In this episode of The Business Startup Podcast, James and Steve return to a more technical discussion, discussing one of the most misunderstood topics in modern organisations: the difference between data migration strategy and data strategy.

They explain why businesses often blur the two, the risks of neglecting long-term data governance, and how leadership ownership, data quality, and continuous investment shape successful digital transformation.

From ETL planning and repeatable migration processes to governance, compliance, and post-go-live data quality, this episode highlights why managing data is no longer optional. It’s essential for trust, insight, and regulatory confidence in today’s world.

In this episode, we cover:

• The difference between data migration strategy and data strategy 01:50

• Why ETL planning, resourcing, and repeatability define successful migrations 02:20

• How data migration creates the opportunity to improve long-term data value 03:00

• Why investing in data strategy is important 3:50

• Data quality tool 05:30

• The risks of poor governance, declining data quality, and bad habits after go-live 07:30

• People and the ownership side of governance 08:24

• Why organisations must invest beyond migration to sustain trusted, accurate data 10:30

EPISODE TRANSCRIPTION:

Disclaimer: This transcript was generated by an AI tool that did its best, but it's never met different British accents it could fully decode. Expect a few funny mistakes. Enjoy!

[00:40] - James B

Hi there. Welcome to the Binary10 podcast. My name is James Blake. I'm the CEO of Binary10.

[00:45] - Steve S

And my name is Steve Smales, and I am the Chief Operating Officer.

[00:49] - James B

Hey, Steve. How are you doing?

[00:50] - Steve S

Hi, Jamie. I'm good. Thanks. How are you?

[00:52] - James B

Good. Yeah. Looking forward to this one. We're going to get technical today, back to our roots.

What's this then? So data migration strategy versus data strategy, yeah are they the same thing certainly not certainly not but it's amazing how we see the waters being muddied you know when we speak with our clients and when we deliver primarily data migration strategies and data migrations how there is an assumption or for a start the thing that really frustrates me is they just call it data strategy and i have to go no no it's a data migration strategy and then oh same yeah same same thing No, no, it's not. It's definitely not. And I think what we're going to do today is try and explore both those areas, show where there are differences, you know, what they mean, but also where they do crossover, because, you know, they do share some similarities as well. So Steve, data migration strategy, what are those sort of key elements of it that are very specific to it and not data strategy purely?

[01:48] - Steve S

Yeah, so I suppose your data strategy is a bit more generic, isn't it? A bit more sort of wider ranging, but with a data migration strategy, video. Focus solely on doing that ETL, that sort of the shifting of data from your legacy systems or spreadsheets or documents, wherever it is, transforming it, get it into your new system, making sure it's reconciled, doing some data quality analysis, data cleansing, data enrichment. But before you do all of that, you need a strategy in place. How are you going to

do it? And you need to plan it out, not just plan the timelines, you need to plan the resourcing and the methodology as well. How are you actually physically going to... transform or get the data the extracts how are you going to transform it how are you going to load it all of that forms your strategy and that sort of length of time where you're doing that etl how many times you're going to do it you want a repeatable process you know you can't just do it once and then put it live so you need to do

this uh you know time and time again and refine it until you're sort of comfortable that the process you've got in place actually works and you're ready to load it into your target system so that's essentially your data migration strategy i mean would you add anything to that no i you know spot on i think it's you know the other way you can look at it as well is just that sort of time boxed event you know a dm strategy is normally related to a project or a program you.

[03:08] - James B

Know where it's a very targeted system or entity that you're trying to move data from A to B. If we then come on to data strategy... So this is something that a business should operate all the time and keep up to date regularly and it should cover all data you know that your business operates but to sort of help simplify it slightly you know it's a way of governing your data and managing it almost keeping it well oiled and a nice way I like to think about it and how you can bring the two together.

Data migration is a great opportunity to clean your data you know make it better obviously you've got to move it into your new system new processes. So at that point, you know, you've already improved and massively increased the value of your data. So think of it as that data strategy can then take that forward. It can keep that value and finesse it and add to it. But it can only do that if you've got a good strategy in place and that you actually, you know, you resource up and you invest

in, you know, whether it's tools, you know, people, technology, to ensure that that data remains consistent, you know, accurate. Because then, you know, people will continue to trust it and it will add value to your business. um but as i said it's amazing how you know we've done some projects and people will contact us maybe just after and say oh some of the data has changed and it's not doing as and you say oh you know what does your how are you addressing that through your data

strategy oh we don't have a data strategy and it's and it's a difficult one because people you know you have to invest in a data strategy and people don't always see the benefits or see the return or the investment return so they're very nervous about doing it but i think in the modern day you know GDPR, DPA 2018, you know, the regulation around data and keeping it secure and safe. It's essential nowadays, you know, businesses really need to start thinking about this. And actually, so

we promote it, you know, when we do our data migrations, we say to our clients, look, just be aware that this is what the data migration will deliver you. Fantastic. It will move your data onto this new system, but it's not going to keep it fantastic. It's not going to, you know, ensure it's well managed, well maintained. You have to put a strategy around that and empower the people within your organization to take up that responsibility to own data and to ensure that it keeps consistent and that it keeps accurate. You have to work at it to make the value come out.

[05:27] - Steve S

Yeah, and it's a good point what you're saying because we do come across it a lot with clients where Part of the data migration strategy in the project, you have to spend a lot of time cleansing the data, enriching the data to get it into a good state to go live on the new system. But then as soon as they

go live on the new system, the old habits start coming in and the data starts sort of, you know, being entered badly in the wrong place or things start getting missed that really should be entered. And it's almost like you've gone through that whole effort to get to a really good point and then you start

letting it slip again. Interesting. We've actually got a tool that we use for data quality analysis, for the actual data migration part, binary view, which you can probably talk a little bit more about. But it's interesting because what we can do is actually tailor that to work not just during the data migration

project, but after the project, after go live, we can actually point that same tool to your target system and then you can actually see the data quality analysis real time going forwards.

[06:29] - James B

Absolutely. And I'm glad you said that because, let's be honest, it's the insight, isn't it people walking around the office, people just doing their day-to-day lives, they're not seeing the data be bad or at inefficiencies. I mean, it might be seen at some level or people just cover over it and get their process done. But it's imperative for delivering data migration. So our tool is fantastic in that we define certain checks we want to do on data, like POs that are really old and out of date and should be closed out. you know there's lots of different checks we can do on on individuals data and making sure they don't have missing data but just as you said steve why then not continue that on

because you spend all that effort you know monitoring that and doing it and and it's amazing how you share you know the dashboards and the reports and your senior management your data owners in the business say oh my god have we really got that bad data right let's fix it fix it fix it but then you

go live and people just carry on being just you know bad habits bad habits You know, don't need to keep looking at that.

[07:27] - Steve S

And like you say, no insight. People just aren't aware, senior management aren't aware that these

issues are happening.

[07:32] - James B

Yeah. And I tell you what, like, I mean, you know, don't quote me on this, but I think, you know, in the future, you think of like standards, you know, in your ISO 9001s and all your different sort of standard bodies. But it'll be the same for data. You know, I think organizations will have to prove a

level of data quality. Yeah. You know, and I think, and that's, you know, we're just talking one area of data strategy here. but you know we would absolutely implore businesses to really consider this you know have reports that check your data on a regular basis and let them you know issue them to you or only issue them to you if it sees discrepancy or it falls beneath the threshold because then you're actively governing your data you're actively managing it and you're reacting if things go wrong or if things go bad so yeah i think and that's probably coming back to what we talked about earlier that is an area where data quality is where data strategy data migration they they both have the same interest you know it's it's certainly super important i mean i think one other area to expand on

data governance is is like the people ownership and everything so data governance is If anyone ever looks up something called the Dharma Wheel, which has been provided by this international institution, if you look at that, it talks about all the different areas of data that you need to manage. But in the heart of that wheel is data governance. Because all these different areas, data management, data security, data storage, whatever it might be, they all need to be governed. They all need responsible people that, as part of their duties, are actively managing. that world and i think that's really important for an organization to kind of drive you know and empower those people to make them realize they've got you know they've got a responsibility about data exactly and if you don't actually sort of put names against responsibilities then nothing's going to happen and you'll just end up in you.

[09:12] - Steve S

Know in a few years time with your data in your new system in just as bad quality as the old system was that you always used to moan about before you you swapped over and said oh i'm glad we're going to a new system so we can sort out the data and have a nice clean start and everything's going to be great but without those responsibilities for keeping that data in a good shape sort of nailed down it will start slipping and it will start slipping from from the first week absolutely.

[09:32] - James B

And i think you know like like with anything the ownership starts at the top yeah so you know all you cfos out there you know hr directors IT directors, that's with you. you know, that is, you know, your responsibility. If anything happens, you know, in a negative way, breaches, things like that with data

in your businesses, it will come back to you. And now that doesn't mean that you've got to spend all your time, you know, fixing data and all the rest of it, but absolutely think about or make sure that you're confident that your teams, you've got the right roles in place, that people understand their responsibilities around data to give you that assurance you need so that you're confident that data is not only adding value to your business and giving you information that you can trust. But actually

that your data is regulatory and it's legislative and it meets all the, you know, criteria put forward by this country. You know, it's so important. And I think that we're just at the start of this, you know, give it another five, ten years, you know, it's going to start getting, you know, controlled even more than

just risk assessed, which it is at the minute.

[10:35] - Steve S

Yeah, no, I totally agree. And I think just a closing point really is this, if you're going to invest so much money in a new system, so much money, you know, so much. money on the data migration part of that and the effort of your own people to do all the cleansing and enrichment. Why wouldn't you just invest a small amount of money afterwards to make sure that that process carries on, that data quality process is carried on a new system in perpetuity?

[10:59] - James B

Indeed. Could not have said it better myself. Well, hopefully that's been of use. I really like talking about that subject because it's really important. And hopefully people have sort of got an understanding there of the difference between those two strategies, but how equally important they are and for different purposes. So, no, really good one that. But I'll see you next time, Steve.

[11:17] - Steve S

See you next time. We really hope you enjoyed this episode. Thank you for joining our podcasts. If you want to see more, please like and subscribe.