hi

i'm adrian cooper i'm chief product

officer for um torrentia

uh hello again if you saw my um earlier

talk um

this in this talk which is called when

data becomes the asset

i'm talking talking about data

management

in the age of agile integration

give me a second

so i want to drill into um

into sort of below the systems level to

the data or the metadata

itself and the importance of data

management data management for for

digital transformation

so you could say that the great digital

innovators have recognized the necessity

uh to connect data and systems not just

for the front end or

customer facing elements but also to

join these together

with the middle or back office systems

they understand this this need to align

online and offline

so it's not really digital

transformation in my view if we're only

changing one part of it

and organizations that only consider

changing the public facing elements are

likely to struggle to deliver value over

time

and i think certainly in my own

experience

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you know a lot of museums have actually

largely focused on that customer facing

or public

uh uh public uh or audience driven part

um digital teams and services have been

established but mostly

you know ultimately another silo and the

rest

of the of the organization has been

business as usual of course at least

until the

the pandemic um so i think collectively

i mean there are obviously

great examples uh and but we need to

work towards what's

what's often referred to as the

connected enterprise or or in this case

the connected museum

in the last talk i introduced this

concept of services as a way to help

organizations

to change their mindset um services

as a macro level uh as a way to

kickstart some of the thinking about key

problems

and develop what i call connected

processes

um so a service that doesn't respect or

follow you know an internal organization

and so if you're thinking about

developing developing something

there's this then an increased need for

organizations to find smarter and easier

ways

to combine the data that's traditionally

held in in these separate

systems internally

modern cloud native applications are

generally designed with this

microservices approach

and microservices are a way to enable

organizations to

orchestrate and bring together and

process

and deliver efficient experiences and

transform workflows

so a microsoft sorry a micro services

approach means that although data and

information sources become more

distributed across the organization

the data itself is brought together or

connected

as needed to support the task the

workflow

um or the overall the overall service

and this is what we mean by um agile

integration

so we can we can we can orchestrate and

bring together data

from a range of different microservices

which each do something specific

in order to present to the user an

interface that allows them to do

what they need to do for the set of

tasks that's part of their service

nothing more nothing less

so if we recognize this need to make the

alignment between the internal

and the external uh if you like the

workplace and the digital space

my data is connected merged or enhanced

through a range of new digital services

what does this mean

to the way we think about and manage our

data or

metadata so really so really what is the

challenge

for for data in that context

so we could say that that data is the

currency

of digital transformation because you

can't do digital

digital transformation without

transforming your approach to that to

the data to metadata

so as we start to plan any migration to

new cloud

native platforms with smart connections

we need to think about how we can ensure

that the data becomes

or remains a key asset so it won't be

smart unless you can unless you can

connect it

so for anything to be an asset it needs

to have value

and and therefore data needs to have

value to be treated um

and seen as an and be an asset

and so i think there are some key

challenges or key things to think about

in terms of what

as a profile we might think about

uh you know in in terms of that data

in this decentralized digital ecosystem

so we need to be able to share data

clearly

we need to be able to trust it uh it

needs to have

meaning and and context um so obviously

that's where

taxonomies and and and controls uh come

in

um it needs to be consistent

comprehensive um

and accurate it needs to be

accessible um controlled uh

secure uh if you think about right

rights and licensing and

all of those things we only only the

people that need to have access

uh we'll get we'll get access

and of course last but not least it

needs to be

machine readable so we need to we need

to have uh

computers to be able to look at data

interpret it know what it means

and to be able to do something logical

with it to process it

in order to to make these make these

connections

so it's clear that we need a more

flexible and

adaptive data management strategy in

order to deliver this agile and trusted

data which then begs the questions you

know who's

who's responsible or accountable how do

we

improve or maintain the quality

and and how do we how do we make these

smart uh connections or automate them

and in term

in terms of what we call the the data

supply chain the joining together of the

data

as needed to support the different

processes that make up

uh an overall service

so i think the ability to manage data as

an asset

across the whole organization depends on

a number of things

technology organizational culture

governance and and sort of staff

employee accountability you know

everyone's got to do their bit and i

think um deliberately in this slide

i've made the organizational culture and

and governance

circles bigger than the technology and

uh because i think that you know

whilst a lot of other people assume that

by upgrading their technology then the

data is going to take care of itself

sadly that's not that's not the case and

and

really it's about organizational culture

and governance

and thinking about things across the

whole organization

that's really going to going to make the

difference

of course technology uh will and can

provide

provide some help in terms of of

cleaning up and sorting data to you know

assuming that there are that there are

rules to follow um

you know data transformation services

can be uh

established and scripts written to try

to clean up poor data and you can

certainly use ai and machine learning to

to to process

um you know data and and and try and um

you know using using machine learning

and ocr and things like that to actually

improve the the tags and and other

metadata

about about the about the assets um and

and we can also use machine learning to

to attempt to create links to

uh linked open data sources um and you

know wiki data

and things like that to it to improve

the uh

the accountability and and add to the

weight

of of that content

so where are we where are we now

i think there are kind of two uh two

types of barriers there's organizational

barriers and there's tech

there's technical barriers to this in

terms of organizational barriers

well really museums aren't any different

to most other sectors

and his data has been managed in roughly

the same way in most

most organizations across all sectors

for this in the same way for the last 20

or 30 years

and that tends to be uh that it's

managed

kind of independently within it within a

departmental focus

uh because in turn that that relates to

the kind of systems that are that are

put in place

and so if you imagine saying in a museum

context uh in typically museum data is

designed and managed in these large

scale

business area specific applications such

as a collections management system

but but even within the same sort of

core area as we say collections

there is you know a lot of cases and

certainly my own experience

as a consultant a strong tendency

towards separate management and control

even within that that area so for

example management of objects archives

books

all part of the overall collection and

and that they're often

handled in in independent uh systems and

i know there are those who say well

there are reasons why but i mean that's

that's more historic i think than than

relevant for

for for the future but but having

independent metadata structures having

separate controlled vocabularies or

taxonomies

uh to to to to describe the data

uh leads to issues where it's hard to

harmonize any of that data

um if if you you know right at the

beginning i said of course services

don't respect organizational culture

so users don't need to know that your

data is held as separately in in

in a library collections or an archive

system they don't care they just want to

find

uh what they're looking for by topic

subject

etc so we need to find ways in order to

bring this data

um together to aggregate it and to

harmonize it

and then on top of the kind of the

thesauri problem as it were with

multiple different

separate systems we then got uh

functions which which are perhaps

duplicated across the organization so

something like like loans might be

managed by you know a number of systems

independently in an organization

or it could be that loans is only

managed in one system and in order for

say an archive object to be loaned it

has to actually be added to the

collection as

a as a temporary record in order to then

have a loan transaction uh carried out

which so

so these things you know are all

examples that

i've i witnessed as as a consultant

and um this siloed approach

as it were is perpetuated by some of

course very basic

and natural tendencies for teams to be

possessive about their data and i think

uh

that's quite understandable and and

there can be a reluctance to share data

or to adapt cataloging practices to

support the organizational wide needs

and and it but in a lot of cases maybe

the organization itself isn't

isn't strong enough or clear enough to

actually articulate what it wants in

order for these

departmental systems to come together

which is then part of this

you know service orientated thinking

let's think more about the outcomes that

we want

rather than thinking in terms of systems

and features and functions because that

just really doesn't help going forward

so um poor integrations and other

technical limitations of course help to

enforce that kind of departmental

outlook so they be it becomes symbiotic

organizational barriers uh are to the

connected uh

museum enterprise are in turn

perpetuated by this

this nature of legacy systems um

some things legacy not not not because

it's old um

it um it it can be it can be legacy for

for a number of reasons um but

um you know they tend to offer this

single interface single database you

know single set of of of modules

um and and often have data entry screens

which are not terribly user friendly or

difficult to use

there's a lack of flexibility and so

this leads to what's sometimes called

field hijacking where users you know out

of out of frustration or you know

uh just just put data where they want to

put it rather than necessary in the

place that

that is designated for it um so that

incoming

in combination perhaps with poor or lack

of data validation tools

uh you know if you're allowed to put a

date in the text field or vice versa

then then of course uh results in the

data quality being uh significantly

reduced that combined with with

terminology control which may not be as

effective as it could be

all points towards lower quality and

dirty data

so you know how do we properly identify

you know people places objects materials

subjects events etc

if the values that we're entering are

are simply strings of characters

that have no in intrinsic meaning that

can be

interpreted by by by other systems and

where the you know the data's in in

fields that

means something different to what they

were intended and that only confuses the

situation

so data managed through a sort of

business specific application

may well also require some form of

interpretation or logic layer to have me

to have meaning if the data is exported

outside the system

it can't necessarily be uh understood um

and and and as you know it's been talked

about a lot today

you know if there are no apis that makes

data hard to integrate

and that results in of course

time-consuming projects

to build inflexible or one-off

integrations between specific systems as

as needed and

there's certainly been a lot of debate

about you know what's the right

level of integration between the cms and

the dems but

from my perspective uh these

point-to-point integrations are

hard to maintain and any change to

either point will result in the

integration breaking and so we should be

thinking more in terms of

of cert of a service oriented view

rather than this system to system

type view because that that really is is

an older way of thinking

so if if given all of that we we look to

compare kind of legacy data

with our data data value profile that i

that i

put up earlier we can see that the

legacy data

uh comes with a number of number of

issues often so

you know it's it's inflexible it's it's

possibly hard to share

it doesn't necessarily have meaning um

it could well be inconsistent because

data isn't in the in the right fields or

it's not being properly validated

so which means that we can't trust it

completely which means that its value is

lower

and and you know possibly you know again

more importantly it isn't machine

readable

because we can't use any logic to

interpret what it

what it means so how can we move towards

uh data being being seen as an asset

and i think as we said if you know

organizations have relied on this

uh business or departmental uh areas

creating and managing their own

applications and data models and tax on

taxonomies uh historically but i think

now there's a need

to change the approach to data

management so that it works more

effectively

across the whole organization and that's

that's going to be a big change for some

and so in the connected organization

data is the most significant and

tangible asset

it needs to become the fuel uh that that

powers multiple use cases initiatives

not not just designed for one particular

use case or project or thing uh new

digital services will only be as strong

as the underlying data

that that fuels fuels them and we need

data to be structured

and machine readable in order to have to

have use

and the value will come from the ability

to collect

and correlate data from different

systems without need for this manual

interpretation

logic and automation so data that's

locked inside systems that has no

intelligent intelligible structure or

meaning can't be connected

um into a digital supply chain which is

then where we have to fall back on

manual

integrations so as organizations shift

from these large monolithic projects to

more agile

and service based approaches the concept

of an organizational

data catalog will become more and more

important

it's going to be important to build and

maintain

this shared data catalog in order to

understand how

key data types key entities gathered

across

the range of systems can be connected to

support the overall data

supply chain and and and support the

needs of specific

uh services so you can see here some

examples of the kinds

of entities i'm thinking about that are

currently held across

uh different systems whether that's

objects or digital assets or

products or locations or customers or

rights you know you know again conscious

that rights are

handled separately in digital asset

managements and collect management and

collection management systems

whereas the reality is that they need to

be brought together need to be properly

harmonized

in in a in a museum context which makes

you know currently makes workflow

uh difficult for those who are trying to

do uh do that role so

we need to think about these key

entities and look where this where this

data is stored

agree how we're going to you know share

share on thing you know on things like

taxonomies across the organization

in order to be able to orchestrate the

data in a way that it can come together

using uh you know machines and services

to deliver the kinds of services that

you know uses

uh and uh expect

so in summary i i think there are

you know there are a few a few key thing

things to think about

um and and so first of all is this

rethinking the data supply chain making

sure that that's done at

an organizational level rather than

necessarily a departmental

or departmental by department level and

i think the service based thinking

where you're thinking about about

specific problems and outcomes

uh will will help with that there's

certainly a need to increase the data

modularity

and and you know to break things down

and move towards this micro services

and focus on on the development of of

apis

uh to to uh to to support this this

bringing together of the data

um and and i need to think more as an

organization about the data

the data governments data is an asset

that everyone has to manage well it's

not it's not

you know somebody over there's

department uh

you know to think to look after um you

know everybody

everybody has to have responsibility and

of course we can use more modern systems

to

to improve that that and keep that keep

that maintained

as we as we go along and last but not

least is the harmonization of metadata

metadata models this is

absolutely critical when we start to

think about shared taxonomies

not only between you know within an

organization but actually between organ

between organizations whilst you know

we've been developing apis some of the

bigger organizations have managed to do

that

there's certainly no uh um

there's no standard around the way that

they've done that so there are already

issues in trying to draw

draw data from different places together

if they're if that if that's not uh

it's self common so within the

institution and then institution to

institution

as we start to collaborate more on on

these on these projects

um so so i hope that's given you uh

some things to think about it's not

always the most exciting part but i

think if we don't get the data element

right

uh then a lot of the other things can't

uh can't follow on

um from that so thank you for for

listening

and good night