Data is central to modern businesses. However, most companies fumble their approach to Data Management and fail to draw insights as a result. Data Management might seem like a back-office function, but it is critical to a modern company’s success. Getting started with or fixing your Data Management processes might seem intimidating. Here are four essential Data Management principles that will clean your workflows and give you the insights you need.

Set Up Infrastructure for Easy Access

Many organizations do a great job of collecting vast amounts of data. However, they store these datasets in opaque data warehouses that silo them from the rest of the organization. For instance, an enterprise with several business lines will likely store datasets per business line in a warehouse designed for a single vertical.

The result is tedious access to data for those in other parts of the organization. Warehouses are great for querying and delivering insights. However, they should not be your first choice of storage. Choose a data lake instead. 

Briefly, a data lake stores data in its rawest form, irrespective of sources or formats. You will have to clean these datasets to work with them. However, a data lake gives you a handy repository to store raw data that everyone in your organization can access. Every vertical, for instance, can access your data lake and transform data for its use.

This process gives everyone in your company equal access to insights, without adding additional burden. Another way to improve data access is to map your data sources and formats to storage locations. This process technically falls under metadata management, but it helps you define data access for those who need it the most.

For instance, a team searching for a particular dataset can review your central data repository and understand where they should search for information in your data lake. The result is fast access and quick insights without generating massive overhead.

Examine Data Quality and Preparation

One of the biggest weaknesses in data analytics workflows lies in the source. Data sourcing is a significant issue for many organizations. They typically resort to a firehose of data from their sources and leave it to downstream apps to clean and transform data.

This approach increases operational inefficiency. For instance, if you have four downstream data-consuming apps, expecting each app to host its own custom ETL process is unrealistic. Data load times will increase, giving you less access to real-time insights.

Instead, analyze data at source and install ETL processes that standardize formats and remove duplicates. Install standard file naming and cataloging conditions so that everyone in the organization is working off the same playbook. Automate these processes so that your teams have more time to conduct value-added analysis instead of clerical work.

Examine Metadata and Documentation Processes

Many companies fail at metadata management and pay the price. Metadata, or data about your data, is critical since it gives your data analysts context into the information they’re viewing. Often, the context in which data was collected skews outcomes, something your business cannot afford.

At the very least, your metadata must include information about the author or creator, field descriptions in business-relevant language, when the fields were created, how they were created, etc. Company data repositories change all the time, and consistent metadata preserves context.

Data lineage may not be at the top of your mind when thinking of data analysis. However, data lineage establishes how the data you’re using came into being. It reveals potential flaws in collection methods that might hobble your current analysis.

For example, a dataset you’re viewing might have been designed for a different or obsolete business use case. Metadata and documentation help you figure out these scenarios and exclude irrelevant datasets before they turn into bigger issues.

Install Robust Security

Data Management is incomplete without a plan to install the right security infrastructure. Cybersecurity is complex currently so having a plan to classify your data assets by risk is critical. A risk-based map will help you prioritize responses to incidents and give your security teams a guide during a stressful time.

Aside from reviewing responses to incidents, take the time to review data access policies. Most companies grant blanket access to executives and those who are higher up in the organization. The issue with this approach is executives rarely access data, and their IDs offer malicious actors a way to infiltrate your system.

Install agile security controls that grant time-based access. Automate ID verification and credential renewals to reduce the burden on your security teams. The modern enterprise uses a mixture of microservices and other machine-based processes that challenge a security team running manual workflows.

Automation is the key to robust security, so make sure you leverage it throughout your security infrastructure.

Modern Organizations Need Modern Data Management

Data Management is critical to analytics success. While gathering data is easy, making sense of it demands preparation. Follow the Data Management principles in this article to ensure you’re always on top of your data and derive the best insights from it at all times.