Operating a Successful Data Management Strategy
Businesses, charities, local government, educational establishments, the National Health Service (NHS); the list could go on, these organizations have all seen the knock-on effect of, if not been directly affected by, cyber-attacks, including those that have made headline news recently.
However, cyber-attacks are just one of many challenges that organizations face daily; successfully managing data and implementing strategies around these challenges is an arduous task and there is often no ‘one shoe fits-all’ solution.
What is a Data Management Strategy?
A Data Management Strategy (DMS), refers to a set of policies and procedures that an organization follows to successfully keep control of and protect data from all threats and challenges. A DMS needs to bear in mind many aspects and each network, site or organization is likely to have a different strategy. Factors to bear in mind may include:
- What sources of data are there?
- How much data is there (new and existing)?
- How long should data be retained for?
- How valuable is the data?
- Who needs to access data?
How to pick a Data Management Strategy to fit your organization
The first step in setting a DMS is to understand the aims of the DMS and to have a full understanding of the data/network that needs protecting. While undertaking a full audit can be time consuming, it will give valuable insight into data, how to protect it and could shine a light on any changes that should be made immediately.
With data growing at such an exponential rate, an estimated 40% year on year, keeping control of data and where it is stored is not easy. For many organizations, data is sprawled across multiple platforms and systems, including for public and private clouds and a data audit can help to identify where data is located and allow centralization and more effective management.
Once a data audit has taken place, data must be categorized and placed into its correct stage in the data lifecycle, this will help to identify further how the data should be protected and whether it needs to be retained, archived or deleted.
- The Data lifecycle is useful to examine in determining when data has been created and how long it should be retained before it is deleted.
As different data sets and sources will have different values and retentions, your DMS needs to take this into account. Similarly, the size of your organization and IT spend should be considered when planning to implement strategies or changes to your data management approach. While there are solutions available that can centralize and automate the whole of an organization’s strategy, these are likely to come at a premium and may not be suitable for all – they may also have functionality that is unnecessary for your business.
To help protect and manage data, it is important to know where it is and if possible reduce the spread of it. Centralizing data will give you more control, allow you to set policies around who can access sensitive information and reduce the risk of an internal data breach. In addition, with data in less locations there are less avenues for external data breaches in the forms of malware or hacking to occur.
Automating data management
While policies and procedures need to be implemented, automation can be a vital tool to ensure this process happens quickly, works effectively and is free from human error. Increasing the number of data management processes through a DMS could have a negative impact for your organization if the strategy is not followed effectively as it may become complex and time consuming.
A successful data management strategy, should simplify processes, increase the protection and visibility of data and help to drive IT efficiency. When deciding on strategy, measurements should be taken as a benchmark and then used as KPIs for regular reviews of the effectiveness of the strategy.
- KPI / Key Performance Indicators are measurable values that demonstrate how effectively a business strategy is working.