Growth and good data practices go hand in hand.
Enterprises that don’t embrace data or are late to the party face serious consequences compared to early adopters.
As to talking about good data practices, most people associate the word with only a few of the multitude of practices that constitute a successfully run, data-driven enterprise.
Besides data analysis, data management is what readily comes to mind. Though equally universal — and perhaps even more critical — data practice is the practice of data governance.
Many confuse the two, merging them, thinking that, essentially, they represent the same set of practices, like two sides of the same coin. However, this is not true.
The two data practices are distinct. And knowing the difference can make-or-break an enterprise.
Let’s take a look.
What is data governance?
Just like you wouldn’t exercise without first creating a plan or schedule and setting a target, the most successful enterprises don’t take business-critical decisions on the go. Particularly, considering the stakes at hand.
Instead, they meticulously plan their course of action, anticipating risks and losses, and figuring out ways to minimize them.
The same principle also applies to the data policies of an enterprise.
Before embarking on their data-driven journey, enterprises ought to plan their course, determine all the ins and outs, risks, and consequences of poor policies.
Only then can they ensure that the data they collect — and eventually analyze — is of the highest quality, which ensures that the decisions based on them are of the highest precision.
That planning, that creation of a robust architecture is what we call data governance.
Data governance solutions impact your enterprise in the following way.
Without a plan, a workforce is at the mercy of its circumstances.
Instead, enterprises must assign a dedicated team or individual — called a data steward — to create a clear, long-term plan that identifies clear targets and processes to achieve those targets efficiently.
That’s how you stay a step ahead.
These are the right structures being put in place.
Provided they are clear and easily communicable, they provide a workforce with a map to success, which, in this case, is maintaining the quality of data.
The processes or standards themselves must be reviewed regularly. They must be constantly monitored and readjusted to minimize errors. This ensures that data is always clean, accurate, and complete.
Re-work and duplication, for example, can be easily avoided by establishing effective processes.
Once the processes are established, the data steward or stewards must also establish a set of rules that describe how the data ought to move in the enterprise.
This involves coming up with questions that concern how reliable the data is, who can access the data, whether it should be retained or archived, or how safe is it.
When the plan is effective and all the right processes and standards are put in place, enterprises must be assured that most of the work is complete. The rest relies on data management.
What is data management?
Once the plan, schedule, and target have been outlined, what remains is actually doing the exercise. That is data management in a nutshell: executing the plan laid out by data governance to maximum effect.
Consistency, though, is key.
Well-thought plans are very likely to be effective and precise anyway. But excellent data management is largely defined by consistency — the ability to store, move, and safeguard data in an enterprise again and again and again while ensuring throughout that its quality is always above a standard threshold.
Here are the impacts of effective data management solutions.
Effective data management encourages data literacy, a massive positive for future growth.
In 2021, and certainly the future, data strategy and management are workforce skills extremely high in demand. Handling data at a high-level day in and out makes a workforce ready for the future.
Well-managed processes are streamlined processes.
Whether it’s IT or HR, the inculcation of good data management practices from the very start goes a very long way in terms of avoiding clutter, redundancy, and the losses in time and space, and therefore money, to which they amount.
There are dozens of amazing data management tools out there that enable enterprises to collect, store, and manipulate data easily and safely. In other words, there are tools that automate data management.
Data management, therefore, could be the gateway to business intelligence or full digital transformation. And in view of the value generated by intelligent processes or automation, using them is a no-brainer.
The difference between data governance and data management
If the most successful business decisions are the sum of identifying a problem and the will and skill to solve it, then data governance is responsible for the former, while data management is responsible for the latter.
In other words, data governance is the act of planning and guiding, while data management is the act of executing or doing what is planned.
One is outlining the blueprint or architecture of an apartment: the other is then constructing it.
Alone, the practices are critical but incomplete. Together, they are most effective.
Done well, data governance and data management lead to:
- High profits
- Improved strategy
- Less risk
- High security
And so, knowing the difference can be make-or-break.