2020 trends for insights and data analytics

The flourishing of Insights and Data Analytics in 2020

New year, new customers, new tools: here are the insights and data analytics trends that are a must to explore and adopt this year!

Why?

We’re witnessing a data bloat effect as an unforeseen consequence of abundance of data. Insights teams are struggling to reconcile so many signals coming from different business areas such as sales, digital traffic, analytics, and so on. Data has become the mantra for the success of innovative businesses, and this year the understanding of consumer data has become more essential than ever. 

2020 is the year! There will be an increase in budget and roles within the analytics teams, along with an increase in demand for stronger storytelling strategy from customers and clients, as data takes a more central role in business. Artificial Intelligence and Machine Learning will be playing major roles and will be increasingly used to personalise products and services.

How will insights and analytics teams handle this large amount of data in 2020? 👇👇👇

👉 Augmented Analytics

First introduced by Gartner in 2017 as ”augmented intelligence”, these tools are able to handle large data sets. The raw data is collected and skimmed through to then extract key data for analysis in real-time. 

Augmented analytics uses Machine Learning (ML) and Artificial Intelligence (AI) to understand complex patterns across various data sets and consumer behaviour to answer questions on demand. For example, it will be possible to have a more accurate conclusion about a causal connection based on the conditions of the occurrence of an effect. In fact, causal inference is the next big thing in analytics. The idea is to use advanced statistical methods to isolate the most likely causes for particular consumer behaviour. 

These real-time insights of consumer behaviour generated with augmented analytics will particularly simplify your decision-making process and will help product teams to prioritise.

👉 Augmented Data Management

Augmented Analytics, Machine Learning and Artificial Intelligence technologies are implemented to automate tedious and arduous background tasks, such as refining data. 

The organisation and maintenance of data quality will be helped by utilising these technologies for data cleaning tasks normally performed by data scientists. By taking these tasks away from them, it could free up their time and increase business productivity in other important areas.  

However, differently from augmented analytics, augmented data management is still a work in progress. Gartner is predicting that augmentation will reduce manual data management tasks by 45% by the end of 2022. 

👉 Graph Analytics and Deep Links Analytics

Reading data can be hard even for a well trained and experienced eye. Having a good data visualisation platform to easily manipulate and view the insights is essential to decrease data understanding error-margins and to maximise time spent on tasks. 

Graph Analytics databases are already quite popular, however, they haven’t quite fulfilled the promise for real-time analytics as they can’t traverse more than three hops, such as multiple degrees of separation possible in a big graph.  

Graph Analytics will see an improvement made possible by the application of deep link analytics that traverses far more than three hops. It will be possible to handle large quantities of data and to ingest data in real-time. By combining graph analytics and deep link analytics, analytics teams will be able to explore, discover and predict very complex relationships in data

Graph Analytics finds its use in various fields including social network analysis, investment management, campaign launches, product launches, and more. 

The application of graph processing and graph databases is predicted to grow at a rate of 100% per year. Due to the increasing adoption of these on-demand tools, teams will see an increase in the speed of data analysis and decision making.

👉 Commercial Artificial Intelligence and Machine Learning

The presence of AI and ML in business is increasing due to the value proven on its applications in different areas of the business.

Open-source platforms dominate the fields of AI and Machine learning, however, commercial AI vendors are providing AI and ML at an affordable price. Tech providers will offer not only an open-source AI platform but capabilities that aren’t currently available in open-source platforms such as bespoke solutions narrowed to the type of business and goals.

These vendors provide greater capabilities for Artificial Intelligence and Machine Learning projects such as project management best practices, model management for Machine Learning, data extraction and qualification, and more.

👉 Explainable AI

In our Bleeding Edge Series: Explainable AI is the key to Social Acceptance of Artificial Intelligence we illustrated why explainability is important to the second wave adoption of AI and some of the major techniques to derive explanations from black boxes. 

Black-box approaches of AI systems lack transparency; thus it’s difficult to grant trust. Explainable AI makes AI systems crystal clear and trustworthy, reducing the risk exposed to businesses for damaging reputations and severe regulatory fines.

👉 Continuous Intelligence

Continuous intelligence involves the integration of real-time analytics such as the above augmented analytics into business operations. For example, when an event occurs, the current and historical data is processed to recommend an action in response. Or if you notice a critical drop in your data, but you don’t know why Machine Learning algorithms can sift through the thousands of possible causes to identify the correct one for you. 

Thanks to the use of cloud platforms, advanced streaming software and the growth of data sources (IoT, web data, and so on), the implementation of continuous systems is possible on a broader scale.

👆 Exciting changes in Insights and Analytics


We’re really looking forward to the development of these analytics trends, both for this year and for the coming years. It’s great to see AI and ML implementation growing in businesses, especially in the areas of insights and analytics. These technologies can bring great value to teams but also throughout organisations, making departments more connected and efficient.

Our mission

Skim’s mission is to empower people to use data more effectively and to demystify artificial intelligence. Rather than holding up the common narrative of machines replacing humans, we see how machines can help humans to have easier lives and better businesses.

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