The 7 Tips For A Successful Master Data Migration And Ongoing Data Quality Management
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The 7 Tips For A Successful Master Data Migration And Ongoing Data Quality Management

Marc Jenny, Senior Director, Customer Engagement Systems & Advanced Analytics Strategy Lead, Interl. & Japan, Alexion Pharmaceuticals
Marc Jenny, Senior Director, Customer Engagement Systems & Advanced Analytics Strategy Lead, Interl. & Japan, Alexion Pharmaceuticals

Marc Jenny, Senior Director, Customer Engagement Systems & Advanced Analytics Strategy Lead, Interl. & Japan, Alexion Pharmaceuticals

COVID-19 has demonstrated how quickly something that was considered normal can change overnight. The ways we used to interact, communicate, and do business have changed without upfront warning. In addition to changing our daily behaviors in our private lives, from a business perspective, the world has seen the acceleration of a global digital transformation process unlike any other. Even before COVID-19, master data quality and data management was already a toppriority, as it is fundamental for the successful end-to-end execution of business processes. The digital transformation acceleration perhaps made clear where data quality is potentially failing, or where necessary customer consent was missing in the execution of anew digital strategy.

At Alexion, we embarked on an 18-month journey to migrate our master data to a new provider, which we completed just before the COVID outbreak. Having undertaken a successful migration, and with a more robust and clean data set, we were able to act quickly to implement our digital transformation, accelerated by the global pandemic. Here you’ll find 7essential TIPS for the execution of a master data project triggered by a legacy data provider change.

ONE–Understand and apply a specific legal framework. Governments have implemented more robust data privacy rules and regulations in recent years. In Europe, the General Data Protection Regulation or GDPR has had ubiquitous impact on business and consumer life. One of the first steps in data management should be to get a clear picture of allexisting data sources and ownership rules. What kind of data from your legacy data provider can be kept unchanged, what must be removed from the system, or what must be kept in the system e.g. due to regulatory and compliance obligations? Make sure to clarify with your legacy data provider how data can be shared with a future data provider to execute the matching process. By doing so, you provide clarity, transparency and facilitate professional collaboration during execution.

before COVID-19, master data quality and data management was already a top
priority, as it is fundamental for the successful end-to-end execution of
business processes


TWO–Scoping – Reflect on what you need. A data migration project is an excellent opportunity to clean your database from unused and outdated data. This would be data that is clearly superfluous and/or data that new privacy regulations would require you to remove anyway. Our experience shows that 50%+ of data is outdated after 24 months if not actively managed by your business. So, go for it: assure to align with your regulatory and compliance obligations (See point ONE) and don’t be shy in culling any and all extraneous data. It’s not an easy task but when it’s done, you’ll see clear benefits.

THREE–Involve all your stakeholders, early on. I recognize this can sound trite but it’s absolutely critical. How often have initiatives just moved forward without genuinely including stakeholders in a concerted and coordinated way? Involvement is not only about informing but involving, taking a journey of collaborative accountability with individuals in your entire relevant ecosystem. Give your stakeholders a strong voice – listen and act according to commonly defined goals. Data migration is not solely an IT project – it is an initiative impacting most business users, so they must be involved. Clearly define the stakeholder roles in the project. Be transparent about the expected effort and time to spend on the project and be generous in your estimations: you will likely hit hidden obstacles that require unanticipated investments of time and resources from all.

FOUR–Conduct a pre-assessment and pressure test. Collect from your key markets, and potentially from niche markets, core data elements that you will test against the new data source. Ask the stakeholders to make the data selection for you, as they know their region best. Get an immediate extract from your new data source as, in the future, you won't give them multiple weeks to update/ refresh the source. Share the output with your project stakeholders and assure a proper review of the assessment. Get 100% buy-in. Yes, 100%. Without unanimity you will almost indisputably face barriers on further execution. Only 100% satisfaction with the pre-assessment will put you in a comfort zone and provide you with the needed support to go ahead with the critical change.

FIVE–Take an Agile, Phased Approach to Execution. A Global Data Migration project is not something that you will implement within a few weeks. It is a multi-month initiative where you will face new challenges along the way. You can anticipate many of these, but you can never fully estimate them all nor how they will influence the project in a particular context. What’s the solution then to anticipating month and year-end closures, potential new product launches, the integration of new data privacy regulations, or any other possible variables? Slice your project into small achievable and flexible steps so that you can proactively react to any upcoming unplanned events. Consistently review what went well during and after each cycle and where plans need to be adjusted or fine-tuned. In our migration at Alexion, we sliced the project into 3 main deployment phases – each phase with a mix of agile and "classic" project approaches. In the end, phase 3 was delivered very differently than phase 1, but it worked and made sense for the different context we found ourselves in for phase 3 (namely product launches). Again, always remember to execute in close, transparent collaboration with all your involved stakeholders. Which brings me to the next TIP.

SIX–Manage Expectations.“A Data Migration project is like open heart surgery – it may involve some risks but it may be life-changing.”The project will impact sales territories, account structures, re-assign key contacts to different account managers, etc. You will need to ensure a proper plan is in place for these and many other similarsituations. Discuss potential upcoming challenges with your stakeholders and agree upfront how you will manage them technically and from a communications standpoint. Assure transparency by putting the facts on the table, using real examples, and explaining why something is happening. Highlight that not everything will be perfect and that there will be a detailed and focused hyper care phase. Remember, you get 99% of things right – but your community will focus on the 1% that went wrong. It is worth investing in pre-empting this 1%, as the return on investment will be 100% satisfaction.

SEVEN–Monitor your KPIs over time. You have never done your last status report! Your third-party data provider is essential in quality data reporting and has a responsibility to do so as part of their service to you. But do not forget that, in most cases, humans are behind those processes and so naturally there is the potential for unintentional errors. During the initial project phases, clarify for yourself and relevant stakeholders the critical elements of your data. What is essential, and does it potentially impact related or dependent processes? Where do you run the risk that errors would again create unnecessary noise? What is vital for your customer engagement teams to excel in the area they care most about? Define your set of KPIs and assure that you will monitor them on a regular basis from day 1. Setup an appropriate organization with data stewards proactively monitoring data and acting immediately in case of any quality deviations.

I hope you find these 7 TIPS helpful in your data migration and data quality management journey. While these initiatives can sometimes feel like an uphill climb, having the right teams, knowledge and equipment will help you get there.

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