In an ever-changing world of big data, AI, new data regulations and increased cyber attacks, enterprises wonder how to protect their data and at the same time benefit from the advantages it offers.
In order to be able to do so, they need to implement the right data management practices, educate employees, and invest heavily into data tools, policies and people. Let’s see what it takes to have and not to have the right approach to using data.
Enterprise Data Management: Key Components
Data management has a very broad definition that is commonly known to include the following components:
- The data itself
- databases (SQL and NoSQL)
- data warehouses
- Tools and processes
- ETLs (Extract, Transform, Load pipelines)
- data architecture
- data modeling
- Policies
- data governance (describes how the data should be gathered, stored, and removed)
- data regulations like GDPR
- Cybersecurity
In other words, data management consists of many things, but it basically boils down to one thing. It describes the right way to manage the data owned or used by the enterprise.
A Bit of Statistics
The global market of data management for enterprises was estimated at almost $90 billion in 2022.
Despite the economic recession, it’s expected to grow at a compound annual growth rate of 12% by 2030.
Statista projects a steady growth of the big data analytics market, with a forecasted market value of more than $650 billion by 2029.
The average cost of a data breach is estimated between $2 mln to $10 mln depending on the industry in 2020-2022, with the most expensive ones happening in healthcare (of course!).
Good Data Management Practices for Enterprises
In this article, we are listing the most important points to consider for your data strategy in an enterprise. Let’s see what they are.
Data Quality Improvement
Raw data is not suitable for analysis or driving insights. It’s imperative to get the data cleaned properly before doing any analysis. The data should be accurate, consistent across different systems, and free of errors and duplicates. Proper data cleaning allows the stakeholders to trust the analysis conducted on such data. Data scientists realize the stakes of having good quality data and spend more than 60% of their time cleaning and organizing it.
Reliable Data Storage
If an enterprise manages a lot of data, it’s important to have one trusted data source to come back to. The team should restrain from making changes to it unless it’s absolutely necessary. Make sure that the database code is written by qualified data engineers, as they would be able to guarantee the code quality. The data should be properly labeled, and the metadata should be available to people working with it. A data warehouse or a data lake is a good way to store multiple data sources.
Data Regulations Compliance
Find out about the laws and data standards applicable to your business and geographic location. The General Data Protection Regulations (GDPR) is the most prominent of recent regulations passed in order to protect customers in the EU. It’s very likely that other countries will follow soon and implement their own data laws. For enterprises related to healthcare or life science, HIPAA compliance is crucial. HIPAA stands for the Health Insurance Portability and Accountability Act and is meant to protect patients’ info from unauthorized access.
Security Prioritization
As we have seen, mistakes in cybersecurity are costly. So, every enterprise needs reliable data security practices. The code must be free of the most critical vulnerabilities, and the most sensitive data should be properly protected (financial data, production secrets, health-related data, etc.), and only authorized people should be given access to it. Some enterprises conduct penetration testing to test the infrastructure against attacks.
Collaboration
Big-scale data projects often require data analysts, developers, and stakeholders to work together to drive the best results. Open communication is a crucial component of success. If several people contribute to data cleaning efforts, keeping a time log and sharing reports is essential. Stakeholders should be available for the data analysis team throughout the project and via different communication channels like meetings, email or chat.
Open Communication
It’s a good practice to be open about what data your website collects, whether you store your customers’ data and for how long, and what happens with the data after it’s analyzed. Don’t hesitate to talk about your data governance practices and policies with your customers and employees, especially if you follow the required regulations. Ideally, the enterprise should provide this information somewhere on a website or in a data management agreement (for employees) so that everybody can access it at any time. This helps to build trust and improve your relationship with everyone involved.
Conclusion
Decent data management policy for enterprises means a whole range of issues to consider. The right approach to data gathering, storing, and processing can offer valuable support for businesses. At the same time, the enterprise should treat the data owners with respect, allowing them to make the decision about the information that belongs to them.
Author:
Irene Mikhailouskaya
Irene is a Data Analytics Researcher. Covering the topic since 2017, she is an expert in business intelligence, big data analytics, data science, data visualization, and data management. Irene popularizes complex data analytics topics such as practical applications of data science, data quality management approaches, and big data implementation challenges.
Are you looking to harness the power of data to protect your enterprise and unlock its potential? Explore our comprehensive data science courses and data science services to gain the knowledge, skills, and tools necessary for effective data management, cybersecurity, and compliance. Take charge of your data-driven future today!