Data Management

What are the main types of data management?

Data management is a broad discipline, encompassing multiple elements. Here are some common data management examples:

• Data architecture: This is a framework that shows how data assets are structured and managed within an organization, including models, policies, standards and rules.
• Data modeling: Data models are visual diagrams of how data flows through an application or organization, with each model representing a dataset or relationship. This helps users to understand the data structure.
• Data ingestion: Ingestion of data into pipelines involves processing data to fix errors, remove duplicates and combine datasets. ETL (extract, transform, load) and ELT (extract, load, transform) are examples of data pipelines that are used to filter, merge and format data for use in artificial intelligence (AI) and business intelligence (BI) analysis.
• Data cataloging: By creating an inventory of your data resources, you can make them more searchable and enable collaboration between users.
• Data storage: Organizations tend to store their data in a data warehouse (a system used to house large amounts of structured data), a data lake (a central repository for both structured and unstructured data) or a data lakehouse (which is a cross between a data warehouse and a data lake).
• Data optimization and maintenance: As your data and usage patterns change over time, your analytics performance can start to degrade. In order to maintain peak performance, you need a plan for tracking and handling changes.

The business benefits of data management

Organizations now have access to vast amounts of data, and without robust management, it’s easy to become overwhelmed and miss out on valuable insights and opportunities. As the volume of your data increases, you’ll need a strategy that covers management and maintenance throughout its lifecycle.

Efficiency and integrity

Successful data management leads to streamlined processes and data integrity, both of which contribute to improved business performance. With the right structures and systems in place, you can organize and use your data much more efficiently.

Data management includes optimizing workflows and automating repetitive tasks, as well as ensuring data is kept in a well-organized, centralized location. This means the process of gathering and analyzing data is quicker, and you won’t have to waste time searching for relevant information.

If you can ensure the quality and integrity of your data, there’s much less risk of duplication or gaps causing costly errors and project delays. You won’t get confused by multiple copies of a file in different locations in different systems, for example.

As well as the productivity boost, better access to data improves collaboration and communication between departments and helps to reduce silos.

Reliability and accuracy

With good data management and maintenance, you can ensure that your information is always accurate and reliable. Thanks to methods like data validation and the implementation of cleansing processes, you can find and fix any errors, inconsistencies or missing values.

A key part of data management for the lakehouse is choosing a format that is versatile, can adapt to changing data and is interoperable across systems. This gives you the greatest flexibility in using analytics and AI tools across your organization, without having to upskill or migrate users to new systems. An interoperable data format lets you maintain a single copy of data for use across the organization, which minimizes duplication of data, reduces storage costs and promotes good data hygiene.

All of this means that you can rely on the data to help you make the right decisions. Having up-to-date, accurate information gives you greater insights and enables you to respond effectively to market changes and customer needs.

Privacy and security

Properly managed data is inherently more secure and works hand in hand with data governance policies. While data governance provides companywide policies and frameworks that support data quality and auditing, data management covers the technical and practical organization of data.

With effective data management and data governance, you will always know where your data is as well as have records of who’s permitted to access it. This makes it easier to spot potential vulnerabilities, discover if information is missing and prevent unauthorized access, which could lead to breaches.

Data management also involves adding security protocols like encryption and data anonymization to guard against cyberattacks. It encompasses the full lifecycle of data in your system — including removing records that have passed the date you can legally store them. By only keeping necessary customer information and maintaining records of their consent, you can ensure compliance with data privacy laws and industry-specific regulations.

Data privacy and security help you protect your business not only from financial penalties, but also from the negative publicity that comes with a breach. If you demonstrate that you can safeguard your data, you’ll build trust with customers and business partners.

Scalability and recovery

Another advantage of data management is that it can help your business to scale. With better visibility and reliable data, you can quickly make decisions, respond to changes and seize new opportunities. You’ll also have more insight into customer preferences, and you can demonstrate your growth and potential to new investors.

Increased efficiency — including automated and repeatable processes — enables you to reduce operational costs and to handle larger amounts of data as you grow. With a cloud platform for data management services, you don’t have to worry about expanding data storage.

Good data management also includes robust backup and recovery strategies, ensuring that you can retrieve your critical data quickly and minimize downtime in the event of a cyberattack or a system failure.

What are the challenges to successful data management?

As your hoard of data grows, it becomes more and more challenging to keep track of where it’s stored and who can access it. Here are some of the key issues that businesses face:

Compliance

Data management is the technical implementation of your data governance strategy — so if you don’t get the governance strategy right, it’ll be harder to manage the data.

There are a lot of regulations to consider, such as the Data Protection Act 2018, the California Consumer Privacy Act, and GDPR (General Data Protection Regulation). And the compliance landscape is always changing, so you have to work hard to keep up-to-date — especially if your business is international.

Data security and privacy

Again, the more data you have, the harder it becomes to keep it safe. If your data management policies aren’t up to scratch, disorganized information will lead to errors and lax security. But with all the other daily tasks you have to complete, security and encryption can sometimes get overlooked.

Poor data management can ultimately cause data loss or complete system failure, putting your company at risk of a breach as well as disrupting your operations (and reducing revenue). Noncompliance with security and privacy requirements can lead to legal action and fines, not to mention reputational damage and loss of consumer confidence.

Data integration

Most organizations use multiple systems to collect and store their data, but it can be difficult to bring it all together for processing or analysis if the systems don’t integrate well (especially with legacy systems). If you decide to consolidate all your data in a single platform or repository, that’s even more of a challenge!

Each of your apps or data management tools will have a different style of database, and there are so many different data types and formats. Before attempting integration, you’ll have to ensure that the data is formatted and transformed where necessary, to avoid errors in comparison and analysis.

Data silos and vendor lock-in

It’s hard to have a solid data management plan when your data is all over the place. Plus, keeping data in separate systems leads to data silos — making it harder to maintain consistency across the organization, to get a companywide overview of your data and to trust that the datasets are accurate.

If data overlaps across silos, you could find that resources are being wasted as two teams end up analyzing the same data. Silos are also detrimental to information sharing and collaboration between departments.

Further issues occur with a lack of data portability, which means that it’s not easy to move data between environments. This might be because the format in which you keep your data is proprietary to a particular vendor, rather than usable across all platforms.

That brings us to the challenge of vendor lock-in, which happens when it’s not viable for you to switch away from a product because it would disrupt your operations or cost too much. In this case, you’re locked into continuing with your current vendor, even if they’re not giving good service.

How can a data management platform help?

A data management platform, such as Databricks, is an integrated digital system that helps you gather, organize and analyze large amounts of data for analytics, BI and AI workloads across your organization. Some common use cases include segmenting audiences to gain insights into customer behavior, monitoring for financial fraud or preemptively addressing supply chain fluctuations.

These data management systems centralize your data so that it’s accessible to everyone in the organization, reducing silos and inconsistencies. They usually come with data security settings such as encryption, and automatic backup and recovery­ — plus ETL and ELT functions and tools for data governance and metadata management. They also may offer autonomous data maintenance and optimization functionality to keep your storage costs low and your query performance high.

As a data management platform, Databricks combines the unique capabilities of data lakehouse architecture with a data intelligence platform powered by AI models that analyze your data alongside how it is used. With the Databricks Data Intelligence Platform, businesses are empowered with natural language access, semantic cataloging and discovery, automated management and optimization, and enhanced governance and privacy.

AI also powers Databricks’ predictive optimization, a tool that automatically optimizes your data by learning from your usage patterns. It predicts the best way to optimize and then performs the right actions. This ensures that you only run optimizations that will bring high ROI — plus, it reduces storage costs and maintains good query performance.

These features contribute to overall data quality and reliable data pipelines across the lifecycle of data management — as do the other features driven by DatabricksIQ, which creates highly specialized and accurate generative AI models that understand your data and your business terminology.

Guarantee success with data management best practices

Data management is a big task, and it’s never done. Here are some ways to ensure your data management efforts run smoothly.

Identify business goals

It’s important to understand companywide objectives so that you can make sure your data management strategy ties in with them. This will help you know which datasets are relevant and therefore worth collecting, keeping and analyzing, which means your data management software won’t become overwhelmed.

Then, you can develop a plan that focuses on the right data and the most relevant KPIs. What insights will be most valuable to the business as a whole? You can also set data-related goals that will contribute to overall company success, such as reducing data duplication by 50% in a year.

Prioritize data quality

Using high-quality data is the only way to find reliable insights and make accurate decisions. So, you’ll need to prepare your data before using it and confirm its integrity. Data preparation includes cleaning, editing, organizing, integrating and merging data — as well as testing it. All of this helps you to ensure that the data is consistent and accurate.

Other processes to improve data quality include training team members on the proper way to input data and performing regular checks for accuracy. You should be able to identify anything incorrect or outdated and look out for inconsistent formatting and spelling errors that will impact results.

Enable interoperability

Data interoperability means that you can exchange and process data across different systems and business processes, even if it’s in multiple formats and locations —­ giving you a unified view of your data. This makes good data management and governance easier to achieve.

As we mentioned earlier, you’ll need to avoid vendor lock-in and go for data management solutions that are interoperable across formats.

For example, Delta Lake UniForm (short for Delta Lake Universal Format) is built on an open source storage framework that provides a live view of data for all users, regardless of format. The seamless unification of table formats means you don’t have to create additional data copies or silos.

Ensure data security

Start by creating policies for security and governance, and train employees on how to handle data securely. You can limit access with different levels of permissions (though make sure to check that everyone has access to the data they need to perform their jobs and explain why there are limits in place).

Choose a data management system with robust security settings, use data encryption and anonymization techniques, and delete information when you no longer need it. Make several backups of your data and put a strategy in place for dealing with a potential breach.

Continue audits and reporting

It’s important to carry out regular audits of your data to maintain reliability and compliance and to generate meaningful reports. Data reporting shows how your business is doing over time, often using visualizations such as charts and tables on an online dashboard. You can also use regular reports to check for anomalies and verify that your data is sound.

Compliance reporting reveals how you collect, store, use and secure your own company data and that of customers. It’s helpful in proving that you’re sticking to all relevant requirements. Analytical reporting lets you analyze a business strategy or process and make data-driven decisions by combining qualitative and quantitative data.

Create a data management strategy

Above all, you need to come up with a plan to guide your data management activities. This roadmap should govern exactly how your organization will collect, organize, use and analyze data, according to documented processes.

The strategy should outline best practices for avoiding the various challenges involved in data management and include official policies and workflows to ensure consistency. These policies should cover data distribution, security and compliance, and specify which tools should be used.

How to build a data management strategy

Of course, every business and its data are unique, so there’s no one-size-fits-all data management plan. However, the basic steps are the same for most organizations.

Start by running an audit or assessment to evaluate your current data infrastructure, including data sources, platforms, processes and capabilities. As well as looking for gaps and security vulnerabilities, you can perform a SWOT analysis to highlight strengths and weaknesses.

Outline your data-related goals and align them with wider objectives. Set up processes for collecting and preparing data, including data transformation and cleaning. Put guidelines in place for checking that it’s accurate, complete and up-to-date. For instance, how will you identify incomplete or inaccurate data?

Include data governance policies to ensure that data is used correctly and consistently across the business, and define the roles and responsibilities of users. Don’t forget compliance — who will check that customers have given permission to collect and use their data?

You’ll also need to consider technology for data storage, processing and analysis, so take the time to research and find a system that enables interoperability. How and where will you store the data, and how will you keep it secure? Make sure it’s easy for teams to collaborate and communicate data insights.

Communicate these policies to all employees, and offer comprehensive training on how to gather, use and secure the data. You may need to hire new staff with specific data management skills or bring in external consultants to oversee the change. Make sure everyone understands the data management strategy and how to perform their role in it.

Finally, it’s important to monitor and evaluate your data management strategy regularly to ensure its effectiveness — you may need to make adjustments based on performance and data accuracy.

How can a data lakehouse improve data management?

As well as following the best practices mentioned above, you can improve your data management efforts by using a data lakehouse. What’s a lakehouse? It’s a type of open architecture that combines the best elements of data lakes and data warehouses.

While warehouses are ideal for structured data, they’re not suitable (or cost-efficient) for other types such as unstructured or semi-structured data. Data lakes are suitable for storing raw data in a variety of formats, but they can’t support transactions or enforce data quality. Lakehouses offer the best of both worlds.

Lakehouses use data management features similar to those of a data warehouse, but they’re built directly on top of low-cost cloud storage in open formats. This makes them scalable, and you can store, refine, analyze and access a wide variety of data types. Your teams can use data without needing to access multiple systems, helping to eliminate silos.

The Databricks Data Intelligence Platform is a unified system that’s built on lakehouse architecture, which means there’s a single architecture for integration, storage, processing, governance, sharing, analytics and AI.

It includes the Delta Lake UniForm feature that enables data portability and interoperability, including the ability to switch between open table formats. You don’t have to worry about vendor lock-in or closed ecosystems, and your data is always within your control — making data management as easy as possible.