10 Key Tech­nolo­gies for 2017

We be­lieve 2017 would be evo­lu­tion­ary in terms of emerg­ing tech and reengi­neer­ing es­tab­lished tech as CIOs and CTOs look­ing at proof points will em­brace newer ones and ju­di­ciously blend old with the new. Wel­come to the age of hy­brid com­put­ing

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It is that time of the year when we look back, to look ahead. In ret­ro­spect, 2016 can be called the year of dis­rup­tion. Many tech­nolo­gies, con­sid­ered as ‘hy­pe­only’ slowly gained main­stream at­ten­tion. While cloud con­sol­i­dated on hy­brid, tech­nolo­gies like IoT saw sig­nif­i­cant traction on the in­dus­trial side of things and started im­pact­ing ver­ti­cals like man­u­fac­tur­ing, health­care, re­tail, et al.

Be­fore div­ing into the tech out­look for the year ahead let’s try and de­code the mar­ket for the adop­tion of emerg­ing tech­nol­ogy. If we di­vide the mar­ket across con­sumers and en­ter­prises, both are re­lated. For in­stance, the im­pact will be felt across the con­sumer seg­ments cut­ting across the de­vices we use. On the part of the en­ter­prise, this will make it hap­pen to con­sumer as well con­sum­ing whole lot of emerg­ing tech to up busi­ness and op­er­a­tional ef­fi­cien­cies.

We have gran­u­lated the fol­low­ing tech trends based on pre­dic­tions ad­vanced by mul­ti­ple re­search com­pa­nies and ven­dors. We be­lieve 2017 would be evo­lu­tion­ary in terms of emerg­ing tech and re-en­gi­neer­ing es­tab­lished tech as CIOs and CTOs look­ing at proof points will em­brace newer ones and ju­di­ciously blend old with the new. Wel­come to the age of hy­brid com­put­ing.


In the cloud evo­lu­tion, this is go­ing to be sig­nif­i­cant. While server vir­tu­al­iza­tion has ra­tio­nal­ized the server sprawl in many en­ter­prises, you still have servers ‘on-prem’. Here is where ‘Serverless Com­put­ing’ comes into play. In this form of com­put­ing, the server re­sides with the cloud ser­vice provider in which de­vel­op­ers run their ap­pli­ca­tions off-prem.

An ex­am­ple of serverless com­put­ing is AWS Lambda, which lets you run code with­out pro­vi­sion­ing or man­ag­ing the servers. In­for­ma­tion avail­able in AWS site sug­gests that by serverless com­put­ing, you pay only for the com­pute time you con­sume - there is no charge when your code is not run­ning. With Lambda, you can run code for vir­tu­ally any type of ap­pli­ca­tion or back­end ser­vice - all with zero ad­min­is­tra­tion.

Serverless com­put­ing is ex­pected to be a sig­nif­i­cant trend as we move for­ward. For in­stance, Gre­gor Petri, a mem­ber of the Gart­ner blog net­work wrote, “We are now a year fur­ther and the term ‘serverless’ is tak­ing on un­ex­pected pro­por­tions. With some even see­ing it as the successor to cloud in gen­eral or at least as a successor to the clouds’ poorer cousin in terms of rev­enue, hype and adop­tion: PaaS…”

The mar­ket is also heat­ing up for serverless com­put­ing; in the fray are IBM, Mi­crosoft, Google and more re­cently Dell.


Hi­tachi Data Sys­tems (HDS) in its Busi­ness and Tech­nol­ogy Trends for Asia Pa­cific 2017 rates bimodal IT as a prom­i­nent trend for the up­com­ing year. Ac­cord­ing to HDS, bimodal IT refers to two modes of IT: Tra­di­tional — em­pha­sizes safety, ac­cu­racy and avail­abil­ity. Non­lin­ear — em­pha­sizes agility and speed.

In the same way, hy­brid cloud will con­tinue to be the pre­dom­i­nant model for years to come, so too will bimodal IT con­tin­ues to be nec­es­sary. While many may wish for the abil­ity to sim­ply do away with legacy ap­pli­ca­tion stacks and start afresh, the re­al­ity of the need for busi­ness con­ti­nu­ity built on well un­der­stood and sup­ported mis­sion-crit­i­cal sys­tems con­tin­ues. IT must be able to man­age both modes and im­ple­ment sys­tems that can bridge be­tween them. Con­verged in­fra­struc­ture so­lu­tions can mod­ern­ize mode 1 sys­tems and bridge to mode 2 ones via or­ches­tra­tion and cloud-ready in­ter­faces says com­pany sources.

Ac­cord­ing to Rus­sell Sk­ings­ley, Chief Tech­nol­ogy Of­fi­cer of Hi­tachi Data Sys­tems Asia Pa­cific, “From a stor­age per­spec­tive, it is im­por­tant that data from both IT modes can be lever­aged, so or­ga­ni­za­tions will look more to sys­tems that can bridge the gap be­tween the two. This means the abil­ity to present cloud pro­to­cols, the ca­pa­bil­ity to be in­stan­ti­ated on-premise or in pub­lic clouds and to fa­cil­i­tate data mo­bil­ity be­tween th­ese en­vi­ron­ments.”

While the need to op­er­ate bi­modally may be seen as a nec­es­sary evil, or­ga­ni­za­tions will not tol­er­ate data be­ing stranded in mode 1 is­lands at the cost of valu­able busi­ness in­sight. Tools like Pen­taho Data In­te­gra­tion, that can bring to­gether the data ware­house of mode 1, with the un­struc­tured data of mode 2 to pro­vide users with a clear view of all their data, will gain sig­nif­i­cant traction.


We are list­ing IoT here on the premise that 2016 was the year of ag­gres­sive con­ver­sa­tions and early adopters in terms of IoT. The year ahead is ex­pected to cre­ate am­ple IoT proof points that will act as a cat­a­lyst for larger adop­tions. With the prospect of a hy­per-con­nected world get­ting real by the day, the ar­ray of in­ter­con­nected de­vices will pose a big se­cu­rity chal­lenge as well. It is in this con­text we be­lieve IoT se­cu­rity will pan out to be a big­ger trend as we move for­ward.

Gart­ner pre­dicts that by 2020, more than 25% of iden­ti­fied at­tacks in en­ter­prises will in­volve IoT, al­though IoT will ac­count for less than 10% of IT se­cu­rity bud­gets. Se­cu­rity ven­dors will be chal­lenged to pro­vide us­able IoT se­cu­rity fea­tures be­cause of the limited as­signed bud­gets for IoT and the de­cen­tral­ized ap­proach to early IoT im­ple­men­ta­tions in or­ga­ni­za­tions. Ven­dors will fo­cus too much on spot­ting vul­ner­a­bil­i­ties and ex­ploits, rather than seg­men­ta­tion and other long-term means that bet­ter pro­tect IoT.


HDS calls this as a sig­nif­i­cant trend. The com­pany says that data is be­com­ing in­creas­ingly valu­able. A re­cent IDC re­search sug­gested that 53% of or­ga­ni­za­tions in the re­gion con­sider big data and an­a­lyt­ics im­por­tant and have adopted or planned to adopt it in the near fu­ture. Com­pa­nies are find­ing new ways to cor­re­late and merge data from dif­fer­ent sources to gain more in­sight, while re­pur­pos­ing old data for dif­fer­ent uses.

“It is a clear les­son learnt from the highly dis­rup­tive in­ter­net-based busi­nesses that the abil­ity to wield data ef­fec­tively is ex­tremely valu­able. Many of th­ese busi­nesses are fun­da­men­tal data, a sim­ple in­ter­face and in­sight­ful busi­ness logic. Tra­di­tional en­ter­prises re­al­ize now that they have not used their valu­able data as ef­fec­tively as they might have,” says HDS’s Sk­ings­ley.

To en­sure the gov­er­nance and ac­ces­si­bil­ity of this data, IT needs to cre­ate a cen­tral­ized data hub for bet­ter man­age­ment, use and pro­tec­tion of their data. This cen­tral­ized hub will need to be an ob­ject store that can scale be­yond the lim­i­ta­tions of tra­di­tional stor­age sys­tems, in­gest data from dif­fer­ent sources, and pro­vide search across pub­lic and pri­vate clouds as well as mo­bile de­vices.

Sk­ings­ley calls this the ‘repos­i­tory of ev­ery­thing an or­ga­ni­za­tion knows’ and be­lieves that or­ga­ni­za­tions can no longer abide by ar­chiv­ing or backup sys­tems that leave this po­ten­tially im­por­tant data stranded.


Ac­cord­ing to Gart­ner, “Ar­ti­fi­cial In­tel­li­gence (AI) and ad­vanced ma­chine learn­ing are com­posed of many tech-

nolo­gies and tech­niques (deep learn­ing, neu­ral net­works, and nat­u­ral lan­guage pro­cess­ing [NLP]). The more ad­vanced tech­niques move be­yond tra­di­tional rule-based al­go­rithms to cre­ate sys­tems that un­der­stand, learn, pre­dict, adapt and po­ten­tially op­er­ate au­tonomously. This is what makes smart ma­chines ap­pear in­tel­li­gent.”

Gart­ner be­lieves that ‘Ap­plied AI’ and ad­vanced ma­chine learn­ing will give rise to a spec­trum of in­tel­li­gent im­ple­men­ta­tions, in­clud­ing phys­i­cal de­vices (ro­bots, au­ton­o­mous ve­hi­cles, con­sumer elec­tron­ics) as well as apps and ser­vices (vir­tual per­sonal as­sis­tants [VPAs], smart ad­vi­sors). Th­ese im­ple­men­ta­tions will be de­liv­ered as a new class of in­tel­li­gent apps and things as well as pro­vide em­bed­ded in­tel­li­gence for a wide range of mesh de­vices, ex­ist­ing soft­ware and ser­vice so­lu­tions.


In­tel­li­gent apps such as VPAs per­form some of the func­tions of a hu­man as­sis­tant mak­ing ev­ery­day tasks eas­ier (by pri­or­i­tiz­ing emails, for ex­am­ple), and its users more ef­fec­tive (by high­light­ing the most im­por­tant con­tent and in­ter­ac­tions). Other in­tel­li­gent apps such as vir­tual customer as­sis­tants (VCAs) are more spe­cial­ized for tasks in ar­eas such as sales and customer ser­vice. As such, th­ese in­tel­li­gent apps have the po­ten­tial to trans­form the na­ture of work and struc­ture of the work­place.

Over the next 10 years, vir­tu­ally ev­ery app, ap­pli­ca­tion and ser­vice will in­cor­po­rate some level of AI. This will form a long-term trend that will con­tin­u­ously evolve and ex­pand the ap­pli­ca­tion of AI and ma­chine learn­ing for apps and ser­vices. (Source: Gart­ner)


Blockchain is a type of dis­trib­uted ledger in which value ex­change trans­ac­tions (in bit­coin or other to­kens) are se­quen­tially grouped into blocks. Each block is chained to the pre­vi­ous block and recorded across a peer-to-peer net­work, us­ing cryp­to­graphic trust and as­sur­ance mech­a­nisms. Blockchain and dis­trib­uted ledger con­cepts are gain­ing traction be­cause they hold the prom­ise to trans­form in­dus­try op­er­at­ing mod­els. While the cur­rent hype is around the fi­nan­cial ser­vices in­dus­try, there are many pos­si­ble ap­pli­ca­tions in­clud­ing mu­sic dis­tri­bu­tion, iden­tity ver­i­fi­ca­tion, ti­tle registry and sup­ply chain. Dis­trib­uted ledgers are po­ten­tially trans­for­ma­tive but most ini­tia­tives are still in the early al­pha or beta test­ing stage. (Source: Gart­ner)


Ac­cord­ing to a def­i­ni­tion ad­vanced by Deloitte, it says ‘Cog­ni­tive an­a­lyt­ics’ is a term used to de­scribe how or­ga­ni­za­tions ap­ply an­a­lyt­ics and cog­ni­tive com­put­ing tech­nolo­gies to help hu­mans make smarter de­ci­sions. While cog­ni­tive as a con­cept has been around for ages, it is get­ting at­ten­tion due to the mas­sive in­crease in com­put­ing power which gives en­ter­prises to lever­age to mine data and gain bet­ter in­sights and out­comes.

Re­search firms like IDC be­lieves that over the next few years, en­ter­prises of all sizes, will have ac­cess to a new gen­er­a­tion of in­tel­li­gent soft­ware tools and ap­pli­ca­tion that will au­to­mate some de­ci­sion-mak­ing and busi­ness pro­cesses to aug­ment the hu­man work in­volved in other pro­cesses. Ac­cord­ing to Dan Ves­set, Group Vice Pres­i­dent, An­a­lyt­ics and In­for­ma­tion Man­age­ment Re­search, IDC, “Th­ese sys­tems are at the fore­front of dig­i­tal trans­for­ma­tion, and en­ter­prises need to un­der­stand where and when th­ese tech­nolo­gies will have the big­gest im­pact on their or­ga­ni­za­tions.”


An­a­lysts term that cloud first strate­gies are the foun­da­tion for stay­ing rel­e­vant in a fast-paced world. “It is clear that the pre­dom­i­nant cloud model for the fore­see­able fu­ture will be hy­brid, as most un­der­stand the agility ben­e­fits of cloud but are not will­ing to move en­tirely to the pub­lic cloud at this point. We ex­pect this to con­tinue through­out 2017,” ob­serves Sk­ings­ley.

As a re­sult, IT man­agers across APAC will be fo­cused on de­vel­op­ing skills in cloud mon­i­tor­ing, cloud work­load per­for­mance and se­cu­rity man­age­ment, along with cloud ca­pac­ity man­age­ment. In­stead of buy­ing in­fra­struc­ture from dif­fer­ent ven­dors and knit­ting them to­gether with man­age­ment soft­ware, IT will want ac­cess to the con­verged sys­tems re­quired to de­liver in­fra­struc­ture-as-aser­vice. Th­ese will en­able en­ter­prises to drive out even more cost and stream­line in­fra­struc­ture op­er­a­tions even fur­ther by com­bin­ing con­verged so­lu­tions.

10. DevOps

DevOps has been the con­ver­sa­tion point for the last two years. DevOps is not a tech­nol­ogy per se. It is a com­bi­na­tion of best pro­cesses, poli­cies and tech­nolo­gies that makes for ag­ile par­al­lel de­vel­op­ment that en­ables high qual­ity soft­ware de­liv­ery. As some an­a­lysts like Gart­ner puts it, “One spe­cific fo­cus of DevOps for some IT or­ga­ni­za­tions is the shift to­wards con­fig­u­ra­tion as code. In this con­text, DevOps bor­rows from the fact that as de­vel­op­ers’ code ap­pli­ca­tions with busi­ness logic, the in­fra­struc­ture can be coded via con­fig­u­ra­tion logic or di­rec­tives.”

IT or­ga­ni­za­tions need to roll out apps with short­est pos­si­ble lead-time and cre­ate an ag­ile and elas­tic IT in­fra­struc­ture. How does one do it? The an­swer lies in DevOps. In 2017, DevOps cul­ture will fur­ther es­ca­late and it will be­come the main­stream soft­ware de­vel­op­ment hy­giene fac­tor.

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