By: Calvin Hoon, Regional VP of Sales, Asia Pacific, Talend Inc.
Data Quality Projects are no longer solely technical projects. They are becoming collaborative and team driven.
As organizations strive to succeed in their digital transformation, data professionals realize they need to work as teams with business operations – since it is the latter who need better data to achieve success in their areas of responsibility.
The fact is that organisations now have troves of raw data combined with powerful and sophisticated analytics tools to gain insights that can improve operational performance and create new market opportunities. Being in the cockpit, Chief Data Officers need to master some simple but useful Dos and Don’ts to ensure their Data Quality Projects deliver the results that business operations can put to productive use.
Asian companies increasingly see the potential value of advanced analytics to their top and bottom lines. A study of the financial statements of more than 2,600 listed companies with revenues above USD 1 billion in 11 sectors and 39 industries across 14 Asian countries confirms the trend.
Recommended steps include the following:
Set your expectations from the start
Why Data Quality? What do you target? How deep will you impact your organization’s business performance? Find your Data Quality answers among business people. Make sure you know your finish line, so you can set intermediate goals and milestones on a project calendar.
Build your interdisciplinary team
Of course, it’s about having the right technical people onboard: people who have mastered Data Management Platforms. But it’s also about finding the right people who will understand how Data Quality impacts the business and making them your local champions in their respective departments. For example, digital marketing experts often struggle with bad leads and low performing tactics due to the lack of good contact information. Moreover, new regulations such as GDPR have made marketing professionals aware of the importance of personal data. Look for tools that can help them clean their data, so that they can act on it without losing control. They will become your allies in your Data Quality Journey.
Deliver quick wins
While it’s key to stretch people’s capabilities and set ambitious objectives, it’s also necessary to prove your Data Quality project will have
Don’t underestimate the power of bad communication
We often think technical projects need technical answers. But Data Quality is a strategic topic. It would be misleading to treat it as a technical challenge. To succeed, your project must be widely known within your organisation. You will take control of your own project story instead of leaving bad communication spreading across departments. For that, you must master the perfect mix of know-how and communication skills so that your results will be known and properly communicated within your organization. The whole enterprise needs to know that your projects have helped marketing suffering from bad leads, operations suffering from missing info, and strategists suffering from biased insights. People may ask you to extend your projects and solve their data quality issues, which is a good reason to ask for more budget.
Don’t overengineer your projects, making them too complex and sophisticated
There are simple and powerful platforms that produce fast results, so you can start small and deliver big. Examples exist of companies that have managed to clean 50,000 records in one day. You don’t necessarily need to
Don’t leave the clock running and leave your team without clear directions
Set and meet deadlines as often as possible. It will realise credibility. As time is running fast and your organisation may shift to short-term business priorities, track your route and stay focused on your end goals. Make sure you deliver your project on time. Then celebrate success. When finishing a project milestone, make sure you take time to celebrate with your team and within the organisation.
The results of BigData can be beneficial to a wide range of stakeholders across the organisation — executive management and boards, business operations and risk professionals, including legal, internal audit, finance and compliance; as well as customer-facing departments like sales and marketing.
While advances in software and hardware have enabled the age of big data, technology is not the only consideration. Companies need to take a holistic view that recognises that success is built upon the integration of people, process, technology and data; this means being able to incorporate data into their business routines, their strategy and their daily operations.