Well into 2020, huge trends such as IoT, big data, and machine learning continue to dominate the data analytics landscape. An overall development throughout 2019 projected to continue well into 2020 is a focus on mature data governance environments and cloud integration. Rather than pursuing short-term goals such as cost reduction and enhanced analytical efficiency for isolated projects, companies will continue to look at overarching, standardized, sustainable analytical environments. Let’s have a look at some of the comprehensive data analytics trends that define the analytics landscape in 2020:
Strategies to enhance insight generation continue to gain momentum. As the placeholder term for insight-enabling technologies such as AI, machine learning (ML), and natural language processing (NLP), augmented analytics is seeing much more attention both in terms of innovation efforts and the implementation of existing models. Instead of drilling through unfathomable amounts of quantitative data, organizations are increasingly utilizing augmented data analytics. They also continue to apply AI-generated suggestions to streamline decision-making.
Back in November 2019, Gartner projected that by 2020, augmented analytics will drive most purchases of analytics and BI. Augmented analytics support data scientists by automating an array of processes related to data preparation, ML, AI development and management—in addition to enhancing user experience within data analytics platforms. So expect this to become a common feature of data-related processes. Further still, this trend is to lead towards more advances in augmented data management.
Another Gartner projection is that by 2022, over 50% of major business systems will implement continuous intelligence (CI). CI uses real-time context data to enhance automated decision-making. CI enables the generation of continuous, high-frequency, intuitive insights from all data by integrating real-time analytics within an operation.
In processing both current and historical data, CI can respond to events fast and provide prescriptive solutions. CI uses augmented analytics, ML, or event stream processing to provide seamless decision automation and assist businesses in real-time decision-making.
Making sense of masses of incoming data, and IoT data, in particular, has become quite an ordeal for companies working within the context of traditional data warehousing solutions or Business Intelligence (BI) tools. To truly make sense of all data, the data has to be incorporated into a data platform with an automated data integration solution encompassing data cleaning, data historization, and versioning. According to leading information technology research firms, towards the end of 2020, we will see an estimated 20 billion connected sensors and endpoints globally, meaning that billions of things will have their digital twins by then.
A digital twin is the digital replica or representation of a “real-world” entity. It is sustained by the real-time data collected by sensors. Digital twins exist at the interstices of our physical reality (as we currently know it) and the virtual realm. Hereby the seamless transmission of data allows these two worlds to co-exist and transition into one another. With improved ability to collect and visualize data, and apply analytics to serve business objectives, digital twins are projected to continue to evolve over the upcoming years.
Data storytelling and data visualization are getting ever more sophisticated. But they are also becoming more accessible as organizations are moving their data warehouses to the cloud. With the need to detect patterns and extract value out of data, or blend disparate data to generate insight, organizations are also facing the challenge of continuously generating data stories that are complete, compelling, and well contextualized.
Today, many cloud-based data platforms offer end-to-end data solutions where data visualization takes place instantly and where no data science knowledge is required to work with these visualizations. This holistic approach to data makes it possible for an array of non-specialist employees to access the data. This is how they glean business-relevant insights and tell impactful stories with that data.
A recent concept, DataOps captures an array of DevOps and agile approaches to the data analytics cycle. These span from data collection and data transformation to reporting. Geared towards data scientists, data engineers, and analysts aiming to analyze data and build models, DataOps seeks to achieve the goals of better data quality and faster, streamlined analytics.
What is the rationale behind this shift? With more and more incoming data, classic data warehousing solutions may prove to be insufficient. These can no longer accommodate the growing need for a continuous, automated data integration of consistent quality. DataOps monitors the data pipeline using statistical process control to deliver that consistent quality.
Generating massive volumes of data to be processed with an eye towards decision-making, big data analytics has become the norm for an array of organizations. Well into 2020, many of these organizations are increasingly looking at hybrid approaches. Hereby data is stored in on-premises data centers whereas the cloud is used for analytics or vice versa. Rather than discarding existing hardware and software to migrate to the cloud, organizations can thus make use of both worlds.
The year’s overarching data analytics trends may also have a lasting impact on the development of business opportunities. One tendency for larger organizations is the use of diversified cloud portfolios. This leads to a thriving market for cloud portfolio management tools. These allow organizations to keep track of different cloud services and vendors.
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We are a Data Science & IoT team based in Frankfurt am Main, committed to helping companies of all sizes to innovate at scale. So we’ve built an easy-to-use development platform enabling everyone to benefit from the powers of IoT and AI.