Not only do businesses collect extensive amounts of data daily, but they also have to access numerous separate data sources. This task alone makes it difficult to define and remember the origins of each data point. By developing a data inventory, companies can keep track of their essential data sources to streamline data collection and analysis.
What is Data Inventory?
Data inventory, also known as a data map, is a source of information that details all of the company's insights. The point of data inventory is to establish a centralized database that employees can access for quick reference. An adequate data inventory details not only information collection but also the storage location and analysis.
Understanding data management keeps employees on the same page and working toward company goals. This, in return, leads to increased operational efficiency, productivity, and decision-making. By utilizing data inventory correctly, businesses can optimize reporting, insight development, and each department's initiatives.
5 Steps to Building Data Inventory
Building an extensive data inventory takes time and practice. Companies can begin by following five comprehensive steps.
1. Establish an Oversight Authority
First, businesses need to establish an oversight authority, as they will have to gather information from various departments. This involves determining the project manager who will orchestrate data collection. Some companies use an oversight specialist to leverage data policies and collect big data.
Depending on what internal and external sources a business needs to access, they must review their industry's data policies as well. By establishing an oversight authority, managers can gain visibility into the data flow without governance issues. However, it is wise to recruit supervisors from each department to avoid any technical issues.
2. Define the Data Inventory Scope
Next, the data inventory team needs to collaborate and establish the scope, deadlines, resources, and other guidelines of their project. The scope refers to what kind and how much data the team needs to collect in order to complete their inventory. As the scope can sometimes be challenging to define, managers should consider the essential components.
Inventory requirements listed in the data policies
Internal versus external data
All data assets
Priority-specific or time-sensitive information
Individual departmental data
Key performance indicators (KPIs)
3. Catalog Data Assets
The supervisor for each department is responsible for defining and cataloging the data within their sector. This saves more time than if one manager went around gathering all company information. After accounting for each department's information, the sole project team then compiles all the data into one inventory. The most organized way to inventory data is through management solutions that structure information in a readable format. Otherwise, owners only have access to large volumes of raw data.
4. Complete Quality Checks
After cleaning and organizing the data, the team must perform quality checks. This includes eliminating duplicate, incomplete, and inconsistent information, increasing the quality of datasets. When databases are to the manager's liking, they can deploy them for the rest of the employees to view.
5. Prioritize Data Initiatives
If the purpose of the inventory was to distribute the information publicly or internally, managers need to prioritize their datasets. This means that the team must determine which information they will release first and to whom. Prioritization considers time-sensitivity, departmental needs, and current initiatives.
The Best Data Inventory Practices
Data inventories are only as impactful as their accuracy. Therefore, businesses should consider the best data inventory practices before delving into their projects.
Inventory All Data
Inventorying data through traditional practices does not enable organizations to collect all available data. This means that companies could be missing out on vital information. Therefore, businesses should implement data management systems in each department to automate data collection, storage, and maintenance.
With an integration tool, they can establish a universal interface to access information from all solutions. Advanced data management systems utilize cloud-computing software to expand data storage and functionality.
Scan vs. Survey
Survey data inventory relies on stakeholders to manually gather information from their own memory. However, because of human error, this method is wildly inaccurate, inefficient, and time-consuming. Surveys also do not account for modifications to data after the gathering stage.
On the other hand, scans utilize software to dig up data from each source and create intricate data maps. Management solutions also actively maintain information to recognize edits, shares, and updates. This technique counteracts data deterioration from stagnant information.
Data maintains a constant growth rate, regardless of the business's size. This means that every organization needs to continuously expand its data inventory capacity to adequately collect information. Otherwise, data can fall between data silos and be lost forever. Again, cloud-based software provides an infinite amount of data storage that companies can expand and retract as needed.
Increase Data Visibility
Sophisticated management systems can define different types of data to perform various analyses and insights. Solutions can even detect interdependent variables, relationships between data points, and sensitive information. This level of functionality improves an organization's visibility into their data, making the information more impactful.
Improve Inventory Value
Building a data inventory in and of itself does not benefit a company. It is how the business plans to use the information and improve their internal processes that matters. Data inventory can enhance an organization's governance, authority, privacy, and even cybersecurity when done correctly.