Regardless of the size of your company, you probably rely on data to fuel the decision-making process. However, dealing with as much digital information as the modern business world currently provides can get rather difficult.
Thus, to stay efficient, an organization should focus on developing and maintaining a comprehensive enterprise data management strategy. Having a coherent approach is just as important for companies that are only starting with data management as it is for those who have already implemented multiple intelligent tools.
Key Highlights
Data governance defines data-related responsibilities for employees, backed by policies and processes, maintaining its integrity, quality, and security.
EDM ensures your data is cleaned, structured, and up to date, so you can deploy AI models whenever needed and gain reliable outputs.
MDM elevates decision-making accuracy through a single, consistent, accurate, and up-to-date data shared across the entire organization.
Cloud-based EDM removes the infrastructure maintenance burden from enterprises, offering scalability, enhanced security, and seamless integrations.
Given the importance of enterprise data management (EDM), in today’s post, we’ll discuss what you should consider when creating a truly competitive strategy and how to do it. For some, it’ll be a good refresher or a way to check if their approach is on track; for others, it will serve as a guideline to rely on when starting their own process. So, let’s get into it.
The True Value of Enterprise Data Management Strategy
Let’s admit it, database management is not a walk in the park, especially on an enterprise level. But it is actually an endeavor worth your time and efforts, simply because it will give you at least these opportunities:
Enhanced decision-making: EDM creates a unified environment for your business data. When everyone works with the same dataset, there is a higher chance of making informed and reliable decisions.
Increased operational efficiency: Having clean, centralized, and governed data means more productive work and fewer errors. EDM also automates data collection and validation, cutting down on manual errors and speeding up the overall process.
Better risk management: EDM strategy clearly defines access controls, thus minimizing the risk of data breaches. Plus, governance frameworks built into EDM strictly follow industry-specific regulations, keeping your business operations more reliable and trustworthy.
Advanced technology readiness: Eventually, having clean, structured, and reliable data means your business is ready to train and deploy AI models whenever needed.
How does AI improve enterprise data management?
AI eliminates manual bottlenecks and accelerates ETL pipelines through intelligent automation. AI agents go even further. They actively monitor and adjust your systems in real time and can independently act to elevate operational efficiency. For example, they may reallocate resources during peak loads to keep data pipelines running without interruption.
Types of Enterprise Data
Broadly speaking, enterprise data can be any type of digital information your business works with. It may come from web platforms, mobile apps, CRM systems, and other software the company employs.
All this data can be categorized into three distinct kinds. In fact, it can probably be split up into more, but we’ll focus on the most popular groups, which are as follows.
Transactional
Transactional data facilitates a company’s ongoing operations. It captures information pertaining to transactions like time, place, price, payment method, and use of discounts. In the context of insurance, transactional data may cover insurance claims, while in banking institutions, it might include information on deposits, withdrawals, or credit.
This type of data is typically stored within enterprise systems and helps automate data processing in sales, customer support, purchasing, and other departments that are focused on daily business activities.
Analytical data is all about values and metrics that deliver insightful business intelligence to improve decision-making. Typically, it is stored in enterprise repositories like data lakes or data warehouses, which allow for the deployment of machine learning algorithms or for different types of data analysis to be carried out.
Finally, master data represents information about business entities and provides context for transactions and daily operations. Concretely, it can include details about products, customers, employees, or suppliers and partners.
Main Components of Enterprise Data Management
Now as you know the different types of data that are out there, let’s move on to how it can all be managed. In simple terms, enterprise data management involves any processes that help define, govern, secure, and maintain the digital information your organization generates and works with.
Working with data can be tricky. Hence, when creating a comprehensive EDM strategy, you’ve got to consider a few key elements that will facilitate success.
Data Governance
Data governance essentially helps ensure that specific data-related activities have the right people responsible for them. It includes policies and processes that help maintain the integrity, quality, and security of a business’s digital information.
These documents should explicitly state an organization’s data guidelines as well as how, when, and by whom they are to be enforced. Overall, data governance accomplishes the following:
Creates internal and external accountability
Protects user privacy
Helps adhere to rules and regulations
Encourages ethical responsibility
During the process of outlining your enterprise data management strategy, you’ll likely need to spend some time on establishing data governance. It’s definitely something that shouldn’t be overlooked.
For organizations actively mapping their data governance as part of an AI initiative, an AI maturity assessment adds a layer of specificity. It scores governance, data quality, infrastructure, and organizational alignment not just as enterprise data management components, but as inputs to a production AI environment.
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In order to reap the rewards of enterprise data management platforms, it’s important to think about data integration. In essence, it means you’ve got to consolidate digital information from disparate sources and various formats in a unified repository so that it is accessible and can be leveraged.
If you haven’t already, you might need to implement a data warehouse or another database in order to efficiently store and transform all of your data. At this stage, data infrastructure consulting can help you assess your current environment, avoid over-engineering, and ensure the chosen solution genuinely matches your operational requirements. This is an important component of a successful EDM strategy.
Master Data Management
Master data management (MDM) is the logical continuation of the integration component. Generally speaking, MDM is a process of ensuring that any decision-making within a company is based on the most current, “true” version of the data. It facilitates the uniformity, accuracy, and stewardship of an enterprise’s data.
Naturally, this is only possible when all the digital information is correctly integrated, duplicates are removed, and records are up to date.
Enterprise data management vs master data management: What is the difference?
EDM is the framework that manages the entire lifecycle of all data types, whether structured, unstructured, transactional, etc. Enterprise data management handles data governance, architecture, security, and compliance.
As for MDM, it’s a subset of EDM. Its main goal is to create a single source of truth for core business entities such as customers, products, suppliers, and the like. This ensures consistency and accuracy across all departments.
Data Quality
Quality is of utmost importance when it comes to data. Incomplete or inaccurate digital information will damage the reliability of analytics and thus negatively affect your business. Moreover, it can slow down the implementation of enterprise data management software and cause project delays, which only incur additional costs.
Hence, you ought to make sure any underlying quality problems are addressed when outlining your data management strategy. Concretely, your team might have to perform data cleansing, enrichment, integrity checks, and quality assurance activities.
Data Architecture
The next component of EDM is data architecture. It defines how exactly your data is stored and processed throughout the organization. Whether to keep your variables centralized or decentralized all depends on your current enterprise purposes.
Many large-scale businesses are actively employing medallion architecture, which organizes and cleans data through three layers: Bronze, Silver, and Gold. This architectural framework can be a perfect solution for businesses that want to structure data within a lakehouse environment.
Intelligent data architecture also provides peace of mind about the scalability and cost-effectiveness of your infrastructure. Here, cloud data management works best by taking the burden of manual infrastructure management off enterprises entirely. In addition, cloud platforms enhance security and may seamlessly integrate across tools and services.
Data Security
Finally, the last component of EDM we’ll discuss today is data security. With the regulatory landscape constantly changing and consumer demands for the safety of personal data increasing, it’s crucial to ensure your systems are well-safeguarded. Data must be protected at all points of the life cycle, whether it’s being stored or is in transit.
Hence, you need to consider which measures you will be implementing into your enterprise data management tools to prevent leaks, theft, or destruction. It might be worth starting with the basics, like implementing encryption and two-factor authentication.
However, if you want to take it a step further, turning to innovative technologies like blockchain can also be an option. Specifically, it allows firms to capitalize on its decentralization and cryptography aspects.
Key Steps of Creating Your Enterprise Data Management Strategy
Now, it’s time to talk about the actual process of developing your EDM strategy. Keep in mind that the following steps will differ depending on how far along you are on your data management journey and the existing tech stack you’ve got. Some steps might have to be done in parallel or in a slightly different order.
However, no matter what your unique situation is, you can definitely use the process below as a guideline and to get a better idea about what awaits you.
1. Assess Current Situation
Often, everything starts with assessing your current data practices, capabilities, and weak spots. Here, your IT team will need to get a clear understanding of how data flows through your organization, what sources are used, and which aren’t but might be beneficial. This step will set the stage for effective data management.
Of course, assessing your current situation might be quite time-consuming. In particular, if you work with a lot of digital information and are only getting started with systemizing your approach to it. Nonetheless, don’t rush this step, as it will ensure that the final result truly caters to your unique needs and challenges.
2. Define Needs and Objectives
After performing your initial requirements assessment, it’s imperative to note them down along with the final objectives you’re hoping to achieve. This will help keep the team on track and also allow you to quickly determine the success of implementing an EDM strategy. So, make sure you answer the following questions:
What are the end goals?
Are there any pressing issues that should be prioritized?
What kind of analytics do you want to run?
Which data is needed, and where will it be stored?
Who will be responsible for ensuring the completion of this undertaking?
Are there challenges you already foresee?
What are the KPIs that will help measure success?
Of course, for some organizations, the implementation of an end-to-end data management strategy may take a lot of time. In that case, stick to the agile approach and define incremental deliverables and goals. In the end, they will all add up and leave you with a success-ensuring plan.
3. Identify the Needed Tools
Once you are clear on the objectives you are looking to reach, it’s time to identify the software that will help you achieve the set goals. Here, make sure you take your time to think over what kind of hardware or software you need to establish a strong data infrastructure.
Perhaps you already have some of the required solutions, and they need minor tweaking or modernization. Or, you might just be working with a few solutions, like a CRM, ERP, and CMS, and are ready to take it up a notch by centralizing everything with a data warehouse. Whatever the case might be, you need to determine if any solutions are missing from your tech stack and decide how you will acquire them.
Do you want a custom-built tool, or will you go for an out-of-the-box one? If it’s the former, do you prefer outsourcing development to an external vendor or handing the project to your internal IT team, if you have one? These are the questions you’ve got to consider at this stage.
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Finally, you can implement the tools you’ve identified in the previous step and start capitalizing on the benefits they bring. At this stage, if you’ve chosen the custom route, your team will focus on the solution’s UI/UX design, backend and frontend development, and testing.
After this part of the process is complete, don’t forget that any software needs to be monitored and maintained. Otherwise, it might quickly become outdated and deliver poor results. So, don’t forget to ensure you’ve got a team keeping track of its performance.
How long does it typically take to implement an enterprise data management strategy?
On average, implementing an EDM strategy requires anywhere from 12 to 24 months. For large enterprises with complex environments, it may take even longer, up to 3 years.
5. Establish Data Governance Policies
Once everything is in place, or even while the needed solutions are being implemented, you can begin establishing your data governance policies. We’ve already touched upon this crucial component of an EDM strategy above, but it’s worth reiterating.
Essentially, determine the necessary standards, policies, and procedures that will be followed by the entire organization to prevent security breaches, data corruption, and loss. Moreover, don’t forget to account for regulatory compliance and include necessary guidelines in your documentation. This is particularly relevant in highly regulated sectors like finance and healthcare.
Of course, your EDM software will likely already be compliant if you’re working with experienced development specialists. However, it’s still crucial for your policies to also incorporate proper data usage procedures. Overall, just focus on outlining how you ensure data quality, security, privacy, and transparency in the processing of digital information.
6. Train Your Employees
Lastly, after you’ve revamped your data management processes and established a thorough strategy, it’s essential to conduct some employee training. Without this step, all of your hard work could be for nothing.
Consider setting up a company-wide education program that will train the relevant staff on any new software and explain why certain changes were enacted. Most importantly, equip the team with the knowledge they need to continuously reach your enterprise data management goals.
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Today, we’ve covered a lot of ground surrounding enterprise data management strategy and hope you’re now inspired to outline your very own approach and begin implementing it.
Remember, no matter the size of your business, you stand to benefit from taking a data-driven approach to decision-making.
At Velvetech, we know how hard it can be to begin your digital transformation process. For that reason, we offer extensive data management services for companies from all kinds of business sectors. So, don’t hesitate to reach out to our team for help. We’re always happy to work on a new project.
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