Modern data management can enable organizations to make better decisions and gain the necessary capabilities to address current operational challenges
What is Modern Data Management Practices?
In -In today’s knowledge economy, few organizations would debate that data
is now one of their most important assets. It is now often critical to service
delivery at many organizations. Processing it too late can be disastrous.
Similarly, seeing it stolen or compromised can be immensely destructive. Just
like any asset, it needs to be managed with care.
Data management is about managing the way data is collected, stored, protected, and processed to optimize operations
and assist decision-making. As the requirements and organizational needs
change, and the volume of data generated and processed by organizations
continues to grow, the approaches to data management have undoubtedly had to
modernize to adapt.
Best practices to Achieve Modern Data
Management
should be crystal clear what our high-level aspirations are with regard to data
before we invest in a system to support those ambitions. What metrics are we
trying to maximize or minimize? How do we want to measure them? What sort of
analysis would help refine our approach over time? What sort of processes do we
want to digitize, and why?
Investing in a new enterprise data
management platform can be daunting at first. Where do you start? We recommend
starting small with use cases that are clear to you now and working up from
there adding more teams, functions, processes, and metrics over time
Documentation: As you develop data architectures
for you, your team, and your use cases, it’s a good idea to invest in clear
naming conventions for folders and data tables, unique identifiers for data
records. This will ensure clear documentation for a more sustainable system
that can continue to function well despite changes in human resources over
time.
Quality: Garbage in Garbage Out: Give careful attention to how you design data collection instruments to
ensure they minimize the potential for human error and stay relevant despite
changes in other parts of the systems.
changes
security.
of systems used
Characteristics of Modern Data
Management
Collecting data for data’s sake adds little value. The data strategy
should reflect organizational objectives and be led not by the IT department
but by business users themselves. Systems should reflect the needs of the users
they are serving. Different teams and people fill different roles. As such,
your enterprise data management platform and approaches should deliver
different processes for different user personas.
undoubtedly greater. A modern data management approach puts security at the
forefront, not only in terms of infrastructure or software-level cybersecurity
provisions but also carefully considered data governance models, ensuring only
the users that need certain data to deliver their functions have access to
them.
cost optimization techniques to a modern data management program is crucial to
reduce costs, maximize the business value, and increase the operational
efficiency of all data management initiatives
modern data management thinking, data silos should be a thing of the past and
mitigated. Disconnected systems not only generate duplication but also confuse
and slow down the ability of an organization to have a single version of the
truth which can drastically affect our ability to respond to challenges and
preempt issues.
First Data should be accessible regardless of where the people that need it
find themselves at the time they need to make a decision
Modern data management refers to all the practices that help streamline
and improve traditional data management processes in order to address the
challenges of today and prepare organizations for the future ones, and help
organizations fully leverage the value of their information
