Did GDPR highlight the robustness of your Data Architecture, the strength of your Data Governance and the fitness of your Data Strategy?

GDPR

So GDPR Day is upon us – the sun still came up and the Earth is still spinning (these facts may be related of course). I hope that most GDPR teams and the Executives who have relied upon their work were able to go to bed last night secure in the knowledge that a good job had been done and that their organisations and customers were protected. Undoubtedly, in coming days, there will be some stories of breaches of the regulations, maybe some will be high-profile and the fines salutary, but it seems that most people have got over the line, albeit often by Herculean efforts and sometimes by the skins of their teeth.

Does it have to be like this?

A well-thought-out Data Architecture embodying a business-focussed Data Strategy and intertwined with the right Data Governance, should combine to make responding to things like GDPR relatively straightforward. Were they in your organisation?

If instead GDPR compliance was achieved in spite of your Data Architectures, Governance and Strategies, then I suspect you are in the majority. Indeed years of essentially narrow focus on GDPR will have consumed resources that might otherwise have gone towards embedding the control and leverage of data into the organisation’s DNA.

Maybe now is a time for reflection. Will your Data Strategy, Data Governance and Data Architecture help you to comply with the next set of data-related regulations (and it is inevitable that there will be more), or will they hinder you, as will have been the case for many with GDPR?

If you feel that the answer to this question is that there are significant problems with how your organisation approaches data, then maybe now is the time to grasp the nettle. Having helped many companies to both develop and execute successful Data Strategies, you could start by reading my trilogy on creating an Information / Data Strategy:

  1. General Strategy
  2. Situational Analysis
  3. Completing the Strategy

I’m also more than happy to discuss your data problems and opportunities either formally or informally, so feel free to get in touch.
 
 


From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

 

An in-depth interview with experienced Chief Data Officer Roberto Maranca

In-depth with Roberto Maranca


Part of the In-depth series of interviews

PJT Today’s interview is with Roberto Maranca. Roberto is an experienced and accomplished Chief Data Officer, having held that role in GE Capital and Lloyds Banking Group. Roberto and I are both founder members of the IRM(UK) Chief Data Officer Executive Forum and I am delighted to be able to share the benefit of his insights with readers.
PJT Roberto, you have had a long and distinguished career in the data space, would you mind starting by giving readers a brief overview of this?
RM Certainly Peter, looking back now Data has been like a river flowing through all my career. But I can definitely recall that, at a certain point in my life in GE Capital (GEC), someone who I had worked with before called me to take a special assignment as IT lead for the Basel II implementation for the Bank owned by GEC in Europe. For the readers not in the Finance industry, Basel II, for most of us and certainly for me, was our Data baptism of fire because of its requirement to collect a lot of data across the organisation in order to calculate an “enterprise wide” set of risk metrics. So the usual ETL build and report generation wasn’t good enough if not associated to a common dictionary, validation of mappings, standardised referential integrity and quality management.

When Basel went in production in 2008, I was given the leadership of the European Business Intelligence team, where I consolidated my hunch that the reason that a 6 months dashboard build project would fail pre-production tests was mainly “data is not good enough” and not our lack of zeal. Even if was probably amongst the first in GEC to adopt a Data Quality tool, you had the feeling that IT could not be the proverbial tail shaking the dog in that space. A few years went by where I became much closer to operations in a regulated business, learning about security and operational risk frameworks, and then one day at the end of 2013, I saw it! GEC was to be regulated by the Federal Reserve as one entity, and that posed a lot of emphasis on data. The first ever job description of CDO in GEC was flashed in front of my eyes and I felt like I had just fallen on the way to Damascus. All those boxes that had been empty for years in my head got ticked just looking at it. I knew this was what I wanted to do, I knew I had to leave my career in IT to do it, I knew there was not a lot beyond that piece of paper, but I went for it. Sadly, almost two years into this new role, GE decided to sell GEC; you would not believe how much data you need to divest such a large business.

I found that Lloyds Banking Group was after a CDO and I could not let that opportunity go by. It has been a very full year where I led a complete rebuild of their Data Framework, while been deeply involved in the high-profile BCBS239 and GDPR initiatives.

PJT Can you perhaps highlight a single piece of work that was important to you, added a lot of value to the organisation, or which you were very proud of for some other reason?
RM I always had a thing about building things to last, so I have always tried to achieve a sustainable solution that doesn’t fall apart after a few months (in Six Sigma terms you will call it “minimising the long term sigma shift”, but we will talk about it another time). So trying to have change process to be mindful of “Data” has been my quest since day one, in the job of CDO. For this reason, my most important piece of work was probably the the creation of the first link between the PMO process in GEC and the Data Lineage and Quality Assurance framework, I had to insist quite a bit to introduce this, design it, test it and run it. Now of course, after the completion of the GEC sale, it has gone lost “like tears in the rain”, to cite one of the best movies ever [1].
PJT What was your motivation to take on Chief Data Officer roles and what do you feel that you bring to the CDO role?
RM I touched on some reasons in my introductory comments. I believe there is a serendipitous combination of acquired skills that allows me to see things in a different way. I spent most of my working life in IT, but I have a Masters in Aeronautical Engineering and a diploma in what we in Italy call “Classical Studies”, basically I have A levels in Latin, Greek, Philosophy, History. So for example, together with my pilot’s licence achieved over weekends, I have attended a drama evening school for a year (of course in my bachelor days). Jokes apart, the “art” of being a CDO requires a very rich and versatile background because it is so pioneering, ergo if I can draw from my study of flow dynamics to come up with a different approach to lineage, or use philosophy to embed a stronger data driven culture, I feel it is a marked plus.
PJT We have spoken about the CDO role being one whose responsibilities and main areas of focus are still sometimes unclear. I have written about this recently [2]. How do you think the CDO role is changing in organisations and what changes need to happen?
RM I mentioned the role being pioneering: compared to more established roles, CFO, COO and, even, CIO, the CDO is suffering from ambiguity, differing opinions and lack of clear career path. All of us in this space have to deal with something like inserting a complete new organ in a body that has got very strong immunological response, so although the whole body is dying for the function that the new organ provides (and with the new breed of regulation about, dying for lack of good and reliable data is not an exaggeration), there is a pernickety work of linking up blood vessels and adjusting every part of the organisation so that the change is harmonious, productive and lasting. But every company starts from a different level of maturity and a different status quo, so it is left to the CDO to come up with a modus operandi that would work and bring that specific environment to a recognisable standard.
PJT The Chief Data Officer has been described as having “the toughest job in the executive C-suite within many organizations” [3]. Do you agree and – if so – what are the major challenges?
RM I agree and it simply demonstrated: pick any Company’s Annual Report, do a word search for “data quality”, “data management“, “data science” or anything else relevant to our profession, you are not going to find many. IT has been around for a while more and yet technology is barely starting now to appear in the firm’s “manifesto”, mostly for things that are a risk, like cyber security. Thus the assumption is, if it is not seen as a differentiator to communicate to the shareholders and the wider world, why should it be of interest for the Board? It is not anyone’s fault and my gut feeling is that GDPR (or perhaps Cambridge Analytica) is going to change this, but we probably need another generational turnover to have CDOs “safely” sitting in executive groups. In the meantime, there is a lot we can do, maybe sitting immediately behind someone who is sitting in that crucial room.
PJT We both believe that cultural change has a central role in the data arena, can you share some thoughts about why this is important?
RM Data can’t be like a fad diet, it can’t be a program you start and finish. Companies have to understand that you have to set yourself on a path of “permanent augmentation”. The only way to do this is to change for good the attitude of the entire company towards data. Maybe starting from the first ambiguity, data is not the bits and bytes coming out of a computer screen, but it is rather the set of concepts and nouns we use in our businesses to operate, make products, serve our customers. If you flatten your understanding of data to its physical representation, you will never solve the tough enterprise problems, henceforth if it is not a problem of centralisation of data, but it is principally a problem of centralisation of knowledge and standardisation of behaviours, it is something inherently close to people and the common set of things in a company that we can call “culture”.
PJT Accepting the importance of driving a cultural shift, what practical steps can you take to set about making this happen?
RM In my keynotes, I often quote the Swiss philosopher (don’t tell me I didn’t warn you!) Henry Amiel:

Pure truth cannot be assimilated by the crowd: it must be communicated by contagion.

This is especially the case when you are confronted with large numbers of colleagues and small data teams. Creating a simple mantra that can be inoculated in many part of the organisation helps to create a more receptive environment. So CDOs should first be keen marketeers, able to create a simple brand and pursuing relentlessly a “propaganda” campaign. Secondly, if you want to bring change, you should focus where the change happens and make sure that wherever the fabric of the company changes, i.e. big programmes or transformations, data is top priority.

PJT What are the potential pitfalls that you think people need to be aware of when embarking on a data-centric cultural transformation programme?
RM First is definitely failing to manage your own expectations on speed and acceptance; it takes time and patience. Long-established organisations cannot leap into a brighter future just because an enlightened CDO shows them how. Second, and sort of related, it is a problem thinking that things can happen by management edicts and CDO policy compliance, there is a lot niftier psychology and sociology to weave into this.
PJT A two-part question. What do you see as the role of Data Governance in the type of cultural change you are recommending? Also, do you think that the nature of Data Governance has either changed or possibly needs to change in order to be more effective?
RM The CDO’s arrival at a discussion table is very often followed by statements like “…but we haven’t got resources for the Governance” or “We would like to, but Data Governance is such an aggro”. My simple definition for Data Governance is a process that allows Approved Data Consumers to obtain data that satisfies their consumption requirements, in accordance with Company’s approved standards of traceability, meaning, integrity and quality. Under this definition there is no implied intention of subjecting colleagues to gruelling bureaucratic processes, the issue is the status quo. Today, in the majority of firms, without a cumbersome process of checks and balances, it is almost impossible to fulfil such definition. The best Data Governance is the one you don’t see, it is the one you experience when you to get the data you need for your job without asking, this is the true essence of Data Democratisation, but few appreciate that this is achieved with a very strict and controlled in-line Data Governance framework sitting on three solid bastions of Metadata, User Access Controls and Data Classification.
PJT Can you comment on the relationship between the control of data and its exploitation; between Analytics and Governance if you will?Do these areas need to both be part of the CDO’s remit?
RM Oh… this is about the tale of the two tribes isn’t it? The Governors vs. the Experimenters, the dull CDOs vs the funky CAOs. Of course they are the yin and the yang of Data, you can’t have proper insight delivered to your customer or management if you have a proper Data Governance process, or should we call it “Data Enablement” process from the previous answer. I do believe that the next incarnation of the CDO is more a “Head of Data”, who has got three main pillars underneath, one is the previous CDOs all about governance, control and direction, the second is your R&D of data, but the third one that getting amassed and so far forgotten is the Operational side, the Head of Data should have business operational ownership of the critical Data Assets of the Company.
PJT The cultural aspects segues into thinking about people. How important is managing the people dimension to a CDO’s success?
RM Immensely. Ours is a pastoral job, we need to walk around, interact on internal social media, animate communities, know almost everyone and be known by everyone. People are very anxious about what we do, because all the wonderful things we are trying to achieve, they believe, will generate “productivity” and that in layman’s terms mean layoffs. We can however shift that anxiety to curiosity, reaching out, spreading the above-mentioned mantra but also rethinking completely training and reskilling, and subsequently that curiosity should transform in engagement which will deliver sustainable cultural change.
PJT I have heard you speak about “intelligent data management” can you tell me some more about what you mean by this? Does this relate to automation at all?
RM My thesis at Uni in 1993 was using AI algorithms and we all have been playing with MDM, DQM, RDM, Metadata for ages, but it doesn’t feel we cracked yet a Science of Data (NB this is different Data Science!) that could show us how to resolve our problems of managing data with 21st century techniques. I think our evolutionary path should move us from “last month you had 30k wrong postcodes in your database” to “next month we are predicting 20% fewer wrong address complaints”, in doing so there is an absolute need to move from fragmented knowledge around data to centralised harnessing of the data ecosystem, and that can only be achieved tuning in on the V.O.M. (Voice of the Machines), listening, deriving insight on how that ecosystem is changing, simulating response to external or internal factors and designing changes with data by design (or even better with everything by design). I yet have to see automated tools that do all of that without requiring man years to decide what is what, but one can only stay hopeful.
PJT Finally, how do you see the CDO role changing in coming years?
RM To the ones that think we are a transient role, I respond that Compliance should be everyone’s business, and yet we have Compliance Officers. I think that overtime the Pioneers will give way to the Strategists, who will oversee the making of “Data Products” that best suit the Business Strategist, and maybe one day being CEO will be the epitome of our career ladders one day, but I am not rushing to it, I love too much having some spare time to spend with my family and sailing.
PJT Roberto, it is always a pleasure to speak. Thank you for sharing your ideas with us today.

Roberto Maranca can be reached at r.maranca@outlook.com and has social media presence on LinkedIn and Twitter (@RobertoMaranca).


Disclosure: At the time of publication, neither peterjamesthomas.com Ltd. nor any of its Directors had any shared commercial interests with Roberto Maranca.


If you are a Chief Data Officer, a Chief Analytics Officer, a Director of Data, or hold some other “Top Data Job” and would like to share your thoughts with the readers of this site in an interview like this one, please get in contact.

 
Notes

 
[1]
 
 
[2]
 
The CDO – A Dilemma or The Next Big Thing?
 
[3]
 
Randy Bean of New Vantage Partners quoted in The CDO – A Dilemma or The Next Big Thing?

From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

 

Link directly to entries in the Data and Analytics Dictionary

The Data and Analytics Dictionary

The peterjamesthomas.com Data and Analytics Dictionary has always had internal tags (anchors for those old enough to recall their HTML) which allowed me, as its author, to link to individual entries from other web-pages I write. An example of the use of these is my article, A Brief History of Databases.

I have now made these tags public. Each entry in the Dictionary is followed by the full tag address in a box. This is accompanied by a link icon as follows:

Data Dictionary excerpt

Clicking on the link icon will copy the tag address to your clipboard. Alternatively the tag URL may just be copied from the box containing it directly. You can then use this address in your own article to link back to the D&AD entry.

As with the vast majority of my work, the contents of the Data and Analytics Dictionary is covered by a Creative Commons Attribution 4.0 International Licence. This means you can include my text or images in your own web-pages, presentations, Word documents etc. You can even modify my work, so long as you point out that you have done this.

If you would like to link back to the Data and Analytics Dictionary to provide definitions of terms that you are using, this should now be very easy. For example:

Lorem ipsum dolor sit amet, consectetur adipiscing Big Data elit. Duis tempus nisi sit amet libero vehicula Data Lake, sed tempor leo consectetur. Pellentesque suscipit sed felisData Governance ac mattis. Fusce mattis luctus posuere. Duis a Spark mattis velit. In scelerisque massa ac turpis viverra, acLogistic Regression pretium neque condimentum.

Equally, I’d be delighted if you wanted to include part of all of the text of an entry in the Data and Analytics Dictionary in your own work, commercial or personal; a link back using this new functionality would be very much appreciated.

I hope that this new functionality will be useful. An update to the Dictionary’s contents will be published in the next couple of months.
 


From: peterjamesthomas.com, home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases

 

The CDO – A Dilemma or The Next Big Thing?

Janus

It wasn’t so long ago that I last wrote about Forbes’s perspective on the data arena [1]. In this piece, I am going to compare and contrast two more recent Forbes articles. The first is 3 Reasons Why The Chief Data Officer Will Become The Next Big Thing by Lauren deLisa Coleman (@ultra_Lauren). The second is The Chief Data Officer Dilemma by Randy Bean (@RandyBeanNVP) [2].

While the contents of the two articles differ substantially – the first is positive about the future of the role, the second highlights some of its current challenges – there are interesting points made in each of them. In the midst of confusion about what a Chief Data Officer (CDO) is and what they do, it is perhaps not surprising that fundamentally different takes on the area can both contain seeds of truth.
 


 
Lauren deLisa Coleman

In the first piece, deLisa Coleman refers to the twin drivers of meeting increasingly stringent regulatory demands [3] and leveraging data to drive enhanced business outcomes; noting that:

Expertise and full dedication is needed particularly since data is threaded into nearly all facets of today’s businesses [4].

She states that appointing a CDO is the canonical response of Executive teams, while noting that there is not full consensus on all facets of this role. In covering the title’s “three reasons” why organisations need CDOs, deLisa Coleman references a survey by Infogix [5]. This highlights the increasing importance of each of the following areas: Metadata, Data Governance and the Internet of Things.

Expanding on these themes, deLisa Coleman adds:

Those who seize success within these new parameters will be companies that not only adapt most quickly but those that can also best leverage their company’s data in a strategic manner in innovative ways while continuing to gathering massive amounts under flawless methods of protection.

So far, so upbeat. To introduce a note of caution, I am aware that, in the last few years – and no doubt in part driven by articles in Forbes, Harvard Business Review and their ilk – most companies have set forth a vision for becoming a “data-driven organisation” [6]. However, the number that have actually achieved this objective – or even taken significant steps towards it – is of course much smaller. The central reason for this is that it is not easy to become a “data-driven organisation”. As with most difficult things, reaching this goal requires hard-work, focus, perseverance and, it has to be said, innate aptitude. Some experience of what is involved is of course also invaluable and, even in 2018, this is a rare commodity.

A sub-issue within this over-arching problem is miracle-worker syndrome; we’ll hire a great CDO and then we don’t need to worry about data any more [7]. Of course becoming a “data-driven organisation” requires the whole organisation to change. A good CDO will articulate the need for change, generate enthusiasm for moving forward and and coordinate the necessary metamorphosis. What they cannot do however is enact such a fundamental change without the active commitment of all tiers of the organisation.
 


 
Randy Bean

Of course this is where the second article becomes pertinent. Bean starts by noting the increasing prevalence of the CDO. He cites an annual study by his consultancy [8] which surveys Fortune 1000 companies. In 2012, this found that only 12% of the companies surveyed had appointed a CDO. By 2018, the figure has risen to over 63%, a notable trend [9].

However, he goes on to say that:

In spite of the common recognition of the need for a Chief Data Officer, there appears to be a profound lack of consensus on the nature of the role and responsibilities, mandate, and background that qualifies an executive to operate as a successful CDO. Further, because few organizations — 13.5% — have assigned revenue responsibility to their Chief Data Officers, for most firms the CDO role functions primarily as an influencer, not a revenue generator.

This divergence of opinion on CDO responsibilities, mandate, and importance of the role underscores why the Chief Data Officer may be the toughest job in the executive c-suite within many organizations, and why the position has become a hot seat with high turnover in a number of firms.

In my experience, while deLisa Coleman’s sunnier interpretation of the CDO environment both holds some truth and points to the future, Bean’s more gritty perspective is closer to the conditions currently experienced by many CDOs. This is reinforced by a later passage:

While 39.4% of survey respondents identify the Chief Data Officer as the executive with primary responsibility for data strategy and results within their firm, a majority of survey respondents – 60.6% — identify other C-Executives as the point person, or claim no single point of accountability. This is remarkable and highly significant, for it highlights the challenges that CDO’s face within many organizations.

Bean explains that some of this is natural, making a similar point to the one I advance above: the journey towards being “data-driven” is not a simple one and parts of organisations may both not want to take the trip and even dissuade colleagues from doing so. Passive or active resistance are things that all major transformations need to deal with. He adds that lack of clarity about the CDO role, especially around the involved / accountable question as it relates to strategy, planning and execution is a complicating factor.

Some particularly noteworthy points arose when the survey asked about the background and skills of a CDO. Findings included:

While 34% of executives believe the ideal CDO should be an external change agent (outsider) who brings fresh perspectives, an almost equivalent 32.1% of executives believe the ideal CDO should be an internal company veteran (insider) who understands the culture and history of the firm and knows how to get things done within that organization.

22.6% of executives […] indicated that the CDO must be either a data scientist or a technologist who is highly conversant with data. An additional 11.3% responded that a successful CDO must be a line-of-business executive who has been accountable for financial results.

The above may begin to sound somewhat familiar to some readers. It perhaps brings to mind the following figure [10]:

Expanded CDO Sweet Spot

As I pointed out last year in A truth universally acknowledged… organisations sometimes take a kitchen sink approach to experience and expertise, a lengthy list of requirements that will never been found in one person. From the above survey, it seems that this approach probably reflects the thinking of different executives.

I endorse one of Bean’s final points:

The lack of consensus on the Chief Data Officer role aptly mirrors the diversity of opinion on the value and importance of data as an enterprise asset and how it should be managed.

Back in my more technologically flavoured youth, I used to say that organisations get the IT that they deserve. The survey findings suggest that the same aphorism can be applied to both CDOs and the data landscapes that they are meant to oversee.
 


 
So two contrasting pieces from the same site. The first paints what I believe is an accurate picture of the importance of the CDO role in fulfilling corporate objectives. The second highlights some of the challenges with the CDO role delivering on its promise. Each perspective is valid. I would recommend readers take a look at both articles and then blend some of the insights with their own opinions and ideas.
 


 
Acknowledgements

I would like to thank Lauren deLisa Coleman and Randy Bean for both reviewing this article and allowing me to quote their work. Their openness and helpfulness are very much appreciated.
 


 
Notes

 
[1]
 
Draining the Swamp.
 
[2]
 
Text is reproduced with the kind permission of the authors.

Forbes has a limited free access policy for non-subscribers, this means that the number of articles you can view is restricted.

 
[3]
 
To which I would add both customer and business partner expectations about how their data is treated and used by organisations.
 
[4]
 
Echoing points from my two 2015 articles: 5 Themes from a Chief Data Officer Forum and 5 More Themes from a Chief Data Officer Forum, specifically:

It’s gratifying to make predictions that end up coming to be.

 
[5]
 
Infogix Identifies the Top Game Changing Data Trends for 2018.
 
[6]
 
It would be much easier to list those who do not share this aspiration.
 
[7]
 
Having been described as “the Messiah” in more than one organisation, I can empathise with the problems that this causes. Perhaps Moses – a normal man – leading his people out of the data dessert is a more apt Biblical metaphor, should you care for such things.
 
[8]
 
New Vantage Partners.
 
[9]
 
These are clearly figures for US companies and it is generally acknowledged that the US approach to data is more mature than elsewhere. In Europe, it may be that GDPR (plus, in my native UK, the dark clouds of Brexit) has tipped the compliance / leverage balance too much towards data introspection and away from revenue-generating data insights.
 
[10]
 
This first version of this image appeared in 2016’s The Chief Data Officer “Sweet Spot”, with the latest version being published in 2017’s A Sweeter Spot for the CDO?.

 

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

 

Sic Transit Gloria Magnorum Datorum

Sic transit gloria mundi

It happens to all of us eventually I suppose.

Just the other day, I heard someone referring to “traditional Big Data”. Since when did Big Data become “traditional”, I didn’t get the e-mail? Of course, in the technology field, the epithet “traditional” is code for “broken”, “no longer of any use” and – most damningly of all – “deeply uncool”. The term is widely used, whether – with this connotation – it is either helpful or accurate is perhaps a matter for debate. This usage makes me recall the rather silly debate about Analytics versus “traditional” Business Intelligence that occurred around 2009 [1].

By way of context, the person talking about “traditional Big Data” was referring to the difference between some of the original denizens of the Hadoop ecosystem and more recent offerings like Databricks or Beam. They also had in mind the various quasi-proprietary flavours of Big Data and/or Big Data plug-ins offered by (that word again) “traditional” vendors. In this sense, the usage is probably appropriate, albeit somewhat jarring. In the more pejorative sense I refer to above, “traditional” is somewhat misleading when applied to either Big Data or – in the author’s opinion – several of its precursors.

Shiny!

While we inhabit a world which places a premium on innovation, favouring the new and the shiny [2], traditional methods have much to offer. If something – a technique or technology – has achieved “traditional” status, it means that it has become part of how things are done. While shaking up the status quo can be beneficial, “traditional” approaches have the not insignificant benefit of having been tried and tested. “Traditional” data tools are ones that have survived some time and are still used. While not guaranteeing success, it should at least be possible to be successful with such tools because other people have done this before.

Maybe, several years after its move into the mainstream, Big Data has become “traditional”. However I would take this as meaning “fit for purpose”, “useful” and “still pretty cool”. Then I think the same about many of the technologies that were described as “traditional” in contrast to Big Data. As ever, the main things that lead to either success or failure in data-centric work [3] have very little to do with technology, be that traditional or à la mode.
 


 
Notes

 
[1]
 
If you have the stomach for it, see Business Analytics vs Business Intelligence and succeeding articles.
 
[2]
 
See also 2009’s The latest and greatest versus the valuable.
 
[3]
 
I itemise a few of these in last year’s 20 Risks that Beset Data Programmes.

 

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

 

A Brief History of Databases

A Brief History of Databases

Larger PDF version (opens in a new tab)

The pace of change in the field of database technology seems to be constantly accelerating. No doubt in five year’s time [1], Big Data and the Hadoop suite [2] will seem to be as old-fashioned as earlier technologies can appear to some people nowadays. Today there is a great variety of database technologies that are in use in different organisations for different purposes. There are also a lot of vendors, some of whom have more than one type of database product. I think that it is worthwhile considering both the genesis of databases and some of the major developments that have occurred between then and now.

The infographic appearing at the start of this article seeks to provide just such a perspective. It presents an abridged and simplified perspective on the history of databases from the 1960s to the late 2010s. It is hard to make out the text in the above diagram, so I would recommend that readers click on the link provided in order to view a much larger version with bigger and more legible text.

The infographic references a number of terms. Below I provide links to definitions of several of these, which are taken from The Data and Analytics Dictionary. The list progresses from the top of the diagram downwards, but starts with a definition of “database” itself:

To my mind, it is interesting to see just how long we have been grappling with the best way to set up databases. Also of note is that some of the Big Data technologies are actually relatively venerable, dating to the mid-to-late 2000s (some elements are even older, consisting of techniques for handling flat files on UNIX or Mainframe computers back in the day).

I hope that both the infographic and the definitions provided above contribute to the understanding of the history of databases and also that they help to elucidate the different types of database that are available to organisations today.
 


 
Acknowledgements

The following people’s input is acknowledged on the document itself, but my thanks are also repeated here:

Of course any errors and omissions remain the responsibility of the author.


 
Notes

 
[1]
 
If not significantly before then.
 
[2]
 
One of J K Rowling’s lesser-known works.

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

 

A further extension of the Data and Analytics Dictionary

The Data and Analytics Dictionary

The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. A larger update is in the works, but for now here are a dozen new definitions:

  1. Binary
  2. Business Analyst
  3. Chief Analytics Officer (CAO)
  4. Data
  5. Data Analyst
  6. Data Business Analyst
  7. Data Marketplace
  8. Data Steward
  9. Digital
  10. End User Computing (EUC)
  11. Information
  12. Web Analytics

As previously stated, 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

 

Draining the Swamp

Draining the Swamp

The title phrase of this article has entered the collective consciousness from political circles in recent months and years. Readers will be glad to hear that the political commentary content of this piece is precisely zero. Instead I am going to talk about Data Lakes, also referred to pejoratively by those who are not fans as Data Swamps.

Having started my relationship with Data matters back in the early days of Relational Databases and having driven corporate success through Data Warehouses and Business Intelligence, I have also done work in the Big Data arena since around 2013. A central concept in the Big Data paradigm is that of a Data Lake; a large Hadoop repository into which all data that an organisation might want to use is poured, often essentially as is. The thinking is that – in a Big Data implementation – storage is cheap [1] and you never fully know what data you might need in advance, so why not save it all?

It is probably fair to say that – much like many other major programmes of work over the years [2] – the creation of Data Lakes, or perhaps more accurately the leverage of their contents, has resulted in at best mixed results for the organisations that undertake such an endeavour. The thing with mixed results is that it is not all doom and gloom, some people are successful, others are not. The important thing is to determine what are the factors that lead to good and bad outcomes.

Well first of all, I would suggest that – like any other data programme – the formation of a Data Lake is subject to the types of potential issues that I review in my 2017 article, 20 Risks that Beset Data Programmes. Of these, Data Lakes are particularly susceptible to risk 16:

In the absence of [understanding key business decisions], the programme becoming a technology-driven one.

The business gets what IT or Change think that they need, not what is actually needed. There is more focus on shiny toys than on actionable information. The programme forgets the needs of its customers.

The issue here is that some people buy into the misconception that all you have to do is fill the Data Lake and sit back and wait for precious Data gems to flow from it. Understanding a business and its key decisions is tough and perhaps it is not surprising that people would like to skip this step and instead focus on easier activities. Sadly, this approach is not going to work for Data Lakes or anything else.
 


 
Dan Woods

However Data Lakes also face some specific risks and in search of better understanding these, I turned to a recent Forbes article, Can Failed Data Lakes Succeed As Data Marketplaces? penned by Dan Woods (@danwoodsearly) [3]. Dan does not mince words in his introduction:

All over the world, data lake projects are foundering, not because they are not a step in the right direction, but because they are essentially uncompleted experiments.

he adds:

The main roadblock has been that once companies store their data in the data lake, they struggle to find a way to operationalize it. The data lake has never become a product like a data warehouse. Proof of concepts are tweaked to keep a desultory flow of signals going.

and finally states:

[…] for certain use cases, Hadoop and purpose-built data lake-like infrastructure are solving complex and high-value problems. But in most other businesses, the data lake got stuck at the proof of concept stage.

This chimes with my experience – the ability to synthesise and analyse vast troves of data is indispensable in addressing some business problems, but a sledge-hammer to crack a walnut for others. Data Lakes are no more universal panaceas than anything else we have invented to date. As always, the main issues are not technology, but good processes, consistent definitions, improved data quality and matching available data to real business questions.
 


 
Paul Barth

In seeking salvation (Dan’s word) for Data Lakes, he sought the opinion of one of my LinkedIn contacts, Paul Barth (@BarthPS), CEO of Podium Data. Paul analyses the root causes of Data Lake issues, splitting these into three main ones [4]:

  1. Polluted data lakes

    Too many projects targeted at filling or exploiting the Data Lake kick off in parallel. This leads to an incoherent landscape and inaccessible / difficult to understand data.
     

  2. Bottlenecked data lakes

    Essentially treating the Data Lake as if it was a Data Warehouse where the technology is designed for different and less structured purposes. This leads to a quasi-warehouse that is less performant than actual warehouses.
     

  3. Risky data lakes

    Where there is a desire to quickly populate the Data Lake, not least to provide grist to the Data Science mill, appropriate controls on access to data can be neglected; particularly an issue where personally identifiable data is involved. This can lead to regulatory, legal and reputational peril.

Barth’s solution to these problems is the establishment of a Data Marketplace. This is a concept previously referenced on these pages in Predictions about Prediction, a review of consultancy Eckerson Group‘s views on Data and Analytics in 2017 [5]. Back then, Eckerson Group had the following to say about the area:

[An Enterprise Data Marketplace (EDM) is] an Amazon-like data marketplace where analysts can seek datasets, see reviews of others, and select the best-fit datasets for their needs helps to encourage dataset reuse, minimize redundancy, and prevent flawed analysis that results from working with less than ideal data. Data cataloging tools, data curation practices, data preparation technologies, and data services will be combined to create a marketplace for data seekers. Enterprise Data Marketplaces return us to the single-source vision that was once touted as the real benefit of Enterprise Data Warehouses.

Enterprise Data Marketplace

So, as illustrated above, a Data Marketplace is essentially a collection of tagged data sets, which have in some cases been treated to increase consistency and utility, combined with information about their contents and usages. These are overlaid by what is essentially a “social media” layer where “shoppers” can search for data and provide feedback on its utility (e.g. a rating mechanism) and also add their own documentation. This means that useful data sets get highly rated and have more explanatory material attached to them.
 


 
Dave Wells

Eckerson Group build on this concept in their white paper The Rise of the Data Marketplace (opens a PDF document), work commissioned in part by Podium Data. In this Eckerson’s Dave Wells (@_DaveWells_) characterises an Enterprise Data Marketplace as having the following attributes [6]:

  • Categorization organises the marketplace to simplify browsing. For example a shopper seeking budget data doesn’t need to browse through unrelated data sets about customers, employees or other data subjects. Categories complement tagging and smart search algorithms, offering a variety of ways to find data sets.
     
  • Curation is active management of the data sets that are available in the EDM. Curation selects and qualifies data sets, describes each data set, and collects and manages metadata about the collection and each individual data set.
     
  • Cataloging exposes data sets for data shoppers, including descriptions and metadata. The catalog is a view into the inventory of curated data sets. Rich metadata and powereful search are important catalog features.
     
  • Crowdsourcing is the equivalent of a social network for data. Data shoppers actively participate in catloging, curating and categorizing data. This virtuous cycle (a chain of events that reinforces outcomes through a feedback loop) continuously improves the quality and value of data in the marketplace.

Back in the Forbes article, Barth focuses on using the Data Marketplace’s interactive elements to identify the most valuable data (that which is searched for most frequently and has the best shopper rating). This data can then be the subject of focussed investment. Such investment is of the sort familiar in Data Warehouse activities, but it is directed by shoppers’ “social media” preferences rather than more formal requirements gathering exercises.
 


 
Dan Woods makes the pertinent observation that:

So, as the challenge now is not one of technology, but of setting a vision, companies have to decide how to incorporate a new set of requirements to get the most out of their data. […] Even within one company, there may be the need for multiple requirements to be met. Marketing may not need the precision that the accounting department requires. Groups with regulatory mandates may have strong compliance requirements that drive the need for data that is 100% accurate, while those doing exploration for product development purposes may prefer to have larger datasets to work with, and 90% accuracy is all that they require. The data lake must be able to employ multiple approaches as needed by different applications and groups of users.

His article finishes with the following clarion call to implement the Data Marketplace vision:

Companies achieve data transparency with data warehouses because of the use of canonical data models. Yet data in data warehouses was trapped in slow processes that lacked agility. The data warehouse data was well understood but couldn’t evolve at the speed of business. The data lake wasn’t able to correct this problem because companies didn’t implement lakes with a sufficiently comprehensive vision. That’s what they need to do now.


 
"Grimpen Mire"

While when I hear about Data Warehouses that take months to change, poor design and a lack of automation both come to mind, it is unarguable that some Data Warehouses can be plagued by long turn-around times [7]. Equally I have seen enough Data Lakes turn into Grimpen Mire to perceive that there are some major issues inherent in an unmodified approach to this area [8]. The Data Marketplace idea is an intriguing one, a mash-up [9] of different approaches that may just yield some tangible results.

I also think that the inherent focus on users’ needs as opposed to technological considerations is the right way to go. I have been making this point for many years now [10] and have full confidence that I will still be doing so in ten years’ time. As with most aspects of life, it is with people, and how a programme interacts with them, that success and failure factors are most readily found. It seems to me that the Data Marketplace approach seeks to embrace this verity, which can only be a point in its favour.
 


 
Acknowledgements

I would like to thank each of Forbes / Dan Woods, Podium Data / Paul Barth and Eckerson Group / Dave Wells for both reviewing this article and allowing me to quote their work. Such generous behaviour is not as typical as one might like to think and always merits recognition.
 


 
Notes

 
[1]
 
Though the total cost of saving such data extends beyond just disk costs and can become significant.
 
[2]
 
See my earlier article Ever tried? Ever failed? for a treatment of what is clearly a fundamental physical constant – that 60- 70% of all types of major programmes don’t fully achieve their objectives (aka fail). Data Lakes appear to also be governed by this Law of Nature.
 
[3]
 
You may need to navigate past a Forbes banner screen before you can access the actual article.
 
[4]
 
The following is my take in Paul’s analysis, for his actual words, see the Forbes article.
 
[5]
 
Watch this space for a review of Eckerson Group’s predictions for 2018.
 
[6]
 
Which I reproduce with permission.
 
[7]
 
By way of contrast, warehouses that my teams have built have been able to digest acquisitions and meet new and onerous regulatory requirements in a matter of weeks, not months.
 
[8]
 
I should stress here a difference between Data Lakes, which seek to be all-embracing, and more focussed Big Data activities, e.g. the building of complex seismological or meteorological models to assess catastrophic insurance risk (see Hurricanes and Data Visualisation: Part II – Map Reading). I have helped the latter to be very successful myself and seen good results in other organisations.
 
[9]
 
Do people still say “mash-up”?
 
[10]
 
For example in my 2008 trilogy:

  1. Marketing Change
  2. Education and cultural transformation
  3. Sustaining Cultural Change

 

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

 

A Retrospective of 2017’s Articles

A Review of 2017

This article was originally intended for publication late in the year it reviews, but, as they [1] say, the best-laid schemes o’ mice an’ men gang aft agley…

In 2017 I wrote more articles [2] than in any year since 2009, which was the first full year of this site’s existence. Some were viewed by thousands of people, others received less attention. Here I am going to ignore the metric of popular acclaim and instead highlight a few of the articles that I enjoyed writing most, or sometimes re-reading a few months later [3]. Given the breadth of subject matter that appears on peterjamesthomas.com, I have split this retrospective into six areas, which are presented in decreasing order of the number of 2017 articles I wrote in each. These are as follows:

  1. General Data Articles
  2. Data Visualisation
  3. Statistics & Data Science
  4. CDO perspectives
  5. Programme Advice
  6. Analytics & Big Data

In each category, I will pick out two or three of pieces which I feel are both representative of my overall content and worth a read. I would be more than happy to receive any feedback on my selections, or suggestions for different choices.

 
 
General Data Articles
 
The Data & Analytics Dictionary
 
August
The Data and Analytics Dictionary
My attempt to navigate the maze of data and analytics terminology. Everything from Algorithm to Web Analytics.
 
The Anatomy of a Data Function
 
November & December
The Anatomy of a Data Function: Part I, Part II and Part III
Three articles focussed on the structure and components of a modern Data Function and how its components interact with both each other and the wider organisation in order to support business goals.
 
 
Data Visualisation
 
Nucleosynthesis and Data Visualisation
 
January
Nucleosynthesis and Data Visualisation
How one of the most famous scientific data visualisations, the Periodic Table, has been repurposed to explain where the atoms we are all made of come from via the processes of nucleosynthesis.
 
Hurricanes and Data Visualisation
 
September & October
Hurricanes and Data Visualisation: Part I – Rainbow’s Gravity and Part II – Map Reading
Two articles on how Data Visualisation is used in Meteorology. Part I provides a worked example illustrating some of the problems that can arise when adopting a rainbow colour palette in data visualisation. Part II grapples with hurricane prediction and covers some issues with data visualisations that are intended to convey safety information to the public.
 
 
Statistics & Data Science
 
Toast
 
February
Toast
What links Climate Change, the Manhattan Project, Brexit and Toast? How do these relate to the public’s trust in Science? What does this mean for Data Scientists?
Answers provided by Nature, The University of Cambridge and the author.
 
How to be Surprisingly Popular
 
February
How to be Surprisingly Popular
The wisdom of the crowd relies upon essentially democratic polling of a large number of respondents; an approach that has several shortcomings, not least the lack of weight attached to people with specialist knowledge. The Surprisingly Popular algorithm addresses these shortcomings and so far has out-performed existing techniques in a range of studies.
 
A Nobel Laureate’s views on creating Meaning from Data
 
October
A Nobel Laureate’s views on creating Meaning from Data
The 2017 Nobel Prize for Chemistry was awarded to Structural Biologist Richard Henderson and two other co-recipients. What can Machine Learning practitioners learn from Richard’s observations about how to generate images from Cryo-Electron Microscopy data?
 
 
CDO Perspectives
 
Alphabet Soup
 
January
Alphabet Soup
Musings on the overlapping roles of Chief Analytics Officer and Chief Data Officer and thoughts on whether there should be just one Top Data Job in an organisation.
 
A Sweeter Spot for the CDO?
 
February
A Sweeter Spot for the CDO?
An extension of my concept of the Chief Data Officer sweet spot, inspired by Bruno Aziza of AtScale.
 
A truth universally acknowledged…
 
September
A truth universally acknowledged…
Many Chief Data Officer job descriptions have a list of requirements that resemble Swiss Army Knives. This article argues that the CDO must be the conductor of an orchestra, not someone who is a virtuoso in every single instrument.
 
 
Programme Advice
 
Bumps in the Road
 
January
Bumps in the Road
What the aftermath of repeated roadworks can tell us about the potentially deleterious impact of Change Programmes on Data Landscapes.
 
20 Risks that Beset Data Programmes
 
February
20 Risks that Beset Data Programmes
A review of 20 risks that can plague data programmes. How effectively these are managed / mitigated can make or break your programme.
 
Ideas for avoiding Big Data failures and for dealing with them if they happen
 
March
Ideas for avoiding Big Data failures and for dealing with them if they happen
Paul Barsch (EY & Teradata) provides some insight into why Big Data projects fail, what you can do about this and how best to treat any such projects that head off the rails. With additional contributions from Big Data gurus Albert Einstein, Thomas Edison and Samuel Beckett.
 
 
Analytics & Big Data
 
Bigger and Better (Data)?
 
February
Bigger and Better (Data)?
Some examples of where bigger data is not necessarily better data. Provided by Bill Vorhies and Larry Greenemeier .
 
Elephants’ Graveyard?
 
March
Elephants’ Graveyard?
Thoughts on trends in interest in Hadoop and Spark, featuring George Hill, James Kobielus, Kashif Saiyed and Martyn Richard Jones, together with the author’s perspective on the importance of technology in data-centric work.
 
 
and Finally…

I would like to close this review of 2017 with a final article, one that somehow defies classification:

 
25 Indispensable Business Terms
 
April
25 Indispensable Business Terms
An illustrated Buffyverse take on Business gobbledygook – What would Buffy do about thinking outside the box? To celebrate 20 years of Buffy the Vampire Slayer and 1st April 2017.

 
Notes

 
[1]
 
“They” here obviously standing for Robert Burns.
 
[2]
 
Thirty-four articles and one new page.
 
[3]
 
Of course some of these may also have been popular, I’m not being masochistic here!

 

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

 

Data and Information

Seasons Greetings for 2017

After having just published three rather lengthy articles in a series [1], here is a piece whose size is at the opposite end of the spectrum.

I am often asked to distinguish between data and information. Indeed this happened just the other day as part of LinkedIn discussions relating to some of my recent articles [2]. In the Data and Analytics Dictionary, I offer the following definition of Information:

Information is the first stop in the journey from Data to Information to Insight to Action. Data may be viewed as raw material, which needs to be refined in order to be useful. Information can be thought of as data enhanced with both relationships and understanding of context.

Here, I will look to be more visual in my definitions, hopefully also embracing the spirit of the time of year. In my opinion, the following image provides a good way to think about the difference between these two related concepts:

Data and Information

Consistent with my Dictionary definition, Information is something you get by organising data based on some knowledge of how it is meant to fit together.

As with most analogies, there are both some interesting ways to extend this and some areas in which it breaks down. In the first column, sometimes not all of the bricks you need are available or the right size (a data quality problem). In the second, you can clearly build a set of Lego bricks [3] into several different forms. It is to be hoped that data, particularly Financial data, is not massaged to provide more than one meaning.

However, I think the up-side of this simple analogy outweighs its fairly obvious limitations. I offer it to readers as a final thought before the 2017 holiday season commences.
 


 
Notes

 
[1]
 
The Anatomy of a Data Function, Parts I, II and III.
 
[2]
 
The discussions may be viewed here (you need to be a member of LinkedIn to view these).
 
[3]
 
Actually Duplo in this case.

 

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