The emergence of Big Data within analytics
Data has now become more available, cost-effective, and vast in size than ever before. As this realisation grows, alongside businesses recognising the importance of data analytics in increasingly competitive markets, datasets threaten to grow larger and larger. The term for this vast amount of collected data, that often ranges in the billions of records, is referred to as ‘Big Data.’
Big Data presents a wide range of challenges for businesses of any size. From questions of storage locations, to advancing functionality so that analysts may interact with stored information, datasets may become unmanageable if not thoroughly navigated.
As current technology and capabilities have evolved to navigate issues such as vast datasets and a wide variety of storage locations, the attention of many has turned to what the future of Big Data and analytics may resemble.
Below, we examine what the future of analytics may involve in 2021 and beyond, as well as how Big Data is expected to continue to play an important role in operations. From Natural Language Processing to shifting perceptions towards cloud storage, the world of Big Data is wide and constantly advancing.
The future of the analytics landscape
Throughout recent years, technology has been forced to adapt to multiple factors and events to stay convenient, reliable, and secure. The inception of the Cloud and the need to work from home during the global pandemic are just two events that have forced many to reconsider how they store, collect, interact, and maintain their data.
Throughout the Big Data landscape, this reactive approach to worldly events and advancements is no different. Alongside the ever-changing capabilities of hardware comes the ability to store more and more data either on-premise or on the Cloud. As this data becomes vaster and vaster, it also becomes increasingly impossible to draw relevant and actionable insights without streamlined analytics integrations.
When considering the future of Big Data and analytics, we expect to see the following emerge into the mainstream landscape:
- The focus on using all data collected, rather than simply the necessary components.
- The need to place increased trust in Machine Learning.
- The development of commercial Natural Language Processing capabilities.
Trend #1 – Using all data collected, rather than simply the necessary components.
Recent advancements within Data Analytics and management have placed a large emphasis on storage and ongoing use.
As a result, enterprises currently navigating large datasets have adapted and found solutions to storing vast amounts of structured and unstructured data, yet they cannot interact with the entirety of collected information – potentially losing out on valuable insights in the process.
As we look to the future, we expect to see the focus shift towards empowering and enabling businesses to meet the full potential of their collected data – rather than the estimated 60% of data currently utilised.
Through leveraging and developing the capabilities of Machine Learning and Artificial Intelligence, enterprises may find a solution that enables them to derive much-needed value from the remaining 40% of unstructured, potentially random data – a key advantage in incredibly competitive global markets.
Trend #2 – The need to place increased trust in Machine Learning.
When Machine Learning was first integrated into Data Analytics and Business Intelligence software, its use was primitive, dependant on ongoing maintenance and supervision, and included a degree of potential error.
Moreover, due to the nature of Machine Learning being a system that learns and develops accuracy and functionality over time, initial results generated may be inaccurate and prone to error.
This can cause issues for analysts and key decision-makers, since once an early inaccuracy is made visible, gaining the repeated and established trust of later results is also made difficult due to lack of confidence.
To counter this, and to re-establish trust, analysts may find value in cross-examining given results. When reported data is validated and seen as correct by internal teams, reliability and confidence in results may be returned, and overall trust in automated processes may increase as a result.
Trend #3 – The development of commercial Natural Language Processing capabilities.
Potentially one of the most exciting innovation regarding the future of Big Data analytics concerns the concept of Natural Language Processing. Variations of this functionality can already be seen in the consumer market, with smart assistants such as Amazon’s Alexa allowing consumers to interact with a wide variety of IoT devices and additional services using natural language, with commands such as:
- What’s the weather like today?
- Do I have any appointments scheduled next week?
- Send a reminder to my phone about Father’s Day.
Recognising the potential within the Big Data landscape, current efforts are underway to integrate Natural Language Capabilities to allows users to interact with millions of rows of data using everyday language.
Supporting analytics through evolving landscapes
With a wide range of advances being made constantly, the Data Analytics landscape is ever-shifting and developing, focusing on allowing users to interact with, and draw the necessary conclusions from rapidly expanding datasets with ease.
As market-leading experts in Data Analysis and Business Intelligence, we are committed to enabling businesses to realise the full potential of their data – gaining access to smarter insights based on trusted intelligence, rather than a gut-feeling approach.
To learn more about how we’ve previously empowered our clients to reach actionable insights, visit our case studies hub, or for any other questions or enquiries you may have, don’t hesitate to contact us. We always aim to respond as soon as possible.