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What Is Data Management And Why Does It Matter to Your Business?
Businesses need to invest in robust data management processes to ensure the data they’re using is accurate, reliable, secure and easily accessible.
Today’s digital, interconnected nature of our world allows businesses access to more data than they’ve ever had previously. This data is a fountain of intelligence and insight for organisations looking to make data-driven decisions to drive success and profitability. However, access to this data is not enough, businesses need to invest in robust data management processes to ensure the data they’re using in their strategies is accurate, reliable, secure and easily accessible.
Defining Data Management
Data management is the ongoing practice of collecting, storing and utilising business data within a business or organisation. The primary purpose of data management is to help organisations optimise their data usage to help them make optimal decisions and implement strategies that will benefit and improve their business.
Having a comprehensive data management strategy that adheres to regulatory requirements is becoming increasingly vital to organisations looking to harness their data strategically.
Data management within organisations consists of an all-encompassing array of tasks and activities relating to data aggregation, analysis, policies and procedures, including:
- Creating, updating and accessing data.
- Aggregating and storing data in onsite storage drives and the cloud
- Generating processes and policies on data recovery.
- Using data across BI tools, apps, analytics platforms and algorithms
- Ensuring data security and privacy.
- Archiving and deleting data according to reforming regulations and compliance requirements.
Collectively, these processes ensure that a business’s data is accurate, accessible and adheres to regulatory policies.
A formal data management strategy considers the activity of both users and staff, the capabilities of existing data management tools and systems, regulations demands, and the organisation’s data needs.
Why Good Data Management is Vital
With data playing an increasingly important role in organisations’ business decisions and processes, proper data management is essential. When harnessed correctly, data can help businesses refine their strategies, improve their campaigns, enhance their operations and reduce their overheads, contributing to more net revenue.
However, inconsistent or inaccurate data management creates data silos, faulty datasets and problems with data quality that limit or prevent businesses from engaging in business intelligence (BI) processes or running analytics programmes, leading to inaccurate findings that could harm business success and output.
Excellent data management is also becoming paramount for businesses to keep up with the growing number of compliance and regulation requirements from government and consumer protection bodies.
Internally, good data management also brings numerous benefits to businesses.
Effective data management can increase the visibility of data assets within an organisation. This makes it easier for staff and admin to search and find the data they need for their purposes, improving productivity.
Data management can help to minimise potential errors caused due to inaccurate data by establishing usage processes. This improves the data’s reliability to the organisation and allows businesses to respond more efficiently to unexpected customer needs or sudden market changes.
Having robust data management procedures in place that utilise encryption and authentication tools protect a business and its employees from data breaches and theft, as well as accidental data losses.
Data security protocols ensure that vital business data is protected, backed up and retrievable if its primary source is somehow affected or goes offline.
With help from data management, businesses and organisations can scale data and usage occasions with established, repeatable procedures to update data.
Keeping these processes simple makes them easy to repeat and reduces the chances of unnecessary data duplication, such as employees conducting the same data research twice or re-running data queries.
Common Data Management Challenges
The benefits of data management don’t mean that it comes without challenges that businesses need to overcome to get the most from their data. These are a few common challenges organisations face when starting their data management journey.
Data is being collected from an increasingly varied array of sources, including social media, smart devices, sensors, drones and video cameras. This data in its raw form is useless and doesn’t provide the insight businesses need.
Data management processes need to be tailored so they provide not just insight, but the right insight that businesses need to make important decisions.
It can be difficult to maintain data management performance levels. As organisations are capturing, aggregating, storing and using data all the time, making maintaining data performance important.
To reinforce data performance and response times, businesses need to consistently monitor and assess the types of questions the database is answering and alter indexes as queries change without affecting the performance of the data.
Compliance regulations relating to data management are often complex and vary across multiple territories, constantly changing. Organisations may find it difficult to stay up to date with changing requirements and will need to be able to easily review their business data and identify areas that need updating.
Data processing and conversion
Simply collecting and storing data won’t provide the value businesses are looking for - the data must first be processed and converted. Data conversion and processing can take time, and if done incorrectly, the potential business value of that data is lost.
Incoming data from multiple sources need to be stored in a secure location that’s easily accessible to organisations. Most businesses use data warehouses and data lakes that store any data of any format in a single repository.
This data needs to be transformed quickly and simply from its original format into the model that businesses need it to be.
Types of Data Management
Data management consists of multiple processes within a business’s data environment. Development of data architecture is generally the first step, which provides a blueprint for the business’s databases and other data platforms that will be deployed. Some other data management processes include:
- Data preparation - cleans and transforms raw data into the right format to be used for analysis, making corrections, combining datasets and taking action.
- Data pipelines - automate and enable the transfer of data from one system to another.
- Data catalogues - work with metadata to create a more comprehensive picture of the data by providing its revisions, locations and quality and making the data easier to find.
- Data warehouses - consolidate various data sources, work with the different data types that businesses store and provide a clear route for analysing data.
- Data governance - establishes and defines standards and procedures for ensuring data integrity and protection.
- Data modelling - documents the flow and movement of data within an organisation or an application.
- Data security - protects data from unauthorised access and data corruption.
Best Practices for Data Management
Addressing data management challenges and reaping the benefits of good data management require businesses to implement a set of best practices that will create a framework for effectively managing data.
- Identify your larger business goals.
The first step to excellent data management is understanding why you’re collecting and working with the data in the first place. This requires businesses to identify and establish the goals they want to achieve through data management.
Having goals in place will help you determine the best processes for aggregating, storing, cleansing, converting, managing and analysing data. Your business objectives will help you ensure that you’re only collecting and organising relevant, useful data.2. Keep data quality in mind.
Putting processes together to provide your organisation with reliable data is taking the necessary steps to improve its quality. First, put the procedures in place to streamline data collection and storage but also conduct regular checks for accuracy to ensure that the data does not become outdated.
These procedures should also identify inaccuracies, such as spelling errors or incorrect formatting, that could impact the final results. To do this, ensure you train your team members on the correct data input and prep processes or utilise data prep automation to ensure data quality from the beginning.
3. Let the right people access your data.
Having accurate, reliable, high-quality data is one part of successful data management. The other part is allowing the right people inside and outside your organisation to access this data whenever they need it.
Rather than setting organisation-wide rules for accessing important business data, it’s better to establish different permission levels of accessibility so each employee can easily access the data they need to do their jobs.
4. Make data security a priority.
Your data should be accessible to the right people, but it should also be secured and protected to prevent it from being stolen by hackers or organisations outside of your business.
Train every employee with any accessibility permission on how to handle data securely and ensure your procedures keep up with changing compliance regulations. Prepare for any worst-case scenarios and establish strategies for handling potential data breaches.