7 Steps to a More Effective Data Management

6 Tips for Making Better Data Management Decisions

Data has become one of the most valuable resources for business, providing key insights about the company and its market. However, with so much data available for analysis, the need for effective data management is becoming increasingly important. Here are some the steps you can take to ensure your company is using best practice when it comes to managing your data.

1. Know what you want to use data for

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Today, companies are gathering enormous quantities of data. Much of that, however, will never be used. To make data management easier, it is important to know what you want from your data so that you keep that which helps meet your objectives and get rid of that which does not. This will help reduce storage costs and make it easier to organise and manage what data is kept. If you have data which is not useful at present but which you want to keep in case it becomes useful in the future, you can always condense it and store it separately.

There are many things you may wish to use your data for. These include data-driven decision-making, automation and processing improvement, customer journey mapping and personalisation, audience targeting, product recommendations and so forth. Knowing what your objectives are will help ensure you only gather and collect the data that you need.

2. Security and compliance are key priorities

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All companies are obliged to comply with data protection regulations, such as GDPR, and this means compliance should be foremost on your list of priorities. Failure to comply or implement stringent security measures can have devastating consequences. Under GDPR, businesses can be fined up to €40 million or 4% of annual global revenue, whichever is the greatest. For British Airways, this was an eye-watering €204.6 million. On top of any fines, there are also reputational damage and case actions to consider.

Working with the right hosting provider, implementing robust security policies and using secure data management software can help your data to be more secure.

3. Data quality is vital

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The quality of the insights your data can provide is based upon the quality of the data you are using. While the first step to improving quality is to limit data to that which meets your company’s goals, it is also vital that data is accurate and up-to-date. Inaccurate or out-of-date information can provide  misleading insights and lead to companies making the wrong decisions. For this reason, cleansing data should be something undertaken regularly.

Quality can also be improved by ensuring that employees know how to accurately gather and input data or that systems which collect data automatically are configured to do so correctly.

4. Eradicate duplicate data

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Not only does duplicate data means you’ll pay for more storage than you need; there’s also the chance that it will be counted twice during any analysis and will, thus, skew the accuracy of any report generated using it. As there are various ways to receive duplicate data, companies need to put processes in place to discover and prevent it from being inputted into the data management system.

5. Managing access to data

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There are two important things to consider when looking at access to data. The first is that, for data analytics to be effective, team members need access to all relevant datasets. This is incredibly difficult to achieve when it is stored in departmental silos where access is restricted to departmental members. Unifying data in a centralised repository, like a data warehouse, removes silos and ensures everyone can have the big picture. It also means data security can be centrally managed.

The second consideration is balancing security with ease of access. This means setting up logical access control where permission to access data and tools is granted on an individual basis depending on the employee’s needs. Not only does this ensure employees only use data they have the authorisation to access; it also means that if their accounts are hacked, hackers won’t have unlimited access to the company’s entire data or its applications.

6. Make sure data can be recovered

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Data loss can happen for a whole host of reasons, including human error, malware, hardware failure, natural disasters, hacking and so forth. While losing personal data can get the company into hot water over compliance, all the other data your company relies on will have significant value too. Losing that data can put a company out of business. To ensure it doesn’t happen, having a remote backup system in place is crucial.

Ideally, you should schedule automated backups to be taken at the intervals which the company needs. For the increasing numbers of businesses who receive data continuously, this will mean having continuous backups taking place, so that, if the worst happens, you have as much recent data as possible to restore. Cloud storage is often the best solution as it is scalable, secure, integrity tested, can be encrypted and is easily accessed for restoration.

7. Choose the right hosting provider

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The hosting provider plays a vital role in ensuring good data management. They will provide and manage the infrastructure needed and implement a range of stringent security measures including firewalls, encryption, intrusion protection, backup services, etc. Those opting for a cloud solution will also benefit from the scalability and payment model of cloud when it comes to storage and processing capacity. Crucially, however, is choosing a host that understands your goals and your needs and which can supply the expertise and computing environment your data management requires.


Effective data management is essential for businesses to make the best use of big data analytics and the insights it provides. Knowing what you want to use that data for, improving its quality, eradicating duplications and making it easier to access are key elements in that management strategy. So, too, are ensuring the data is secure, setting up logical access, making remote backups and choosing the right hosting provider.

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