Podcast: How to prepare your data for digital transformation


In this podcast with Chris Gorton, EMEA managing director at Syniti, we look at how to optimise data for digital transformation.

We talk about how planning is key, in particular to visualise the future needs of the business and the data it will require, then to plan, prepare, transform and migrate data to achieve those aims.

Gorton also talks about cleansing, harmonisation and migration, as well as preparing the business for the change that will come with digital transformation.

Antony Adshead: What are the key preconditions for dealing with data in a successful digital transformation project?

Gorton: This will probably come as no surprise, but preparation is key.

We often talk about having a data-first thought process and strategy whenever moving into a digital transformation.

So, what we’re essentially trying to educate the market to do and be more thoughtful about is how you bring data into the forefront of how you’re going to meet your digital transformation objectives.

And often people will choose technology to run, whether it’s ERP [enterprise resource planning], B2C [business-to-customer], UX [user experience] – all of these different decisions that they have to make in digital transformation – they lead with the primary technology and data is left as an afterthought.

We’re trying to make people realise that data is integral to the success of digital transformation. I often say to people I’m speaking to – be it in SAP or Oracle, all the systems that make up digital transformation initiatives – all these applications tend to work perfectly until you start to put data into them.

So, why would you park data as the last part of your planning and not bring it to the beginning? You actually use data to inform the design and make sure you can bring the desired outcome, the business value and business benefits.

Optimising data 

What should customers be doing to their data to make it optimal for digital transformation?

Gorton: I have a number of steps that I go through when I’m speaking to an organisation.

The first is, know your data footprint. Organisations collect and amass lots and lots of data. Have you done logical housekeeping; archiving and managing that data down to a suitable size before you even start thinking about transferring that data into its future state.

And then I talk a lot about using and shifting left the complexity in data by what I call “right-to-left mappings”.

Start to look at data in its future data model and future state outside of the target application to understand where there are gaps, [and] test the business requirement.

Then, as you build confidence around that, you can start to educate the business around what the future looks like; build confidence in the change that’s needed for big digital transformations.

But, more often than not, you can actually start to use data for analytics and smaller business change. For instance, supplier harmonisation. If you can harmonise your suppliers early, why would you not start to use that data to optimise your business ahead of the digital transformation completion?

What are the key phases regarding data for an organisation preparing and carrying out digital transformation?

Gorton: We start with preparation. There are three or four key elements I always talk about.

We talk about right-sizing; making sure the right housekeeping has been done, because you don’t want to take your old data across to your new system if it’s not going to give you any value. There are lots of solutions that are doing different things. You’ve got to do some kind of assessment, there will definitely be cleansing to do, and some kind of harmonisation.

And then you move into the migration phase. There are lots of ways people talk about how you migrate.

In all the different digital transformation states, the reality is that a good-quality business process-driven approach to migration is key.

And I also talk about migration being in some cases a once-in-a-generation opportunity to transform your data at scale. There are so many lessons that can be learned about what was wrong with that data.

So, do the analysis, find out what was wrong, why it was wrong, and make sure that’s built post-go-live into your governance process – be it MDM [mobile device management], data quality, how you’re going to handle GDPR [General Data Protection Regulation], and how you govern around your analytics.

Those are all by-products of how you can become more mature in data but ensure you can be more successful, not only going live in a digital transformation, but then maintaining high data quality as you move forward.

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