One of the things I've been pushing for years is the idea that data strategy should itself be data driven. In other words, if we are claiming that all these expensive data and analytics initiatives are driving business improvement, let's see the evidence, and let's have a feedback loop that allows us to increase the cost-effectiveness of these initiatives. This is becoming increasingly important as people start to pay attention to the environmental cost as well as the monetary cost.

This idea can be found in my ebook How To Do Things With Data and my articles for the Cutter Journal, as well as on this blog.

I doubt anyone will be surprised by Gartner's recent survey, showing that although over 90% of the respondents acknowledged the importance of being value-focused and outcome-focused, only 22% were measuring business impact. So they clearly aren't eating their own dog food.

And the same thing applies to the current hype around AI. Tech journalist @LindsAI Clark asks will we be back in another 10 years wondering who is measuring the business value of all that AI in which organizations have invested billions?

Lindsay Clark, Data is very valuable, just don't ask us to measure it, leaders say (The Register, 21 Feb 2025)

Richard Veryard, How To Do Things With Data (LeanPub) 

Richard Veryard, Understanding the Value of Data (Cutter Business Technology Journal, 11 May 2020)

Wikipedia: Eating your own dog food

   

We can find a useful metaphor for data ethics in Tolkein's Lord of the Rings. Palantíri are indestructable stones or crystal balls that enable events to be seen from afar. They also allow communication between two stones. The word comes from Tolkein's invented language Quenya - palan means far, tir means to watch over.

The stones are powerful but dangerously unreliable. Even in the hands of an evil wizard such as Sauron, the stones cannot present a completely false image, but they can conceal enough to mislead, and at one point in the story Sauron himself is deceived.

This links to my oft-repeated point about data and dashboards: along with the illusion that what the data tells you is true, there are two further illusions: that what the data tells you is important, and that what the data doesn't tell you is not important. (See my eBook How To Do Things With Data.)

Joseph Pearce notes the parallel between palantíri and another device whose name also denotes watching from afar - television. "The palantir stones, the seeing stones employed by Sauron, the Dark Lord, to broadcast propaganda and sow the seeds of despair among his enemies, are uncannily similar in their mode of employment to the latest technology in mass communication media" Pearce p244.

The big data company Palantir Technologies was named after Tolkein's stones. It has pitched itself as "providing the power to see the world, without becoming corrupted by that power" Maus. Not everyone is convinced.

Denis Campbell, NHS England gives key role in handling patient data to US spy tech firm Palantir (Guardian, 20 November 2023)

Maus Strategic Consulting, A (Pretty) Complete History of Palantir (27 April 2014)

Joseph Pearce, Catholic Literary Giants: A Field Guide to the Catholic Literary Landscape (Ignatius Press 2014)

Wikipedia: Palantír, Palantir Technologies

   

The concept of Dynamic Pricing has been around for at least 25 years. I first encountered it in Kevin Kelly's 1998 book, New Rules for the New Economy.

Five years ago, when I was consulting to a large UK supermarket, this concept was starting to be taken seriously. The old system of sending people around the store putting yellow stickers on items that were reaching their sell-by date was seen as labour-intensive and error-prone. There were also some trials with electronic shelf-edge labels to address the related challenge of managing special offers and discounts for a specific stock-keeping unit (SKU). At the time, however, they were not ready to invest in implementing these technologies across the whole business.

The BBC reports that these systems are now widely used in other European countries, and there are further trials in the UK. This is being promoted as a way of reducing food waste. According to Matthias Guffler of EY Germany, around 400,000 tonnes of food is wasted every year, costing German retailers over 2 billion euros. Obviously no system will completely eliminate waste, but even reducing this by 10% would represent a significant saving.

Saving for whom? Clearly some consumers will benefit from a more efficient system of marking down items for quick sale, but there are concerns that other consumers will be disadvantaged by the potential uncertainty and lack of transparency, especially if retailers start using this technology also to increase prices in response to high demand.

Update October 2024

Dynamic pricing has recently hit the headlines following some extreme examples of surge pricing for concert tickets. See commentary from Hannah Downes of the Consumers' Association.

Mabel Banfield-Nwachi, Happy hour in reverse: where dynamic pricing may creep further (Guardian18 November 2024)

MaryLou Costa, Why food discount stickers may be a thing of the past (BBC News, 30 November 2023)

Hannah Downes, Oasis tickets: Ticketmaster's 'in demand' pricing could be in breach of consumer law (Which? 10 September 2024) Dynamic pricing: how does it work and is it legal? (Which? 2 October 2024)

Matthias Guffler, Wie der Handel das Problem der Lebensmittelverschwendung lösen kann (EY-Parthenon, 28 March 2023)

Related posts: Dynamic Pricing (April 2006), The Price of Ev,erything (May 2017)

Wikipedia: Dynamic Pricing

   

A couple of questions came in on my phone, and I thought I'd share my answers here.

When is a Business Requirements Document good enough?

Here are a few considerations to start with

Fit for purpose - What is the document going to be used for - planning, estimating, vendor selection, design or whatever? Does it contain just enough for this purpose, or does it dive (prematurely) into solution design or technical detail?

SMART - are the requirements specific, measurable, and so on, or are they just handwaving? How will you know when (if) the requirements have been satisfied?

Transparency and governance - is it clear whose requirements are being prioritized (FOR WHOM)? Who wrote it and what is their agenda? (Documents written by a vendor or consultancy may suit their interets more than yours.)

Future-proof - are these requirements short-term and tactical, are all the urgent requirements equally important?

Complete - ah, but what does that mean?

When is a Business Requirements Document complete?

Scope completeness - covering a well-defined area of the business in terms of capability, process, org structure, ...

Viewpoint completeness - showing requirements for people, process, data, technology, ...

Constraint completeness - identifying requirements relating to privacy, security, compliance, risk, ...

Transition plan - not just data migration but also process change, big bang versus phased, ...

Quality and support plan - verification, validation and testing, monitoring and improvement. What is critical before going live versus what can be refined later.

RAID - risks, assumptions, issues and dependencies 

Important note - this is not to say that a business requirements document must always be complete in all respects, as it's usually okay for requirements to develop and evolve over time. But it is important to be reasonably clear about those aspects of the requirements that are assumed to be more or less complete and stable within a time horizon appropriate for the stated purpose, and to make these assumptions explicit. For example, if you are using this document to select a COTS product, and that product needs to be a good fit for requirements for at least n years, then you would want to have a set of requirements that is likely to be broadly valid for this length of time. (And avoiding including design decisions tied to today's technology.)

   

Following my previous posts on Netflix, I have been reading a detailed analysis in Ed Finn's book, What Algorithms Want (2017).

Finn's answer to my question Does Big Data Drive Netflix Content? is no, at least not directly. Although Netflix had used data to commission new content as well as recommend existing content (Finn's example was House of Cards) it had apparently left the content itself to the producers, and then used data and algorithmic data to promote it. 

After making the initial decision to invest in House of Cards, Netflix was using algorithms to micromanage distribution, not production. Finn p99

Obviously something written in 2017 doesn't say anything about what Netflix has been doing more recently, but Finn seems to have been looking at the same examples as the other pundits I referenced in my previous post.

Finn also makes some interesting points about the transition from the original Cinematch algorithm to what he calls Algorithm 2.0.

The 1.0 model gave way to a more nuanced, ambiguity-laden analytical environment, a more reflexive attempt to algorithmically comprehend Netflix as a culture machine. ... Netflix is no longer constructing a model of abstract relationships between movies based on ratings, but a model of live user behavior in their various apps Finn p90-91

The coding system relies on a large but hidden human workforce, hidden to reinforce the illusion of pure algorithmic recommendations (p96) and perfect personalization (p107). As Finn sees it, algorithm 1.0 had a lot of data but no meaning, and was not able to go from data to desire (p93). Algorithm 2.0 has vastly more data, thanks to this coding system - but even the model of user behaviour still relies on abstraction. So exactly where is the data decoded and meaning reinserted (p96)?

As Netflix executives acknowledge, so-called ghosts can emerge (p95), revealing a fundamental incompleteness (lack) in symbolic agency (p96).

Ed Finn, What Algorithms Want: Imagination in the Age of Computing (MIT Press, 2017)

Alexis C. Madrigal, How Netflix Reverse-Engineered Hollywood (Atlantic, 2 January 2014)

Previous posts: Rhyme or Reason - The Logic of Netflix (June 2017), Does Big Data Drive Netflix Content? (January 2021)