Data Management 101: Why It Matters to Your Business

Data Management 101: Why It Matters to Your Business

Cole Lehman
Cole Lehman

Data has become the lifeblood of modern business and managing that data and ensuring its accuracy, completeness, and accessibility is one of today’s biggest challenges. Data management involves everything from modernizing your data stack to putting together the right data analytics team. It’s a combination of all the tools, processes, and people who make data useful to your business.

The benefits of effective data management include better decision-making, fewer errors, and reduced risk of fraud or misuse of customer data. Here’s what you need to know about effective data management in your business.

What Is Data Management?

Data management is the process of collecting, organizing, moving, and storing data in a secure method. It's vital to modern businesses because it helps them make better decisions by providing accurate and up-to-date information. The core of data management practices are focused on by data scientists, data analysts, data engineers, and machine learning specialists.

Data analytics teams can automate many parts of data management processes using tools like Shipyard, Fivetran, and Snowflake. With a modern data stack and a DataOps team focused on data management best practices, you can turn your company’s unstructured data into useful, secure datasets. Here are some of the main components of data management you need to get right:

  • Data ingestion — Data ingestion is how you collect, structure, and move all types of data from many different data sources to different target locations or a central source of truth that makes the data useful for business processes and business decisions.
  • Data infrastructure — Data infrastructure is the ecosystem of technology, processes, and people responsible for the collection, storage, maintenance, and distribution of data within an organization. When well-structured and managed consistently, data infrastructure unlocks crucial insights about company performance and uncovers opportunities (or problems) hidden in patterns of big data.
  • Data pipelines — A data pipeline is a series of actions that move data from multiple sources to a preferred destination. It automates multi-step processes, including aggregating, cleaning, formatting, processing, visualizing, and analyzing data as it moves from source to destination. Data pipelines can include extract, transform, load (ETL), ELT, or Reverse ETL processes to move datasets to new locations for data analysis.
  • Data analytics team structure — A data analytics team performs specialized tasks to make your business data usable for digital products, business units, departments, and decision-makers. It can also be called a dataops team, analytics team, data science team, or a business intelligence team. There are several common roles on a data analytics team — ranging from data scientist to chief data officer.

With all of these in place, you can work on overall data quality through the whole data lifecycle, improve workflow efficiencies across your company, and build new data-driven products faster.

Why is data management important?

Data management is important because it makes your data useful to the business — enabling everything from decision-making to new product development. Data management best practices can help businesses optimize their data use, protect their data from security threats, and improve their data quality.

Data management systems can help businesses overcome these challenges by providing tools for managing enterprise data, integrating different data sources, and governing big data. Data analytics teams can also support data management efforts by turning raw data into useful data models with machine learning algorithms.

Data analytics team members should focus on things like data ingestion, data preparation, data storage, data processing, and presentation to manage unstructured data more effectively. Data integration can also help reduce the problem of too much data by linking disparate datasets together, so they work as a cohesive whole. With all of these aspects of data management covered, you reap a long list of benefits for your business.

What are the benefits of data management?

The benefits of data management range from improved decision making to increased scalability for your whole organization. When your data is clean, organized, and stored well, the limits on how you can apply that data are limited only by your imagination.

Here’s a short list of the benefits  companies  experience when they adopt data management and the tools to do it well.

  • Streamline compliance and data governance
  • Make decisions faster, collaborate better, utilize sharper business intelligence, and lower costs associated with storing data
  • Create new sources of revenue and new products out of new use cases discovered in big data patterns
  • Deliver accurate data on time, every time
  • Combine data from a variety of sources in an automated way
  • Use data analytics tools more effectively, reduce costs by eliminating redundancies, and save time with automated data management
  • Innovate more rapidly than ever before through self-service access to consolidated data
  • Increase data privacy and data security across the business

While there are  many benefits, there’s also a list of challenges.

The main challenges of data management

Companies that do not properly manage their data risk a number of issues. These include difficulties in sharing information with other departments or even overpaying for services because they did not have access to all the information necessary to make an informed decision.

A lack of coordination among departments when collecting and storing important data also leads to wasted time and effort — especially when teams are trying to answer specific questions about customers or product quality.

Here’s a quick list of the main challenges in data management:

  • Eliminating data silos
  • Creating cross-functional workflows
  • Legacy data sources (data warehouses, data lakes, unstructured data)
  • Broken data schema
  • Data integration
  • Source data formatting
  • Data quality
  • Data integration

In order for a company's analytics team to be able to use data for continuous improvement effectively, data management must be in place so that there is enough high-quality raw material available on which they can build their findings.

Thankfully, with data management best practices and data management solutions in place, these challenges gradually get smaller and smaller.

5 best practices for data management

Every data science team has their own approach to data management. Individual tactics and business strategies use slightly different language, but some of the core practices are always neccesary. Here are five of the best practices you can implement to get data management right at your company.

1. Set up continuous monitoring — This is one of the most basic steps to detect issues quickly and respond before they become a larger problem. When you’re continuously monitoring your data science pipeline from start-to-finish, you can manage problems as they come up before they cause cascading issues in your data workflows.

2. Automate your data ingestion pipelines — Make sure to give your data engineers tools to automate data ingestion and data workflows. This helps everything from data visualization for business intelligence to running automated security checks for data governance requirements. Data automation frees your data scientists to use their full skillset instead of wasting time pulling reports and manually updating data sources for the business.

3. Choose a single source of truth for business users — Choose a central location for data storage, data analysis, and data processing where your business users can source their metrics. Marketing and product teams can also use this central data location to build their real-time and personalized customer experiences without constantly requesting resources from data engineering teams.

4. Create a master schema for data elements — The schema defines exactly what fields exist within each table and what type of field it is (text, number, date/time, etc.). A master schema keeps consistency across tables by ensuring every record includes certain fields while others are optional.

5. Sync data management to business strategy — It’s easy to get lost in data science and forget that the business could have simple needs your data could solve today. Or to get ahead of the business because your teams are operating in vacuums. Establish a healthy connection between your business initiatives and data management projects to scale steadily and in ways that deliver value.

All of these best practices are easier to implement with the right data management solutions.

Tools for data management

Does your data stack have all the tools you need to ingest, store, and monitor your data? Do you have a cloud data warehouse that meets your needs?

If you’re looking for some new solutions (or to streamline your data architecture) here is a collection of our favorite tools to help you with data management.

Snowflake Cloud Data Platform

Snowflake is a fully managed cloud data service that’s simple to use but powers a near-unlimited number of concurrent workloads. It’s your solution for data warehouses, data lakes, data engineering, data science, data application development, and securely sharing and consuming shared data.

Fivetran Data Pipelines

Fivetran helps data engineers effortlessly centralize  data so your team can deliver better insights faster. It helps you securely access and send  data to one location to instantly connect hundreds of  powerful databases and data sources.

dbt Cloud Data Transformation

dbt is a development framework that combines modular SQL with software engineering best practices to make data transformation reliable, fast, and fun. With dbt, data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows.

Shipyard Data Orchestration

Shipyard integrates with Snowflake, Fivetran, and dbt Cloud to build error-proof data workflows in 10 minutes without relying on DevOps. It gives data engineers the tools to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds (without the headaches).

Your individual requirements and business initiatives will determine which combination of these tools works best for your DataOps team.

How to get started with data management

Start by getting your data infrastructure  right, choosing the right analytics team, and automating your data ingestion pipelines. From there, you can build in complexity as your DataOps team takes on new challenges and the business asks for more from your data management efforts.

Shipyard is a solid data management solution with data tools and integrations that work with your existing data stack (or modernize your existing legacy systems).

Sign up to demo the Shipyard app with the Developer plan that’s free forever (no credit card required). Immediately start to build data workflows (in 10 minutes or less), automate them, and see if Shipyard fits your business.