Make More of Your Data: Digital Transformation Tips
As business leaders race to make the most of their data, the routes toward value creation become increasingly convoluted. Advancements in tools, techniques and working philosophies are giving rise to new roles, responsibilities, and in some cases, entirely new functions.
Data should play a starring role in a transformation process, yet it tends to take a backseat, often finding itself managed reactively as other initiatives take precedence.
From cultural shifts to operational overhauls, a good data strategy can help you deliver a more streamlined business transformation. Check out the insights from our consultants below.
Starting Your Data Journey
Many leaders struggle with knowing exactly where their data journey should begin. Should we start with the data we have and go from there? What kind of data do we need to make informed decisions?
Those at the start of a transformation project have a significant advantage – they have an outcome in mind. Whether it’s trying to change a process, a function, a technological infrastructure, or any aspect of their business, they have a question that needs answering.
For example, say you were in the middle of a cultural shift and wanted to diversify your workforce – you have the objective, and now you know which data is most relevant. In this case, that could be the number of underrepresented groups in your workforce, turnover rates between demographics, or workplace satisfaction scores broken into diversity metrics.
Starting with the ‘why’ can help you better align your data analysis and collection, supporting a smoother and more predictable transformation process while you’re at it.
Standardisation
Who owns what data? Standardising the way your teams work with (and store) data will drive transparency and make sure everyone is on the same page. Not that this means centralisation is the way forward – it’s different for everyone, and larger companies typically benefit from a decentralised approach, like data mesh architecture.
With data privacy concerns at DEFCON 1, safe and secure data handling has never been so important. Standardisation can help you decide ownership, take accountability, and ultimately ensure high-quality data is accessible when you need it. Plus, it makes it easier to scale your data strategy as your transformation gets underway. And speaking of high-quality data…
Data Cleansing
Is your data fit for purpose? Even the most advanced strategies or technologies won’t make up for bad data. During a transformation project, you’ll likely run into data from multiple systems, which could include legacy platforms.
The better the data, the easier it is to build effective automation, analytics models, and metrics. Data cleansing can help you get here. This might consist of conducting a data audit, establishing uniform formats for data entry, or deduplication, among others.
Interoperability
Are your new systems harmonising? If you’re integrating new systems during your transformation process, they must communicate with your existing platforms. This can be an arduous process without the right measures in place, especially if you’re migrating data away from legacy systems or transitioning to the cloud. A data-savvy task force is key.
We would recommend:
1. Auditing your existing data – this can help you identify the data you need to move, what you’re missing, what the structure of your data looks like, where it’s stored, and what format it’s in. After that, you can start cleansing your data before the migration. This will help you figure out which migration process is best for you (phased, big bang, hybrid, etc).
2. Data Governance and Ownership – high-quality data starts with high-quality governance. How are you handling your data? It’s important to develop a framework that determines role responsibilities, policies and processes. Once you’ve done that, you’ll have an easier time maintaining your data quality without having to worry about breaching compliance. For more regulatory direction on how to handle data safely and compliantly, you can check your relevant authority’s website. For example, in the UK, it’s the ICO.
3. Set up your target environment – where is your data off to? You should consider security, compliance, and any relevant tools you’ll need to move your data to your target environment. You can find some nifty examples: here.
4. Testing is critical before, during, and after the migration – testing ensures that your data has transferred correctly. Compromised data integrity can lead to a clunky, lengthy process that will likely stall your transformation process. Testing and monitoring in real-time can help you respond quickly. This might include unit testing, data validation, regression testing, and user acceptance testing (UAT).
5. Migration – understand your resource allocation, timelines, priority data sets, and roles and responsibilities. This may involve loading your data into a staging area. Validation checks are essential, as is placing a rollback mechanism in place if anything goes awry.
6. Post-migration optimisation and evaluation – is your data looking like it should be? A quality assessment can help you identify any areas that need maintenance, and this should include a performance analysis (you’ll want to check for pesky bottlenecks in particular). This should be an ongoing process that encourages user feedback.
Support from Trinnovo Consulting
Don’t get left behind. Data-driven transformations are on the rise as organisations discover new ways to create revenue streams and make more out of their existing data.
Whether that’s repackaging your existing data for commercialisation, upgrading your enterprise resource planning system (ERP), or extracting insights from your digital products, doing more with data will be the key to operational resilience in the coming year.
A rigid approach won’t work in a fast-moving environment. As your dedicated change enabler, Trinnovo Consulting’s financially sustainable approach brings the agility and technical expertise needed to help you build a defensible, data-optimised business.
