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Data Management

What is data management and why should organizations learn its importance?

Data management is the process of collecting, managing, and securing your organization's data for the use of marketing, sales, and analysis. With a lot of data security breaches, most countries have come up with strict regulations on how data should be stored, accessed, and secured.

A proper data management strategy is not only crucial but also mandatory to maintain the reputation of your organization.

What is data?

Traditionally, there were concerns regarding the safekeeping of data because it holds important information about customers, business transactions, and more. However, with the rise of AI, data is becoming the driving force behind predictive and AI-based engines that aid in building effective models and solutions for various business processes.

Let's take a self-driving car as an example. Without millions of datasets like driving patterns, accident patterns, item recognition, and similar data, a car cannot drive on its own without crashing. So, the use of data has evolved over the years, and security is mandatory because the business USP and workflow depend on it.

Why is data security a concern?

With the rise of digital technology, organizations are slowly adopting various cloud-based software to run different aspects of their business. This exponentially increases their data footprint, putting them at a huge risk of data loss and theft.

Data security statistics are urging organizations to start enforcing diligent data management processes.

• 94% of organizations that suffer from data losses are unable to retrieve all of their data (Source Truelist).
• Over 60% of SMEs are under the risk of ransomware attacks (Source: SMEHorizon).
• Enterprises lose around $15 million dollars every year due to data quality issues.
• The top three reasons for data loss is some kind of human error (Source Truelist).

Need for data management strategy

Data management is not a simple feat. Depending on the size and nature of your business, there are various kinds of data used by different teams. Earlier, one was required to have an on-premise server to keep data in a centralized location to access it. It was not always easy for a lot of non-technical organizations to achieve this because of the cost it incurred, as well as the need to hire IT personnel to manage the data and the IT infrastructure.

With the rise of SaaS solutions, the problem was addressed where organizations were able to look beyond using their local systems and could bring their data onto the cloud. This made collaboration easier and data was available for wider use. Having your data on the cloud doesn't necessarily mean it's secured or analysis is easy, though. Some of the various shortcomings of the cloud are:

• Security concerns of the public third-party cloud that stores your data.
• Data silos because data access resides with different data owners due to different service providers.
• Lack of clarity with respect to data governance to ensure the right people have access to data.
• Improper data lineage results in the lack of understanding of what happens to the data.
• Data loss due to improper data backup process.
• Formatting inconsistencies in data because of multiple sources of data.

Managing all these aspects of data is not easy for most organizations without a dedicated process to enforce data management in a seamless manner.

Effective data management strategy

Even if there are no processes to manage data, there is some sort of data management that happens across various departments with different stakeholders. However, to scale your business or to be compliant with various data policy regulations, the informal data management setup will not help. One needs to look at a holistic approach to the organization and a vision to come up with a data management process that works.

Data management best practices to build a fool-proof strategy

Step 1: Identify different data tools and techniques used currently in the organization.

Step 2: Figure out the current and future data needs of the organization.

Step 3: Build data management infrastructure and define processes to manage data.

Step 4: Identify stakeholders who are responsible for the data and set up governance.

Step 5: Catalog and label the data for easy retrieval.

Step 6: Set up periodic backups to ensure data is safe.

Step 7: Periodically educate stake holders about data management best practices.

Benefits of good data management

Following a dedicated data management strategy helps your organization stay transparent, efficient, and secured. With organized data, your organization has the following benefits:

• Better decision-making based on accurate data.
• Increased accountability with proper data governance.
• Automated data backup to safeguard your organization from data loss.
• Compliance with various global data regulations.
• Easy and faster data retrieval with proper cataloging.
• Less chance of duplicated efforts by ensuring every team has access to clean and ready-to-go data.

The future of data management

With data accumulating at a fast pace and the incredible evolution and adoption of AI, the importance of data management is going to increase drastically. In the next few years, more countries will have their own data protection laws to enforce proper data privacy and security.

Some data trends to look forward to are:

• Most organizations will start using cloud data management and analytics tools.
• AI is going to play a crucial role in all industries.
• Big data will be adopted by organizations of all sizes, and they will start looking at data to make key business decisions.
• The importance of data privacy and governance will increase drastically to ensure data is safe and secured.

How Zoho DataPrep can help your data management strategy

Data preparation is an important part of your data management process. It is where all of your data is organized and cataloged for easy analysis. Zoho DataPrep is a self-service data preparation tool that can easily help get your organization data-ready. You can:

• Consolidate and standardize data from different sources.
• Remove duplicate and invalid data.
• Catalog your data to segment them effortlessly.
• Create automation to ensure there is a seamless flow of prepared data.
• Set data governance to ensure only approved people have access to the data.
• Easily export prepared data to the data warehouse of your choice for the safekeeping of data.