Data Management: Ultimate Guide

The rapid growth of data and the speed at which it enters a business means that data management should be a top priority. Data can only provide value when it is accessible and usable. In order to make this happen, data management principles must be adopted and executed.

Here, we will cover everything you need to know about data management and touch on the types of tools that can help your business leverage data to its utmost abilities.

Coming Up

1. What is Data Management?

2. History of Data Management

3. What are the Challenges of Data Management?

4. What are Data Management Best Practices?

5. What are the Benefits of Data Management?

6. What are the Types of Data Management?

7. What are the Data Management Tools?

8. What are Data Management Challenges and Risks?

9. Data Management Tasks

10. Importance of Data Automation

11. Final Words

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What is Data Management?

Data management refers to the process of collecting, validating, processing, and securing data. The goal of data management is such that users can access necessary data in a timely manner and be able to trust that it is accurate and reliable. Data management also takes into consideration the cost of having data and looks to achieve cost effectiveness in doing so.

Every type of business has some form of data. No matter how much data is acquired, data plays a major role in informing key decisions. But, if you have data with no way to use it properly, then you run the risk of wasting time, money, and energy of valuable resources. By incorporating data management into your business practices, you have everything to gain.

Organisations of all sizes are adopting a data management system because the benefits of using data management software are far-reaching.

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History of Data Management

While there isn’t one set day where we can surely say data management entered the world, there are periods of time in which we can see its evolution coming into play. During the 1960s, the Association of Data Processing Service Organizations (ADPSO) started to provide professionals with advice about data management. By the 1970s, data management systems were developed and operational. These databases stored information in data warehouses and allowed for reports to be generated at specific times.

Data management has since evolved massively and includes functionalities like:

• Batch processing
• Structured query language (SQL)
• Non relational databases
• Data catalogs
• Cloud computing
• Data streaming
• Enterprise data warehouses, data fabrics, and data lakes

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What are the Challenges of Data Management?

Business happens fast. So does data. To be able to keep up with the influx of data and render it usable requires more than manual effort. As such, data automation solutions and data management software is key to successfully dealing with data.

This being said, there are more data management challenges that can arise, including:

• Knowledge: Data comes from various sources. From emails, social media, text, photos, and more, data exists everywhere. Many organisations are unaware of what data it has and what to do with what they have.
  
  
• Compliance: Of primary concern is the security of the data that your organisation is collecting. Compliance and regulations are constantly changing and adapting to keep up with the nature of business and data collection. This is especially true within industries like finance where data is sensitive.
  
  
• Scalability: With a set amount of data, an organisation may be able to manage it well. But, data is like a living and breathing organism that multiplies quickly. So, the scalability of data management must be considered when approaching data from the get go.
  
  
• Storage: Data can be stored in different systems, from warehouses to data lakes. Yet, data is most valuable when it is being analysed and utilised together. To perform accurate analysis, an automation solution can be used to pull data from multiple sources in real-time so you have reliable information at your fingertips when you need it most.

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What are Data Management Best Practices?

A robust approach to data management begins with the acknowledgement of best practices. We’ve pulled together some of the most important points gleaned over time from working with organisations around the world:

• Data Identification: Create a discovery layer so that your team can browse datasets and easily find what it is needed at any given time.
  
  
• Automation Solutions: Robotic process automation and intelligent automation have reshaped how organisations can utilise and manage their data. With automation, databases can be accessed in real-time and analysts can focus on high-level tasks rather than be bogged down by repetitive and manual labor.
  
  
• Data Repurposing: Automation solutions also make it possible to perform data transformation in a fraction of the time that it would take a human to complete. This means that you can streamline data analysis and hasten the time it takes to get insights from raw data. It also makes it feasible to test different data models more quickly so that your organisation can remain adaptive and proactive, rather than reactive.
  
  
• Data Compliance: Since data can exist in multiple systems, the connections made between data must be secure and compliant. Tools can take care of data discovery and detect connections between data to maintain compliance.

• ‍Common Query: Even though data may exist in different locations and be stored based on different structures, database management software is equipped with a common query layer. This allows the system to be able to pull data from various sources and transform it into a singular format so that the data is usable.

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What are the Benefits of Data Management?

Being able to best serve your customers and employees relies on having an understanding of their needs and the value that your business can provide. Since you can’t expect to talk to every customer individually about their desires and experience, data fills in the missing gaps.

Through both structured and unstructured data, you have the potential to find answers. But, it’s basically like code, so you need a data management system to be able to decipher the raw information to transform it into digestible facts.

Beyond the major benefit of being able to enhance and personalise the customer experience, the benefits of data management span:

• Identifying the root cause for business failures and issues in real-time
• Boosting customer engagement
• Improving customer loyalty
• Increasing employee satisfaction
• Growing revenue
• Decreasing costs
• Remaining compliant
• Avoiding data breaches and hacks
• Aiding in better overall business performance
• Achieving operational efficiency

Data management is the combination of applying technology to a data management strategy to achieve business goals. While the software can do most of the heavy lifting, it’s still necessary to define your data goals and know what type of data you need to collect.

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What are the Types of Data Management?

Data management involves many techniques and technologies to work properly. Here’s a brief overview of all the aspects and considerations that are involved in data management:

Database

A database is the computer system in which structured data is stored electronically. The database is controlled by a database management system (DBMS).

Database Administration

Database administration refers to the function of managing the database management systems. In many instances, an organisation will employ someone who is an expert in information technology to perform this function and serve as the database administrator.

Database Management System

Database management system is the technology and software that stores, pulls, defines, and maintains data within a database. There are different types of database management systems, including: relational database management system (in which information is stored in tables of rows and columns), NoSQL databases (can store unstructured data), and in-memory databases (where data is stored in the server’s memory).

Data Modelling

Data modelling is the process of developing a model by which data can be stored within a database. It’s akin to creating an organisational structure within a filing cabinet or spice cabinet, let’s say. You define the association between different data objects and rules by which you will organise each piece of data.

Data Integration

Data integration is pulling data from various sources into a single centralised location to be able to use it. This way, users can access data with consistency. Data integration allows for you to automate processes.

Data Governance

Data governance consists of policies, processes, and the people that dictate how you manage your data. In order to align business goals with your data uses, you can outline rules by which your technology and team are to adhere to with respect to data and information.

Master Data Management

Master data is an organisation’s core data which its operations are based around. Master data domains involve customers, locations, products, and other related information. Master data management is the process of storing and ensuring that the master data is accurate and consistent.

Data Quality

Data quality is focused on making sure that data is usable and relevant for business purposes. From the moment data is collected to the time it is being used, data quality is required which means that data must be complete, accurate, and relevant. If data is not of good quality, then it can lead to costly mistakes.

Data Preparation

In order to analyse data, data preparation occurs. This involves how data is collected from various sources, cleaned, and transformed into a unified structure to be analysed. With an automation solution, all of these tasks can be complete without the need for time-consuming manual work.

Data Storage

Data storage refers to the physical hardware required to store data.

Data Security

Data security is everything that’s done to protect data so that only authorised users can access the data.

Data Manipulation

Data manipulation involves reorganising and structuring data so that it is readable. Automation and data management software uses programming and code to manage this task.

Data Aggregation

Data aggregation brings together data so that it can be presented in a summarised view. Data analysis requires that data be of high quality and necessitates bringing together data from various sources. In order to ensure results are significant, there must be enough data to analyse. This is why data aggregation is so important, especially when it comes to financial decisions and business strategy.

Data Wrangling

Data wrangling consists of the steps it takes to clean data to be accessed and analysed. It begins with data collection (or acquisition), involves the combination of edited data, and results in data cleansing so that bad data can be removed before the data is processed and analysed.

Data Transformation

Since data is collected in different formats, data transformation takes place so that data can be structured into a unified format for usage. Data transformation is part of data wrangling, data integration, and data warehousing, to name a few.

Manual Data Processing

Like the name implies, manual data processing is the non-technological process of humans managing data. People may use physical filing cabinets, writing utensils, and paper to achieve manual data processing. As opposed to automatic data processing, manual data processing is time-consuming and error-prone. It’s also less easy to standardise, protect, and share.

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What are the Data Management Tools?

Data management tools come in different forms. From data management systems to data modelling tools, there are various components to properly managing data. Here’s a look at what’s typically involved:

Database Management System

As alluded to earlier, there are a few data management systems (DBMS) to consider. The most commonly used DBMS is a relational database management system. Relational databases store information in columns and rows.

Rather than having to devise duplicate data entries, tables can be connected. NoSQL systems also exist. The four main types of NoSQL systems are document databases, wide column stores, graph databases, and key-value databases. NoSQL databases can store all types of data.

Data Warehouses and Lakes

Data warehouses pull together data from disparate operational systems. In most cases, a data warehouse is used for business intelligence and reporting. Data lakes are places where data is stored and used for analytical applications like machine learning and predictive modelling.

Data lakes usually hold data in its raw form. For this reason, it’s not uncommon to need a data scientist or analyst to get involved in order to manually prepare data for analysis.

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What are Data Management Challenges and Risks?

Without proper organisation for data, there are many risks that can arise. For starters, without an integrated system, data will exist across platforms and be siloed. Without data integration, the insights gleaned from analysis will likely be inaccurate, and therefore, should be deemed unusable and unreliable.

Additionally, many departments within an organisation need to share data. But, if they are each using their own systems or personal spreadsheets to maintain data, then there’s no real-time updates and various users may have different information to work with. Herein lies the problem of using disparate data and making important decisions based on incomplete information.

To overcome the challenge of siloed data, automation software has the functionality to collect, transform, and store data from disparate systems in a centralised repository for usage. When data is stored in the cloud, it becomes easier and faster to access.

However, organisations often find it overwhelming or daunting to move data from legacy systems to the cloud. By moving data from on-premises systems to the cloud, data becomes more cost effective to store. It’s also an option to continue to use your existing toolstack and choose an automation tool that can connect with your data through APIs and integration without posing any risk to your data.

When you store data in the cloud, there’s the potential to expose data and suffer from a breach only if it’s not properly managed. Software systems should be deployed to account for these risks. With the right software partner, you can trust that your data is stored safely and that only users who are authorised will be able to enter the system and access the data.

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Data Management Tasks

From all the nuances involved in data management, you probably understand that there are many tasks and roles involved with data management. Depending on your organisation’s size and resources, you will choose how to approach data management.

Within the industry, some roles that are related to database management involve: data modellers, database administrators, database developers, data integration developers, data engineers, data stewards, and data architects. Whether you have access to these personnel or not, you can leverage all-in-one solutions that can fulfill the functions that these roles are responsible for managing.

Automation software has been expertly designed so that there’s no/low code required to get up and running to manage your every data need. From data collection to transformation to storage and analysis, the software can be programmed using a simple user interface to cover your functions.

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Importance of Data Automation

‍Data automation is the application of technological solutions and equipment to collect, process, and store data. Designed with artificial intelligence, data automation software utilises robotic process automation to execute tasks that would otherwise be performed manually by humans. As data grows, the tool becomes more successful in its predictions and analysis, thanks to machine learning.

In any industry, data automation can maximise performance while decreasing inefficiency. Data automation offers business leaders and teams with what they need to make the best business decisions in real-time.

Data automation makes it possible to automate entire processes from start to finish. Gone are the days where you relied on a single person to get a task done and suffered when they were out of office on vacation or for an illness.

Automation tools also help to reduce compliance risk by standardising processes and securing data. No matter where or when a process takes place or a report needs generating, the data automation tool is programmed to execute the tasks in the exact manner you desire.

You can use automation tools to connect data across your existing toolstack, reconcile big data in record time, and assist humans in getting their day-to-day activities done while freeing up their time to focus on high value-added activities.

By utilising an automation tool, you can say goodbye to manual data processing. Instead, you can use the software to manage your data tasks like onboarding, records and reporting, data extraction, data consolidation, and data modelling, to name a few use cases.

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Final Words

Data management has the power to make or break an entire business. With each passing day, database management becomes more paramount given the amount of data that is produced. In order to take advantage and reap the benefits of having access to data, it needs to be made usable and properly maintained.

Automation software and database management software can take care of all the components of data management for your organisation. Take a look at how SolveXia can help your organization of any size.