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At the heart of every digital transformation project lies a valuable asset: data. But although we know that data drives business, all too often addressing the quality of that data can be de-prioritised during a transformation programme.
Don’t get me wrong, I get it. We start off with good intentions, and then reality hits, with data quality quietly slipping off the list of ‘must-haves’. But what if I told you it should be your top priority - that every digital transformation should be a data transformation?
Because without addressing your data quality you risk the success of the whole project. Your costs increase by migrating the wrong data, issues you experienced with your old system often remain, and when your new system starts struggling, it is harder to get buy-in from the rest of your organisation. Ultimately it becomes a reputation issue for the IT team.
Why prioritise data?
A report by HFS Research has found that 95 percent of global business leaders believe their companies would be more competitive, more innovative, and make faster decisions if their data was more accurate. The same group of executives says only 60 percent of their data is actually usable.
And although accurate, high quality data is valuable at any time, when you are considering a transformation project, it becomes even more crucial. Prioritising data before, during, and after transformation can bring higher success rates, lower implementation costs, and faster migration.
Data quality projects can seem like an uphill struggle - the sheer volume of data can be overwhelming. But there are some steps to take early on, that the customers I work with have found help to keep focus and make headway.
1. Get to know your data
To limit disruption, be clear on which data is business-critical and which data must be as accurate as possible. This means you can focus your efforts on the data that matters, and be sure that data quality, integrity, and availability are maintained throughout the project.
Think about which data links directly to your organisation’s business objectives, how it supports decision-making, and where poor data accuracy has the most impact.
How accurate is that priority data now, so that you understand the scale of the task ahead to remediate and the effort required? Find out who owns it, who creates it, and who uses it so you understand now who you are going to have to influence later.
2. Set data quality goals
Once you have identified your priority data and its current state, it becomes a bit easier to set meaningful data quality goals, and decide on the steps to take to achieve them.
Make sure you understand which data has to be 100 percent accurate at all times, set alerts to help you spot when your data quality has dropped, and use a framework like Six Sigma to highlight how far your data deviates from your target so you can quickly take appropriate action.
Engage with the C-suite. Data quality is not just the IT team’s responsibility: organisations with long-lasting digital transformation success tend to secure C-suite ownership of data quality goals. This encourages the rest of the organisation to work towards them too. Buy-in at all levels, where everyone has a stake, significantly increases success rates.
3. Measure and adjust
Set yourself review points to measure the accuracy of your data against your goals, and use KPIs to spot whether the approach you’re taking is working or whether it needs adapting. Don’t be put off if your data is not 100% accurate overnight. Be prepared to be in this for the long haul, and take a sustained and steady approach.
4. Focus on the benefits
You could meet resistance from the rest of the organisation - after all, this is another thing on their to-do list. Make this relevant for them, and explain how managing data will help them to be more successful in their own work.
Encourage your C-suite to show how key data is used to make business-critical decisions, and make the connection between accurate data and good business outcomes. Demonstrate how trust in data helps with regulatory compliance in the GDPR and CCPA space.
Give real life examples of how accurate data has made a tangible impact on business outcomes. I’ve worked with an organisation that optimised purchasing payment terms and lead times by de-duplicating vendor master records. And a company that realised it was offering an unintended discount to its customers due to manual data error. What examples do you have that could engage your people?
5. Data accuracy message on repeat
You’re asking for company-wide behaviour change, and any change expert will tell you that this is a long-term commitment. Bringing the business onboard is not a one-off activity. Get ready to consistently reinforce your message.
Be smart with your communication. Identify the teams with the biggest impact on the accuracy of your priority data and engage with them directly.
Join up with your internal communication team (if you have one) and work with them to tell the stories that will resonate most with your colleagues, and time your communication activity so it’s not fighting to be heard.
Celebrate progress and any significant achievements, praising individuals or teams that can help motivate others.
Digital transformation is complex, anyone who’s been through it will tell you, but by prioritising data quality before, during, and afterwards, IT teams massively improve success rates. And bringing the C-suite and the rest of the organisation along with you, frees up time for you and your team to focus on implementation and migration, and demonstrate your value to the business.