What is Data Management
Nowadays, companies have access to more data than ever in this digital economy. Businesses must invest in data management solutions that improve visibility, reliability, security, and scalability to ensure employees have the correct data for decision-making. Data management is gathering, organizing, safeguarding, and storing an organization’s data to analyze it for business decisions.
Today’s most advanced data management software ensures that businesses use reliable and up-to-date data to drive decisions. The software assists with everything from data preparation to cataloging, search, and governance, allowing users to find the information they need for quick analysis. Data management is an essential first step in implementing effective data analysis at scale, which leads to important insights that add value to your customers and improve your business.
Data Management Types
A data management discipline has a broad scope, and a robust data management strategy typically implements the following components to streamline its process and operations across an organization:
Raw data is ingested from various data sources during this stage of the data management lifecycle, including web APIs, mobile apps, Internet of Things (IoT) devices, forms, surveys, and more.
- It is then usually processed or loaded using data integration techniques like extract, transform, load (ETL) or extract, load, transform (ELT) (ELT).
- While ETL has historically been the standard method for integrating and organizing data across different datasets, ELT has grown in popularity as cloud data platforms have emerged and the demand for real-time data has increased.
Regardless of the data integration technique employed, the data is typically filtered, merged, or aggregated during the data processing stage to meet the requirements of the intended purpose.
While Businesses can store data before or after processing, the type of data and its purpose usually dictate which storage repository to use.
- Data warehousing, for example, necessitates a defined schema to meet specific data analytics requirements for data outputs such as dashboards, data visualizations, and other business intelligence tasks. Business users typically direct and document these data requirements in collaboration with data engineers, who will execute against the defined data model.
- A data warehouse’s underlying structure is typically organized as a relational system (i.e. in structured data format), with data sourced from transactional databases.
Other storage systems, such as data lakes, incorporate data from both relational and non-relational systems, serving as a testing ground for new data projects. Data lakes are especially useful for data scientists because they allow them to incorporate structured and unstructured data into their projects.
Data governance is a set of standards and business processes that ensure data assets are effectively leveraged within an organization. This includes functions relating to data quality, data access, usability, and data security.
- Data governance councils, for example, tend to agree on taxonomies to ensure that metadata is added consistently across multiple data sources. This taxonomy should also be documented further through a data catalog to make data more accessible to users and facilitate data democratization across organizations.
Data governance teams also assist in defining roles and responsibilities to ensure that data access is provided appropriately; this is especially important to maintain data privacy.
Data Security is the installation of safeguards to protect digital information from unauthorized access, corruption, or theft.
- As digital technology becomes more prevalent in our lives, modern businesses’ security practices are scrutinizing to ensure that customer data is protected from cyber criminals or disaster recovery incidents.
While data loss can be devastating to any business, data breaches, in particular, can have costly financial and brand consequences. Data security teams can improve security by incorporating encryption and data masking into their strategy.
Importance of Data Management
Data is viewed as a corporate asset that a business can use;
- To make better business decisions.
- Improve marketing campaigns.
- Optimize business operations.
- Cut costs, all in the hopes of increasing revenue and profits.
However, a lack of proper data management can leave organizations with
- Incompatible data silos.
- Inconsistent data sets.
- Data quality issues, limiting their ability to run business intelligence (BI) and analytics applications – or leading to incorrect findings.
Data management has also grown in importance as businesses face increasing regulatory compliance requirements, such as GDPR and the California Consumer Privacy Act.
Furthermore, businesses are capturing larger volumes of data and a broader range of data types, both of which are hallmarks of the big data systems that many have deployed. Such environments can become cumbersome and difficult to navigate without proper data management.
Data Management Benefits
Gain a competitive advantage: A well-executed data management strategy can assist businesses in gaining potential competitive advantages over their competitors by improving operational effectiveness and enabling better decision-making.
Provide agility: Organizations with well-managed data can also become more agile, allowing them to spot market trends and move faster to capitalize on new business opportunities.
Avoid data breaches: Effective data management can also assist businesses in avoiding data breaches, data privacy issues, and regulatory compliance issues, which can harm their reputation, add unexpected costs, and put them in legal jeopardy.
Improved business performance: Finally, the most significant benefit a solid data management approach can provide is improved business performance.
Challenges in Data Management
The ever-changing landscape of information technology is ever-changing, and data managers will face numerous challenges along the way.
There are four major data management challenges to be aware of:
Huge Data systems:
The amount of information available can be overwhelming. It’s difficult to overestimate the amount of data that must be managed in a modern business, so when developing systems and processes, be prepared to think big. Third-party apps specializing in integrating big data are critical allies.
Different requirements across departments:
Many businesses isolate data. The development team may work from one set of data, the sales team from another, and so on. A modern data management system requires access to all of this information to develop modern business intelligence. Real-time data platform services enable teams to stream and share clean data from a single, trusted source.
The transition from unstructured to structured data can be difficult. Unstructured data frequently floods into organizations. Data preparation is required before it can be used to generate business intelligence: Data must be organized, de-duplicated, and cleaned in other ways. Data managers frequently rely on third-party partnerships and tools designed for on-premises, cloud, or hybrid environments to assist with these processes.
Managing culture is critical to data management. All the processes and systems in the world won’t help if people don’t know how — and perhaps more importantly, why — to use them. Managers engage team members as essential pieces of the information process by making team members aware of the benefits of data management (and the potential pitfalls of ignoring it) and fostering data-use skills.
These and other obstacles stand in the way of traditional business practices and initiatives that harness the power of data for business intelligence.