might the centre of the storm be? how big might the storm be? how powerful might the storm be? in which direction might the storm move? Layering all of these onto a single exhibit while still rendering it both legible and of some utility to the general public is not a trivial exercise.

The cone of uncertainty is a precise chart, so long as the reader understands what it is showing and what it is not. Perhaps the issue lies more in the eye of the beholder. However, having to annotate your charts to explain what they are not is never a good look on anyone. The NWS are clearly aware of the issues, I look forward to viewing whatever creative solution they come up with later this hurricane season.
 


 
Acknowledgements

I would like to thank Dr Steve Smith, Head of Catastrophic Risk at Fractal Industries, for reviewing this piece and putting me right on some elements of modern hurricane prediction. I would also like to thank my friend and former colleague, Dr Raveem Ismail, also of Fractal Industries, for introducing me to Steve. Despite the input of these two experts, responsibility for any errors or omissions remains mine alone.
 


 
Notes

 
[1]
 
I also squeezed Part I(b) – The Mona Lisa in between the two articles I originally planned.
 
[2]
 
I don’t mean to imply by this that the estimation process is unscientific of course. Indeed, as we will see later, hurricane prediction is becoming more scientific all the time.
 
[3]
 
If both methods were employed in parallel, it would not be too surprising if their central predictions were close to each other.
 
[4]
 
A gas or a liquid.
 
[5]
 
Cone

A shape traced out by a particle traveling with constant speed and with a circle of increasing radius inscribed around it would be a cone.

 
[6]
 
Latitude and Longitude

The distance between lines of longitude varies between 111 km at the equator and 0 km at either pole. This is because lines of longitude are great circles (or meridians) that meet at the poles. Lines of latitude are parallel circles (parallels) progressing up and down the globe from the equator.

 
[7]
 
At a point in time of course. Hurricanes change in size over time as well as in their direction/speed of travel and energy.
 
[8]
 
I am rounding here. The actual threshold values are 63 kmph and 39 mph.
 
[9]
 
Using the definition of size that we have adopted above.
 
[10]
 
Their use of capitals, bold and multiple exclamation marks.

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

A Nobel Laureate’s views on creating Meaning from Data

Image © MRC Laboratory of Molecular Biology, Cambridge, UK

Praise for the Praiseworthy

Today the recipients of the 2017 Nobel Prize for Chemistry were announced [1]. I was delighted to learn that one of the three new Laureates was Richard Henderson, former Director of the UK Medical Research Council’s Laboratory of Molecular Biology in Cambridge; an institute universally known as the LMB. Richard becomes the fifteenth Nobel Prize winner who worked at the LMB. The fourteenth was Venkatraman Ramakrishnan in 2009. Venki was joint Head of Structural Studies at the LMB, prior to becoming President of the Royal Society [2].

MRC Laboratory of Molecular Biology

I have mentioned the LMB in these pages before [3]. In my earlier article, which focussed on Data Visualisation in science, I also provided a potted history of X-ray crystallography, which included the following paragraph:

Today, X-ray crystallography is one of many tools available to the structural biologist with other approaches including Nuclear Magnetic Resonance Spectroscopy, Electron Microscopy and a range of biophysical techniques.

I have highlighted the term Electron Microscopy above and it was for his immense contributions to the field of Cryo-electron Microscopy (Cryo-EM) that Richard was awarded his Nobel Prize; more on this shortly.

First of all some disclosure. The LMB is also my wife’s alma mater, she received her PhD for work she did there between 2010 and 2014. Richard was one of two people who examined her as she defended her thesis [4]. As Venki initially interviewed her for the role, the bookends of my wife’s time at the LMB were formed by two Nobel laureates; an notable symmetry.

2017 Nobel Prize

The press release about Richard’s Nobel Prize includes the following text:

The Nobel Prize in Chemistry 2017 is awarded to Jacques Dubochet, Joachim Frank and Richard Henderson for the development of cryo-electron microscopy, which both simplifies and improves the imaging of biomolecules. This method has moved biochemistry into a new era.

[…]

Electron microscopes were long believed to only be suitable for imaging dead matter, because the powerful electron beam destroys biological material. But in 1990, Richard Henderson succeeded in using an electron microscope to generate a three-dimensional image of a protein at atomic resolution. This breakthrough proved the technology’s potential.

Electron microscopes [5] work by passing a beam of electrons through a thin film of the substance being studied. The electrons interact with the constituents of the sample and go on to form an image which captures information about these interactions (nowadays mostly on an electronic detector of some sort). Because the wavelength of electrons [6] is so much shorter than light [7], much finer detail can be obtained using electron microscopy than with light microscopy. Indeed electron microscopes can be used to “see” structures at the atomic scale. Of course it is not quite as simple as printing out the image snapped by you SmartPhone. The data obtained from electron microscopy needs to be interpreted by software; again we will come back to this point later.

Cryo-EM refers to how the sample being examined is treated prior to (and during) microscopy. Here a water-suspended sample of the substance is frozen (to put it mildly) in liquid ethane to temperatures around -183 °C and maintained at that temperature during the scanning procedure. The idea here is to protect the sample from the damaging effects of the cathode rays [8] it is subjected to during microscopy.
 
 
A Matter of Interpretation

On occasion, I write articles which are entirely scientific or mathematical in nature, but more frequently I bring observations from these fields back into my own domain, that of data, information and insight. This piece will follow the more typical course. To do this, I will rely upon a perspective that Richard Henderson wrote for the Proceedings of the National Academy of Science back in 2013 [9].

Here we come back to the interpretation of Cryo-EM data in order to form an image. In the article, Richard refers to:

[Some researchers] who simply record images, follow an established (or sometimes a novel or inventive [10]) protocol for 3D map calculation, and then boldly interpret and publish their map without any further checks or attempts to validate the result. Ten years ago, when the field was in its infancy, referees would simply have to accept the research results reported in manuscripts at face value. The researchers had recorded images, carried out iterative computer processing, and obtained a map that converged, but had no way of knowing whether it had converged to the true structure or some complete artifact. There were no validation tests, only an instinct about whether a particular map described in the publication looked right or wrong.

The title of Richard’s piece includes the phrase “Einstein from noise”. This refers to an article published in the Journal of Structural Biology in 2009 [11]. Here the authors provided pure white noise (i.e. a random set of black and white points) as the input to an Algorithm which is intended to produce EM maps and – after thousands of iterations – ended up with the following iconic mage:

Reprinted from the Journal of Structural Biology, Vol. 166. © Elsevier. Used under licence 4201981508561. Copyright Clearance Center.

Richard lists occurrences of meaning being erroneously drawn from EM data from his own experience of reviewing draft journal articles and cautions scientists to hold themselves to the highest standards in this area, laying out meticulous guidelines for how the creation of EM images should be approached, checked and rechecked.

The obvious correlation here is to areas of Data Science such as Machine Learning. Here again algorithms are applied iteratively to data sets with the objective of discerning meaning. Here too conscious or unconscious bias on behalf of the people involved can lead to the business equivalent of Einstein ex machina. It is instructive to see the level of rigour which a Nobel Laureate views as appropriate in an area such as the algorithmic processing of data. Constantly questioning your results and validating that what emerges makes sense and is defensible is just one part of what can lead to gaining a Nobel Prize [12]. The opposite approach will invariably lead to disappointment in either academia or in business.

Having introduced a strong cautionary note, I’d like to end this article with a much more positive tone by extending my warm congratulations to Richard both for his well-deserved achievement, but more importantly for his unwavering commitment to rolling back the bounds of human knowledge.
 
 
If you are interested in learning more about Cryo-Electron Microscopy, the following LMB video, which features Richard Henderson and colleagues, may be of interest:


 
Notes

 
[1]
 
The Nobel Prize in Chemistry 2017.
 
[2]
 
Both Richard and Venki remain Group Leaders at the LMB and are actively involved in new scientific research.
 
[3]
 
Data Visualisation – A Scientific Treatment.
 
[4]
 
Her thesis was passed without correction – an uncommon occurrence – and her contribution to the field was described as significant in the formal documentation.
 
[5]
 
More precisely this description applies to Transmission Electron Microscopes, which are the type of kit used in Cryo-EM.
 
[6]
 
The wave-particle duality that readers may be familiar with when speaking about light waves / photons also applies to all sub-atomic particles. Electrons have both a wave and a particle nature and so, in particular, have wavelengths.
 
[7]
 
This is still the case even if ultraviolet or more energetic light is used instead of visible light.
 
[8]
 
Cathode rays are of course just beams of electrons.
 
[9]
 
Henderson, R. (2013). Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noise. PNAS This opens a PDF.
 
[10]
 
This is an example of Richard being very, very polite.
 
[11]
 
Shatsky, M., Hall, R.J., Brenner, S.E., Glaeser, R.M. (2009). A method for the alignment of heterogeneous macromolecules from electron microscopy. JSB This article is behind a paywall.
 
[12]
 
There are a couple of other things you need to do as well I believe.

 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary

 

The revised and expanded Data and Analytics Dictionary

The Data and Analytics Dictionary

Since its launch in August of this year, the peterjamesthomas.com Data and Analytics Dictionary has received a welcome amount of attention with various people on different social media platforms praising its usefulness, particularly as an introduction to the area. A number of people have made helpful suggestions for new entries or improvements to existing ones. I have also been rounding out the content with some more terms relating to each of Data Governance, Big Data and Data Warehousing. As a result, The Dictionary now has over 80 main entries (not including ones that simply refer the reader to another entry, such as Linear Regression, which redirects to Model).

The most recently added entries are as follows:

  1. Anomaly Detection
  2. Behavioural Analytics
  3. Complex Event Processing
  4. Data Discovery
  5. Data Ingestion
  6. Data Integration
  7. Data Migration
  8. Data Modelling
  9. Data Privacy
  10. Data Repository
  11. Data Virtualisation
  12. Deep Learning
  13. Flink
  14. Hive
  15. Information Security
  16. Metadata
  17. Multidimensional Approach
  18. Natural Language Processing (NLP)
  19. On-line Transaction Processing
  20. Operational Data Store (ODS)
  21. Pig
  22. Table
  23. Sentiment Analysis
  24. Text Analytics
  25. View

It is my intention to continue to revise this resource. Adding some more detail about Machine Learning and related areas is probably the next focus.

As ever, ideas for what to include next would be more than welcome (any suggestions used will also be acknowledged).
 


 

From: peterjamesthomas.com, home of The Data and Analytics Dictionary