Data Management Best Practices: Strategies & Tools (2023)

The A-Z Guide To Master Data Management

Big data, as the name implies, is huge and keeps growing. But as every business moves towards a data-driven strategy, the handling vast amounts of data properly is mission-critical.

How can you leverage a modern data stack to work faster and more efficiently? It's not enough to have data, but how can you use it to improve decision-making? You need to make it as actionable as possible. Data can't merely exist in a silo — it must be fully integrated. Data management to yield useful data insights for stakeholders.

To do this, it helps to understand proper data management principles, tools, and strategies. You must also be able to establish and maintain an ever-evolving and continuously improving data framework for your company.

Here is a brief rundown of everything you need to know to master data management and establish it as a solid discipline across your company.

Big Data Management Processes

Before we get ahead of ourselves, here's the scoop on managing big data:

Big data management refers to the overall organization, maintenance, and governance of huge volumes of data. The data managed could be of any type, structured or unstructured, and could come from multiple different sources.
Big data management aims to uphold high data quality and easily implement analytical solutions such as business intelligence. Like any data management process, it consists of the various stages of data collection, processing validation, storage, integrating different data types, and making it accessible for easy usage.
Additionally, big data management processes require security and privacy countermeasures to protect large amounts of data. You should also have the proper backup and disaster recovery plans to ensure high data availability.

Here are the major cogs that keep big data flowing:

Data warehouses

When working with relatively smaller datasets, you might find simple databases sufficing for your use. But when working with datasets of the scale and volume of big data, you need data warehouses. These large, centralized data storage systems act as the central repository for all the data collected from varying sources.

Data warehouses are crucial to your overall data management system, allowing you to perform quick data extraction and complex queries on huge amounts of data.

Data management systems

Data management systems are software applications that help collect, manage, and store data so that it is readily accessible for any business-related function.

Some key features of a data management system include data integration, security, data retention, automated data analytics, real-time data capabilities, storage, reporting, and monitoring features.
There are many different types of data management systems, such as marketing automation systems, CRMs, databases, big data management systems, and so on.
Design and use a proper data management system that applies well to your particular use case.

Data sources

A data source can be anything from a flat file to a cleanly processed database can be a data source for your bigger data management system.
Depending on your applications and use case, your data management system should be able to account for all the relevant data sources.
For instance, e-commerce data include website data, product information, social media interactions, and more as its data sources to ensure they have a clear idea of its customer profiles and user behavior.

Data formats

Data management can involve inspecting various types of data formats, which can be structured and unstructured.
Data coming from different data types should be converted to compatible types and will be stored as such in the data warehouse.

Pillars of Data Management

The main goal of data management is to enable data-driven decision-making. This involves the consideration of three pillars of data management comprising all the major activities that are taken to ensure data management goals are achieved.

I. Data Collection

Data collection and data integration are the first stages of any data management process. Now, collecting data is no easy process. You have to first identify your data sources, merge data from multiple sources, and ensure they are accurate, clean, and compatible.

This involves various stages of data processing, like data cleaning, data merging, removing redundancies, noise, inaccurate data, transforming data, and so on.
The data collection and integration process can be the most time-consuming and needs to be planned well.
You need to select the right tools, hire the right resources with data skills, and set up a data pipeline that can be automated to keep the system going efficiently.

II. Data governance and data security

The data you collect, process, analyze and get insights from can be sensitive and confidential. Besides the data governance policies that your company may have, there are also several government-regulated data standards to follow.

You should also be careful with the data channels and access mechanisms you use so as not to cause any data leaks or unauthorized usage.
Setting up the proper security mechanisms, such as access control and authenticated access, defining data usage policies and data retention policies, and implementing them properly are all crucial for the success of your data management projects.
Data protection includes other functions like maintaining proper backups, recovery and restoration, cloning, data striping, encoding, encryptions, and more.

III. Data quality

Besides the correctness and accuracy of the data used, data quality parameter in the context of data management also applies to how to fit the data for analysis and decision-making.

Your data must be able to drive decision-making in a relevant way.
It should be sufficient to form actionable insights to share with stakeholders.

Effective Data Management Best Practices

Establish business goals

Everyone would agree that you need to have a data strategy. But it is also important to stress that your strategy needs clear-cut metrics, initiatives, and a roadmap. Make smart, measurable goals aligned with your business and have effective, actionable items based on your data analysis and business intelligence insights.

Some key questions to consider when setting your goals could be:

What problems do you intend to solve?
What are your current data requirements?
Do you need real-time data capabilities?
What are your immediate actions to be taken based on your data analysis?

Harden critical data storage access

More than a best practice, you should use this as an essential security measure to protect your data. A data breach can be catastrophic for a business, depending on its relevance to your business functions and reputation.

Having unauthorized access to your data storage in any way can put you in trouble with several regulatory bodies.
Data privacy is a real concern, too. Make sure to prioritize data protection when designing any data operation, tool, or policy.
Besides employing secure software solutions, you should also employ the right security personnel, conduct regular audits and penetration testing, and have a proper monitoring and incident report system.

Leverage a data warehouse

Proper data management does not start until you have set up a proper data warehouse.

Data warehousing solutions help you avoid silos, reduce redundancy and ensure consistency at all times.
They also help with easy access to data and can be a great way to reduce rework across the organization.

Remove duplicate data

Removing redundancy is an essential part of the data collection and cleaning process.
Take extra care to remove duplicate data so that your storage is optimized and processing performance can also be improved.

Perform real-time data analysis

The entire point of data management is to make your business decisions more effective, data-driven, and, thus, more successful. But when you deal with old data, the level of relevance could go down, and you might be working from a delayed perspective.

Real-time data analysis empowers you to make quicker decisions that can match up to the rapid changes going on in the market.
It can help you improve your business agility, optimize your day-to-day operations and be able to catch hold of any operational issues quickly.
You can identify any short-term changes in the market and use them to your advantage.
Real-time analytics can also improve your customer support operations by enabling you to access up-to-date data.

Set up continuous data collection

When you start as a small company, it might seem easier to carry out all data operations manually. But as you scale up, achieving the same results within the same period will be very difficult. You cannot simply go on a hiring spree hiring more data engineers to handle the extra load. The much better option would be to automate whatever that can be automated.

Automate your ETL processes for data collection, data sets creation from source, metadata handling, and more.
Automation allows error-free, faster data collection, which can greatly boost your overall performance.

Create high-quality reporting for stakeholders

One major point that data analysts fail with respect to gaining traction for their findings is that they don't use proper reporting mechanisms.

Good reports should be able to communicate the data to non-technical stakeholders just as it does to a seasoned data scientist.
It should be easy to digest and relevant to the problem you are trying to solve. Include high-quality curated information along with easy-to-understand visualizations.
Understand your audience and create reports that are presented in a way that resonates with your audience.

Work with a master data management (MDM) consultant

While you may have your data team, they may still lack the expertise to create a highly-tuned MDM strategy for your business. Hiring expert MDM consultants to help you understand your business goals and devise the data management strategies that best suit your operations and resources.

Types of Data Management Software

When you design your data management strategy, you will also have to choose the various tools and software solutions you will use. Here are some important types of data management software that you should be aware of:

Data integration

Data integration software merges and manages data from multiple sources into a single data warehouse or platform. A good amount of data collection software such as CRM, automated data collection solutions like Zapier, ETL solutions, and so on can be your best aid towards efficient data integration.

Data transformation

Data transformation in data management is the process of transforming data from multiple sources and varying formats into a compatible format that can be a better fit for use in analytical applications.

Data transformation software applications allow you to specify the various rules, business logic, and data transformation specifics to convert data formats. It is basically the T part of the ETL pipeline. Some popular data transformation tools include Portable, dbt, Airflow, Hevo, EasyMorph, and Dataform

Data security and privacy

As mentioned earlier, data security and privacy are two important data management functions that must be well-planned and catered to. You must ensure that your systems comply with data regulations such as GDPR and SOC2 to get the necessary approvals required to run your business.

While this can be quite a hassle to accomplish manually, there are software solutions that can help you stay compliant with minimal effort.

Data workflow automation

Most business operations consist of workflows that are well-suited for automation. Any process with repeatable and consistent steps can be easily automated.

The data passed through each step can be well-tracked and accounted for with the help of a proper data workflow automation tool. You can use your data platform or data warehousing solution to set up efficient workflow automation.

Business intelligence tools

Business intelligence tools are the direct result of motivation towards a data-driven decision-making process. These tools help sensibly ingest data with the help of easy-to-understand views like dashboards and graphic visualizations like charts and graphs.

Some popular BI tools include PowerBI and Tableau, which can be used for stakeholders meetings and business discussions.

Data Protection Principles

The General Data Protection Regulation, GDPR, puts forth seven key principles for data protection. They are:

Lawfulness, fairness, and transparency
Purpose limitation
Data minimization
Accuracy
Storage limitation
Integrity and confidentiality
Accountability

You need to set up the proper data policies and tools to uphold these important data protection principles. Some of those crucial steps to be taken to ensure data are:

Rigorous data access permissions

Whatever your preferred data storage location or system is used, you must ensure that it follows the rigorous data access policies you have set up for your organization. For instance, if data needs to be moved to any hardware storage device such as a USB, it must be done only with the right access level and privilege and be properly accounted for.

You should clearly define the various roles, the access rights and privileges associated with each role, and the proper security mechanisms to enforce the same. All your data storage facilities must be well guarded, both physically and software-wise, to ensure only authorized data access.

Establish a data breach protocol

You must have a data breach protocol as part of your data management strategy to handle every security incident properly. These protocols should clearly define the steps to minimize the impact, take recovery actions, inform the relevant stakeholders, and ensure transparency throughout the process. It also includes various employee training, password policies, data monitoring and reporting features, patch management, breach recovery plans, and security tools usage.

Archive unused, non-actionable data

Data archiving is the process of moving inactive or unusable old data out of live production systems to some back office long-term storage systems. Archiving unused data frees up your data warehouse storage capacities and allows you to optimize the performance of your data operations. It also ensures that you only work with relevant and up-to-date data.

Limit raw customer data to stakeholders

Customer data can contain several sensitive pieces of information, and sharing it without restrictions can cause privacy concerns. It also makes no sense to share raw data, which can be difficult to make sense of comprehensively. Present only relevant data in the right format such that it can be used for the purpose it is meant for.

Tips for Working With Multiple Enterprise Data Sources

Use a cloud-based ETL data platform

When working with multiple enterprise data sources, you need an effective data collection and integration tool that can help you collect data from multiple sources and integrate them seamlessly.

Cloud-based ETL tools are easy to deploy and can be used across various data sources. Portable is an excellent tool that you can use for all your ETL operations. With over 300 connectors, Portable allows you to integrate data from 300 varying data sources into a cloud-based data warehousing solution.

Stay focused on core business initiatives

Always ensure your activities and data operations align well with your business goals. The core functionality of data management is to improve decision-making in your business operations. Monitor your progress by collecting the right metrics, metadata, and other relevant information.

Enforce proper data management practices

While planning is only the initial phase of data management, it is through effective enforcement of your plans that you truly achieve what you seek. Pay attention to data quality, data integration, data privacy issues, and make sure all these concerns are addressed in your data management strategies.