• Rowen Grierson, Senior Director and General Manager at Nutanix


• 17.11.2023 10:45 am
#data #security

It’s hardly surprising given the increasing amounts of data flowing through IT systems that at some point organizations are going to get bogged down. How much is too much? What is relevant? Data can be both a blessing and a curse and it comes down to levels of effective management, as to which camp organizations find themselves in.

According to McKinsey, “investing in internal data initiatives provides a significant opportunity to maintain consistent growth, adopt new business models to support cash flow, improve efficiency, and provide the level of data security that customers demand.”

That makes sense given GDPR and data privacy concerns but also concerning how data can drive decision-making and customer insight. In short, data needs to work for the business. It needs to help the business be competitive. If it isn’t, something is going wrong. To that end, here are five tell-tale signs that an organization’s data management is stuck in a rut and needs a rethink.

1. Managing data takes up more time than actual data analytics

Data silos and inconsistent or incompatible data can undermine any organization’s efforts to use data for business intelligence. If you spend a lot of time trying to find relevant data or standardize data for analytics, then there is a data management problem.

Poor data quality can lead to inaccurate decisions as the analytics will be based on wrong, or incomplete information. The biggest culprit of this is legacy or siloed data centers. Organisations need to re-evaluate data center performance and determine whether or not keeping legacy technologies alive is a false economy?

There is also the issue of using up valuable resources. At a time when skills are at a premium, organisations need to look to automation to drive efficiencies.

2. Unstructured data dominates your datasets

There is nothing wrong with unstructured data, as it can be a source of meaningful insights that organisations can use for competitive advantage. However, it is possible to have too much unstructuredorganizations data. 

Managing this data can be a challenge and traditionally organizations struggle given its very nature. Unstructured data is qualitative and cannot be processed using conventional tools. You cannot search it either, as it tends to include images, social media posts, and audio files. This can lead to unnecessary use of resources, increasing costs and risk.

Again, siloed and legacy technologies are exposed by their inability to help, when it comes to managing unstructured data. Alternatively, Database as a Service (DBaaS) can be an effective way to combine databases across multiple departments in the cloud. This creates a single-hosted database management system, which helps users access and manage unstructured data sets more effectively.  

3. Energy bills are sky-high

Energy costs are a good indicator of data center efficiency. If bills are still high, regardless of any unit price changes (although we are all living in a volatile energy price world) it’s a clear sign something is afoot. It’s almost certainly time to look more closely at data storage provision as this could be struggling to cope with disparate data.

According to the National Grid, up to 90% of the energy consumed by data centers is when they are idle. It’s a source of huge waste, in terms of cost but it also leads to unnecessary carbon emissions. Having multiple, physical data centers is an obvious issue as this creates inefficiencies, with data storage not being optimized for organizational workloads.

Consolidating big data into the cloud and using hyperconverged infrastructures is an effective way to confront these issues, increasing energy efficiency and reducing emissions, while also improving data security. 

4. Remote or hybrid working is slow and risky

It’s become a fact of modern working life, thanks in large part to the COVID-19 pandemic, that most organizations offer employees remote and hybrid working options. During the pandemic, this led to a patchwork of quick-fix solutions, and with that came data management and security issues. Post-pandemic and many of those issues remain.

Organizations now have to revisit remote working tools to ensure fast, efficient, and secure access to data, regardless of location. Finding a data management solution that minimizes the latency of application access to storage is crucial. 

5. Lack of confidence in GDPR compliance

Building trust in data handling is key to customer loyalty. Data governance is increasingly vital for organisations given regulations and potential fines. This also encompasses data sovereignty issues, where personal data needs to reside within the borders of its origin.

This year to date, approximately €1.6 billion in GDPR fines have been imposed. It’s a policy that has teeth but it also makes business sense to do everything you can to ensure customer and company data is well managed and protected.

Data security and privacy technologies and processes need to be built into any digital transformation as data governance is a vital aspect of any data management strategy. Organizations need visibility. How is the data being managed? Are there any potential areas of exploitation? Understanding how data is moving across systems, stored, and being used by applications is now a fundamental responsibility. Without visibility, how can organizations know what is happening with the data?