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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




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