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RESEARCH
ANALYSIS
The US analysis and consulting company Gartner, well- insights and models are increasingly used with the help
TRENDS
known for its in-depth studies such as the "Magic of augmented analytics. However, the explainability of
Quadrant" or technology forecasts like the "Hype these insights and models (e.g. their derivation) is
Cycles", provides some seminal solutions for these crucial for trust, compliance with legal regulations, in
challenges and ‘Natural Language Processing’ (NLP) other worlds, the management of the brand reputation.
and ‘Conversational Analytics’, our topics of the month, The fact is that inexplicable decisions, which are made
play an important part here. by algorithms, often do not trigger enthusiasm in most
people: some AI applications are said to reinforce
Natural Language Processing (NLP) is a branch of certain prejudices or "learn" from training data.
linguistics and computer science that deals with the Explainable AI is a model whose strengths and
interactions between computers and human (natural) weaknesses can be identified. The likely behavior of
languages. Currently, companies are particularly such a model can be predicted as well as possible
concerned with the question of how to program distortions. An explainable AI makes decisions of a
computers to process and analyze large amounts of descriptive, predictive, or prescriptive model more
natural language data. This applies to search engines, transparent. In this way, important factors such as the
voice commerce, voice assistants as well as analytics accuracy, fairness, stability, or transparency of
applications, especially since Gartner claims that 50% algorithmic decision-making can be ensured. By 2022,
of analytics queries will be generated in systems via more than half of all major new business systems will
search, voice input (NLP) or automatically this year. have continuous intelligence that uses real-time context
The end customer trend of voice control in the car, for data to improve decisions. Continuous Intelligence
instance, via smartphone or smart speaker, is indeed combines raw data and analysis with transactional
getting more and more popular in B2B analytics business processes and other real-time interactions.
applications. Hence, it is predicted that already by next Methods such as event stream processing (a method
year, processing natural language will increase for real-time analysis), business rule management (rule
acceptance of analysis and business intelligence -based decision systems) and of course machine
software from 35% of employees to over 50%. This will learning are used for this. Continuous intelligence can
make analytics more usable for new user groups such also be described as a further development of
as managers, salespeople, or creative people. Such operational intelligence.
NLP functionalities are offered, for instance, already by
companies like Qlik or Tableau. Augmented Analytics & Data Management
Commercial AI & ML In general, data analysis is complex and requires one
or more data scientists who can extract value from
Currently, many popular Artificial Intelligence (AI) and large amounts of data. The complexity is mostly
Machine Learning (ML) software frameworks are still because data is collected from different sources such
supported by open source (e.g. TensorFlow from as web analysis, enterprise resource planning (ERP),
Google or Caffe by Berkeley AI Research). By 2022, product information management (PIM), marketing
75% of new end user software (e.g. apps & websites) software or social media. Due to the high manual effort
that use AI and ML technologies will work with for the preparation, cleaning and merging of data, data
commercial rather than open source software. Hence, scientists spend most of their time with such tasks,
Gartner forecasts that commercial cloud-based which is estimated to be up to 80%. Augmented
services from major providers (especially Amazon, analytics can help here to reduce workload with
Google, or Microsoft) will reach the turning point of 20% machine learning that enables data scientists to invest
market share in the data science platform market by more work in the search for actionable insights. By
2022. Especially since these large tech companies 2020, Gartner expects augmented analytics to be a
have long recognized the potential of data science and dominant driver for business intelligence purchasing
started to work on the commercialization of their self- decisions, as well as data science and machine
developed frameworks early. learning platforms. Augmented data management
can help reduce the manual effort described above by
Explainable AI & Continuous Intelligence
cleaning and merging large amounts of data from
Gartner expects more than 75% of large companies different sources with machine learning and automated
hiring their own AI specialists in areas such as IT workflows.
forensics or data protection by 2023 to reduce risks for
the reputation of their brand. Automatically generated
May 2020: Natural language processing & conversational analytics: data quality beyond reproach 6