For manufacturers, Data Governance is the difference between a data-driven future and falling behind the sector’s digitization. Here, Amplifi shares their 6 tips to getting it right.

Data Governance is the linchpin of any data strategy. Take Data Governance out of the equation, and everything else falls apart. Data Quality declines, data platforms lose their value, and data-driven actions like automation and analytics start to go wrong. It’s integral regardless of what you’re trying to achieve with data, or what technology you’re using to process it: it doesn’t matter if you’re storing asset information on a spreadsheet, or on an MDM platform, you will always need Data Governance if you want that data to be correct.

But what is Data Governance, exactly? When people hear the term, they often think of an audit, an inspection, or a kind of harsh foreman who keeps data on the straight and narrow. Yet successful Data Governance – much like a good foreman – is less dictatorial and more democratic. It’s not really about laying down the law, as much as it is about finding the best ways of working with data and helping your organization to embrace them.

For manufacturers, Data Governance is the difference between using good data to steer your organization into the future – strengthening supply chains, introducing automation, boosting profitability through efficiency, and keeping up with compliance – and having bad data scupper your plans for digitization (not to mention the risks of falling foul of regulations).

So how do you implement it? Data Governance may be different for every manufacturer, but there are some key aspects that every Data Governance initiative needs to thrive. Here are 6 tips for implementing successful Data Governance.

1. Address your Data Quality first

Data Quality and Data Governance go hand in hand. Data Governance may be essential to making sure that Data Quality is maintained over time, but it’s also a good idea to introduce a Data Quality initiative before you embed your governance framework. A Data Quality will project will help you identify what data you need and what ‘good data’ looks like for your organization, making it easier to ascertain the behaviors and processes needed to keep that quality up in the long term.

2. Write your rule book

Once you know what good data looks like, create a framework to ensure that it’s always available whenever and wherever it’s needed. A Data Governance framework establishes a glossary and applies policies, rules, and definitions to your data, creating clearly signposted flows of information throughout the business.

3. Establish ownership

Who is responsible for that data? This is one of the hardest behaviors to change in manufacturing, as it requires an organizational shift from thinking of data as an IT problem, to taking ownership of data accuracy at an individual level. Anyone who uploads, accesses, and edits data need to understand their responsibilities to that data, and why it matters.

4. Explain why

As mentioned before, treating Data Governance purely as a rulebook that has to be followed is never going to work. People need to understand the reasoning behind the rules and why it matters to them if you want to rely on those rules being followed. Data Governance is about transformation: take people on a journey and show them the positive impact it will have on their day-to-day jobs. This is particularly important in manufacturing when data can initially seem like an abstract until it is related to the potential for automation and improvements.

5. Make sure best practice reaches the top

Lead by example. Make sure that your Data Governance has buy-in at the C-suite level in order for best practices to be clearly actioned at the top. Appointing data champions is also a good way to make sure that best practice is upheld throughout the business: incentivize people from all levels to take the lead and serve as touchpoints.

6. Look to the long term

Data Governance is not a check-box exercise: it’s a long-term part of your data strategy that needs consistent upkeep, revision, and communication. An initial Data Governance project will help you to put your framework in place and set the course for a long-term Data Governance transformation.

Data Governance is a big step toward creating a data culture for Manufacturers. Building a clear framework for data, allows you to communicate the importance of good-quality data and inspire a new way of thinking about data as a business asset. To find out what else you need to do to embrace data in your manufacturing enterprise, download our guide: Culture clash: Creating a data culture in enterprise manufacturing.