6 key data management principles

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Data is king. Organizations cannot succeed in today’s economic climate without making data-based decisions.

“As the world becomes smarter and smarter, data becomes the key to competitive advantage, meaning a company’s ability to compete will increasingly be driven by how well it can leverage data, apply analytics, and implement new technologies,” writes strategic business and technology advisor Bernard Marr.

The problem that many businesses have when transitioning to a data-first approach is managing a sudden abundance of data. They have more data than they know what to do with, which can be just as paralyzing as having no data at all. To effectively capitalize on the opportunities that data provides, organizations must develop road maps for collecting, analyzing, and managing their data.

Here are six data management principles that can guide companies as they structure their data management processes.

1. Create a data management strategy

One of the most important data management principles is developing a data management plan. To be effective, organizational initiatives require a strategic approach to data management. It’s essential to an organization’s success to build a solid foundation through a data strategy that provides the framework for using that data.

Some of the key components of a data strategy are

  • Defining a vision and roadmap for data usage 
  • Identifying which data to use and when and how to use it 
  • Planning for storage, security, and documentation
  • Ensuring data quality

Together, these elements create a blueprint for managing an organization’s data throughout a project’s or program’s life cycle.

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2. Define roles in the data management system

Good data management requires you to clearly assign roles to individuals within the data management system. Managing data is a team effort, and everyone’s role is “unique yet interdependent,” writes Brenda Reeb, senior data management consultant at IData Incorporated.

Reeb explains that the three most common roles that need to be defined are

  • Data owners. Every database needs a data owner who is accountable for the data and is the authority on who gets access to it and how it is used.
  • Data stewards. Data stewards are responsible for the quality and meaning of the data.
  • Data custodians.  Data custodians manage the archiving, recovery, maintenance, and security of the data. They don’t analyze it or use it to make decisions.

When each role is clearly understood, individuals can successfully perform their data management duties.

3. Control data throughout its life cycle

Another important data management principle is controlling data throughout its life cycle. By putting the proper policies and procedures in place, organizations can ensure that data is stored, validated, and managed until the end of a project, when it can be archived or destroyed.

Michael de Ridder, senior software engineer at YouTube, explains the six steps in data lifecycle management:

  1. Data creation: capturing and acquiring new data values
  2. Data storage: processing the data without deriving value from it and storing it so that it can’t be altered
  3. Data use: mapping who can use the data and how
  4. Data sharing: governing how data is shared
  5. Data archiving: storing data after it is no longer useful
  6. Data destruction: destroying active and archived data that’s no longer needed

Organizations will get the most value from their data by following these data life cycle phases.

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4. Ensure data quality

Assuring the quality of data is another important data management principle. Meaningful data interpretations can only happen through high-quality data, writes Clara Beck, business manager at marketing solution provider Thomson Data. For data to be considered good, she says it must be accurate, timely, non-repetitive, complete, and consistent. Data with these characteristics can lead to positive business outcomes, such as more informed decisions, higher profits, and a competitive advantage in the marketplace.

To ensure the quality of data, organizations must develop an organized data system that profiles and controls all incoming information. That system checks the quality of data against predetermined benchmarks before data is accepted. The data then needs to move through a pipeline that consistently reinforces the quality of that data — these checks and balances are the only way to ensure quality.

Bad data going in equals bad data coming out, so it’s essential to build data sets using only the highest quality data.

A man and a woman examining data printed on paper
Photo by UX Indonesia on Unsplash

5. Collect and analyze metadata

Data that describes another set of data is called metadata. It gives data users a deeper understanding of a data set. It tracks all aspects of that data — such as how it has been collected and analyzed — giving insights into the content, characteristics, and uses of the data. Metadata is invaluable to a successful data program.

“The value of metadata lies in its ability to more efficiently classify and organize information, as well as to yield deeper insight into the actions taking place across your business, providing more intelligence and higher quality information to fuel big data initiatives, automation, compliance, data sharing, collaboration and more,” writes the team at information management solutions company M-Files.

Overlooking or ignoring metadata can diminish the quality and value of data, which is why companies must develop a detailed approach to managing metadata that complements the data strategy.

6. Maximize the use of data

None of the other data management principles matter if a company doesn’t maximize the use of the data it collects. Data has no value unless it’s used, so organizations need to ensure that data is accessible and usable for anyone who needs it.

Matt Kendall, content developer and strategist at Wizeline, shares different ways that companies can ensure they’re getting the most value out of their data:

  • Set business goals that inform the data strategy to make sure data is actually being used, not just stored.
  • Standardize data collection to create clean, user-friendly databases.
  • Make data analytics a core competency of the company.
  • Educate everyone from the C-suite down the ladder on how to use data meaningfully.

Collecting data for the sake of possessing it doesn’t do anybody any good. By maximizing the use of data, organizations can better harness its power and reap its benefits.

These data management principles are best practices in maximizing the effectiveness of data and helping organizations achieve business goals. Without a structured approach to data management, it’s easy to become overwhelmed trying to implement a data program.

Photo by Sora Shimazaki

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