Businesses Data Management: The Complete Guide

Businesses Data Management: The Complete Guide

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Want to start implementing data management? You’re not alone. Effective data management is no longer a “nice-to-have” but a must for every business. In this guide, you’ll learn what data management is, why it is important, how to use it effectively, how to address its challenges, and what the future of data management tools looks like. Let’s go into each one by one

What is data management?

It seems that everybody is talking about data management these days, but if you’re new to data management, getting started can feel overwhelming. There are so many new terms to learn. So many new ways to store data, organize it, and label it. Not to mention new processes to set up and keep track of!

But in reality, data management is not that complicated. In fact, it’s really only about three things: data quality, data governance, and data optimization.

Before we go into each one, let’s talk about why data management is important.

Why is data management important?

It’s simple.

Data management keeps all your data organized, accessible, and secure.

Think of it as having a well-organized filing system where everything is properly labeled and stored, so you can easily get what you need—when you need it.

This will drive faster business decisions, fewer data security breaches, and guaranteed compliance with regulations and integrity of all your data systems.

A few facts to consider.

Effective data management can increase your operational efficiency by up to 60%, while poor data management can cost you up to $15 million per year. Oh, and only 3% of companies’ data meets basic quality standards. Ouch.

But how do you make sure your data is well-organized, accessible, and secure? In other words, how do you use it effectively?

How to use data management effectively

As we mentioned before, the backbone of effective data management consists of data quality, data governance, and data optimization.

To use data management effectively, you have to make sure you do all three well. This will keep your data not only accurate and accessible but also aligned with your business goals and compliant with regulations.

Let’s take a look at each in detail.

Data quality

The quality of your data is critical for smart business decisions, like figuring out what your most popular features are, how to improve your customer service, when to plan new releases and sending out marketing emails.

When your data quality is poor—not accurate, complete, consistent, or up to date—you risk opportunity, financial, and efficiency losses, amongst others.

So how can you make sure your data is high quality?

Here are three ways to do this:

Aspects of Data Quality
Aspects of Data Quality

1. Data monitoring and observability

Data monitoring and observability involve tracking and analyzing the state and health of all your data systems to ensure they operate correctly.

In the context of data quality, this means you need to check data flows for issues like inaccuracies, inconsistencies, or delays that could indicate underlying problems with your data systems. By using monitoring and observability, you can proactively identify and address data quality issues, ensuring the reliability and trustworthiness of your data.

2. Data contracts

Data contracts define the expected format, structure, and other quality criteria for data shared between all your systems. They serve as formal agreements that dictate how data should be created and consumed, including rules for data validation, transformation, and usage.

Data contracts support data quality by setting clear standards and expectations, reducing the likelihood of data errors and inconsistencies that can arise from misinterpretation or misalignment between different data sources and consumers.

3. Shift left

"Shift left" means performing quality checks earlier in the data lifecycle rather than later—during the design and development phases.

Most businesses wake up to the need for data governance too late—when something breaks. By now data issues have propagated through later lifecycle stages and have become much harder and more expensive to fix. This is a reactionary approach.

You need to be proactive and start earlier, to catch and fix errors right from the start—from the moment the data is first gathered. This stops quality problems from spreading and becoming bigger issues later on, saving you time and money. It also makes sure you can use your data used for fast decision-making and high-quality analysis. And it trains your team to think about data quality from the beginning when you're setting up all your data processes and systems.

Data governance

Data governance is the practice of creating high-level policies for using and managing data in your business. In other words, it’s about deciding who can see your data, who can use it and update it, and how it can be protected.

Effective data governance involves four things:

Aspects of Data Governance
Aspects of Data Governance

1. Privacy

You must keep personal and sensitive information in line with privacy laws like GDPR or CCPA. This includes stopping unauthorized access or misuse of data and letting users control their own data.

To do this, you’ll need to classify data, control access to it, and encrypt it—to protect it from the moment it was collected to the moment it gets thrown away. This builds trust and makes sure your business follows the law, reducing the risk of data leaks and fines.

2. Compliance

You must comply with data regulations like GDRP, CCPA and follow data compliance rules, regulations, and laws.

In other words, you must create processes and policies to make sure your data complies with requirements like data security controls, data access management, data encryption, data classification policies, and others.

3. Access controls

To keep sensitive information safe, you need to set up robust mechanisms to manage who can view and modify your data.

This involves defining user roles and permissions, ensuring that people have access only to the data necessary for their role. Strict access controls can prevent unauthorized data access and modifications, reducing the risk of data breaches and ensuring compliance with data protection regulations.

4. Data lineage

You need to track the journey of data from its origin through various transformations and to its final destination.

This transparency will help you understand how data has been altered and ensure it remains accurate and consistent over time. Keep clear and up-to-date records of your data's evolution, and you’ll be able to troubleshoot data issues more effectively, meet audit requirements, and maintain trust in your data's reliability.

Data optimization

Data optimization helps your data systems run smoothly and deliver top-quality data.

This means you need to change how data is stored to access it faster or make data workflows simpler to avoid duplication. It also means you need to make sure your data is accurate and reliable, use good practices to enhance data safety, and follow rules so you can get the most business value from your data.

Data optimization involves optimizing three things:

Aspects of Data Optimization
Aspects of Data Optimization

1. Cost

Your cost includes taking care of data resources like hardware, software, staff, and running costs.

To cut costs, you need to find ways to lower these expenses without hurting the quality or availability of your data. To do this, you can use cost-saving storage options, remove duplicate data to free up storage, or use cloud storage that can grow with your needs and might cost less with pay-as-you-use rates.

2. Performance

Your data needs to be reached, used, and studied quickly. Hence, performance in the context of data optimization means the speed and ease of using all your data systems and processes.

To boost performance, you can use indexing, data caching, smart query design, and quicker storage technologies. The goal here is to make sure that data workflows are smooth so that decision-making isn’t slowed down by any data delays.

3. Utilization

Use your data resources and infrastructure well—to make sure storage, computing power, and data aren’t used too little or too much.

To do this, spread the workload evenly, adjust resources based on what you need, and remove any old or unnecessary data. This way your business will get the most out of data management, avoid waste, and have resources ready when and where they're needed.

But wait. There is more!

Aside from data quality, data governance, and data optimization, a proper data management system is impossible without a solid system for data movement.

It consists of three things: data storage, data processing, and data reporting.

Let’s take a look at each in order of your data flow.

Data storage

Data storage is the preservation of input and output data on various media types. This includes the architectural design, implementation, and maintenance of solutions that keep your data structured and accessible.

There are many data storage systems—from local databases on individual computers to distributed systems in multiple locations, managed by cloud storage providers.

A few data storage examples are:

  • Databases (RDBMS) like MySQL, Oracle Database, or Microsoft SQL Server (data organized into tables with predefined relationships) and NoSQL databases like MongoDB, Cassandra, or DynamoDB (designed for unstructured or semi-structured data to offer more flexible schemas and scalability)
  • Data lakes like Amazon S3, Microsoft Azure Data Lake Storage, or Databricks Lakehouse (manage data as objects within a flat namespace)
  • Data warehouses like Snowflake, Amazon Redshift, or Google BigQuery (optimized for analytical queries and BI)
  • Storage systems like SAN (Storage Area Network) or cloud-based block storage solutions (high-performance, low-latency storage), or file storage systems like NAS (Network Attached Storage) that organize data into a hierarchical file and directory structure
  • Cloud services like Google Cloud Storage, Microsoft Azure Blob Storage, or Amazon S3 (scalable and secure cloud object storage solutions for storing and accessing data from anywhere)

Data storage supports your entire data lifecycle, from creation and use to archiving and disposal. Whatever storage solution you choose will depend on the specific requirements regarding your data’s volume, speed, accessibility, and compliance needs.

Data processing

Data processing is the transformation of raw data into useful information through a structured sequence of operations. It involves collecting, manipulating, and analyzing data to get insights and help support decision-making.

The process typically includes data ingestion when data is acquired from various sources, data cleansing to remove inaccuracies and inconsistencies, data integration to combine disparate data, and data transformation to convert data into a suitable format for analysis.

Other steps may involve complex analytical processes, leveraging algorithms, and computational techniques to identify patterns, trends, and correlations within the data. The final output is then structured for interpretation and presented through visualizations or reports.

Let’s look at them in detail.

1. Ingestion and ETL

Ingestion is pulling data from all kinds of different sources and bringing it all into your system for processing. And ETL (Extract, Transform, Load) is a type of ingestion that first extracts data from its sources, transforms it for storing in the proper format or structure for the purposes of querying and analysis, and then loads it into the final database, data warehouse, or data lake.

2. Cleansing

Cleansing is identifying and correcting (or removing) errors and inconsistencies in your data to improve its quality. This means fixing typos, removing duplicate entries, or dealing with missing values to ensure the data is accurate, complete, and reliable for analysis.

3. Integration

Integration is combining data from different sources to provide a consolidated view. This is especially important when different departments in an organization have their own data systems, and you need to collect the data needs to create a comprehensive view for analysis and business decision-making.

4. Transformation

Transformation is converting data from its original form into a format or structure that’s easier for analysis. This can involve aggregating, summarizing, or reorganizing data to improve its value and usability and make it ready for the tools and techniques that you want to apply when analyzing it.

Data reporting

Data reporting is simply organizing data into informational summaries to see how different areas of your business are performing.

It involves presenting data in formats that all stakeholders can easily understand—like tables, charts, and graphs—to make faster data-informed decisions.

Here are its main four parts:

1. Summarization

Summarization is condensing large datasets into smaller, comprehensible formats. This means creating executive summaries that highlight key performance indicators (KPIs) or trends, so you can get quick insights into an issue without a deep-dive analysis (when you need to make a decision fast).

2. Visualization

Visualization is turning data into visual formats like charts, graphs, and dashboards. This way you can use data to illustrate patterns, trends, and outliers, making it easy for all stakeholders to understand complex information at a glance and spot areas that need attention or change.

3. Updates

Regular updates are what will keep your data current and reliable. You can schedule updates at set intervals with automated reporting systems, which will give you and your users real-time or near-real-time data. This is very important if your business is a startup competing in a fast-paced market where data can change lighting fast. ⚡

4. Communication

Last but not least. Effective communication of your findings to all stakeholders is key. This is what reports, presentations, or dashboards are for. The goal here is to ensure that all data insights are clearly presented, so that everyone is on the same page and can take swift action based on the reported information.

To summarize:

  • Data quality is the condition of your data based on its accuracy, completeness, consistency, reliability, and timeliness.
  • Data governance is the framework of policies, standards, and procedures that dictate how your data is managed and used.
  • Data optimization improves the efficiency and effectiveness of data storage, access, and processing. effective data management intertwines three critical aspects to uphold the integrity and utility of data within an organization.
  • Data storage is the foundational infrastructure that shows you how and where your data is kept, making sure it's secure and retrievable (resting in the right mix of databases, data lakes, and cloud services).
  • Data processing transforms raw data into actionable insights through ingestion, cleansing, integration, and transformation, making it ready for analysis.
  • Data reporting organizes and presents processed data in a clear visual format, enabling stakeholders to make informed decisions based on the most relevant information.

How to address data management challenges

Implementing a data management system will likely make you face some of the most common data management challenges like:

  • Data silos and legacy systems
  • Data quality and consistency issues
  • Data volumes and complexity
  • Data literacy

How to deal with them?

Let’s tackle them in order.

To solve the problem of separate data and old legacy systems, create a united data space so that data moves smoothly across different areas of the business. To do this, connect both your new and old data management systems. Moving data from old systems to new ones can also help eliminate separations. Or you set up a strong data control system. This will make sure that data is the same and easily accessible across all systems.

To improve data quality, set clear data standards, regularly clean data, use data management tools to automatically find and fix errors, train employees on the importance of data quality and define who is responsible for it, and keep it up-to-date. This will keep your data accurate over time.

To handle the constantly growing data volume and complexity, find solutions that can grow with it. Use cloud storage and computing to get the flexibility you need. Add advanced data tools with AI and machine learning to handle and use all this complex data—like deciding what data to keep, archive, or delete. This will help you manage your storage and makes sure you can easily find the data you need.

And finally, to help your team accept new ways of managing data explain clearly why these changes are good. Train your team and give them the tools they need to succeed. Recognize and praise people for following the new rules to create a positive outlook on data management.

What is the future of data management tools?

Finally! Let’s talk about the future of data management tools.

We believe it’s going to be shaped by a few emerging trends—the hottest of them being AI.

Here are five key directions:

  1. Integration of AI and ML. Automated data management tools will increasingly incorporate AI and ML algorithms to automate data processing, cleansing, and analysis tasks, making them more efficient and less prone to human error.
  2. Enhanced data security and privacy. As data breaches become more common and regulations like GDPR and CCPA more strict, future data management tools will likely offer more advanced security features and privacy controls to protect sensitive information and ensure compliance.
  3. Cloud-native and multi-cloud solutions: The shift towards cloud-based data management will keep growing, with tools becoming more cloud-native and offering seamless multi-cloud capabilities to let your businesses store, process, and analyze data across different cloud environments.
  4. Edge data management. With the rise of IoT devices and edge computing, data management tools will simply have to adapt to handling data processing closer to data source, reducing latency and bandwidth usage.
  5. Data mesh and data fabric. Future tools might embrace data fabric and data mesh architectures, giving you a more integrated and flexible approach to data management that supports decentralized data ownership and governance but still keeps your data easily accessible and usable.

Chat with us

You made it to the end of this very long data management guide. Congratulations!

We hope you’re excited to implement more effective data management for your business. If at first it’ll seem hard, it’s okay. It’ll take some trial and error to get it right. But the results are worth it, and now it’s more important than ever to keep your data up-to-date.

Want to learn more about data management and how we can help you with it?

Chat with us! We’d love to hear from you.

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