Exploring the Future of Business Intelligence Applications

8/11/2019 Exploring the Future of Business Intelligence Applications

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an Enterprise Applications eBook

Exploring

the Futureof Business IntelligenceApplications


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2 Business Intelligence, Data Warehousing andData Virtualization

6 Business Intelligence Challenged by Social, Mobile Data

9 Rethinking Mobile Business Intelligence

12 HTML5 Will Transform Mobile Business Intelligenceand CRM

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Contents

This content is adapted from the Enterprise Apps Today website.

Contributors: Wayne Kernochan and Drew Robb.

Exploring the Future of BusinessIntelligence Applications

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2 Exploring the Future of Business Intelligence Applicationsa QuinStreet Enterprise Applications eBook. 2013 QuinStreet, Back to Contents


ne of the most fundamental decisions

that business intelligence implementers

in IT make, at the beginning of every

new BI initiative, is whether the new

data involved should be copied into a central data

mart or data warehouse or accessed where it is.

The advent of software as a service (SaaS) and the

public cloud has added a new dimension to this

decision: Now the BI implementer must also decide

whether to move the data physically into the cloud

and de-link the cloud and internal data stores. In

fact, this decision is no longer the purview solely of the

CTO the security concerns when you move data to a

public service provider mean that corporate needs to

have input into the decision. However, fundamentally,

the decision is the same: Move the one copy of the

data; keep the one copy where it is; or copy the data,move one copy and synchronize between copies.

Almost two decades of experience with data

warehousing has shown that these decisions have serious

long-term consequences. On the one hand, for customer

buying-pattern insights that demand a petabyte of

related data, failure to copy to a central location can

mean serious performance degradation as in, it

takes hours instead of minutes to detect a major cross-

geography shift in buying behavior. On the other hand,

attempting to stuff Big Data like the graphics and videoinvolved in social networking into a data warehouse

means an exceptionally long lag time before the data

yields insights. The firms existing data warehouse

wasnt designed for this data; it is not fine-tuned for

good performance on this data; and despite the best

efforts of vendors, periodic movement or replication of

such massive amounts of data to the data warehouse

has a large impact on the data warehouses ability to

Business Intelligence, Data Warehousing

and Data VirtualizationBy Wayne Kernochan

O

perform its usual tasks. Above all, these consequences

are long-term applications are written that depend

for their performance on the existing location of the

data, and redoing all of these applications if you want

to move to a different database engine or a different

location is beyond the powers of most IT shops.

The reason that it is time to revisit the move or stay

decision now is that business intelligence users, andtherefore the BI IT that supports them, are faced with

an unprecedented opportunity and an unprecedented

problem. The opportunity, which now as never before is

available not only to large but also medium-sized firms,

is to gather mammoth amounts of new customer data

on the Web and use rapid-fire BI on that data to drive

faster customer-of-one and agile product development

and sales. The problem is that much of this new data


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is large (by some estimates, even unstructured data

inside the organization is approaching 90 percent of

organizational data by size), is generated outside theorganization, and changes very rapidly due to rapid and

dangerous changes in the companys environment: fads,

sudden brand-impacting environmental concerns, and/or

competitors who are playing the same BI game as you.

How do you get performance without moving the data

into the organizations data warehouse? How can the

data warehouse support both new and old types of data

and deliver performance on both? Most importantly, how

do you keep from making the same mistakes in move or

stay decisions that make present-day data warehousingso expensive and sub-optimal for your new needs?

Business Intelligence Political Wars

Todays business intelligence users, wowed by case

studies of great analytics insights leading to major cost-

cutting and add-on sales, are likely to view these move

or stay decisions as the property of IT, to be decided

after the CEO or CMO decides how best to use the

latest dashboard, Facebook data miner or performance

management tool. In turn, BI IT has tended to viewthese decisions as more short-term and ad-hoc, meant

to meet immediate urgent needs. Alas, past experience

has shown that not only is such an approach to business

intelligence implementation unwise, it is also futile.

An old story is of the new pastor, some of whose

congregation ask him to change the location of the altar.

He asks an older pastor what the tradition is, and the

pastor, instead of answering, says to try it that way. The

result is a massive argument among the congregation.

He goes back, and the older pastor says to put itback. Instead of dying down, the argument gets even

hotter. He goes back again, and says, whats the

tradition? My congregation is fighting like mad about

this. Ah, says the older pastor, thats the tradition.

In the same way, when data warehousing was first

introduced, CEOs deferred the move or stay decision

to IT, which attempted to shoehorn all data into the

central data warehouse. Lines of business, of course,

resisted the idea that corporate IT should be gatekeepers

over their data, making them wait for weeks for reports

on their local sales that used to take a day. The result

was that CEOs were being frequently appealed to by IT

or by lines of business over the matter that became

the tradition. Eventually, the advent of data marts andthe fact on the ground that data warehouses could not

handle new data from acquisitions ended the arguments,

at the cost of BI that was poorly equipped to handle

new data from outside the organization and executives

and lines of business that under-used corporate BI.

To avoid these political wars, the BI user needs to set

out a long-term approach to move or stay that should

inform implementation decisions at both the corporate

and IT level. Instead of I have a hammer, everything looks

like a nail, this approach stresses maximum flexibilityand agility of any BI solution which, in turn, translates

to asking BI IT to deliver, not the highest performance,

but reasonable performance and maximum architectural

flexibility to accommodate new types of data.

The Business Intelligence Technology of theDecade

To make such an approach effective, the BI-using

enterprise needs to understand just what IT can

and cannot do to make the architecture flexibleby move or stay decisions. Here, the good news

The BI user needs to set out a long-term approach

to move or stay that should inform implementation

decisions at both the corporate and IT level.


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is that software technology developed over the

last decade has given IT a lot more ability to make

their architectures flexible if they will use it.

In my view, the greatest business intelligence technology

advance of the last decade is not cloud, analytics, BI

for the masses, or near-real-time BI, but a technology

originally called Enterprise Information Integration (EII)

and now called data virtualization. This technology

makes many disparate data stores appear as one to the

user, developer and administrator. In order to do so, it

also demands that the data virtualization user develop

a global metadata directory that catalogs not only data

copies, but also variant storage formats. Thus, in master

data management, data virtualization allows a wide

variety of ways of representing a customer, many of them

from acquired companies with their own approaches

to customer data, to have a common master record.

In business intelligence, data virtualization allows Big

Data from outside the organization to be combined with

new types of operational data that are not yet stored in

the data warehouse and with data-warehouse data in

carrying out near-real-time data mining and analytics.

The practical effect of data vir tualization is that in the realworld it delivers the move or stay flexibility that data

warehousing alone never could. It does this in two ways:

1. It gives the BI end user a third option: not just

copy new data to the data warehouse and wait

until the data warehouse allows you access

to it or dont copy it and have no access to

it, but you also have the option not to copy it

and have slower-performance access to it.

2. It makes IT and the organization aware of itsdata assets, allowing IT to provide high-level BI

interfaces below which the datas location can be

changed as needed, and allowing the organization

to understand better the BI opportunities

afforded by new data they hadnt known about.

There are data virtualization products available

today, as well as master data management solutions

embeddable in the products of business intelligence

vendors. At the same time, BI buyers should bear

in mind that a Metadata Officer to abet storage of

metadata information in the repository and enforce

corporate standards for master data management is

needed and is a good idea in the long run anyway.

The second advance that BI users should know about

is a work in progress, and is also a bit trickier to

describe. The essence of the problem with scattering

data copies across geographies is that either you have

to make it so that every time one copy of the data is

updated, it appears to the user as if all other copies are

updated simultaneously, or you have to deal with the

difficulties that result when one user thinks the data

has one value, and the other, another. For example,

suppose your bank receives a deposit to your checking

account in New York to cover a withdrawal, and the

withdrawal itself in Shanghai immediately after. If

the Shanghai branch doesnt receive the notification

of the first update in time, you will face overdraft

fees and will be rightfully annoyed at the bank.

For at least the last thirty years, software folks have been

wrestling with this problem. The solution that covers allcases is something called the two-phase commit, but it

requires two back-and-forth communications between

New York and Shanghai, and is therefore so slow in

real life that it can only handle a small percentage of

todays data. In the late 1990s, Microsoft and others

found a way to delay some of the updates and still

make it look like all the data is in synchronization in

the local area networks that support todays global

organizations. Above all, over the last few years, the

need to support distributed cloud data stores has led to

identification of many use cases (often associated withthe noSQL movement) in which two-phase commit

isnt needed and so-called eventual consistency

is OK. The result is that, on the cloud, you are much

more able to keep multiple copies of data in different

locations without slowing down business intelligence

performance unacceptably. Of course, this is still a long

ways from the cloud hype of your data is somewhere

in the cloud, and you dont need to know the location


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and it is likely that in the real world we will never get

to the point where data location doesnt matter.

Thus, the business intelligence-using organization should

expect IT to be able to use data vir tualization to deliver

much greater BI flexibility, in the cloud or outside it, and

should demand that IT consider the flexibility benefits

of noSQL-type data copying in certain use cases but

should not expect cloud data-location nirvana.

The Business Intelligence User Bottom Line:Think Long Term

So move or stay is an important decision for thebusiness intelligence buyer or upgrader; it should be

made up front by corporate as well as IT; there are much

better solutions today that allow smart BI implementers

to avoid many past mistakes; and the key to move or

stay decision success is to emphasize flexibility over

raw performance. Suppose you do all that; now what?

The first thing you find is that decisions like cloud BI or

not cloud BI become a lot easier. With less dependence

on data-location-dependent apps, moving these apps

and their data from location to location becomes, if noteasy, at least doable in a lot more cases. So where now

it makes sense to move only a small subset of mission-

critical Big Data to a small cloud BI provider, because

otherwise the coordination between that and your data

warehouse becomes unwieldy, now you can make the

decision based more on potential cost savings from the

cloud vs. the overall performance advantages (smaller

than before) of a single massive data warehouse.

The second thing you discover is that you have

created some new problems but also some newopportunities. The old, dying data warehouse plus

operational database model of handling enterprise

data had its drawbacks; but compared to the new

architectures, complexity was not one of them.

However, well-designed new architectures that include

moved, copied, and accessed-in-place data also allow

the BI user to constantly change the proportions

rapidly to adapt to new data. In this case, the agility

benefits far outweigh the complexity costs.

The third thing you see is that corporate is being

forced to think about BI as a long-term investment,

not an endless series of fad tools and thats an

excellent thing. All too often, today, the market for

BI in general and analytics in par ticular is driven by

one-time cost-cutting or immediate needs for insights

and rapid decisions. The key difference betweenorganization flexibility and organizational agility is that

the latter makes the organization constantly change,

because it is always focused on the change after this

one. Move or stay decisions by IT can make your

business intelligence and enterprise architecture more

flexible; a long-term mindset by corporate that drives

the move or stay and other BI decisions makes BI and

the whole organization more agile. And data on agile

software development suggests that agility delivers

greater cost, revenue, and customer satisfaction benefits

than flexibility, both in the short and long term.

A final thought: a popular term a few years ago was

glocal, in which the most effective enterprise was the

one that best combined global and local insights. Move

or stay is the equivalent in business intelligence, a way of

tuning BI to combine the best of internal/local and Web/

cloud/global data for better insights. In essence, move

or stay success is a way to achieve information glocality.

Given the importance today of long-term, customer-

information-driven competitive advantage, a key action

item for every business inteligence user, in corporateand IT, should be to redesign the BI architecture in

accordance with more agile move or stay norms.

Its not administrivia; its about the long term.


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2010 IBM survey found that 76 percent of

CEOs view insight and intelligence as

key to corporate success over the next five

years slightly behind getting close to the

customer and people skills and that, in response,

83 percent of CIOs correctly perceive that leveraging

business intelligence (BI) is thebest way to implement these

strategies.

But whats the best way to

leverage BI? A recent Gartner

report, flawed or not, suggests

that many business intelligence

projects fail, or are perceived

as doing so. And the risks of

failure, and consequences of

success, are not negligible:another IBM study indicates

that improving business agility

by such strategies as agile

BI can lead to long-term

improvements in top- and

bottom-line business metrics

by as much as 60 percent.

How do successful BI implementers operate? A recent

MIT study suggests that they create and maintain a data-

driven decision process. In other words, best-in-classBI implementers consciously view their organizations

as machines that scoop up information and funnel it

to decision-makers, and so they design a machine that

will carry out such a process as quickly as possible,

with as little information loss as possible, and with the

right networks to aim each piece of information at the

right decision-maker. To support this machine, they buy

software and hardware that will not only process fast and

accurately, but also have the smarts to figure out who can

use the information best, and pre-analyze for the target

decision-maker.

Now, however, this beautiful model of how to operate,

developed over the last 20 years and well served by

existing business intelligencefirms, may be breaking down.

The problem is that much of

the valuable information about

a firms customers, suppliers

and environment exists

outside the firm but is not

being captured by the existing

machine and more new types

of information are arriving out

there, faster and faster. The

Other Category of information,not handled by existing data

warehouses, is now beginning

to determine corporate

customer relationships social

information as in Facebook,

sensor information as in

Google Maps, smartphone information as in cellphone

videos and snapshots, or rapid-fire text conversations

via tweets and texting. How should IT buyers keep the

machine from breaking down as its asked to handle

information it was never designed for, or, even worse,churning away at information that is less and less relevant

to the needs of the firm?

It is hard to find an answer among the clouds of cloud

BI hype, the flashbulbs of BI vendor hype and the

distractions of hot-topic hype. But I believe that certain

principles will serve the best-in-class BI buyer well, now

and in the future.

Business Intelligence Challenged

by Social, Mobile DataBy Wayne Kernochan

A


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A Modest Other Proposal for BusinessIntelligence

I would propose three additional criteria for the BI buyer

who has exhausted the usual criteria of fit to purpose,

scalability, robustness, flexibility and conformance to

government and business requirements. The first is:

more truly agile is better than less truly agile. That is, a

BI solution that helps the firm make itself not only faster

to respond to the customer but also more effective in

anticipating customer changes is to be preferred.

An example might be the whole hot topic of agile

business intelligence that I have discussed in the past.As others have noted, some firms try to do agile on the

cheap by setting loose programmers with any kind of so-

called agile development process to constantly customize

the vendors existing or latest BI solution. Others

seek to complement existing BI solutions with agile

development, constantly involving customer input and

vendor advice, with the responsibility shifting from the

vendor to the firm as IT gains experience. Commentators

have noted that this kind of approach tends to work out

better, and that vendors who can support agile advice

and responsibility shifting are to be preferred.

The second criterion is: third-party is better than one-

stop lock-in. I dont mean that you shouldnt buy from

a BI vendor that can supply everything you need in-

house. No, what Im talking about is the likely result in

your architecture of adopting the BI vendor, and the

attitude of the vendor. Will your choice of a vendors

solution component foreclose some other databases (say,

noSQL databases), or does your vendor try to use the

sale to crowd out all other vendors in a particular area,

as, say, Oracle has been accused of doing in databases

in the past? The ability of the vendor to handle todays

Other Category data (e.g., unstructured and semi-

structured Web Big Data) and integrate with the Web

sources supplying that data and the open-source tools

that can also provide access to that data is a good test

of whether the vendor is open to present and future third

parties.

And finally, my third criterion would be: How well does

the vendor handle the other other category? Because

the fact is that there will always be an important other

category of information that you will need to seek out,

no matter how much of it you cover today. Necessarily,

todays business intelligence solutions, having focused

on structured data, will focus only on certain parts of


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todays Other information social media, Hadoop

that are easier to aggregate, and more important right

now. For example, IBMs present definition of Big Data

provides a separate Big Data platform, but that platform

draws its information most easily from such sources as

geospatial and Facebook data stores, not smartphone

texting. A vendor who supports open-ended tools such

as agile development environments, data integration

with unstructured data support, and BI-driven CRM is

more likely to allow you to evolve your BI as new types of

information arise and become important.

IT Buyer Bottom Line: Let the Customer Hug

You BackThe most popular current CEO strategy getting close

to the customer often seems to the BI implementer to

mean hug the customer more closely: respond more

quickly to new information from the customer, analyze it

better so as to understand the customer, and ensure a

longer and longer relationship involving more and more

add-on sales. But the importance of todays Other

Category to business success indicates that there is

another side to the customer relationship: following the

customer as the customers needs and wants change overtime. That, after all, is why the Other Category arose in

the first place because the advent of the Web provided

a new, faster-changing customer environment that

outdated the business data warehouse model.

Thus, the IT buyer cannot assume that simply

implementing a Big Data platform, or a series of cloud

BI projects, will be a long-run best practice. Instead, the

IT buyer should attempt to assure, by adding my three

criteria or in some other way that future Other Categories

will be handled better.

The same surveys that show CEOs ideas of successful

business strategies also show that they view the

environment of the business, not the business itself,

as the source of the greatest business risk because itkeeps changing faster and faster. As it does, customer

needs and wants will inevitably do so as well, and

probably faster and faster. Your business intelligence

that analyzes these needs and wants must be open to

the customers indication of those changes which often

show up as information in an Other Category. And if you

want to hug the customer closer, you need to ensure that

the customers changes result in the customer finding you

to be an even better fit for purpose, and thus hugging

you better. To do this, pick business intelligence solutions

that will continue to handle the Other Categories of thefuture. Your customers may well hug you for it.


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assumptions implicit in todays vendor visions of mobile

BI. In particular, we need to shed the assumption of one-

size-fits-all mobile hardware.

Death of the Laptop Exaggerated

The basic approach of many mobile BI vendors and IT

users today reflects a sense that the mobile workers

future all-purpose mobile computing device will bethe smartphone. After all, its consumer installed base

is far greater than that of the PC, it is used for other

sophisticated apps like performing transactions in bank

accounts or (in Europe) waving the cell phone in front of

a sensor to pay for purchases, and some predictions are

that laptop sales will start to decline, while smartphone

purchases continue a steep rise. If there is any future

competitor, the conventional wisdom asserts, it is the

Rethinking Mobile Business IntelligenceBy Wayne Kernochan

As if the advent of agile business

intelligence wasnt enough of a

puzzle, IT buyers are now being asked

to wrap their minds around the notion

of mobile business intelligence. There are several

head-scratching features of mobile BI as it is

advertised today:

Todays hot analytics tools are all about

deeper, more ad-hoc analysis based on

data-stuffed enterprise reports, and floods of

just-in-time data alerting real-time enterprises

and driving more immediate decisions in

other words, lots of data with complicated

relationships. How do you fit all that on a

cell phone screen? How do you perform

complicated analyses without a keyboard?

Whats the target form factor? First

it seemed to be the iPhone, then the iPhone

plus iPad and now it appears to be a stew of

iPhone, Android, iPad and other approaches.

Whats the use case? Is it sales-type customer

interaction, with just-in-time price comparisons and

local inventory search? Is it global marketing, with

the executive travelling light and always in touch

with local markets and corporate financials? Is it the

manufacturing process, with supply-chain analytics inthe middle of the factory floor or the warehouse? Is it

glocality, with cheap cell-phone access driving thriving

developing-country offices? Whats the killer app?

I believe that there are straightforward answers to each of

the above questions that should improve an enterprises

implementation of mobile business intelligence. However,

to get to these answers we need to rethink some basic

W


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iPad, which presently has some limits on its ability to

perform enterprise apps and is larger (and therefore less

mobile).

However, research that looks more closely at how workers

are using mobile devices today paints a very different

picture. To handle their mobile needs, these workers

employ what I call a three-legged stool:

For wandering around in the immediate

area, such as around the office or factory floor,

mobile workers use smartphones, because they

are light, dont require placement on an object

for use, and can use the Internet constantly in

pretty much any area via cell phone networks.

For moving around locally and then finding a stable

site to work from, a netbook (and now possibly

an iPad) is preferred. They can be carried for long

distances (say, in a bag), they have larger screens

for more complicated output, and netbooks have

keyboards for complicated input. By tethering

a cell phone or getting a special attachment, the

netbook user can get the same access to the Internet

as a smartphone. At present, the netbook lacks thetouch screen capabilities of the iPhone and iPad,

but there are indications that within a year both

hardware and operating system (Windows 8) vendors

will support touch screens in laptops and PCs.

For traveling beyond the local area, a full-fledged

laptop is preferred. Users view both the smartphone

and netbook as too crippled to do all of the

corporate tasks that they may want to do when

they are away from home for a long time. Again,

note that the keyboard is an important plus in these

cases, due to the use of legacy corporate user

interfaces, and tethering or attachments will yield

Internet access anywhere. In effect, the destination

of the travel becomes a new office away from

office, with a laptop replacing the PC and a

smartphone still used for wandering around locally.

In fact, it is possible to view todays mobile user

as creating a mobile mobile office, where ones

traditional physical office with its PC, extended

locally by the smartphone and netbook, can now

be mimicked halfway around the world. And

that, in turn, means that there will still be plenty

of demand for laptops in the near future.

Fine-Tuning Mobile Business Intelligence

This vision of a mobile mobile office suggests some

answers to the questions I posed at the start of this

article. Lets take each of them in turn:

How do you fit BI on a cell phone screen? You dont. All

you need to support is the simple business intelligence

needed when one is wandering around in onesimmediate area do we have this in stock? Is there a

problem in the store? For those spare minutes where you

as a manager need to dig deeper, theres the netbook.

Whats the target form factor? For the immediate future,

it appears that mobile workers mobile mobile office

will not change drastically. Long-distance travelers will

still want keyboards, and the smartphones advantages

of touch interfaces and ubiquitous Internet access are

vanishing. It is possible that extensions to the netbook

The basic approach of many mobile BI vendors

and IT users today reflects a sense that the mobile

workers future all-purpose mobile computing device

will be the smartphone.


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will make it serve adequately as both an iPad and a full-

fledged laptop. Therefore, the mobile BI implementer

should consider targeting at least two and probably three

form factors (note the netbook/laptop typically already

has a fair amount of enterprise IT support). The cell

phone and laptop platforms have a ways to go to merge,

but via such offerings as Googles Android cell phone

OS and Chrome network computer OS, it is possible to

envision a one API to choke common platform across

the two, to which the implementer can port enterprise

business intelligence client software and mini-apps.

Whats the use case? In fact, with solutions for each type

of mobility, the answer is: all of the above. The sales call

is local; the manufacturing floor is immediate-area; the

long-distance traveler is a full-fledged mobile mobile

office, and so is the developing-market physical office.

And that, in turn, means that there is no single killer app

for mobile BI. All of the above use cases will benefit from

providing such a mobile extension to the existing BI

solution to the mobile worker.

The Mobile BI Bottom Line

The message of users employment of mobile technologyis that the way is now clear to providing a reasonable

equivalent to the key promised benefit of mobile

business intelligence in-depth, ad-hoc analysis of key

data, no matter where you are, moving or not. Rethink

mobile BI, tune the implementation plan to support the

three-legged stool I have cited above, and there is a clear

path to such a mobile BI solution.

That doesnt mean that implementing such a solution

will be easy. To cite just a few difficulties: you need to

minimize the costs and time of developing new apps for

multiple, disparate mobile platforms; you need to identify

the specific use cases for each platform for your own

business; and you need to future-proof the platform, as

it is likely that one or more of the three platform types in

the three-legged stool will change significantly over the

next year or so, in predictable (Windows 8 touch screens)

and unpredictable (will Android succeed?) ways.

However, implementation difficulties should not be a

barrier to starting to create your three-legged stool

today. In fact, the fast pace of change in the mobile

market only makes a quick start more urgent. Remember,

the smartphone app market is consumer-driven, as is

your mobile technologys enterprise use. The more you

lag the market, the less able you are to support the

mobile data analytics that your companys consumers/

employees may well do with you or without you not to

mention without adequate corporate security.

And, best of all, with an adequate mobile BI plan in place

you can go back to scratching your head about agile BI.


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HTML5 Will Transform

Mobile Business Intelligence and CRMBy Drew Robb

TML has evolved considerably since it

was first mapped out by Tim Berners-

Lee more than 20 years ago. Now were

up to HTML 5.0, which could have a

significant effect on the business intelligence and

CRM landscape.

HTML5 is a big push forward, especially considering

how it handles different media as well as cross-device

portability, said Tiemo Winterkamp, senior vice

president of global marketing at business intelligence

(BI) vendor arcplan. Both are key areas to help us

with mobile scenarios.

In the past, browsers were dependent on other

technologies such as Flash, Silverlight or Java to

render rich Internet applications (RIAs). This created

problems such as Flash not being supported oniPhones or iPads. One big benefit of HTML5 is that

browsers will be able to integrate additional content

like multimedia, mail and RIAs with enhanced rendering

capabilities. And plans have been made to allow future

HTML5 browsers to securely access sensor and touch

information, which makes HTML5 a viable alternative to

native application development for such functions.

The result: With HTML5, nearly every piece of Internet

content we can envision today will be able to be coded in

HTML, Javascript and Cascading Style Sheets (CSS), andtherefore automatically portable to all environments and

browsers supporting HTML5.

This approach is very attractive for BI vendors who

aim to provide business critical information anywhere,

anytime and on any device, said Winterkamp. The

result is an attractive, multi-functional user interface with

as little design and deployment effort as possible. And

more importantly, you only need to develop these apps

once for all devices.

He notes that HTML5 is very much a standard on the

rise. It is progressing fast, with supporters such as

Apple, Microsoft and Google, but the different browser

vendors are currently cherry-picking the HTML5 features

that best fit their current roadmap. Thus the degree

of HTML5 support varies within some browsers, and

an official release date when well see browsers witha broad implementation is hard to predict. But the

good news for business applications is that many of the

features available today thanks to HTML5 are sufficient

to implement modern business intelligence and CRM

applications.

For standard PCs or notebooks, current HTML is fine in

terms of delivery of most relevant business intelligence and

H


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CRM functions. However, factors such as screen resolution

and device size make mobile devices more of a challenge.

HTML5 will simplify things and incorporate zooming

technologies and gestures (pinching, double tap, turning,

and so on) natively provided by the different devices.

The advent of HTML5 will also be good for app

development. It will act as an impetus for innovation

among CRM and BI application developers.

Improved browsing technologies will force apps to

evolve, said Thomas Husson, an analyst at Forrester

Research. In addition to talking to the local device, next-

level apps should also talk to other apps through openAPIs and interact with other devices.

Empowering Mobile Salespeople

Looking ahead, HTML5 will make it easier to bundle

various types of media content into one client mashup.

For example, it becomes possible to list the nearby

customers of a salesperson based on current GPS

information and further visualize the different locations

within Google Maps.

You could even go as deep as mashing up the current

total turnover of the customer with embedded chart

visualization, said Winterkamp. You may even embed

content such as voice comments, pictures or bar codes as

attachments in the database which is currently used on

the server side.

Yes, some current applications can do all of that. But

those are typically custom-built native apps that run on

a specific device or platform. With the new HTML5, the

door is opened to the creation of such applications oncefor them to be deployed everywhere.

Take the case of App Store tools that were specifically

created for iPhones. When the iPad hit the streets, those

apps had to be adjusted to work on that platform. And

in many cases, they could not be reused for Android or

Blackberry devices. Each of the apps for these different

platforms and devices has to be maintained over time.

HTML5 simplifies takes away that complexity, reducing

the maintenance cost for mobile BI.

CRM, BI Vendors Roll Out HTML5 Apps

What is going to happen on the vendor front? More than

likely, the established CRM and business intelligence

players will be slower to adopt HTML5, as they already

have plenty on their plates to deal with maintaining and

upgrading their existing products. So look for the smaller

players to be the ground breakers in this arena.

Arcplan, for instance, completed a review of its mobile

BI options in 2010 and decided to go the web-app route

using HTML rather than harnessing native applications.

The result is that arcplan Mobile can run on any browser

providing sufficient HTML support. This includes WebKit-

based mobile browsers on iPhone, Blackberry, Android,

Windows Phone 7.5 Mango and Bada.

We are not offering every HTML5 function right now, but

with each update of arcplan Mobile, well integrate new

functions as they become available, said Winterkamp.

One thing that is high on our wish list is to have local and

secure data storage in the browsers. This will then allowusers to create uniform offline analytical web apps.

Another potential addition would be to have something

on a smartphone or tablet PC called a mobile BI Wall.

This could give users widget-like snippets of dashboards

and reports on their computers that are automatically

updated and fed by a BI data warehouse. Typically

this requires animated charts, local data storage,

personalization, and collaboration features.

Overall, it may be a little while before this technologybreaks into the business intelligence and CRM

mainstream.

HTML5 will greatly improve the audio and video

capabilities of mobile browsers, said Husson. However,

it will be at least three years before the technology fully

matures. It has to reach critical mass on consumers

mobile handsets and in developers minds.

Documents

Exploring the Future of Business Intelligence Applications