Data and Analytics Trends: The Future of Data for Business


Even fully established data-driven organisations need to keep an eye on the horizon. Fail to prepare for what’s coming next and you risk missing out on lucrative opportunities or fall behind your competitors. What future trends should you be looking out for?  Here are a few of the top trends in data analytics.
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Augmented Analytics

Augmented analytics enhances the value of data by enabling machine learning (ML) and natural language processing (NLP) to enhance data analytics, data sharing and business intelligence. Data can be processed much faster using augmented analytics than it can by humans, so, with data scientists currently reportedly spending 80% of their time preparing data, augmented analytics presents great efficiency opportunities.

Continuous intelligence

Achieving continuous intelligence (CI) from your data refers to the approach of using machine-driven analytics in a continuous and frictionless way to process data, enabling users to gain fast access to any data and analysis, no matter where the data is hosted, how many sources exist and how much data there is.

Explainable AI

In recent years AI technology has attracted a lot of negative media scrutiny which has served to erode public trust. To overcome this, there’s an increasing requirement for AI developers to improve transparency around how AI systems process data and derive recommendations. Unlike “black box” ML solutions where it may not be possible to inspect the decisions an algorithm is taking, explainable AI provides insights into the data, variables and decision points machines use.

Augmented data management (ADM)

Augmented data management (ADM) involves using ML and AI solutions to automate manual data management tasks, including data quality checks, metadata and master data management, and data integration “self-configuring” and “self-tuning”.

NLP / Conversational Analytics

Voice activated technologies have been widely adopted in the home. Now businesses are starting to get on board and it’s not just improving voice-driven customer service solutions they are looking at. Among a number of natural language processing use cases being developed, data driven businesses are now using voice technology to provide non-technical staff with easy access to data and information.

Blockchain

Until now, blockchain technology - an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way – has been primarily used for cryptocurrencies, most notably bitcoin. Now other business use cases are emerging. Within financial institutions blockchain is being deployed to speed up back office settlement systems. Beyond this, solutions are also being rolled out to execute smart contracts and manage supply chains.

Graph databases

Relational data structures have long been difficult for developers to work with. Graph is one of the fastest growing database technologies. It maintains data in relationships rather than in relational tables so you no longer need to translate business relationship objects into relational tables and back again. The benefits are improved performance, flexibility, and developer productivity.

Data Fabric

Data Fabric simplifies and integrates data management across cloud and on-premises. With integrated hybrid cloud data services businesses can achieve high quality data visibility and insights, plus high-level data access and control and data protection and security.

Commercial AI / ML

Commercial AI vendors are increasingly making affordable AI and ML available to regular business users.

Persistent memory servers

Persistent memory (or PMem) is a new type of memory technology that delivers a unique combination of affordable large capacity and persistence. PMem contents remain even when system power goes down in the event of an unexpected power loss, user-initiated shutdown, system crash, and so on. This unique characteristic means PMem can also be used as storage.

Future job roles

The data-driven movement is also having implications on job roles companies are hiring for. Demand for data scientists has risen in recent years, a trend that will continue as more organisations undergo transformation. More specialised job roles within data are also emerging. Data roles dominate the top of the list of the fastest-growing jobs in the UK according to LinkedIn’s 2020 Emerging Jobs Report, with the role of AI Specialist ranked #1.
LinkedIN-jobs-listMore regulation

With more data to analyse, the future will see greater scrutiny over what it’s being used for and where it was sourced. Previous high-profile cases have put data security under a spotlight so it’s likely further regulation will follow, which will impact the types roles data-driven organisations will be hiring for, and the processes they put in place to ensure effective data governance across the business.

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Becoming a Data Driven Organisation: The Challenges and Opportunities 

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  • Which emerging trends are shaping the future of data analytics
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