Managing data is a near-universal problem among tech companies. In fact, one recent survey found that a whopping 95 percent of organizations report difficulties managing their data and point to these difficulties as a significant business impediment. 

If you find yourself among this enterprise majority, the good news is that a few simple steps can help put things right. Take a look at the guidelines below to learn more about organizing your data environment and ensuring you’re putting your best data foot forward. 

Determine Your Goals 

Before embarking upon any data initiative, it’s important first to determine what your goals are. Oftentimes, this entails meeting with stakeholders from various departments, as well as the C-Suite. Once you have identified your goals, you need to discuss them with the data science team to ensure everyone is aligned on the type and quality of data required for the project. It’s also a good idea to create a schedule for each task and set deadlines for reaching these goals. 

Identify the Sources 

If you want to manage your data effectively, you need to be organized, and knowing where each piece of information comes from is critical. This step is beneficial for following up on specific points of information and checking the reliability of the data, which also falls under data management best practices. 

Keep the Data Secure 

Human error. Natural disasters. Cyber attacks. Any one of these can result in lost or inaccurate data, leading to an enterprise headache at best or a major PR disaster at its worst. In light of this, it’s important that companies address data security, ensuring that they have a good internal backup system and that firewalls, antivirus software, and other considerations are implemented to protect data from external parties. 

Remove Irrelevant Data 

Separating relevant and irrelevant data is an essential pre-analysis step and also goes a long way in helping organize the information environment. Once your project goals are defined, set criteria for the type of data required. These often relate to data quality, the source of the data, or its recency.

Consider Data Visualizations 

Once you’re ready to analyze your data, consider the use of visualizations to make the results more readily apparent to non-technical stakeholders. When key insights and forecasts are contained in graphs, charts, or other visual displays, the data analytics team will spend less time communicating the results, making the entire organization more agile in its use of the resulting insights.

You can read more on the above practices, as well as other data management tips here.