Over view: After the Internet of Things (IOT), Analytics yo nan Bagay sa yo (AOT) se pwochen etap la ki lojik pou antrepwiz yo. In fact, san yo pa AOT a, li difisil a reyalize potansyèl la ak tout IOT la. Li se pa ase yo jis akimile yon anpil nan done ki sòti nan aparèy, men antrepriz bezwen fè sans soti nan done yo ak fè yon bagay ki fè aparèy sa yo pi efikas. Also, done yo pwodwi gen potansyèl la yo amelyore yon bann bagay sa sou biznis la. Sa a se kote AOT se konsa enpòtan,,en,Biznis bezwen analytics son ki amelyore fason yo li kouri biznis la ak an jeneral,,en,liy anba a,,en,kòm antrepriz plan yo tounen vin jwenn AOT,,en,yo fè fas a defi anpil sou wout la,,en,IOT tèt li se toujou en ak AOT se nan anfans li,,en,Konsa, va gen yon anpil nan konfizyon ak move konsepsyon ki mennen ale nan envèstisman mal nan lajan ak efò,,en,Enterprises bezwen envesti sou teknoloji ak MANPOWER kalifye yo ka resevwa pi bon an soti nan AOT,,en,Yon anpil tan ak pasyans nesesè sou wout la,,en,Kesyon an pral,,en,konbyen kapab antreteni tèmpo a pou ki long,,en,Analytics de bagay sa yo,,en,AOT - Ki sa ki li aktyèlman vle di,,en,Analytics de bagay sa se pa gen anyen men IOT analytics,,en,An tèm senp,,en,AOT vle di génération analytics soti nan done yo ki te pwodwi pa IOT a,,en. Businesses need sound analytics that improves the ways it runs the business and overall, the bottom line.
However, as enterprises plan to turn to AoT, they face numerous challenges on the way. IoT itself is still evolving and AoT is in its infancy, so there will be a lot of confusions and misconceptions leading to wrong investments of money and effort. Enterprises need to invest on technology and skilled manpower to get the best out of AoT. A lot of time and patience is required on the way. The question will be, how many can sustain the tempo for that long?
Analytics of things
AOT – What does it actually mean?
Analytics of Things is nothing but IoT analytics. In simple terms, AoT means generating analytics from the data generated by the IoT. IOT vle di ke plizyè aparèy ki konekte nan entènèt la, epi yo transmèt done nan yon kote,,en,jis jwenn done a se premye etap la,,en,Enterprises bezwen analize done yo fè aparèy yo pi entelijan ak pi efikas,,en,se rezilta a nan IOT analytics tou yo itilize pou pran desizyon pou dwa nan sitiyasyon diferan,,en,si nou eskli la ',,en,Bagay sa yo ',,en,pati soti nan tèm nan,,en,Lè sa a, tout rès la se sèlman ',,en,Analytics ',,en,ki se byen menm jan an nan nati a nenpòt analytics done lòt,,en,Isit la nan ',,en,yo pa gen anyen men IOT aparèy,,en,Menm jan ak analytics done lòt,,en,AOT ka nan diferan kalite tankou deskriptif,,en,dyagnostik,,en,prediksyon oswa kontreyan,,en. Now, just obtaining the data is the first step. Enterprises need to analyze the data to make the devices smarter and more efficient. The result of IoT analytics is also used to make right decisions in different situations.
Now, if we exclude the ‘Things’ part from the term AOT, then the rest is only ‘Analytics’ ,which is quite similar in nature to any other data analytics. Here the ‘Things’ are nothing but IoT devices.
Similar to other data analytics, AoT can be of different types like descriptive, diagnostic, predictive or prescriptive. For example, dyagnostik ak kontreyan analytics ka fè avèk èd nan aparèy medikal IOT ak prediksyon ki kapab fèt ki baze sou done yo ki te pwodwi pa endistriyèl aparèy yo IOT elatriye,,en,nou dwe sonje sa ke tout fòm sa yo nan IOT analytics / AOT sont toujou en ak mande pou siyifikatif kantite lajan nan tan ak efò yo ka resevwa valè reyèl biznis,,en,Ki sa ki defi yo,,en,Lè nou pale sou nan,,en,'Analytics de bagay sa,,en,gen sitou de pati nan li,,en,analytics pati,,en,ak lòt la se nan,,en,koleksyon done pati,,en,ki te pwodwi pa bagay / konekte aparèy yo,,en,Se yon pati nan analytics rezonab ase matirite men obstak nan pi gwo se yon pati nan koleksyon done,,en,ki mond lan ap fè fas a analytics pou ane,,en,nou ap aktyèlman iteration menm pwoblèm nan fin vye granmoun pandan y ap kouri dèyè AOT,,en. men,, we must remember that all these forms of IoT analytics/AOT are still evolving and requires significant amount of time and effort to get real business value.
What are the challenges?
When we talk about the ‘Analytics of Things‘, there are mainly two parts in it, one is the analytics part and the other is the data collection part, generated by the things/connected devices. The analytics part is reasonably matured but the biggest hurdle is the data collection part, which the analytics world is facing for years. So, we are actually iterating the same old problem while pursuing AoT. Analytics moun ki ta ka gen yon anpil nan lide inovatif sou analize done yo ak ap resevwa Sur bèl bagay soti nan li,,en,Men, reyalite a tè se,,en,sof si nou gen yon enfrastrikti bon ak konpetans yo jwenn ak analize done ki nesesè,,en,AOT se san sans,,en,se pou nou divize defi yo an de kategori laj,,en,se yon sèl sou bò òganizasyonèl ak lòt la se sou teknoloji ak aplikasyon bò,,en,Ann kòmanse ak defi yo òganizasyonèl premye,,en,Defi a pi enpòtan an se yo bati yon solid AOT ka biznis konvenk òganizasyon an,,en,Li pral fasilite envestisman an ak pwochen ankourajan nan AOT vizyon,,en,se envestisman an premye oblije deplwaye aparèy yo IOT nan tout tanp zidòl apwopriye ak detèktè pran done,,en,Yon fwa aparèy yo yo pare,,en. But the ground reality is, unless we have a proper infrastructure and skill to acquire and analyze necessary data, AoT is meaningless.
Now, let us divide the challenges into two broad categories, one is on the organizational side and the other is on the technology and implementation side.
Let’s start with the organizational challenges first.
The most important challenge is to build a solid AoT business case to convince the organization. It will ease the investment and future nurturing of AoT vision. The first investment is required to deploy the IoT devices in proper places with sensors to capture data. Once the devices are ready, òganizasyon bezwen yo ki ap pèmèt mouvman an done ki sòti nan sous,,en,IOT aparèy,,en,nan destinasyon,,en,pouvwa gen yon DB Platfòm oswa done depo oswa kèk lòt depo,,en,yon estrateji bon gen yo dwe bati eseye figi konnen ki jan yo kapab depo a ak analytics dwe jere,,en,Koulye a, kite nan pale sou kèk nan defi yo sou teknoloji ak aplikasyon bò,,en,Done defi,,en,Volim a nan done chak Capteur jenere se gwo,,en,kesyon an se,,en,yo tout done sa yo vo transmèt,,en,Repons lan se 'Non',,en,se konsa nou bezwen konnen,,en,Ki jan intelijans nou ka transmèt sèlman done ki nesesè yo ak sans,,en,Li, sa pral lakòz yon analytics pwòp san yo pa tretman done tenten,,en,sekirite defi,,en,Sekirite ak vi prive nan Capteur pwodwi done enpòtan anpil,,en,Espesyalman,,en (IoT devices) to destination (may be a staging DB or data warehouse or some other storage). finalman, a proper strategy has to be built to figure out how the storage and analytics can be managed.
Now let’s talk about some of the challenges on the technology and implementation side.
- Data challenge: The volume of data each sensor generates is huge. men,, the question is, are all these data worth transmitting? The answer is ‘No’, so we need to figure out, how intelligently we can transmit only necessary and meaningful data. It will result a clean analytics without processing junk data.
- Security challenge: Security and privacy of sensor generated data is very important. Specially, lè se done sa a pwodwi nan aparèy sansib ekipe nan zòn konfidansyèl oswa kritik,,en,done yo ka vini soti nan kèk aparèy ekipe nan yon entansif,,en,Entansif Inite Swen,,en,oswa nan yon ayewopò oswa li kapab nan kèk enfrastrikti endistriyèl kritik,,en,Nan tout ka sa yo,,en,sekirite done gen yo dwe asire yo pwoteje entegrite nan nan sistèm lan,,en,Analytics defi,,en,Li se pi plis ki gen rapò ak filtraj analytics tout pwosesis la,,en,Defi a se,,en,kote nou ka fè tout sa yo analytics,,en,Petèt,,en,ka kèk pati dwe fèt nan aparèy yo,,en,se konsa ke done yo ap vini soti nan aparèy sa yo yo filtre nan yon sèten mezi,,en,nou kapab desine kouch analytics separe yon fwa yo fin done a te rive chanje,,en,ak Lè sa a fè filtrasyon yo etap pa etap,,en,Epi finalman,,en,fè analytics yo ak done yo pwòp,,en,Normalizasyon / pwotokòl defi,,en. For example, the data may be coming from some devices fitted in an ICU (Intensive Care Unit) or from an airport or it can be from some critical industrial infrastructure. In all these cases, data security has to be ensured to protect the integrity of the system.
- Analytics challenge: It is more related to filtering the entire analytics process. The challenge is – where we can do all these analytics? Maybe, some part can be performed within the devices, so that the data coming out of these devices are filtered to some extent. ou, we can design separate analytics layers once the data is reached unchanged, and then perform the filtrations step by step. And finally, do the analytics with the clean data.
- Standardization/protocol challenge: Normalizasyon ak pwotokòl se youn nan pi gwo defi yo pou AOT siksè,,en,Nou bezwen estandadize pwotokòl la kominikasyon ant aparèy,,en,Li pral ede tout aparèy yo nan kominike youn ak lòt transparans,,en,Apa de pwoblèm yo ki pi wo a,,en,nou ap tou ale nan fè fas a yon anpil nan nouvo defi nan jou kap vini,,en,Kòm nou avanse pou pi devan,,en,nou pral gen nouvo aparèy IOT,,en,nouvo fòma done,,en,nouvo pwotokòl ak anpil plis,,en,li pral evantyèlman pote nouvo obstakl simonte,,en,Ki sa ki pwomès yo,,en,Tankou nenpòt ki vizyon nouvo,,en,AOT tou te gen yon anpil nan pwomès li kapab akonpli,,en,ka vo a nan AOT fèt sèlman reyalize ak tan,,en,men nou deja gen kèk egzanp nan plas ki fè yo vrèman pwomèt,,en,analytics yo prediksyon a AOT te pwouve vo li yo nan anpil nan kote tankou ATM machin,,en,Odinatè rezo sistèm elatriye,,en,Oto-kondwi machin,,en. We need to standardize the communication protocol between devices. It will help all the devices to communicate with each other seamlessly.
Apart from the above issues, we are also going to face a lot of new challenges in the coming days. As we move forward, we will have new IoT devices, new data format, new protocols and many more. So, it will eventually bring new hurdles to overcome.
What are the promises?
Like any new vision, AoT also has a lot of promises to fulfill. Although, the worth of AoT can only be realized with time, but we already have some examples in place which are really promising. The predictive analytics of AoT has proven its worth in lot of places like ATM machine, Computer network system etc. Self-driving cars, sistèm enfòmasyon trafik yo se kèk nan lòt zòn yo kote AOT se deja nan plas,,en,Li se tou k ap antre nan nan endistri medikal,,en,endistri lwil oliv,,en,kondisyon fizik sektè elatriye,,en,AOT a ap grandi kòm IOT ap grandi,,en,prediksyon,,en,milya dola konekte,,en,Bagay sa yo,,en,Pral nan itilize nan,,en,moute,,en,pousan nan,,en,AOT se kwè yo gen yon anpil nan potansyèl ak li pral byento vin yon pati nan lavi nou,,en,wout la pi devan,,en,nou ka byen fasil konprann ke nan anviwònman an konplèks nan IOT,,en,AOT se yon nouvo entrant ak jis te kòmanse evolye,,en,defi yo evidan nan aplikasyon pral gen simonte,,en,Gen kèk nan defi yo se sou bò biznis la,,en,ki ka jere ak yon ka itilize apwopriye ak jistifikasyon,,en,defi yo teknolojik ka rezoud ak estrateji apwopriye,,en,platfòm teknoloji ak resous kalifye,,en. It is also entering into medical industry, oil industry, fitness sector etc.
The AoT will grow as IoT grows. According to Gartner prediction ‘6.4 Billion Connected “Things” Will Be in Use in 2016, Up 30 Percent From 2015’. So, AoT is believed to have a lot of potential and it will soon become a part of our lives.
The way forward
From the above discussion, we can easily understand that in the complex environment of IoT, AoT is a new entrant and just started to evolve. So, the obvious challenges of implementation will be there to overcome. Some of the challenges are on the business side, which can be managed with a proper use case and justification. And, the technological challenges can be solved with proper strategy, technology platform and skilled resources.
Òganizasyon bezwen reyalize potansyèl la nan AOT, li mete apwopriye enfrastrikti IOT an plas,,en,Si nou tounen gade dèyè,,en,lè sa a nou ka byen fasil konprann ke précoces yo byen bonè nan done gwo te genyen anpil,,en,Yo te kapab pran avantaj ki genyen nan konpetitif ak jwenn nan biznis,,en,Menm a se laverite pou IOT ki te swiv pa AOT,,en,AOT a pwal nan prensipal la nan koup pwochen de zan,,en,li nan moman an dwa pran plonje a, epi fè yon siksè vizyon AOT nan plas,,en,Analytics de bagay sa,,en. If we look back, then we can easily understand that the early adopters of big data have gained substantially. They were able to take the competitive advantages and gain in business. The same is true for IoT followed by AoT. AoT is going to be in the mainstream in next couple of years. So, it’s the right time to take the plunge and make a successful AoT vision in place, otreman li kapab twò ta rantre nan ras la.
