Insights gained from data have enabled the transformation of the relationship between customers and businesses, streamlining the flywheel approach to attract, serve and retain customers.
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Data analytics has risen to become the main driver of competitor differentiation and lasting success across all industries. Insights gained from data have enabled the transformation of the relationship between customers and businesses, streamlining the flywheel approach to attract, serve and retain customers. The methods used to meet these ends are seemingly unlimited.
With rapid, on-going innovation a key feature of the data world, new trends are continuously emerging to take advantage of this immense well of potential.
Whether gaining better insights, boosting efficiency or complementing human ingenuity, data science is still on course to keep transforming how businesses interact with their customers.
Here’s our pick of the top data-analytics trends to look out for in 2020.
Inclusion not exclusion
In 2019, advances in machine learning and AI have led to concern about millions of people losing their jobs. However, more positive analyses suggest that 2020 will be a year of ‘Augmentation’ rather than ‘Automation’, predicting that the debate will focus on what advanced technologies can do with humans to help humans, rather than replace them. Building on this, Gartner forecasts that by 2020, 40% of firms will shift from designing tools for humans to designing technology for augmentation.
Continuous Intelligence is real-time analytics which allows businesses to feed off informed insights to enhance and support decision-making. It uses different advances, including augmented analytics, optimisation and business-rule management. According to Gartner this will allow AI to identify a person’s emotions and tailor online adverts accordingly. Such ‘hyper-personalisation’ will allow adverts to be a lot more intelligent and targeted, producing better results and increased revenue.
Internet of Things
By 2020, Analytics Insight predicts the world will be home to over 20 billion IoT devices, representing an enormous source of data information. Meeting this increased influx will require enhanced analytical methods to extract information in the most open and transparent way. Unfortunately, 75% of companies won’t be able to tap into the full advantages of IoT due to a lack of data experts. This coming year will bring improvements and transformation in this field, but until more people are well-versed in data modelling and programming, progress is relatively limited.
Gartner analyst and VP, Rita Sallam claims that by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management.
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Increased In-memory Computing
In-memory computing uses a middleware software which enables companies to store data in RAM. This data can then be processed at the same time across multiple computers. As of this year, in-memory computing has turned into a mainstream technological solution and is expected to become even more popular in 2020.
While it is an expensive investment, it offers businesses powerful mass-memory to aid in complex and high-performance tasks. With in-memory computing, businesses can increase performance, analyse huge volumes of data in real-time at record speeds, and make quicker, more accurate decisions. In turn, this results in better user experience and higher customer satisfaction.
The rise of citizen data scientists
While citizen data scientists analyse data to create data and business models for companies, they are not necessarily data-science or business-intelligence experts. By the end of 2020, Gartner forecasts that over 40% of data science tasks will be automated, which will not only result in higher productivity but necessitates an increase in citizen data scientists.
The rapid rise in this demand can be attributed to two main factors—data scientists are hard to find and often work with data outside of a business context. This is where citizen data scientists step in. They bridge the gap between traditional data analytics services used by businesses and professional data analysis experts. As a result, it is expected that companies in 2020 will try to simplify their data-science products facilitate citizen data scientists.
Augmented Analytics and Data Management
Augmented Analytics combines machine learning and AI to enhance data analytics, data sharing and business intelligence. It is becoming increasingly popular due to the rising trend in using customer data to illuminate and inform business decisions. And on top of this, it can be utilised in a wide range of industries, from finance to transportation to defence.
Similarly, Gartner analyst and VP, Rita Sallam claims that by 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management. Augmented data management will automate complex data tasks, freeing up highly skilled technical staff to focus their efforts on tasks with higher value.
Throughout 2020, data analytics will continue to drastically transform and impact the business industry. In order to retain customers and keep a competitive edge, companies will have to stay on top of the latest developments in data analytics. This requires efficient testing and planning, future-forward thinking and an open-minded approach to change.
redk has over 15 years’ experience as technical and consultancy CRM experts, including technology strategy and data. We aim to support companies seeking to enhance efficiency and profitability through world-class tools that optimise team performance across your organisation.
CRM Transformation Practice at redk
Hideki applies over 15 years of experience in the field of CRM and Customer Experience to overcome business challenges in the customer cycle.