October 2014, IDC #AP2578213VE
Excerpt
Big Data Professional Services: Market Forecast and Key Players in Asia
Prabhitha Sheethal Dcruz Mayur Sahni
IDC OPINION
IDC believes that the Big Data market in Asia/Pacific (excluding Japan) or APEJ is still at nascent
stage and has yet to experience large-scale adoption. With very few early adopters able to justify their
investments, others are still struggling to address technology and business challenges. Nevertheless,
as these technologies evolve, businesses continue to show interest and actively research and explore
how Big Data and analytics (BDA) initiatives will fit into their organization. IDC has observed the
following trends in the region:
IDC expects that the Big Data services market in APEJ will grow from US$228.16 million in
2013 to US$644.75 million in 2017.
IDC's study reflected that only a small percentage of businesses in APEJ are actively pursuing
opportunities with Big Data and analytics. Most of these engagements are in the pilot stage
and hence ad hoc in nature, which implies that businesses in the region have not yet
developed an enterprisewide strategy for Big Data.
The huge investments and complexities associated with Big Data implementations are forcing
organizations to turn to professional services firms that will help them develop a clear business
case, identify data sources (internal and external), and help choose the right mix of
technologies and solutions for their organization. As such, organizations across APEJ are
showing significant interest in consulting-led engagements.
Majority of the current Big Data deployments do not adhere to the 4Vs — volume, variety,
value, and velocity. The bulk of these initiatives mostly involve internal/enterprise data and do
not include any external data, thus limiting the outcomes of Big Data projects. Businesses
continue to view BDA technologies as a hardware/software solution rather as a business
process solution. BDA projects should be followed by a business process change to reap the
true benefits derived from faster and more reliable technology solutions.
Hiring and retaining talent is a major challenge for all organizations across APEJ. Bulk of the
skills required for Big Data implementations are still mostly nonexistent in the marketplace.
IDC believes majority of the talent will be supplied by the vendor community, which is currently
investing in developing talent through trainings and alliances with universities.
The industry is currently vendor-driven and vendors are making significant investments in developing
and marketing their BDA services portfolio. While BDA initiatives can singly deliver business
outcomes, IDC recommends that businesses invest in other 3rd Platform technologies, such as cloud,
that will aid in the faster delivery of scalable solutions.
©2014 IDC #AP2578213VE 1
IN THIS EXCERPT
This is an excerpt from an ongoing report series on service providers offering Big Data services in
Asia/Pacific (excluding Japan) or APEJ. As part of the study, IDC Asia/Pacific profiled HP as one of the
key players in the region
Big Data Definition
The intelligent economy produces a constant stream of data that is being monitored and analyzed.
Social interactions, mobile devices, facilities, equipment, research and development (R&D),
simulations, and physical infrastructure all contribute to the flow. In aggregate, this is what is called Big
Data. IDC's Big Data technology and services document size and forecast the technology and services
for managing, analyzing, and accessing Big Data, not the data itself. However, for the purpose of this
document we have only included numbers for Big Data services. IDC defines Big Data technologies as
a new generation of technologies and architectures designed to economically extract value from very
large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis.
Following IDC's Big Data definition, we created criteria to determine whether a use case and
associated technology and services should be included in the Big Data market sizing.
Step 1 evaluates three scenarios:
Deployments in which the data collected is over 100 terabytes (TB) (we use data collected, not
stored, to account for the use of in-memory technology in which data may not be stored in a
disk.)
Deployments of ultrahigh-speed messaging technology for real-time streaming, data capture,
and monitoring (this scenario represents Big Data in motion — as opposed to Big Data at
rest.)
Deployments in which the data sets may not be very large today but are growing very rapidly
at a rate of 60% or more annually
Step 2 evaluates whether, for each of the three scenarios of step 1, technology is deployed on
dynamically adaptable infrastructure. This could be a scale-out or a non-scale-out infrastructure.
Step 3 evaluates two scenarios:
Deployments that include two or more data types or data sources
Deployments that include high-speed data sources such as clickstream tracking or monitoring
of machine-generated data
IDC's definition for Big Data, the market sizing criteria, and the three steps for evaluating use cases
are depicted in Figure 1.
©2014 IDC #AP2578213VE 2
Data is received
via ultrahigh-
speed
streaming
FIGURE 1
IDC's Technologies and Services Market Sizing Criteria
Source: IDC, 2014
SITUATION OVERVIEW
The Big Data Market Today
In the past 24 months, businesses in Asia have embarked on their journey toward the implementation
of 3rd Platform technologies. Although cloud and mobility solutions have made headway into most
organizations across APEJ, Big Data is yet to get its due respect. Early adoptions of BDA have been
mostly in silos with no enterprise support for measurement tools or methods to gauge outcomes and
effectiveness. In addition, the return on investments (ROI) associated with each of these deployments
seem to vary and remain elusive in most cases. This is mainly because the process of integrating
analytics across the business requires change in the business process too; guaranteeing ROI will
largely depend on how organizations have been able to use these high-speed technologies to drive
business and manage the change associated with them.
All enterprises own data, which until recently was collected mostly for reporting purposes. Big Data
projects are all about data and the actionable insights derived from this data. So logically the first step
in any Big Data project is to understand what data is available and what data will be used that could
©2014 IDC #AP2578213VE 3
help achieve business objectives. As organizations moved up the maturity curve they started using
analytics to derive value from this data. Traditionally different lines of business (LOBs) have used
different tools to analyze data with certain structure. Although this has worked well in the past with
structured small sets of data, the Big Data scenarios require analytical solutions that can run on large
sets of structured and unstructured data with high speed to provide real-time insights. Hence, the
traditional tools used for analyzing data may no longer be relevant in the case of Big Data. However, it
is not just the tools — even the traditional infrastructure will not be able to support Big Data. Hence,
Big Data deployments will require organizations to rework their entire architecture encompassing the
software and hardware aspects.
Enterprises today sit on heaps of data that is growing exponentially every day. Although the declining
costs in storage have helped to store all of this data irrespective of its usability, the key challenge with
Big Data deployments is to understand the quality of the data being analyzed. This is challenging
primarily due to two reasons. First, this data exists in pockets and there is no single centralized view
available for this data, hence the inability to correlate all of this data. Second, not all data is reliable
and accurate. Thus, ensuring that the data analyzed is of right quality and the technology has the
ability to extract value from data in real time are the key challenges in Big Data deployments.
As organizations across APEJ increase their spending in 3rd Platform technologies, Big Data
technologies are also climbing up the priority ladder in the CIOs' agenda. Despite CIOs acknowledging
the benefits associated with Big Data and analytics, very few of them have actually taken the plunge. A
recent IDC survey revealed that a large percentage of respondents have not yet implemented any Big
Data and analytics solutions in their organizations (see Figure 2). Well, this does not come as a
surprise as Big Data is currently vendor-driven and businesses have yet to develop a clear
understanding about Big Data and its associated technologies. With most of them following the
wait-and-watch approach, current deployments are restricted to the telco, finance, and public sectors.
©2014 IDC #AP2578213VE 4
FIGURE 2
Big Data Deployment Plans
Source: IDC APEJ IT Services Survey, 2013
Although actual deployments may be relatively low, there is a significant buzz surrounding Big Data
and its benefits. Everyone understands that the data they own can be put to use — to gain actionable
insights and make informed decisions. However, to reach a stage in which they can make sense of the
data they own is the first challenge.
The second most important aspect of Big Data deployments is the speed at which insights are
obtained that will help in decision making. However, to derive business outcomes obtained from these
high-speed technologies requires a major change in the way businesses operate. At present, business
users tend to make decisions based on the limited information they have or mostly from their previous
experiences. With Big Data, business users will be forced to change the way they make decisions —
from decisions based on intuitions to ones based on facts. Change management then becomes a
crucial part of determining the success of Big Data investments. The speed at which insights are
obtained will mean nothing if organizations do not change their business processes accordingly.
Having said thus, the major challenge lies in integrating these technologies with the existing enterprise
architecture. Businesses will increasingly turn to service providers that are able to provide them a road
map on how these technologies can be integrated into their current enterprise architecture. Big Data
comprises a set of genuinely new technologies and a convergence of more mature technologies. To
embrace Big Data fully, organizations need to be dedicated and determined to embrace a more
information-led culture. Big Data reflects both the complexity of data sets and the new technologies
and techniques to manage the growth, variability, and velocity in those data sets. An organization may
face a Big Data challenge if its current IT infrastructure is not fit to handle the increasing requirements
for availability and performance-related to growing volumes of data.
Currently implementing
(9.2%)
Not implemented
as yet, but plan to implement in the next 6 -
12 months (10.1%)
Already implemented and operating it right now
(15.6%)Not
implemented as yet, but plan to implement
after 12 months (22.1%)
Not implemented
as yet and neither plan to
implement (43.1%)
Total = 100%
©2014 IDC #AP2578213VE 5
Challenges
Big Data presents several opportunities to transform businesses. However, harnessing Big Data is not
an easy task and requires CIOs to work with multiple LOBs to develop enterprisewide architecture. Big
Data technologies are complex and are still evolving; hence there is a significant gap between
customers' understanding of these technologies and the technology limitations. Businesses
understand the competitive advantage Big Data can bring to the table but handling increasing volumes
of data to obtain real-time results that can be used to keep them ahead of the competition is very
challenging.
Customers
IT versus LOB funding. One of the primary challenges with Big Data projects is obtaining
sponsorship. Because most believe that Big Data is a solution waiting for a problem and Big
Data solutions are expensive, there is a constant tiff between the IT and the business
regarding who should be funding these projects since both these units have different
expectations from BDA projects. Hence, for BDA projects to be successful there has to be
continuous collaboration between the business and the IT.
Buy-in from multiple stakeholders. Since Big Data is all about data that is not owned by a
single team, Big Data and analytics projects require support from multiple LOBs. Also, since
the benefits of BDA projects are realized across multiple LOBs it is important to get buy-in
from multiple stakeholders.
Lack of business case. Although there has been an increase in the adoption rate of analytics
and Big Data technologies, ROI in most cases remains elusive. With most business cases
targeted at only acquiring sponsorship for the projects, businesses often miss out factoring in
all the pros and cons of their investments in the new technology. Hence, developing a clear
business case through an in-depth analysis of the problem and how businesses put together
resources and technologies to achieve goals becomes crucial. The business case should also
be able to identify costs for the overall management and integration of Big Data with the
enterprise's existing ecosystem. This will help businesses to get a realistic idea regarding the
ROI. Also, the business case should identify how to quantify the benefits through these
projects, and most importantly, on how these benefits will be realized by both IT and business.
Big Data use cases do not adhere to all the four Vs. As businesses try to compete against
each other they often give in to the hype of Big Data and fail to address all the four Vs —
volume, variety, value, and velocity — in their Big Data implementations. Most current use
cases are targeted at volume or at best include two Vs only — and often end up investing too
much in a solution that is no different from their present business analytics solution. Hence,
businesses must adhere to the four Vs to achieve business outcomes associated with the BDA
technologies.
Data management. Big Data is all about using data as an asset. Data could be both internal
within an organization and external to an organization. For some businesses, beginning with
internal data gives them a sense of control and security, although this may not completely
adhere to the definition of Big Data and the results may not be very different from their current
traditional relational database results. Although there is no dearth of data, the challenge is to
prioritize these different data sources to be analyzed while ensuring that data quality is
maintained.
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Vendors
Lack of experienced vendors. As is the case with customers, most vendors while busy
marketing their Big Data capabilities have forgotten to invest in developing skills and
intellectual property that can aid them in their BDA projects. Customers always like to engage
with vendors that can show them successful references; however, in the case of BDA projects,
since the technology is still evolving it is hard to find experienced vendors.
Nonavailability of skilled resources. One of the key challenges with BDA projects has been the
lack of skilled human talent. The adoption of BDA is on the rise, but the supply of talent has
not kept pace with the rise, resulting in a big gap in the talent pool. The skill gap is across the
board — from data scientists to Hadoop architects to data warehousing specialists for various
platforms — which has a direct impact on vendors' ability to deliver quality BDA projects.
Attracting, developing, and retaining the right talent will be critical to all vendors as they vie for
greater market share in the BDA segment.
However, vendors are now investing heavily to bridge the gap between the adoption rates and
the talent supply. Some of the significant steps include: conducting training programs with
alliance partners on emerging tools, influencing the curriculum to expose students to this
growing opportunity, holding boot camps for deep technology skills; and providing solution
architect training to develop comprehensive solutions, and hands-on opportunities in emerging
areas such as visualization and Big Data, and so forth.
Lack of innovation in sales and delivery. Sales personnel who are used to selling traditional IT
products and services will also need to adapt and evolve to be able to sell 3rd Platform
technologies. Sales personnel need to be trained to sell to the business; they need to talk the
language of business solutions as opposed to the language of technology. Similarly, services
providers will have to improvise on their delivery (by means of tools and accelerators/agile
delivery models/cloud delivery models) to deliver faster and better services.
Technology
Technology maturity. In the early days, Big Data was synonymous with Hadoop. However, due
to advancements this has slowly changed to include a number of different technologies, which
are still evolving and yet to attain complete maturity. The lack of clarity regarding these
technologies has inhibited the large-scale adoption of BDA.
Privacy concerns. Security concerns surrounding data are currently inhibiting the adoption of
Big Data on a large scale. With growing concern among individuals regarding how their
personal data is being used, businesses have to be cautious about the way data is handled.
Big Data adopters will have to provide complete disclosures on how data is being collected,
stored, and shared. Though privacy concerns about data are not completely new, businesses
will have to work hard to pass on tangible benefits to the customers, which would lead to
lowering the overall expectation of privacy of users.
Cost of technology infrastructure. BDA technologies enable organizations to store and analyze
vast amounts of data irrespective of their structure, thus opening new avenues of business
opportunities. In an age in which businesses manage petabytes of data, the traditional data
warehouses become incompatible for managing the storage requirements. Although BDA
solutions are a perfect fit to the growing volume of data by providing the required flexibility and
scalability, these come at a high price; hence the high costs associated with the technology
are deterrent to the adoption of BDA on a large scale.
©2014 IDC #AP2578213VE 7
Big Data Services
IDC expects the Big Data technology and services market in APEJ to grow from US$548.4 million in
2012 to US$2.38 billion in 2017 at a compound annual growth rate (CAGR) of 34.1%. IDC takes into
account infrastructure (the fastest-growing segment of Big Data), software, and services to derive the
total revenue associated with Big Data technology and services. The total revenue associated with Big
Data services, specifically, is expected to grow from US$183.9 million in 2012 to US$644.8 million in
2017 at a CAGR of 28.5% during the forecast period.
TABLE 1
Asia/Pacific (Excluding Japan) Big Data Services Revenue, 2012—2017 ($M)
2012 2013 2014 2015 2016 2017 2012–2017
CAGR (%)
Big Data
Services
183.9 228.16 290.39 380.82 486.85 644.75 28.5
Source: IDC, 2013
Per IDC estimates in 2013, services constitute 30% of the total Big Data revenue. Project-based
services will be increasingly sought by customers to help them with their Big Data initiatives.
Customers will look to service providers that can offer them a road map — on how these initiatives will
be integrated with their existing software/hardware stack, how it will help them achieve their business
outcomes, what technology solutions suit their business best, and what data can be used to provide
actionable insights. For vendors, this means that having consulting capabilities is no longer optional
but is now a must-have offering among their 3rd Platform service offerings. With business taking a
front seat, these investments in business consulting capabilities, along with IT consulting capabilities,
will help vendors guide their customers and build a clear business case.
In IDC's discussion with vendors we noted that vendors are actively building their consulting
capabilities by building tools and use case repositories that will help them in their road map services
for Big Data. As mentioned earlier, data exists in silos in most organizations across APEJ. In some
organizations in which investments have been made to integrate enterprisewide data, the current
investments on analytics are still in silos. Big Data solutions, in contrast, will use data from a wide
variety of sources including mobile, sensors, videos, enterprise, and others. Enterprises have to take
caution in using this data derived from multiple sources to protect user privacy by using the right
security solutions and services.
Going forward, as enterprises integrate data, they will be faced with the challenge of using the right
data and tools. Service providers with the right expertise will be able to guide their customers on their
Big Data journey. Once these solutions have been deployed, integration across the existing enterprise
architecture will be another crucial factor that will decide the success of these investments.
©2014 IDC #AP2578213VE 8
The declining costs of storage have helped businesses to store huge amounts of data. But with the
ever increasing volumes of data the existing datacenter architectures will not be able to cope and will
need transformation. One of the key prerequisites of Big Data is storage that is able to handle huge
volumes of data, scale up or down based on requirements, and deliver analytical results at faster
speeds. As businesses move away from traditional analytics platforms to Big Data platforms, network
consulting and integration service providers will be increasingly sought out to address the networking
requirements of low latency and datacenter transformation.
The challenge does not end at having the right technology and the right data. Getting the right skills
also matters. Deployment of Big Data solutions requires people with the right mix of industry and
functional knowledge. With the growing concern over talent shortage, vendors will continue to
aggressively attract talent from competition. Several skills will be difficult to find in the marketplace and
a large number of vendors will increasingly compete to secure access to a small talent pool, often by
offering premium wages. To alleviate skills supply shortage, vendors have already invested in training
programs and workshops and built alliances with universities and government agencies to hone talent.
Big Data solutions at the moment are currently vendor-driven and are yet to enjoy mass adoption in
APEJ. As the adoption rates increase, customers will have a plethora of vendors to choose from. For
vendors this means a need to have differentiated services and solutions offerings to gain more market
share. As such, vendors will have to invest in building an ecosystem of partners, come up with
industry-specific solutions to differentiate themselves, and build competencies across technologies.
Vendor Profile
HP
The HP Analytics and Data Management Services Practice is crucial to the company's long-term
goal — to evolve into a key partner for its customers. Given the investments that HP has been making
in its Enterprise Services (ES) organization, the company is more strategically positioned in the market
today. What differentiates HP from its peer services organizations is its hardware and software
ownership.
However, although customers will expect HP to emphasize its own hardware and software products,
the company's strategy is to provide a best-of-breed approach, which will involve other technology
partners. Evidence of this is observed in the multiple technical alliances that HP has set up with Ab
Initio, Informatica, SAP, SAS, TIBCO, and Hortonworks in Asia/Pacific (including Japan) (APJ).
Another factor unique to HP is the fact that it does not emphasize a lot on external unstructured data.
Rather, HP shared that it is helping organizations realize the level of uncaptured data within the
organization, which has direct relevance for businesses for driving lower total cost of ownership (TCO)
through improved operational insights and higher ROI by mining business-relevant data for improving
investment potential. This is observed in the use cases that HP has undertaken (see Table 1).
©2014 IDC #AP2578213VE 9
TABLE 1
Common Use Cases for Big Data Services: HP
Vertical Use Case
Telecom Efficiencies in traditional data, content management, and
customer analytics
Banking and financial services Compliance solutions, information management solutions
including enterprise data warehouse (EDW), and customer
analytics
Public sector Efficiencies in traditional data, content management, records
management, and analytics for security and situational
awareness
Manufacturing Customer analytics and integrated supply chain analytics
Retail Customer analytics and store performance analytics
Source: IDC Big Data Services Vendor Interviews, 2014
As an organization that emphasizes Big Data as one of its strategic bets, HP is building out its
resource base in the region. The HP Analytics and Data Management Services Practice is part of HP
Enterprise Services and works in collaboration with HP Software for HP Autonomy and HP Vertical
products, and HP Hardware unit for infrastructure to deliver Big Data and analytics projects. Through
its industrialized delivery system, HP provides its analytics and data management services to 500
clients globally that are supported by its 1,200 global analytics professionals and more than 3,600
consultants worldwide. Within APJ, HP has the advantage of access to global delivery talent — in
India, China, and the Philippines — that delivers to clients across regions. HP claims that its Global
Analytics organization in India has over 1,000 data analysts/scientists.
HP provides advisory services through its HP Big Data Discovery Experience offering that aims to help
accelerate customers' entry and journey to Big Data analytics. The fact that these services are made
available through a cloud delivery model or on-premise make them an attractive proposition for first-
time customers who want to assess the value of data before investing.
In addition to the advisory services, other services offered by HP include: HP actionable analytics
services, HP Information Management Services, HP Information Governance Services, customer
analytics offerings, HP Interactive Media Command Center (IMCC), archival services, situational
awareness, and records management.
The HP HAVEn platform empowers HP's partners to build solutions for the analytics platform, helping
them to monetize Big Data. HP realizes that its partners are also important in helping the company to
innovate, as validated through HP's investments with its partners that range from HP ES developing
core capabilities on their partner technologies to developing joint solutions and platforms.
©2014 IDC #AP2578213VE 10
IDC believes that HP has emerged as a strong player in the Big Data professional services segment.
This is largely because HP is not only able to provide end-to-end services for analytics and data
management but also has a wide range of Big Data offerings across its hardware and software stacks.
In summary, IDC views HP as a key partner for businesses that are getting into their Big Data journey.
In cases in which customers have significant investments on the HP stack, the firm should be a de
facto partner.
FUTURE OUTLOOK
Despite the buzz surrounding Big Data technologies, these have not yet completely evolved and are
currently climbing up the technology maturity curve. Businesses, enthusiastic though they may be
about moving to the new world of Big Data technologies, cannot ignore their current software/hardware
stack for data warehousing. Traditional database architectures and Big Data architectures will continue
to coexist; managing this environment will prove to be increasingly complex for most enterprises,
hence they will turn to service providers that can help them implement and manage the new enterprise
architecture and has advanced analytical capabilities, manages huge volumes of data from various
sources, and is hosted within the company's datacenter or delivered via cloud. The low maturity level
of associated technologies, huge investments, elusive ROI, and the dearth of skills have been a major
driver for professional services engagements.
The current approach to Big Data and analytics is in silos, often ignoring the overall enterprise
architecture. Although this might work well in the short run for certain use cases in some enterprises to
drive the business, in the long run this approach will create significant challenges. Since 3rd Platform
technologies are disruptive in nature, it is essential to take a holistic approach spanning across
business, information, application, and technology in order to transform business (Figure 3).
©2014 IDC #AP2578213VE 11
FIGURE 3
Moving from Diagnostic Analytics to Predictive Analytics
Source: IDC, 2014
Going forward, the proliferation of sensor, mobile, wearable, and embedded devices (i.e., Internet of
Things or IoT) will become a significant driver of the Big Data market. The Internet of Things will create
huge amounts of data, most of it unstructured, providing opportunity to enterprises to analyze new data
sets that will help them in their decision-making process. To address data from the IoT, there will rise a
need to invest in technology for both data in motion and data at rest. As a result, enterprises will move
from traditional client-server architectures to high-speed server-to-server architectures. Hence,
enterprises will have to address the changing landscape of the datacenter and will end up increasing
their infrastructure investments. Adding to this are the complexities in managing user privacy. Although
on one hand, businesses will look at consolidating their application portfolio and centralizing their data;
on the other hand, this might make them more vulnerable to stealth and malware attacks. This will lead
to an increased demand for security applications spanning cloud and traditional datacenters. In light of
the increasing complexity associated with managing data storage and network, organizations will have
to engage in datacenter transformation. Since datacenter transformation is a costly affair, small and
medium-sized enterprises might turn to cloud service providers to provide them with the analytical
capabilities using cloud delivery models.
A key attribute of Big Data is velocity. Since speed is a critical factor, the decision and
recommendation solutions are not the only solutions that have to be automated — businesses will look
at having rapid deployment models with some automation for the development and testing of their Big
Data and analytics solutions. These solutions will utilize a mix of cognitive computing, rules
management, analytics, biometrics, rich media recognition software, and commercialized HPC
infrastructure. Vendors offering Big Data services should be able to offer rapid product development
Data collection and reporting
Basic analytics
Big Data —Advanced analytics
Run the
business
Grow the
business
Transform the
business
©2014 IDC #AP2578213VE 12
and testing cycles with shorter release cycles. IDC believes that the number of vendors offering Big
Data services will triple over the next three years (see IDC Big Data Predictions 2014: Beyond
Irrational Exuberance — Opportunities in the Big Data and Analytics Markets, IDC#WC20131211,
December 2013). With vendors competing against each other for greater market share, what vendors
can bring to the table will be a key differentiating factor alongside their intellectual property and
industrialized solutions. Improvements in automation, growth in appliances, and cloud deployments
will help vendors and businesses alleviate the problem of skill shortage to a certain extent.
The four pillars of the 3rd Platform — cloud, mobile, Big Data, and social — are disruptive. IDC
believes that as organizations transition from the 2nd Platform to the 3rd platform, cloud will likely
emerge as the delivery platform for these technologies. Professional services engagement will be
sought by organizations to substitute and complement their existing IT capabilities. Although service
providers will be able to help organizations in their 3rd Platform projects, the measurement of success
will depend on the organizations' capability to support these initiatives.
ESSENTIAL GUIDANCE
For CIOs
Before embarking on Big Data deployments, businesses should understand the actual benefits of Big
Data and not get carried away by the hype. The availability of a large number of platforms, tools, and
resources makes the task even complex.
Hence, it is imperative that businesses invest in the right tools and solutions and engage with a service
provider that is able to help them justify their investments.
Begin by evaluating the current data practices in your organization. This will include
understanding how data is currently handled in the organization, how decisions are made —
are they based on data or on intuition — and what are the current policies surrounding
data/information management.
Big Data initiatives will require CIOs to explore the business case associated with these new
technologies. This will categorically include two aspects. First, to understand the different data
sources that is accessible to the organization internally and externally:
Data sources that you know exist and are captured
Data sources that you know exist but are not captured
Data sources that may be valuable but have been left unexploited
Second, the use of the right analytics and technologies to gather actionable insights that are
delivered in real time. The success of BDA initiatives is also dependent on how organizations
are able to fine-tune their business processes to accommodate the business outcomes
brought about by these initiatives. Hence, BDA initiatives should also include a process
redesign plan.
For businesses taking the piecemeal approach, although the platform is able to cater to the
current requirements, consider as well how it will pan out in the future. Adequate data
©2014 IDC #AP2578213VE 13
governance and controls will need to be put in place to ensure that the same set of standards
and processes are maintained during cross-functional adoption.
BDA initiatives are different from the traditional enterprise resource management (ERM) and
customer relationship management (CRM) deployments. Hence, businesses will need to
understand that ROI calculation from these initiatives have to be done differently as compared
with traditional technology deployments.
BDA initiatives will require multiple teams to work in collaboration with each other; hence it is
imperative that before starting on the Big Data path there is an organizationwide commitment
and support for these initiatives.
IDC suggests that CIOs engage in proof of concepts (POCs) before actual adoption to
evaluate the benefits of BDA deployments. Consulting-led engagements in particular will help
businesses to derive better benefits from their engagements.
For Vendors
The current adoption rate of BDA is low, with most adoptions being in the evaluation stage. However,
IDC expects the BDA services market in APEJ to triple its value by 2017. To be able to tap into this
growing market, vendors will have to invest in people, process, and skills to position themselves as an
end-to-end services provider of Big Data services. As vendors continue to drive BDA adoption, IDC
recommends that vendors focus on the following areas:
Assessing clients' maturity and building the business case. Before beginning with any
implementation, vendors will have to gauge customers' maturity and current data practices,
and build a clear business case. Vendors will also have to develop benchmarking tools to
identify where the current organization is in relation to industry standards and future business
expectations, including organization, process, infrastructure, leadership, and risk/reward
assessments.
Building industry-specific expertise. Industries differ in their use and need of Big Data
technologies. Vendors need to develop industry-specific tools and knowledge to help their
customers. When mass adoption of BDA kicks in, intellectual property can go a long way in
differentiating a vendor from its immediate peers.
Creating innovations in sales and delivery. Most Big Data implementations will be funded by
business as opposed to IT units. Hence, vendors will have to train their sales force to ditch the
language of technology and talk the language of business solutions. Vendors will also need to
educate IT resources and to improve the IT team's influence on people in the business units.
In addition, vendors will also have to focus on shortening their development and test cycles
through reusable and repeatable frameworks to aid them in quicker and quality deliverables.
Investing in training to develop the required skill sets. Although cloud solutions and automation
might alleviate the scarcity of human skills, it is crucial that vendors have a qualified workforce
that can help guide customers through all stages of their implementations. Training programs,
partnerships with universities and education centers, and retaining good talent will help
vendors face powerful competitive headwinds.
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Building partnerships to form an ecosystem of partners. Vendors will have to work on
partnering with product and solution firms that can support their customer base. These
partnerships will help the service provider be product/solution-agnostic and help customers
choose the right product for their enterprise architecture.
Building on consulting capabilities. 3rd Platform technologies transform business and have a
large impact on the business. Hence, customers will increasingly have consulting-led
engagements. It is no longer enough to have systems integration capabilities alone; consulting
capabilities are also becoming crucial in deciding the vendor's position in the marketplace.
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