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In this day and age, organisations are creating and consuming more data than ever before. As that data flows between applications and locations, data management solutions continue to develop and progress.
A number of data management trends have made their way into enterprise tools, such as: migration of data, processing to the cloud, as well as adoption of machine learning and artificial intelligence (AI) solutions.
There are also a number of trends, which companies have been utilising for years. These standard approaches to data management include processes such as:
- Data warehouse modernisation
- Metadata management
- Hadoop adoption
- Cloud usage & cloud adoption
Like any technology, there have been newer trends in data management which are aiding businesses to modernise their systems. Newer data management trends include:
- Internet of Things (IoT) data
- Machine Learning and automation
- Data lakes
Expert views of data management and insight
New products and services are constantly emerging to help businesses gain more insight from the reams of data they’re collecting and storing. Using expert opinions on what’s next in data management, we’ve compiled the top trends to watch out for this year.
1. Data warehouse modernisation
One of the biggest trends over the last few years that doesn’t look like it will be slowing down any time soon, is data warehouse modernisation. Modernising data has many benefits for businesses today, whether you’re looking to be more cost-effective with your businesses data, or even hoping to utilise Big Data. The fact of the matter is that traditional data warehouse systems are failing to meet requirements of modern businesses, with more and more companies looking towards modernisation as the solution.
Businesses around the world are becoming aware of the need to modernise in order to be competitive and responsive to the marketplace. It can seem like a huge task to modernise your data warehouse environment, but it’s a task which many businesses are undertaking as they become more aware of the benefits.
As Greg Hoffer, VP of Engineering, Globalscape, said: “We continue to see companies emphasising modernisation and digital transformation in order to realise the benefits of availability, resilience, performance and cost management. There will continue to be challenges to face, though, in ensuring that digital transformation results in appropriate solution architectures that provide appropriate security, visibility and compliance.”
Hoffer points out that there are many benefits to modernising your data warehouse. Key benefits for businesses are:
- Accommodating Big Data – modern businesses need to house massive data sets, so in order to use this data for business advantages, organisations need to upgrade and modernise their systems.
- Real Time Data – The need for real-time access to data, is huge in data-driven organisations. Utilising modern data warehouses enables your business to access features like self-service data access, giving you the ability to access your data whenever you need it.
- Advanced Analytics – an enormous benefit of going ahead and modernising your data warehouse environment is the ability to use advanced analytical tools. Predictive modelling, data mining, entity analytics optimisation and machine learning are just a few of the tools you’ll have at your disposal to gain better business insights.
2. Data protection will be a bigger priority
It seems like we hear about the security breaches in business and government data every day: many businesses are vulnerable to attacks that can result in the loss of important corporate and personal data. A recent study from GIGYA found that 68 per cent don’t trust brands to handle their personal information appropriately.
With the vast amounts of data now generated by businesses, it’s likely that this year we’ll see a rise in data privacy and regulation. Organisations will start to prioritise and implement comprehensive data protection plans as they create and store data across enterprise applications, social media platforms, cloud applications and elsewhere.
Businesses will need to take a wide approach to data protection and touch on every aspect of safeguarding their data from corruption, disaster recovery and theft.
3. Data governance will change the way the business consumes data
One of our predictions for data management in 2018 is that data governance will evolve in a new direction. It used to be that data governance meant data security and control, but now the primary focus is shifting towards value.
With this new focus, it’s safe to say that we’ll see data governance take on even greater urgency for organisations of every size. In this day and age businesses are realising that data governance should be about ensuring that data adds value to their enterprise, as well as enabling the enterprise to derive value from data.
As Adam Famularo, CEO of Erwin, said: “The business will begin to treat its data assets in the same way it treats physical assets to reduce the regulatory and reputational risks to the organisation, as well as see it as a valuable resource that helps employees excel in their day-to-day jobs.”
4. New data infrastructure will be needed for Internet of Things (IoT) data
Some businesses are already utilising the Internet of Things (IoT) technology into their products, processes and workflows. Research firm Gartner predicts that there will be a huge interest in ‘things’ connected to the internet in the next few years. It predicts that there will be more than 11 million ‘things’ connected to the internet in 2018 and, according to this Gartner research, we’ll see even more integration of Internet of Things into our everyday lives.
With Internet of Things technology being adopted on a wider basis this year, we’ll also see an increase in IoT data collected. Currently, connected products only collect sensor data, which is all well and good, but the end goal for businesses is to gain insights from that data. This will require new data infrastructure to be designed and implemented. Companies need to implement technology that can handle the constant stream of data, in addition to finding more effective ways to analyse that data.
In 2018, it’s likely that we’ll see businesses use machine learning and deep learning within their connected products in order to get data that is useful and insightful.
5. Machine learning algorithms in data management
There are many time-consuming tasks that developers need to complete again and again. Mapping sources to targets, categorising data, remediating data anomalies and creating metadata to represent new data from new sources, are just a few of these.
According to a Gartner study, Artificial Intelligence will be a major driver in the next five years. It seems inevitable that data management will be combined with machine learning on a wide scale in the coming years. Advancements in machine learning are slowly being incorporated into data management development tools to provide automation, which in turn leads to greater developer productivity.
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