4 Best Practices to Optimize the Data Management Process
If your business is like many organizations today, it has some data management tools and procedures in place. It's capable of collecting, processing, analyzing, and reporting on data of various types to make informed business decisions.
But if you're also like many businesses today, your data management practices fall short of perfection. They may be good, but they're not exceptional – and you'd like them to be better.
How, exactly, can you do that? How do you transform data management from being "good enough" to "exceptional"?
There are no simple answers. But there are pragmatic steps organizations can take to optimize data management and analytics, as this article explains.
Defining Exceptional Data Management
Let me begin by stating something that might sound obvious, but is easy to overlook: The meaning of "exceptional" data management varies widely.
After all, there are many ways to be exceptional. Maybe optimizing the productivity of your data analysts because you have a small, overstretched team is what would make your data management outcomes exceptional.
Perhaps improving data quality due to inconsistent or inaccessible data sources is a top priority. Maybe it's improving the accuracy of business intelligence reporting. And so on.
My point here is that the first step in deciding how to optimize data management is determining what, exactly, you want to optimize for. There are many possible goals, and they vary from one organization to the next.
Best Practices for Optimizing Data Management
That said, the core strategies for improving data management are consistent regardless of which types of improvements you're striving for. Here's a look at several such practices.
1. Strive for incremental change
Often, when data management outcomes are not as good as a business would like them to be, the knee-jerk reaction is to upend everything. The business might bring in a new manager or director, for example, with the goal of overhauling the organization's approach to collecting, processing and analyzing its data.
This rarely results in measurable, long-term change. It's more likely to replace one set of suboptimal practices with another one, with the added downside of creating resentment among data analysts and engineers, who don't typically like to be told that everything they are doing is wrong.
A better approach is to implement change incrementally. Measure your current data management processes, identify which ones you want to update, update them and then continue measuring to confirm that the changes actually moved the needle in the right direction. A slow, steady approach is what leads to real optimization.
2. Quantify time expenditures
Along similar lines, knowing exactly how long it takes to perform various tasks within data management workflows is critical. Not only does it help you determine which tasks are taking longer than they should, but it also helps you establish accurate timelines for new projects.
For this reason, don't simply ask your engineers to estimate how long they spent on a process like preparing data or building data infrastructure. Continuously track their effort so you have detailed, quantifiable data.
It's worth noting that engineers often don't like having their time tracked extremely closely (and you can't blame them, because humans in general don't enjoy being watched constantly). To ease this friction, emphasize how quantifying time expenditures benefits individual engineers by helping leaders to avoid over-assigning tasks to one engineer while another sits idle.
When done right, careful time management benefits workers as well as the business as a whole.
3. Minimize planning time
In general, the bulk of the personnel time spent on data management should focus on implementation, not planning what to implement. If your engineers and analysts spend most of their days in meetings planning sprints (meaning units of work that they intend to complete within a fixed timeline) or projects, they're not able to deliver the greatest value, leading to a suboptimal data management process.
To this end, working to reduce planning time and maximize implementation time is one way to optimize data management workflows and outcomes. For instance, if planning sprints is taking longer than it should, consider breaking your sprints into smaller units of work. This makes the sprints easier to manage and simpler to plan, allowing your team to focus on actually doing the work instead of just talking about it.
4. Adapt and deviate from plans
The final key best practice for data management optimization is not being afraid to deviate from fixed playbooks or procedures. Even by the standards of the broader IT industry, which no one would call simple, the world of data management is especially inconsistent and complex.
Every project is different because every data set is different. Plus, variables like the inability to predict the quality of data before you begin processing and analyzing it can lead to challenges that are impossible to anticipate ahead of time.
For this reason, it's important not to become wed to a specific set of data management tools or processes. Adapt and deviate from standard plans where necessary. It's OK – and, in some ways, necessary – to treat each project as a special snowflake. Doing so means you won't be able to establish highly consistent, standardized processes, but you'll optimize your outcomes nonetheless because you'll be able to follow whichever processes are ideal for a given project.
Getting the Most From Data Management
To borrow a clichéd phrase, there is no one-size-fits-all approach to data management. But there is a data management ideal that virtually all organizations share: The goal of making data management as efficient and effective as possible.
Exactly what that means will vary depending on what your organization's goals and priorities are, as well as which unique challenges it faces. But when you adopt practices like a healthy approach to change management, careful time tracking and a highly flexible data management playbook, you set your organization up for optimized data management, whatever that happens to mean in your company's case.
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