6 Vital Data Management Best Practices

6 Vital Data Management Best Practices

Follow these six best practices and you'll ensure that your data management strategies are meeting the needs of today's demanding businesses.

Digital data is now the lifeblood of any business. In today's environment, it's easier than ever to generate huge amounts of information about customers, employees and the wider marketplace that can be used to transform decision-making, boost efficiency and deliver a better service.

But with so much data being collected by firms, this can lead to a range of challenges. Aside from having the physical infrastructure necessary to store, transfer and process this data, it can be difficult to separate the most relevant information from the noise, or determine how accurate some data sets are.

Therefore, having an effective management strategy is an essential part of operating in the age of big data. That's why we've put together a few key best practices that all professionals should keep in mind when seeking to make the most of their data.

1. Don't dive straight into the deep end

With so much data available, it can be tempting for companies to jump straight into the data lake and start experimenting. However, this is an approach that's unlikely to see success. Instead, it's best to start small, with a couple of clearly-defined projects that can tackle a known problem. Find something that can offer a quick win and you can enjoy a good return on investment and gain valuable knowledge of what you need to make your data work effectively before rolling out large-scale plans.

2. Focus on data quality

One of the biggest barriers to success in data management is data that is poor quality, inaccurate or outdated. Cleaning up data before it is used in any analytics processes is therefore essential. There are a large number of issues that can result in poor-quality data, from inconsistent and non-standard formats to spelling errors, so having the right tools to identify and fix these issues is vital.

3. Keep access as simple as possible

Having the right data won't be valuable to a business unless the relevant personnel are able to access it easily as and when they require it. Developing effective access controls is always a tricky balance between convenience and security, but it is well worth taking the time to ensure that each individual has the level of access that's appropriate for their role, rather than implementing blanket policies which are either too restrictive for some people or may allow others to access data they should not be able to view.

4. Take steps to reduce redundancies

As data volumes grow, it's likely that so too will the amount of duplicated data across the business. Particularly in environments that are transitioning from highly siloed structures to a more centralized system, redundant data takes up space and slows down processing. Therefore, data management strategies must have plans in place for identifying duplicate data and deleting it without impacting any processes that rely on it. Similarly, being able to spot data that's no longer relevant and remove it also needs to be a part of this.

5Make data security a top priority

Any data management strategy must take into account security considerations, but with the introduction of tough new regulations such as the EU's General Data Protection Regulation - which will impact any business that has customers in the EU, not just those that are based there - this has become more critical than ever. As well as access control and encryption where appropriate, physical security must not be overlooked.

6. Have a strong recovery plan in place

With every company now a data-driven business, losing access to your data can be one of the most damaging problems a firm can encounter. Whether it is human error, hardware failure or power issues, any interruption to a company's data flow can render an organization completely unable to do business. Therefore, no data management strategy is complete until it includes a clear backup and recovery plan that spells out exactly how often different types of data need to be backed up and what steps need to be taken to recover them in the case of a disaster.

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