[audience applauding]
- Hello everyone. So can logic and creativity co-exist?
I am left-handed.
I am therefore in theory,
right-brained holistic synthesizing intuitive thinker.
And yet I vividly remember the day at primary school,
a very long time ago now,
when I was introduced to Boolean logic for the first time,
first described by George Boole in his 1847 book,
"The Mathematical Analysis of Logic".
I was completely bewitched. I fell under its spell.
And really that's why right now
I am enthralled with the job I do every day,
working in data, technology and analytics,
because I feel like it enables me
to bring my whole self to work.
For me, great analytics requires that we are
curious about the world in which we live,
that we have the creativity to pose new
and provocative questions.
And I believe the magic combination of scientific inquiry,
algorithmic thinking when combined with human intuition
and creativity is where you really
do uncover new and special insights.
For me, data really has the power to drive change.
And for me, that is really, really exciting.
So a little bit about Sainsbury's,
which is where this all begins.
We are a beloved British institution
with a long standing reputation
for purveying fine quality products at fair prices
from our 1400 plus stores for over 150 years.
But in the modern day incarnation of the business,
we also have this stable of great brands.
But our challenge within the technology space
is that we exist in a highly disruptive
and competitive market.
So our challenge and it's a challenge held in common
with other large scale enterprises,
is how do we scape the narrow confines
of a siloed structure that is formed
of those very successful and well-defined brands?
How do we more nimbly provide a unified customer experience
that crosses them?
And this is why we have brought our technologies together
for the first time in a progressive new tech organization
called Sainsbury's Tech.
Yeah, we're removing inefficiency,
driving more structural change
within our technology organization.
Moving our technologists out of what has historically been
individual technology teams facing into the brands
into this new unified organization
with the idea that that means we can accelerate
the way we think about our processes,
our systems, and importantly, in my space,
more effectively pool our data than ever before. Yeah?
So this is it, this launched about a month ago,
but what is the purpose for Sainsbury's Tech,
other than escaping, as I say, the rigid confines
of a historically siloed brand led structure?
What we've had to do in creating that structure
is really think about how we align our business vision,
our strategy, and indeed our investment
with a really bold review of how we work as technologists.
And this is just to give you a flavor of some of the things
that we've had to think about
and really thinking about what it means
to create a technology organization that can build
the capability that will enable us to compete.
Particularly with organizations now who are competitors,
who were born at the digital age, rather than 150 years ago
when John, James and Maryanne started Sainsbury's.
But it's the customer that lies at the heart
of this organization.
So Sainsbury's Technology is an organization
that is gonna build the capability to power,
to be the engine room and the catalyst for change
to help Sainsbury's become
the UK's number one multi-brand, multichannel organization.
But it's the customer that lies at the heart of
our purpose and endeavor.
This is a quote here by our CIO, Phil Jordan.
And he's talking here about the fact
that it's imperative now
that we start to build our technology in such a way
that it is multi-brand and multichannel.
That we can delight our customer
whenever and wherever they choose to shop.
And that that brand experience
at the technology level cannot be different anymore
at a brand level.
We want to be able to see the customer in 360 degrees
at all times, irrespective of the brand or channel
that they're choosing to shop with.
And we really, really want to develop our systems,
our processes, our tech, anchored around the idea,
what would our customers think of this?
So if we translate that broader technology landscape,
this idea that we're creating
this new integrated technology organization
for the group for the first time,
if we take that now down,
it's what it means in the data space.
As Matt has eloquently said,
my job really is to help realize
the organization strategic ambition,
that we should know our customers
better than anyone else does.
If I translate that into data terms,
this is what I would describe our data vision to be.
So, as you can see, it says at the heart,
we want to know our customers better than anyone else.
That means at the data level,
we need to be operating from one ecosystem.
And really converge on the golden facts
on which we're choosing to run our business.
So there shouldn't be a difference
in the way that we define sales as a simple concept
between the Algo's retail business
and the food retail business, yeah?
Secondly, we want to really empower our colleagues
to satiate their curiosity about our business
and the way it works by bringing information
into the palm of their hands.
Automating access to insight,
shifting the paradigm away from
the insight being the preserve of the special few
who can write brilliant code and democratizing it
into the palm of the hands of every store manager,
every colleague in commercial finance.
And then lastly, if we're gonna know our customers
better than anyone else,
so as to better anticipate and meet their needs,
as data level,
what anticipation and meeting needs means
is it means better prediction
and it means better optimization.
So we want to start reducing human decision-making
in the loop of the most complex end-to-end network decisions
in our business.
Now, all three of these things are completely independent,
completely dependent on one another.
We can't be running large scale predictions
without our phenomenal data asset.
We won't build the business buy-in
into reducing or changing human decisioning,
unless colleagues can satiate their curiosity
about why things are happening in our business
the way they are.
Yap, so that, that is our data vision.
But what I want you to think about for one second
is the amount of data an organization like ours throws off.
Yeah, so we've got the UK largest loyalty scheme.
We've got the second biggest
German merchandise and clothing business in the UK.
We have a bank.
We have hundreds of stores, thousands of colleagues,
millions of customers, and billions of transactions.
It is a phenomenal data asset,
I would argue but then I'm biased.
It is probably the preeminent data asset in the UK.
The challenge is that we're not yet maximizing the value
of that data asset.
So my job, why, why my role exists?
Why Sainsbury's is investing in a role like mine
is to start to bring this data together in a way
that makes it useful and consumable by all,
not that it's locked away in the vertical silos
of our brands or indeed functions within brand.
Yeah, because it's in democratizing access to this,
to this data, turning this data into information
that we will change the way our business works
ultimately to the benefit of our customers.
It's the, the delighting of our customers
that is the purpose behind this endeavor.
And the way we're going about that
is building this ecosystem, which we call Aspire.
So what we're essentially doing at the moment
is building a ton of data pipelines
out of every single transactional system
across our multi-channel, multi-brand business.
And we're publishing that data, firstly,
to our lake and then from our lake,
into our warehouse Snowflake.
Very tailored to three discrete types of user needs.
So in my space, my customer is the consumers of information.
And they are, at the most nuance level,
our scientists and machine learning engineers
who want access to large scale granular data.
You then have the analytical community,
those colleagues who are able to write code
to interrogate large-scale data assets.
And then the last group is the group I referred to earlier,
the store managers,
the colleagues who work in our trading division or finance,
who don't have the capability to write code themselves
to interrogate data,
but want information served up
in an extremely accessible way.
So the ecosystem that we're engineering here
that you can see is very much tailored
to those three different users.
Now, I touched on this earlier,
I'm gonna touch on it again now.
Nectar is one of our preeminent sources of data
in the organization.
Alongside our digital channels,
it gives us an enormous amount of information
about the way the British public lives their lives.
From the asset we can tell how little somebody is,
what their buying behaviors are,
what their tastes and preferences are.
And then you can see a few of the stats here
about the scale of it.
But a couple of years ago, now,
we recognized that we needed to fundamentally rethink
the way Nectar was being used by customers,
because we felt that
the historical card-based loyalty scheme,
wasn't truly reflective
of the way customers want to shop these days.
So we started a program to digitize,
digitize the proposition,
to reflect the way customers want to shop.
In Sainsbury's Tech,
We have a culture of experimental curiosity,
and we also believe very deeply
that when you're trying to develop a truly compelling
and attractive proposition, data is the answer to that.
So we took a three-staged approach to the development
of the Digital Nectar Program.
So in April, it should say 18, apologies.
In April 18, we launched a trial on the Isle of Wight,
that was quite deliberate.
It has a very specific population, doesn't it?
And if you're gonna really be bold and experimental
about digitizing a very traditional
at the time loyalty scheme,
that was a real proven game to understand
how far we could really push
a pure play digital loads proposition.
From there, we then went into Wales.
And from Wales, we have subsequently gone to
national rollout.
But very deliberate three-phase approach,
constant iterations and changes to proposition
being made on the basis of data.
And this is what we've launched nationally recently.
What I wanted to touch on was the comment there,
personalization driven by machine learning.
In Sainsbury's Tech, and in my data technology domain,
we're very focused on the idea that the days
of operating with and to the average have long gone.
And it's imperative that we deliver to the organization,
the capability to execute at the most granular atomic level.
So we are making sure we're building capability
to enable granular prediction at scale,
building systemic, automated, robust solutions
to really complex recurring problems.
And this is an example of one of those problems.
So what we have been doing
within the personalization dimension of the proposition
is we've been saying, over 30,000 skews that we sell
in the food business and our 19 million loyalty customers,
which of those skews are those customers
likely to be most interested in at an individual level,
in any given week, given their purchase history,
their affinity to use offers
and their own personalized buying behaviors
in terms of their recency, frequency and value?
So 30,000 skews, 19 million people,
real-time prediction about the best fit
between customer and skew at an individual level.
And that was very very exciting
because that was probably the largest scale
machine learning activity that we had landed at the time.
And that is all predicated on that data lake
that I mentioned within Aspire.
So look, why is Sainsbury's Tech
in a really exciting place to work for me?
It's a really exciting and rewarding place to work for me
because it's, it is, it is not abstract.
It is about 26 million customers a week.
It is about facilitating the way Britain shops.
It is about directly affecting real lives.
And it's about delivering the technology
that supports the communities we serve.
It's a pretty thrilling place to work.
Thank you.
[audience applauding]