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Going beyond the limitations of traditional RPA

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Craig Le Clair of Forrester has determined a set of rules in aid of discovering the right use of RPA for the company or industry, to squeeze out the most value.     No more than 5 decisions RPA works better with simple applications that operate in high volume.   Limitations Requires Rules Management system Lack of support for unstructured data No more than 5 apps RPA doesn’t rely on APIs, which means they are sensitive to changes in applications.   Limitations Desktop front-ends susceptible to change Constant change = always fixing bots Lack of RPA support for API's   No more than 500 clicks RPA bots record the way an employee moves through repetitive tasks.   Limitations Unmanageable complexity Lack of ROI   One of the critical success factors would therefore be to overcome the rule of five limitations and deliver fully end-to-end intelligent automation. Incorporating Business Intelligence and scalability, SmartFlow provides a customer journey p
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Future of Data Analysis

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Thе bіg ԛuеѕtіоn іѕ whаt dоеѕ thе futurе hоld іn ѕіght for dаtа аnаlуѕіѕ? In аll hоnеѕtу, fоr data analytics, thе future will bе a long evolutionary process whereby thоѕе fіrmѕ thаt іnvеѕt іn and trust thе results from аdvаnсеd analytics will ѕlоwlу рull ahead іn tеrmѕ оf outsized fіnаnсіаl реrfоrmаnсе, subsuming thоѕе whо remain tіеd tо іntuіtіоn-bаѕеd, mіddlе-mаnаgеr drіvеn decision mаkіng. Thе рrосеѕѕ hаѕ аlrеаdу begun in terms оf thе 'tесh gіаntѕ' having ѕhоwn thе outsized bеnеfіtѕ of іnvеѕtіng іn аnd drіvіng dесіѕіоnѕ through advanced аnаlуtісѕ. Here аrе mоrе thoughts оn the futurе оf data analysis; Analytics tесhnоlоgіеѕ wіll bе able tо abstract out a lоt of thе еffоrt thаt needs tо be рut into сhооѕіng what bеѕt аlgоrіthmѕ tо uѕе for mоdеlіng аnd visualizing the data, turning thеm іntо соmрutаtіоnаl optimizations, allowing аnаlуѕtѕ tо fосuѕ more on thе higher level ԛuеѕtіоnѕ , lіkе what thеу want tо ask frоm thе dаtа, hоw thеу wаnt tо merge аnd grоuр the dаtа, аnd w
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Cognitive Automation

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Cognitive automation is based on software bringing intelligence to information-intensive processes . It is commonly associated with Robotic Process Automation (RPA) as  the conjunction between Artificial Intelligence (AI) and Cognitive Computing . By leveraging Artificial Intelligence technologies, cognitive automation extends and improves the range of actions that are typically correlated with RPA, providing advantages for cost savings and customer satisfaction as well as  more benefits  in terms of  accuracy in complex business processes that involve the use of unstructured information . What is Cognitive automation and  what it is not Cognitive automation is not machine learning . Cognitive automation  leverages   different algorithms and technology approaches  such as natural language processing, text analytics and data mining, semantic technology and machine learning. What does cognitive automation mean for the enterprise? The integration of different AI features with
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