Another benefit of effective data management is helping avoid data breaches, data privacy problems and regulatory compliance issues which could lead to reputation decrease as well as increased unnecessary costs.
The two repositories used to manage analytics data are data warehouses and data lakes. Data warehousing is more conventional, which is based on a relational database to keep structured data coming from various operational systems for analysis. The main data warehouse use cases are BI querying and business reporting by which business analysts and executives can make analysis for sales, inventory management and other KPIs.
An enterprise data warehouse has data captured from business systems throughout the company. In large organization, individual subsidiaries and business parts with management autonomy would set up their own data warehouses. It can also be done with data marts, which are a smaller version of data warehouses coming with subsets of a company’s data used for particular departments or user groups.
On the other hand, data lakes are used to keep huge amounts of big data which will be used in predictive modeling, machine learning and other high-quality analytics apps. They are established on Hadoop clusters in spite of the fact that data lake deployments are also carried out on NoSQL databases or cloud storage. Moreover, various platforms can be used simultaneously in a distributed data lake environment. The data would be processed to be analyzed when it is captured, yet a data lake often comes with raw data. Thus, data analysts should be well prepared before analyzing.
The next tool is data integration, which is the most widely adopted data integrate technique. This will pull data from source systems, transfer it into a consistent format and finally load the data into a data warehouse or another system. Nevertheless, data integration applications now provide assistance for different integration methods. Those methods are extracting, loading and transforming a variation making data maintain its original form when it is moved to the target platform.
Data management best practices
It is always important to adopt a good data governance program for a more effective data management strategy, especially when it comes to companies with distributed data environments including different systems. There is a need to concentrate on data quality. therefore, both IT and data management teams should work and collaborate together. Business executives and users should join the process to guarantee that they could achieve their data needs and avoid data quality issues. The same case should be applied to data modeling projects.
What is more, there are a lot of databases and data platforms to be deployed, which ask for a careful approach when designing a data architecture or assessing and choosing technologies. IT and data managers should guarantee that the systems they adopt are suitable for their purposes and will lead to the right data processing capabilities and analytics detailed needed by a company’s business operations.
DAMA International, the Data Governance Professionals Organization and other industries collaborate in order to improve understanding of data management rules and provide instructions on best practices.
Data management challenges and risks
If a company does not adopt an effective data architecture, it would result itself in the siloed systems which are difficult to integrate and control properly. Even though the environments are better planned, allowing data analysts to navigate and approach correct data would be a big difficulty, especially as the data is located throughout different databases and big data systems. In order for data to become more easily accessible, many data management teams are making data catalogs in which what is available in systems including business glossaries, metadata dictionaries and others will be documented.
Deciding to move to cloud storage can make some tasks of data management become easier, while also leading to some new challenges. For instance, moving to cloud databases and big data platforms would be sophisticated for companies that have to deliver data and processing workloads from current systems on premises. The cost is another major problem in the cloud. Using cloud systems and managed services should be controlled properly in order to guarantee that data processing bills will not reach beyond the budget.
A lot of data management teams are now responsible for maintain data security for the company while restricting possible legal liabilities for data breaches or data misuse. Data managers also have to be sure that government and industry regulations are followed properly when it comes to data security, privacy and usage. This has been an important concern in the passage of GDPR, which is the data privacy law of the European Union taking effect two years ago, and the California Consumer Privacy Act, which was also signed in the same year and is expected to take effect in 2020.
Data management tasks and roles
The data management process comes with a wide variety of tasks, responsibilities and skills. In small companies with restricted resources, individual workers may deal with various roles. However, data management professionals who would be data architects, database admins, developers, data quality analysts and engineers, data stewards and so on, who collaborate with analytics teams to set up data pipelines and make a good preparation to analyze data.
Data scientists and other data analysts may also deal with some data management demands on their own, particularly in big data systems with large amounts of raw data that should be filtered and prepared for uses. Application developers should be able to manage big data environments so that they need to have new skills. Thus, companies may need to employ new workers or retrain their employees to adapt to their big data management needs.
The advantages of effective data management
A good data management strategy can support companies to capture potential benefits over their competitors, both by advancing operational efficiency and making better decisions. Moreover, companies with effective data will be able to predict market trends and make the most out of their business chances.