Icho chinoshaikwa chinongedzo mukuwana dhata-inotyahwa yekutenga chaiyo
Gadzira maitiro ako ekutenga, asi zviite nemazvo
Vatungamiriri vemazuva ano vekutenga zvinhu vanotoziva kuti iro ramangwana rekutenga rinotungamirwa nedata. Asi ngatitorei zvakananga kweminiti. Chii chinonzi dhata-inotungamirwa kutenga chaizvo? Ndeapi matanho chaiwo aunoda kuti uone izvi? Uye maererano nezera rekukura, uripi izvozvi?
Mazuva ano, hazvifungidzike kuve pachiitiko uye usingaone imwe yeanotevera mabuzzwords: yekunyepedzera ungwaru (AI), muchina kudzidza (ML), bhizinesi njere (BI) uye zvimwe zvakawanda. Izvi zvinonzwika kuva zvinozivikanwa here? Hazvina kuitika kuti aya mazwi anogona kuwanikwa pane chero bhenera, flyer kana promo vhidhiyo uye kuti inogona kutanga iwe. Ivo anotonhorera, ari kufamba uye iro ramangwana rinonyatso kuve rakazara navo. Nekudaro, kuenda nechirongwa ndiko kujairana nehunyanzvi uhu uye kugona kunzwisisa kuti vangabatsira sei bhizinesi rako uye nekuita kwezuva nezuva. Paunoita, chiito chinonzwisisika kutanga nacho, kutarisa pane izvo zviri panheyo yezvinhu izvi zvitsva: kuwana nyore nyore kushandisika, data remhando yepamusoro.
Maalgorithms uye data - zvinhu zvekuziva kana iwe uchida kuti ivo vafare vakaroora
Maalgorithms anogona kukupa iwe inoitika inoitika. Semuenzaniso, vanogona kuona (muswe) kushandisa mapatani, kufungidzira shanduko mune zvinodiwa nevatengi uye kuona zvipingaidzo mukutenga nzira vasati vasimuka. Kana yaitwa nemazvo, aya matekiniki akakosha zvakanyanya uye akakosha kuti inyatsoita nzira yekutenga.
Nekudaro, isu tinoona mazvikokota mazhinji ekutenga anotambura kubva kune yakaderera-dhata dhata hwaro iyo inowanzo ine yakasviba- uye yakaipa data remhando risingakwanise (uye nekukurumidza) kuwanikwa. Maalgorithms anogona kunge akangwara, asi iwo achiri michina. Izvi zvinoreva kuti kana iwe ukavafudza marara (semhedzisiro yedhata yakaipa data), ivo vanokupa iwe marara sekuburitsa. Izvi zvinonzi iyo marara mu = marara kunze musimboti, uye mamiriro ezvinhu ausingade kuzviisa iwe semutungamiriri wekutenga. Chaiwo zviratidzo zvekuve ne-sub-optimal data hwaro hwatinoona, uye kuti iwe ugone kuziva, mukuita ndeiyi:
- Zvinotora mavhiki uye dzimwe nguva pamwe nemwedzi kuwana data rakakodzera
- Hazvina kukwana data uye data kushomeka
- Yakasviba- uye yakaipa mhando yedhata, ine mijenya yekushayikwa uye isiriyo tsika
- (Yakavanzika) inonzwisisika uye nekudaro isingasvikike data
- Nguva inopedza trajectories nemaitiro emukati ekuwana mukana kune data rakakodzera
Iyo yakasimba nheyo yako department rekutenga inoda
Inotaridzika sei ramangwana, inoshanda nzira yekutenga? Sezvineiwo, mumwe angade kuve neakasimba data hwaro nekuwana nyore nyore kushandisika uye data remhando yepamusoro kuti akwanise kuona hunyanzvi-hunofambiswa nedata nemabuzzwords ataurwa (eg AI, ML, BI nezvimwewo). Uine hwaro hwakasimba hwedata, data remhando yepamusoro rinokupa mhedzisiro yemhando yepamusoro uye nzwisiso dzinoitika dzinozosimudzira dhipatimendi rekutenga uye rinokupa mukana wakakura mukuenzanisa neavo vachiri kushaya hwaro hwakakodzera hwedata.
Saka tinoita sei izvi nemazvo?
Cheni yakasimba sekubatana kwayo isina kusimba. Uye muketani yekutenga, mazhinji maumboni atovepo uye zviri nyore kuita. Nekudaro, pane imwechete inonetsa link iriko. Iwe unogadzira sei yakasimba data data uye ungatangira kupi semutengesi wekutenga?
Zvichienderana nezvinonetsa dhipatimendi rekutenga nekutambura naro, Syntho inogona kukubatsira kuti umise iyi yakasimba data data. Mimwe mienzaniso inotsigirwa naSyntho:
- Kugadzira (zvakavanzika) data rakapfava kuwanikwa nyore pasina kurasikirwa nehunhu
- Kurumidza kumhanyisa data ku (sensitive) dhata kubva kumavhiki (uye dzimwe nguva mwedzi) kusvika maawa
- Nesimba gadzirisa dhata mhando yemhando dzakadai sekushaikwa / zvisirizvo tsika
- Kana paine dambudziko rekushomeka kwedata (kudzidzisa semuenzaniso algorithms), isu tinokwanisa kunyorera sub-setting / kuwedzeredza uko kwakawanda kwemhando yepamusoro yekudzidzisa dhata iri yezvakakosha
- Kugadzira yakawedzera enjere synthetic data nemapateni akafanana, hunhu uye hukama hwehukama se data rekutanga rauinaro
Iwe unoziva here zvipingamupinyi zvatataura? Uye ichi chinyorwa chinokupa iwe zviri nani pfungwa yerwendo rwako kuenda kune data-drive kutenga uye yako yazvino nhanho yekubereka? Tinoda kunzwa paumire, ndeapi matambudziko aunotarisana nawo uye neyako mhinduro. Naizvozvo, Syntho achave aripo kuDPP Procurement Conference munaGunyana 15th uye 16th. Ndapota inzwa wakasununguka ku taura nesu uye tibvunzei mibvunzo yese yaunayo. Ingo svitsa ruoko kuburikidza ne DPW-chikuva or taura nesu zvakananga kuenderera zvakadzama mune ramangwana rekutsvaga kunotungamirwa nedata.
Dhata ndeyekugadzira, asi timu yedu ndeyechokwadi!
Bata Syntho uye imwe yenyanzvi dzedu ichasangana newe nekumhanya kwechiedza kuti uongorore kukosha kweiyo synthetic data!
Unoda kudzidza zvakawanda nezve mhando ye data rekugadzira? Tarisa uone vhidhiyo yeSAS inoongorora yedu synthetic data!
Iyo data yemhando yedata rekugadzira mukuenzanisa neyekutanga data yakakosha. Ndosaka isu munguva pfupi yapfuura takabata webinar neSAS (mutungamiri wemusika mune analytics) kuratidza izvi. Nyanzvi dzavo dzekuongorora dzakaongorora dzakagadzirwa synthetic datasets kubva kuSyntho kuburikidza neakasiyana analytics (AI) ongororo uye vakagovana zvabuda. Iwe unogona kuwana pfupiso yeizvi muvhidhiyo iyi.
