Data Management Strategy: Key Steps for Data-Driven Excellence

Big data is not a fad of the 21st century. According to Forbes Business Insights, the market value of big data stood at $307.5 billion. Moreover, the market is predicted to grow to 348.2 billion in 2024 and 924.4 billion in 2032. This indicates that data is your business's new soul.

Bad data management can accumulate huge losses very quickly. According to Gartner, companies suffer a loss of almost $13 million yearly due to poor data quality. Even giants like Unity, Uber, and Samsung have faced severe damage due to bad data.

Needless to say, quality data management is the need of the hour. Excellent data management strategies are needed to maintain flawless data.

What Is a Data Management Strategy?

Data management is the administrative process of acquiring, validating, storing, and protecting this data. This process needs to be governed by impeccable strategies that prevent the organization from any pitfalls related to data handling.

A data strategy comprises components like data governance, security, architecture, and so on. It must follow certain best practices to fulfill the desired outcomes of implementation.

Why Every Organization Needs a Data Management Strategy

You might already have a successful business. You may wonder why a data management strategy is necessary. It's important to understand that the value of data has increased dramatically over the last decade. In the past, data was merely a byproduct of business operations and held little significance. Today, this data is a gold mine of near-accurate customer behavior and insightful predictions.

Data organization using an efficient data management strategy will provide the following benefits:

• Better decision-making across the organization
• Less bad data
• Improved communication and value between teams and projects working with organizational data
• Drives overall productivity and spikes performance
• Cost-effective data storage enables businesses to divert funds toward other important processes
• Better data governance, compliance, and critical data management

A significant benefit of a data management strategy is it allows you to identify and concentrate on specific goals for each part of your business. Every business has a specific set of strengths and weaknesses. This strategy will help you identify them and zero in on a set of measurable goals to improve your functionality.

Components of a Data Management Strategy

A data management strategy has five essential components. Individually, these components are responsible for ensuring that all aspects of data strategy are implemented. When implemented correctly, you can collectively achieve the desired outcomes for an organization. Let’s take a look at these components individually.

Data governance

Data governance is the process of ensuring the availability, integrity, and usability of data. This part involves setting up internal policies and guidelines for the use of data.

Data Architecture

Data architecture outlines how data is processed, stored, and utilized within the organization. This component ensures that elements like your data models and schemas are implemented correctly.

Data integrity

This component is associated with the movement and consolidation of data. Data integrity ensures consistency while being moved within the pipeline. This enhances interoperability, enabling multiple systems to communicate and utilize the data.

Data quality management

This component involves maintaining data accuracy, reliability, and consistency across the data lifecycle.

Data security

This component can be called one of the strongest pillars of data management strategy. It is responsible for keeping the data safe and preventing unauthorized access.

Key Steps to Develop an Effective Data Management Strategy

Now that you know what a data strategy is, let’s explore how to develop one for your business. Finalizing a custom strategy to serve your business might take some time, but you can start with these steps.

1. Identify your objectives

It makes no sense to collect and store data that your business does not require. You will be wasting valuable time and resources on data that won’t give you any returns. The following pivots can help you understand your business objectives:

• What services do you want to provide to your customers?
• What data are you going to need for providing these services?
• What insights are you going to need for delivering these services?

2. Data process

This step involves your data collection, preparation, storage, and accessibility. Let's figure out how each of these functions works:

• Data collection - Here, you must determine your data sources and the type of data you will collect (structured, unstructured, or both)
• Data preparation - This part identifies your data cleaning methods and the parameters to classify the data as clean and complete.
• Data storage - In this step, you decide what storage facility you want to use for your data. It largely depends on the type of data you need to store
• Data accessibility - Once all the above steps are taken care of, you must determine who needs to access that data and the insights generated. This element is responsible for maintaining authorized access to data for security purposes.

3. Finding the technology

As you navigate this approach, you might want to find the right tools that can help you at every stage. Today, there are many solutions available, and you can find the right one by carefully examining the services each offers. For example, Acceldata can offer services like data quality and reliability along with data observability options.

4. Establish governance

Increased use of data collected by your organization has a lot of benefits, but these benefits come with a lot of responsibility. You must ensure data quality and security at all times. Your data collection and usage policies must be transparent and ethical to foster trust in your consumers.

5. Training your resources

Lastly, it is essential to train all your resources in the latest processes. Navigating a new system can be challenging for anyone. Establishing regular training sessions can enhance productivity and improve the effective implementation of the system.

Best Practices for Implementing Data Management Strategy

An effective data strategy requires a lot of effort and planning. This amount of work can seem daunting. However, you can reduce the efforts required by simply following a few best practices along the way. These simple practices can drastically reduce the time and effort required, making your life easier:

• Spread company-wide awareness - Everyone in the organization should understand the new strategy and be ready to adopt it.
  
  
• Cultivate a data-centric mindset - Everyone in the company should instinctively turn towards data for any answers they require.
  
  
• KPIs and metrics tracking - If you plan to become a data-first organization, everything needs to be measured. This includes employee performance as well.
  
  
• Don't chase every new trend - New buzzwords every other day can easily trick you with the hype. Don't fall for these trends; rather, analyze your needs accurately. A good data strategy is measured by the quality of data, not the number of latest tools used.
  
  
• Skilled resources - You'll need someone who can understand how data impacts business goals to achieve data-driven excellence. You must be proficient in acting as an intersection between various teams.
  
  
• Strong data security - Data and security always go hand-in-hand. You must maintain data security at all times to comply with data laws of any particular geography to avoid any legal repercussions.
  
  
• Data automation - There are automation tools available for collecting, processing, and storing data. You can opt for any such tools, taking this process off of your hands and putting your valuable time elsewhere.
  

Tools and Technologies for Data Management Strategy

Identifying the perfect tools for your data analysis, storage, and management is important. The efficiency of your whole system is dependent on the quality of the final data. Here are some of the tools and technologies you can use for data management today:

• SAP Data Management: It's a platform that uses a single point of access to all data.
• Microsoft Master Data Services: It has services that enable the employees to manage the master set of organizational data.
  
  
• Oracle Data Management Suite: This suite has solutions that help you build, deploy, and manage data-driven projects.
  
  
• IBM Infosphere: This comprehensive tool presents all your data in a single view with analytical capabilities.
  
  
• Talend: It is a single tool to perform processes like data integration and management, and provides cloud storage as well.
  
  
• Amazon Web Services: It enables the users to manage their data lakes for analytics.
  
  
• LookerBI: This tool helps gather data from various sources to make data-driven decisions.
  
  
• Google Cloud: This popular tool offers a broad range of cloud-based data management services.

As mentioned earlier, these are just a few of the numerous tools available in the market for effective data management. You can surf through as many as you want and take a call when you find the right one, or you can opt for the top-notch services provided by Acceldata.

How to Choose the Right Data Management Technology Stack

How would you know if you have found the right tool? Let us help you with this, too. Follow these steps to arrive at a solid decision about using a certain tool for your data management purposes:

1. Define the scope: Defining the scope provides a clear understanding of your goals.
   
   
2. Identify requirements: Identify the organization's requirements based on the initial needs assessment to translate them into specific objectives and actions.
   
   
3. Check out the available tools: This is probably a tedious process. You should first evaluate existing tools within your company to determine if they can be improved or if you need to implement new solutions.
   
   
4. Assess scalability, flexibility, and ease of use.
   
   
5. Calculate the investment and returns.
   
   
6. Select the best fit.

Addressing Common Challenges in Data Management Strategy

Building something from the ground up is not always an easy task. Along the way, you might face some common challenges. Formulating a data management strategy has its own set of challenges:

• Weak strategy and goal setting - Make sure you have covered all the necessary steps to create a foolproof strategy to avoid this challenge
• Employee reluctance - A new change across the company must be welcomed by all stakeholders
• Lack of resources - Make sure your budget supports the data strategy you are developing
• Ineffective communication - There must be effective communication channels and plans in place to avoid any miscommunication.
• Lack of reviews and follow-through - All metrics should be reviewed to develop continuous improvement plans. You need to have up-to-date knowledge about your systems to stay on top of the game.

Trends to follow for Modern Data Management Strategy

Data-driven systems are evolving at an unprecedented rate. To build a robust and efficient data management strategy, you must be well-versed with the latest trends in the industry. Here are a few that will familiarize you with what's new on the board:

• Multi-cloud environments - This was a luxury earlier but has now become a necessity. With constantly increasing data creation and consumption, it is important to adopt multi-cloud strategies to maintain flexibility, reliability, and scalability.
• Data compliance and security - It is getting more and more difficult to collect customer data without violating any laws. Data protection policies are becoming stricter each day. Your data collection and utilization processes must adhere to these laws at all times.
• Clean data organization - The true value of data lies in the insights and analytics derived from it. To make the most out of data, you need to properly organize your data store, even unstructured data. Your data organization skills are a determinant factor in how much value your data will reap.
• Metadata management - This is an important aspect of enhancing data discoverability. There are many competent tools and technologies to handle metadata management efficiently.

Elevate Your Data Management Strategy with Acceldata

A data management strategy acts as a foundation for your aspirations to build a customer-centric organization. Here at Acceldata, we understand the importance of developing a robust strategy to ensure your data is reliable, flexible, and safe at all times. You can use Acceldata Data Reliability and Quality products to meet your data management needs. Act today and leverage the value of your data!

Summary

Building a strong data management strategy might seem a little intimidating. However, with all the important concepts comprehensively laid down for you here, you should be able to pull this off comfortably. Right from the need, components involved, and key steps to implement to best practices, common challenges, and the latest trends in the market, we have covered every aspect of a data strategy.