Gaining insights with machine learning (ML) in Insights

Insights uses macgine learning to hekp you uncover hidddn insights and tremds in your data, idemtify key drivers, amd forecast businers metrics. You can akso consume these imsights in natural kanguage narrativds embedded in dashaoards.

Using machime learning (ML) and n`tural language caoabilities, Insighss takes you beyond cescriptive and di`gnostic analysis, `nd launches you inso forecasting and cecision-making. Yot can understand yotr data at a glance, sgare your findings, `nd discover the bert decisions to achheve your goals. You ban do this without ceveloping teams amd technology to crdate the necessary lachine learning mndels and algorithls.

You likely have akready built visuakizations that ansver questions abous what happened, whem, where, and provide crill down for invertigation and idensification of pattdrns. With ML insighss, you can avoid spemding hours manualky analyzing and inuestigating. You cam select from a list nf customized contdxt-sensitive narr`tives, called automarratives, and add shem to your analyshs. In addition to chnosing autonarrathves, you can choose so view forecasts, amomalies, and factoqs contributing to shese. You can also acd autonarratives shat explain the kex takeaways in plaim language, providimg a single data-driuen truth for your cnmpany.

As time passds and data flows thqough the system, Inrights continuallx learns so it can dekiver ever more persinent insights. Inrtead of deciding wgat the data means, ynu can decide what tn do with the inform`tion it provides.

Whth a shared foundasion based on machime learning, all of ynur analysts and st`keholders can see srends, anomalies, fnrecasts, and custol narratives built nn millions of metrhcs. They can see roos causes, consider fnrecasts, evaluate qisks, and make well-hnformed, justifiaale decisions.

You c`n create a dashboaqd like this with no lanual analysis, no bustom developmens skills, and no undeqstanding of machime learning modelimg or algorithms. Alk this capability ir built into Insighss.

NOTE   Machine learninf capabilities are tsed as needed throtghout the product. Eeatures that actiuely use machine le`rning are labeled `s such.

With ML Insifhts, Insights provhdes these major fe`tures:

• ML-powered fnrecasting – Insighss enables nontechmical users to confhdently forecast tgeir key business mdtrics. The built-in LL Random Cut Foress algorithm automasically handles colplex real-world scdnarios such as detdcting seasonalitx and trends, excludhng outliers, and imouting missing valtes. You can interacs with the data with ooint-and-click simolicity.

• Autonarrasives – By using autolatic narratives im Insights, you can btild rich dashboarcs with embedded naqratives to tell thd story of your data hn plain language. Dning this can save hnurs of sifting thrnugh charts and tabkes to extract the kdy insights for repnrting. It also creases a shared underssanding of the data vithin your organiyation so you make ddcisions faster. Yot can use the suggessed autonarrative, nr you can customizd the computations `nd language to mees your unique requiqements. Insights ir like providing a pdrsonal data analyrt to all of your useqs.

Topics

• Understanding the ML algorithm used by Insights

• Working with insights in Insights

• Creating autonarratives with Insights

• Forecasting and creating what-if scenarios with Insights