Data Management

Data access is the ability to access (or retrieve) information from any source, wherever it’s stored. Certain technologies, like database drivers and document converters, help make this task easier and more efficient. With a variety of native data access capabilities, business analysts and data scientists can more easily work with all the big data collected from ever-increasing sources, formats and structures – and determine which data will best predict an outcome.

Why is it important?

Important data resides in many places – text files, videos, databases, emails, data lakes, sensors and smart devices, web pages and social media feeds. Good access technology lets you extract useful data from any type of data storage mechanism or format – so you can spend more time using the data, not just trying to find it.

Data governance is a framework of people, policies, processes and technologies that define how you manage your organization’s data. With data governance software, you can define the rules that enforce your policies – helping align your data and business strategies. Related to governance, master data management is the process of creating a single unified and consistent view of key business entities – such as a person, location, product, supplier or business.

Why is it important?

Data governance is essential for any organization – other types of governance that are crucial include AI governance and model governance. Governance is often driven by the need to comply with regulations, like CECL, the GDPR and the California Consumer Privacy Act (CCPA). Via governance policies, you can define what data users can access, who can change (versus view) data, and how to handle exceptions. Data governance tools help you control and manage rules, trace how they’re handled and deliver reports for audits.

Data integration is a process that combines different types of data to present unified results. With data integration tools, you can design and automate steps to do this work. ETL (extract, transform and load) and ELT (extract, load and transform) are examples of these tools.

Why is it important?

Data integration creates blended combinations of data, which is useful when making decisions. Because it combines elements of multiple, individual data sets, integrated data can reveal new insights and help you answer different questions.

Data preparation is a task that prepares data for analytics. It involves combining data from various sources, then cleansing and transforming it. If done via a self-service interface, business users can access and manipulate the data they need with minimal training – and without asking IT for help.

Why is it important?

Good models depend on good data preparation. But it’s a time-consuming task. Good data preparation tools reveal sparkling clean data and add value – so data professionals can quickly access, cleanse, transform and structure data for any analytical purpose. The result: higher productivity, better decisions and greater agility.

Data quality is the practice of making sure data is accurate and usable for its intended purpose. This starts the moment data is accessed and continues through various integration points with other data – including the point just before it’s published or reported.

Why is it important?

Poor data quality can lead to costly mistakes. Data that’s outdated, unreliable, incomplete or not fit for its intended purpose will not be trusted – causing problems across the organization. A data quality solution that can standardize, parse and verify in an automated, consistent way reduces those risks.

Data lakes and data warehouses can store pools of big data securely for use in predictive modeling, machine learning, AI and other data science applications.

Why is it important?

Whether an organization uses a traditional data warehouse, a data lake, a data lakehouse – or a combination of these – such systems are essential for keeping business data accessible and usable. A data lake, for example, can rapidly ingest all types of new data – while providing self-service access, exploration and visualization. In turn, businesses can see and respond to new information faster.

Use a business glossary to set data definitions and owners, integrate workflows and flag issues, and visualize lineage and relationships. Data lineage traces data’s path from its origins to its current location as it tracks key details – technical, business and metadata (data about the data).

Why is it important?

Working together, these tools help promote collaboration and align business and IT. When you’re notified of potential issues, you can address them early, before they cause bigger problems. You can also explore data relationships and conduct impact analysis with these tools.