The surge in the amount of data available for businesses across all industries has led to the emergence of a relatively new but extremely important strategy: implementing data governance.
Organizations that want to get the most out of their data analytics programs now see the need to use data governance. Simply put, it’s a way of establishing consistent rules and processes for the collection and analysis of data.
Part of what drives the need for better data governance is the sheer complexity of current information systems. Most organizations aren’t dealing with a single data warehouse. Instead, they handle data from multiple sources, all of which need to be analyzed properly to power operational intelligence.
The diversity of information has created a situation where a governance plan is necessary to understand the details of each data process flow, as well as assure both the validity of data sources and the security of information.
What is Data Governance?
Data governance is separate from data management. The latter provides plenty of challenges by itself, with the need to manage both data storage and the sharing of data across multiple platforms, to name just two vital areas.
But governance goes beyond this. It establishes control and authority over how the various areas of data management will be handled. This typically requires a shared vision with input from people in all departments that touch the data management process.
Some of the areas a data governance plan would oversee include:
- Data architecture
- Data development
- Data quality
- Data security
- Data warehousing
- Data used for business intelligence
- Document and content management
It’s a widespread effort. But creating a shared plan that establishes strong parameters and consistent rules for the handling of data is necessary with so many people involved. Otherwise you get a situation where data is “siloed” in different departments and everyone is running operations off their own game plan.
What Data Governance Can Achieve
In general, data governance involves these fundamental issues:
- Assuring the source of data is valid and accurate
- The establishment of parameters for collection and analysis of data that are strong, consistent and repeatable
- Establishing rules around data that support improvement to customer experience (which should always be the goal)
- Establishing consistent processes that lead to real insight from data analysis
In many ways, it’s still early days of data practices in general. Many companies are deep into data collection. That’s led to accumulation of billions of data points stored on servers but, in many cases, there’s a lack of real insight from that data because it hasn’t been leveraged properly.
Data scientists are in great demand for this very reason. Everyone can collect data and distribute analytics reports with plenty of numbers for people to contemplate. But data scientists and analytics experts are needed to find ways of making use of that information.
Establishing strong data governance is one of the first steps they take to reach that goal.
Data Governance Best Practices
No two companies are completely alike in how they establish data governance, but there are some key areas to keep in mind.
- Communication of purpose: As with any big project, communication with everyone involved is key. This often proves more difficult than people first imagine, but it’ss necessary for everyone involved to understand the purpose of creating strong data governance before the process even starts.
- Strong leadership: Once the process starts to create a data governance plan, managers, directors and executives must demonstrate a strong commitment to the process. That’s because it will not be easy getting everyone across the various departments onboard and consistent with their input.
- Bottom up: Even with strong leadership – and especially without it – it is helpful for those working directly with data collection and analysis to drive the need for data governance from the bottom up. Front line workers know the issues better than anyone. Their input is needed to get data governance right.
As can be seen, most of the issues involving data governance are related to people, not technology.
Innovation and increased sophistication in technical systems has given organizations the tools they need to collect and analyze data. It’s up to the people involved to create a plan for how to best leverage that tool in a way that provides insights into an organization’s operations.