Document 1169230

November 2013
Analytics and Big Data for the Mid-Market
The topic of “big data” has grabbed headlines over the past few years, but most of
this attention has focused on a small percentage of large enterprises. However, big
data is not just an issue for the select few, as mid-market organizations
increasingly deal with many of the same data pressures as their larger compatriots
while being forced to find solutions without the benefit of relatively larger
resources. Aberdeen’s recent research study, Data Management for Analytics
(September 2013), shows that the mid-market is turning to both big data and
analytic solutions to solve their information needs and drive business value out of
their data. However, determining which solutions will best help extract business
value from their data is challenging. The variety of options — and the diverse
characteristics of those options — is considerable. This report, focused on 69
mid-market organizations, offers guidance to these smaller companies on how
they might narrow the options by revealing which technology enablers are
prevalent in the mid-market, investigating which features are most used by top
performing companies, and showing how these solutions provide tangible benefits
to line-of-business operations.
This document is the result of primary research performed by Aberdeen Group. Aberdeen Group’s methodologies provide for objective fact-based research and
represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc.
and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.
Analytics and Big Data for the Mid-market
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© 2013 Aberdeen Group.
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Fax: 617 723 7897
Analytics and Big Data for the Mid-market
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© 2013 Aberdeen Group.
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Telephone: 617 854 5200
Fax: 617 723 7897
Analytics and Big Data for the Mid-market
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The Value of Data and Analytics in the Mid-Market
The “big” aspect of “big data” can be misleading, as many companies believe that
big data is only for the Fortune 500 and global organizations with vast datacenters
and petabytes of data. However, in most cases, big data is purely relative — “big”
is in the eye of the beholder, and simply refers to data demands that outstrip an
organization’s ability to handle them in a reasonable, cost-effective manner. For
mid-market companies that self-describe as struggling with big data, this usually
means data well within the terabyte range, and datasets less than two terabytes in
size (see sidebar on page 2). However, the rapid increases in size, speed, and
demand for new data sources and analysis have all contributed to a “big data”
scenario and stress on their IT infrastructure.
Aberdeen’s latest research study on Data Management for Analytics (September
2013) has shown that there is a convergence between big data and analytic
solutions for these companies. They first invest in scalable, flexible data
management technologies to solve their immediate data pain points. Then they
spend on new methods of accessing, analyzing, and visualizing their data, moving
beyond just the mitigation of their problems to the discovery of new ways to
improve their business.
These two groups of solutions have wonderful synergy, as analytics can help
identify which data sources are most valuable to the organization, and better data
management in turn means more quality information feeding the analytic engine.
The Challenge of Determining Solution Fit
Mid-market organizations are quick to recognize their problems and the
inadequacies of their IT infrastructure. However, it can be more difficult for them
to find which solutions are good fits for their budgets and needs. For instance, the
top data-centric business pressures cited by these organizations include:
© 2013 Aberdeen Group.
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• The inability to use or analyze unstructured data (reported by 46%
of companies)
• Poor data quality affects decisions (45%)
• Business users do not get information fast enough (39%)
• Unable to meet the demands of the modern customer (30%)
This diverse set of needs adds a layer of complexity to the buying and decision
process, as no single solution meets this catalog of requirements.
© 2013 Aberdeen Group.
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To provide practical guidance to smaller companies on technology-enabler
selection, Aberdeen divided the 69 mid-market organizations in the Data
Management for Analytics research study into two groups: the Leaders (36
respondents) and Followers (33 respondents). As the sidebar on page 1 shows,
organizations were classified based on their data management performance in
regards to agility (integrating new data sources), reliability (delivering information
on time), and annual improvement (increase in time-to-decision). The following
sections will dissect the strategies and investments made by Leader organizations
in regards to both their analytic and big data initiatives.
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© 2013 Aberdeen Group.
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Factors to Consider When Evaluating Analytic Solutions
Analytic solutions see their greatest impact when they are chosen and deployed in
response to specific goals, whether those goals are organization-wide or
department-specific. In the mid-market, three of the largest factors for
organizations to consider are:
• Ability to improve customer engagement
• Cost and deployment method
• Overall fit in long-term analytic vision
Of these, Aberdeen’s research has shown that impacting customer engagement is
one of the fastest growing areas of analytic adoption and one of the fastest ways
to generate return on investment (ROI) for analytic projects.
Figure 1: Top Goals for Analytics in the Mid-Market
66%
Percent of respondents n=69
MidMarket <$1B
59%
44%
46%
32%
22%
0%
Better insight into
customer behavior
Better insight into
internal operations
Improve effectiveness
of marketing
Source: Aberdeen Group, September 2013
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Nearly two-thirds (59%) of mid-market companies focus on improving their
understanding of their customer’s behavior, demands, likes, dislikes, and buying
habits. This makes analytic solutions that can combine historical transaction records
with customer profiling and social media chatter particularly enticing. Other popular
use cases for analytics involve optimizing specific business units or job functions,
such as marketing or product development. In fact, the mid-market currently uses
analytics in a number of specific departments (see sidebar), and so assessing the
needs of these units is a critical part in choosing the right analytic solution. For some
of these units, speed of analysis is the most important function; for others it might
be the ability to access unstructured data or easily share and disseminate insights
among employees.
Cost Involves More than a Price Tag
In the mid-market specifically, another important criteria to selecting the right
technological fit is cost. The sad reality is that while data volume is growing at 51%
annually, IT budgets are not nearly growing as quickly. In fact, according to Data
Management for BI: Getting Accurate Decisions from Big Data (January 2013), the
budget for analytics and data management was projected to grow less than 6% this
year for mid-market companies, which is 2% less than the growth for large
enterprises. Even with the price per gigabyte for data storage dropping every year,
this makes the cost implications of deployment more important than ever before.
Traditional on-premise deployment still remains a viable — and even the most
common — method, but involves significant hardware investment, employee
resources, and software licensing costs. Given that the number of employees
requiring access to data analysis tools grew over 15% in the last year,
organizations using this approach should consider licensing options that are
flexible and not cost-prohibitive to scale up to a larger number of permitted users.
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Hosting analytic solutions in the cloud, or renting Software-as-a-Service (SaaS)
solutions, are approaches where the mid-market slightly outpaces larger
companies. Twenty-five percent (25%) of mid-market Leaders indicate that one of
their top data strategies this year is exploring Cloud services to host data and
analytics, compared to 20% of large enterprises. The lower cost, easy scaling, and
pay-as-you-go model that SaaS offerings often provide can be very enticing to
companies in this market segment.
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One creative approach to minimizing cost involves simply securing budget
elsewhere. This larger trend reveals that analytic decision makers and budget
increasingly emanate from the line-of-business side of the house. While 54% of midmarket companies report that leaders of analytic initiatives and the corresponding
budget still come from IT (compare this to 70% of large enterprises), other business
units have realized the powerful benefits these solutions can provide and are
providing their own cash. Twenty percent (20%) of mid-market companies have
budget coming from sales or marketing, while 23% are funded by finance or
accounting. An additional 13% are controlled by a cross-functional team that
combine the resources, needs, and skills of both IT and line-of-business
stakeholders. This flexible approach often has a fringe benefit: A successful analytic
deployment in one business unit can serve as a positive proof-of-concept and make
it easier to get approval for subsequent rollouts to other areas of the company.
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Develop a Long-Term, Flexible Analytic Vision
As for specific analytic features being deployed in the mid-market, Aberdeen’s
research shows that Leader organizations take a much more aggressive approach
in investing in advanced capabilities (Figure 2).
Figure 2: Mid-Market Leaders Embrace Advanced Analytic Enablers
70%
Leaders
66%
63%
Followers
60%
48%
44%
35%
32%
29%
23%
19%
13% 4%
0%
7%
Analytic
applications
Data mining
tools
Data
visualization
Predictive
analytics
Social media
monitoring tools
Hadoop cluster
Source: Aberdeen Group, September 2013
The first three features listed show a preference for solutions that combine both
power and simplicity. Self-contained analytic applications are often designed to be
user-friendly for the typical business analyst or even a tech-savvy line-of-business
employee. Data mining tools focus on allowing a user to explore data, and can
be as simple as an interactive dashboard or report that allows the user to drill
down from a high-level to specific transactions or events in order to answer
business questions.
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And while tens of thousands of data points can often be consolidated into a
mathematical equation describing statistical probabilities, it is far faster and easier
to digest this information using a time series, cluster graph, heat map, or another
data visualization technique. As such, solutions with intuitive interfaces are rapidly
growing in use. Part of the reason for this move is that mid-market companies are
less likely to be able to find and afford a full-time data scientist. These data
scientists — often defined due to their advanced degrees in mathematics,
computer science, or economics — can be found in over 36% of large enterprises,
but less than a quarter (24%) of the mid-market. Instead, 37% of mid-market
companies develop analytic knowledge and skills in-house, having a formal
program for training up their best and brightest. Additionally, 45% more of these
mid-market companies plan to implement such a training program within the next
two years — making solutions with an accessible interface, easy maintenance, and
minimal requirements for a technological background a great fit for this next
generation of analytic users.
The last three features listed in Figure 2 show capabilities that are still in the early
adoption phase for the mid-market, but importantly, the leading companies adopt
these capabilities at markedly higher rates than their lower-performing compatriots.
This shows that they are not only concerned with meeting immediate needs and
maximizing their current resources, but they invest in a long term plan. By becoming
early adopters of these powerful new tools, they are looking to secure a
competitive advantage for the coming years.
Predictive analytics allows companies to move beyond historical reporting to
identify patterns of market or customer behavior, becoming more proactive and
less reactive when it comes to major events. While less than half (44%) of Leaders
have adopted this technology, they have done so at 6.3-times the rate of Followers,
meaning these solutions have a very high correlation to top performance. Similarly,
the interest in collecting and analyzing social media data has never been higher,
especially given its value in understanding customer behavior and making
recommendations. The use of social media monitoring tools has sky-rocketed in
the past few years, and projects continue to grow at a rapid pace; especially for
Leader organizations.
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Finally, Hadoop has received a lot of attention for its powerful analytic capability
and its open-source framework that runs on commodity hardware. While this
combination of power and low-cost would seem tailor-made for the mid-market,
the simplicity part of the equation isn’t quite there yet, as it still requires a lot of
effort and a specialized skill set to install and maintain. However, 48% of midmarket companies are currently evaluating Hadoop with plans to deploy by 2015,
so companies would do well to keep an eye on Hadoop integration capabilities
when evaluating other analytic solutions.
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Mid-Market Performance Gains From Analytic Solutions
Unsurprisingly, Leader companies in the mid-market are able to meet their top
analytic goals (listed in Figure 1) through the tactical investment in the powerful,
scalable, intuitive tools discussed above.
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Figure 3: Leaders Meet their Data Goals Through Analytics
Year-over-year change n=69
26%
Leaders
23%
21%
Followers
20%
17%
15%
13%
2%
0%
Quality /
accuracy of
business
decisions
4%
Total customer
base
5%
Customer
satisfaction
1%
Customer
retention
4%
Marketing
effectiveness
Source: Aberdeen Group, September 2013
As Figure 3 shows, they had a much better grasp of their customer’s behavior and
demands, growing their total customer base by 21% in the last fiscal year, and
reporting similar levels of improvement to both customer satisfaction and
retention. Their marketing effectiveness, defined by the number of leads marketing
contributed to the sales pipeline, showed almost 4-times the improvement of
Followers. And for other operational tasks, their speed, quality, and accuracy of
business decisions increased by a staggering 23% in the last 12 months.
What’s more, these Leaders achieved all those performance gains while at the
same time maintaining large-scale analytic initiatives and providing data access to a
larger percentage of their employees (Table 1).
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Table 1: The Benefits of Better Data Analysis Tools
Performance Metrics
Leaders
Followers
Performance
Difference
Percentage of employees with
access to BI
29% of employees
18% of
employees
1.6-times more
Percentage of sales team
meeting annual quota
(YoY change)
20% improvement
8%
improvement
2.5-times greater
increase
Total product sales
(YoY change)
22% improvement
2%
improvement
11-times greater
increase
Source: Aberdeen Group, September 2013
By understanding their customers, involving employees from multiple business
units — from product development to marketing to sales — they were better
able to match their products with customer demands. More of their sales team
met their annual quota, and their total product sales increased by 22% in the last
year. All this contributed to these mid-market Leaders reporting a 21%
improvement in organic revenue in the last 12 months, a 7-times greater increase
than Followers (3%).
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Factors to Consider When Evaluating Big Data Solutions
Of course, all this analytic firepower could misfire or merely fizzle out unless the
data feeding the systems are accurate, reliable, and well-managed. This poses a
significant challenge for mid-market organizations that are seeing their data
environments rapidly run out of their control and into the territory of big data. As
mentioned above, they are faced with competing concerns around storing this
information cost-effectively, keeping the level of data quality high enough to enable
good business decisions, and delivering data access to end-users at a faster and
faster pace. Simply scaling up the IT infrastructure in a linear fashion — adding
new servers and storage arrays — is no longer cost effective or fast enough to
deal with this data growth.
As a result, the mid-market turn to solutions marketed to this type of big data
challenge, and embrace tools to enable rapid data access and control rapid
data growth.
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Baseline Technologies for Big Data
First, both Leader and Follower organizations implement baseline technologies to
help curb the data expansion; both groups reported 38% to 40% adoption on data
compression and data de-duplication tools. However, Leader organizations lead
the pack when it comes to understanding that not all information is created equal.
Some data is valuable only for a short time, and other data has little use but must
be stored for compliance reasons, for example. Over half (51%) of Leaders had
filtering tools to define and identify high demand, high value data streams,
compared to only 20% of Followers. This allows them to maximize their valuable
high-speed resources by only storing this critical information in the expensive
systems. Furthermore, this type of filtering allows the organization to re-prioritize
what data is valuable as they scale up and grow their analytic efforts to include
other business units or new data streams.
Enablers to Support Big, Fast Data
In addition to filtering tools, Figure 4 displays the other data management
technologies that Leaders in the mid-market deploy to address their big data
issues. First and foremost, they address issues of data errors and inaccuracies
through governance and quality tools. As the volume of data scales up, issues of
poor data quality can quickly get out of hand, resulting in more time wasted
searching for correct information, errors in order fulfillment, and flawed analysis
and decision-making. Leaders are also 5-times more likely to automate elements
of data cleansing, such as ensuring that information is in the proper format at the
point of capture, before it enters an analytic system or software application.
Removing as many manual operations as possible over the data lifecycle is essential
to increasing data processing speed and reducing human-introduced errors.
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Figure 4: Mid-Market Leaders Invest in Enablers to Support Big, Fast
Data
Leaders
66%
Followers
65%
57%
50%
44%
33%
22%
29%
32%
28%
19%
14%
0%
Data governance
tools
Data quality
tools
Real-time data
integration tools
Real-time
analytic platform
32%
7%
6%
In-memory
analytic tools
Master data
management
Source: Aberdeen Group, September 2013
Next, mid-market Leaders have invested in tools that speed up and streamline the
entire process from data capture to delivery to end-users. They are 2.6-times
more likely than Followers to have real-time integration tools, such as an
Enterprise Service Bus (ESB), change data capture, or data replication features.
Given that 31% of mid-market companies reported that they need near real-time
insight after a business event, and 46% said they need that data within an hour,
real-time integration tools help accomplish the first step of getting data into the
company in a fast, seamless manner.
A third (33%) of Leaders have also implemented real-time analytic platforms, or
systems that can run algorithms and pattern recognition against data streaming
into the organization. This way they can raise alerts when certain events happen
or pre-determined conditions are met, allowing them to react quickly to prevent
negative situations from spiraling out of control. Similarly, they can take immediate
steps to leverage a favorable business opportunity, such as providing customized
discounts and product offerings to customers browsing an eCommerce site.
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Leaders are also among the early adopters for in-memory analytic technology,
which can bring hundreds of gigabytes or even terabytes of data into the random
access memory (RAM) of a server or analytic appliance. This bypasses many of the
bottlenecks that can slow down the processing of data, and brings the data as
close to the server cores as possible. Aberdeen’s research into In-Memory
Computing: Enabling Real-Time Access to Big Data (March 2013) reported that
companies using this type of technology had average data access times 100-times
faster than companies without these tools.
Finally, a little under a third of Leaders (32%) have implemented Master Data
Management (MDM) systems to serve as a trusted, central repository of business
data to feed their most critical applications. While MDM systems have traditionally
been marketed to large enterprises with complex data ecosystems, more products
are now aimed at the mid-market. As Aberdeen’s Master Data Management (April
2013) research recently reported, having this type of authoritative repository not
only allows for data consistency across different applications and content channels,
but allows for faster deployment of new data initiatives. With new channels of
customer communication springing up every day in the mobile and social space,
having this solid data foundation can allow companies to embrace an agile, omnichannel approach to customer engagement.
© 2013 Aberdeen Group.
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Performance Gains From the Right Data Solutions
As Table 2 shows below, the impact of these data management tools crossed the
entire organization and directly mitigated the most pressing big data issues the
mid-market face. In terms of data quality, not only did the Leaders report 50%
fewer major data errors in their records, but they also improve the levels of data
accuracy and completeness by 19% a year — all this despite the massive growth in
data volume. Likewise, thanks to the real-time technology implemented not only
at the point of capture, but also for analysis of streaming data in-flight and in later
batch processing, their average data access times could be measured in minutes
while Followers took multiple hours to process the same requests.
Table 2: The Impact of Data Management Tools on Big Data
Performance
Metrics
Leaders
Followers
Performance Difference
Percentage
of business
records with
significant errors
10% of
records
15% of records
50% better
Overall data quality
(YoY change)
19%
improvement
1%
improvement
19-times greater increase
Average time
to respond to
data request
6 minutes
4.2 hours
41-times faster
Time-to-information
(YoY change)
24%
improvement
No change
24-times better
Quality and
relevance of data
analysis
(YoY change)
22%
improvement
1%
improvement
22-times greater increase
Source: Aberdeen Group, September 2013
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This all feeds back into the analytic tools discussed above, as the impact of high
quality, large volume, fast paced big data was shown to improve overall data
analysis by an impressive 22% in the last year.
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Summary and Key Takeaways
Mid-market organizations today struggle with rapidly growing data environments,
increasing demands from end-users, and new requirements for analytic
performance — all with an IT budget that has barely grown at all. For mid-market
companies trying to mitigate their data and/or “big data” pain points and gain
competitive advantage through analytics, they should identify and deploy
technology-enabled solutions that both address their top challenges yet remain
affordable and straight-forward to implement. Knowing the important
characteristics to consider and narrowing the options is no small feat. To
accomplish this, Aberdeen recommends the following:
• Identify major business goals — such as customer engagement.
Mid-market companies use better data management and analytics in a wide
variety of use cases. Companies should determine their top use cases and
data requirements as the first step. The types of data — customer data,
internal operational data, unstructured information — and how fast they
must be analyzed will identify what analytic features should be targeted in a
solution. Customer analytics is the most popular use case (listed by 59% of
all companies), but almost any business unit, from sales to marketing to
finance to product development, can benefit from an analytic solution.
• Investigate methods of reducing cost and maximizing existing
resources. Mid-market Leaders outpace large enterprises in terms of active
initiatives for hosting their data and analytic solutions in the Cloud, mostly
for reasons of cost and easy deployment. They are also 1.6-times more likely
than large enterprises and 2.9-times more likely than Followers to train
existing employees in analytic skills, leading them to adopt solutions that are
easy to learn, and need a minimum of technical requirements to operate.
Finally, see if line-of-business units are willing to become involved in analytic
initiatives, and fund projects that can directly benefit their daily operations.
• Develop a long-term vision for analytics and big data, and invest
accordingly. There are a wide number of possible data management tools
for companies to invest in, but the ones that have the largest correlation
with mid-market success revolve around data quality and data speed. Leaders
© 2013 Aberdeen Group.
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are twice as likely as Followers to have implemented data governance and
cleansing technology, and up to 2.6-times more likely to have invested in realtime tools for integration, on-the-fly analysis, and high speed batch
processing. This has allowed them to analyze data an average of 42-times
faster than Followers. However, in addition to these baseline capabilities,
Leaders are also more likely to be early adopters of new powerful tools such
as predictive analytics and in-memory computing. Finally, keep an eye on
whether a solution has the ability to integrate with Hadoop, given the high
levels of mid-market interest in this relatively new technology.
For more information on this or other research topics, please visit
www.aberdeen.com
© 2013 Aberdeen Group.
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Related Research
Business Analytics and Unstructured Data: Are You
Asking the Right Questions?; September 2013
Seeing the Big Picture: Visualization for Big Data;
July 2013
In-Memory Computing: Enabling Real-Time Access
to Big Data; March 2013
Big Data Trends in 2013; February 2013
Data Management for BI: Getting Accurate
Decisions from Big Data; January 2013
The Big Data Imperative: Why Information
Governance Must be Addressed Now;
December 2012
Big Data for Small Budgets; December 2012
Go Big or Go Home? Maximizing the Value of
Analytics and Big Data; September 2012
The State of Big Data: Video Benchmark; July 2012
Agile or Fragile? Your Analytics, Your Choice; July 2012
Beyond Agile Analytics: Is Agile Data Integration Next;
June 2012
Managing the TCO of BI: The Path to ROI is Paved
with Adoption; May 2012
Enabling Access to Big Data with Data Integration;
April 2012
High Performance Organizations Empower
Employees with Real-Time Mobile Analytics;
April 2012
The Little Elephant in the Big Data World: Hadoop
1.0 Goes Live; March 2012
Author: Nathaniel Rowe, Research Analyst, Enterprise Data Management
([email protected])
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