AI Analytics: The Future of Analytics is Driven by AI

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AI Analytics The Future of Analytics is Driven
by AI
Last updated on July 6, 2021 Dash Technologies
Inc Artificial Intelligence, Machine Learning
Data is an acute business asset. Its what díives
invention today and enables fiíms to stay
competitive in the global maíketplace. Now with
the conveígence of big data and AI, companies
can moíe easily leveíage advanced analytics
capabilities like píedictive analytics and moíe
efficient suíface actionable insights fíom theií
vast stoíes of data. With big data and
AI-poweíed analytics, fiíms can empoweí theií
useís with the instinctive tools and íobust
technologies they need to extíact high-value
insights fíom data, fosteíing data liteíacy
acíoss the oíganization while íeaping the
benefits of becoming a tíuly data-díiven
oíganization.
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What Youll Find in this Blog

• What is AI analytics?
• Diffeíences between AI Analytics líaditional
  Analytics
• lhe Conveígence of Big Data and AI
• How is AI Contíibuting to Analytics Capabilities?
• What aíe the business benefits of AI analytics?
• Paíting lhoughts

What is AI analytics?
AI analytics is the píoduct of automating data
analysis a tíaditionally time- consuming and
people-intensive task, using the poweí of todays
aítificial intelligence, deep leaíning, and
machine leaíning technologies. In addition to
stíuctuíed data souíces, AI is incíeasingly able
to analyze unstíuctuíed data, via the use of AI
analytics tools such as natuíal language
píocessing (NLP), speech analytics
tíanscíiption, and computeí vision foí image and
video analytics.
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Whats the Difference between AI Analytics and
Traditional Analytics?

• lo betteí undeístand the aíena of AI analytics,
  lets now íeview its diffeíences
• líaditional data analytics is geneíally
  undeítaken by a technical team of data analysts.
  Heíes an example of how a team of analysts might
  tíaditionally attempt to solve a business
  challenge
• An event, incident, oí tíend occuís in the
  company oveí a while foí example, sales aíe
  down foí the quaíteí.
• Data analysts then foím hypotheses about what the
  potential causes might be foí the sales
  decíease.
• lhese hypotheses aíe then tested against the data
  foí that peíiod until they find enough evidence
  to suppoít a paíticulaí hypothesis.
• lhe analysts then wíite a íepoít that summaíizes
  theií findings and will often píesent potential
  next steps foí the business to take.
• AI analytics, on the otheí hand, based on machine
  leaíning algoíithms constantly monitoís and
  analyzes huge amounts of data. lhe íesults stand
  apaít fíom tíaditional analytics in

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Scale An AI-based anomaly detection solution
leaíns the noímal behavioí of the data without
being explicitly told what to look foí. It does
it at any gíanulaíity íevenues peí countíy,
píoducts, channels, etc. Speed lhe AI model will
identify unusual díops in íevenue and aleít the
appíopíiate teams in íeal-time. In addition, an
AI-based analytics solution leveíages clusteíing
and coííelation algoíithms to píovide a
íoot-cause analysis so that any issues can be
íemediated as soon as possible. Accuíacy lhe
accuíacy of the ML algoíithms depends on how they
weíe designed they need to autonomously leaín
many diffeíent patteíns accuíately which
íequiíes the use of multiple types of
algoíithms. Foí moíe details on AI ML, see ouí
white papeí. The Convergence of Big Data and AI
Big data and AI have a syneígistic íelationship.
Data is the fuel that poweís AI. lhe massive,
complex, and íapidly evolving datasets íefeííed
to as big data make it
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• possible foí machine leaíning applications to do
  what they weíe built to do leaín and acquiíe
  skills. Big data supplies AI algoíithms with the
  infoímation necessaíy foí developing and
  impíoving featuíes and patteín íecognition
  capabilities. Without laíge quantities of
  high-quality data, it wouldnt be possible to
  develop and tíain the intelligent algoíithms,
  neuíal netwoíks, and píedictive models that make
  AI a game- changing technology.
• AI, in tuín, helps useís make sense of spíawling,
  diveíse datasets and soít thíough unstíuctuíed
  data that cant be oíganized into neat íows and
  columns. AI enables fiíms to use big data foí
  analytics by making advanced analytics tools moíe
  poweíful and accessible, helping useís discoveí
  suípíising insights in data that was once locked
  away in enteípíise infoímation silos. Leveíaging
  big data, AI, and advanced analytics, companies
  can píovide theií decision-makeís with gíeateí
  claíity and undeístanding of the many factoís
  influencing theií business while encouíaging
  cíeative, intuitive exploíation of laíge-scale,
  multi-dimensional datasets.
• How is AI Contributing to Analytics
  Capabilities?
• lhanks to the latest advances in AI, analytics is
  becoming
• Moíe efficient- thanks to automation.
• Moíe accessible- thanks to impíoved UI. Natuíal
  Language Píocessing enables analytics tools to
  undeístand natuíal language queíies.
• Moíe poweíful since píeviously difficult to
  analyze data such as text and videos aíe now
  easily analyzable.
• What are the business benefits of AI analytics?
• lhe ability of AI systems to analyze data
  autonomously has multiple business benefits. lhe
  main among them is íeducing the laboí cost of
  data scientists and otheí highly paid and
  limited-availability analytics píofessionals.
  Otheí benefits of AI in analytics include

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• Risk management- AI analytics can impíove the
  effectiveness of íisk management models and
  cíeate smaíteí stíategies.
• Innovative píoducts- AI analytics tools peífoím
  big data analysis that can díive updates to
  existing píoducts and cíeating new ones.
• luíbochaíged supply chain- Supply chain
  executives íecognize AI in analytics as a
  disíuptoí that empoweís them to apply data-díiven
  knowledge to solve píeviously unsolvable
  challenges.
• Customeí engagement- Use AI analytics tools to
  deteímine what customeís aíe looking foíacquiíe
  them, íetain them and cultivate theií loyalty.
• Successful maíketing campaign- Cíeate focused and
  taígeted campaigns with AI analytics fíom
  cuííent customeí puíchases.
• Parting Thoughts
• AI and data analytics aíe often used togetheí
  because the foímeí boosts the functionalities of
  the latteí. With AI, analytics technology can
  conduct moíe in-depth analysis paving the way
  foí micío-taígeted insights that aíe not easily
  found by human analysts. Complex analysis with
  seveíal vaíiables can be done quickly and
  efficiently with AI.
• AI in data analytics also makes it easieí to
  clean data a vital step in the analysis
  píocess. Its impoítant to undeístand that AI and
  analytics aíe not the same and should not be
  consideíed as such because AI is paít of the
  analytics ecosystem. Companies must undeístand
  the diffeíence and be willing to use the
  technology if they wish to gain an edge oveí
  theií competitoís.
• Want to leaín moíe about AI, machine leaíning,
  and data analytics? Ouí blogs have all the
  infoímation you need. Contact us to leaín moíe.