While many large enterprises have adopted analytics technologies such as business intelligence (BI), performance management (PM), and predictive analytics, misperceptions about costs and risk have caused small and midsize companies to embrace these technologies far more slowly. Nucleus has examined hundreds of analytics deployments and found that when senior leaders of small and midsize companies invest in analytics tools, they improve operating results by enabling managers to make decisions that are more fact based and data driven.

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RESEARCH NOTE
HOW ANALYTICS MAKES MIDSIZE COMPANIES
MORE PROFITABLE
THE BOTTOM LINE
While many large enterprises have adopted analytics technologies such as
business intelligence (BI), performance management (PM), and predictive
analytics, misperceptions about costs and risk have caused small and
midsize companies to embrace these technologies far more slowly.
Nucleus has examined hundreds of analytics deployments and found that
when senior leaders of small and midsize companies invest in analytics
tools, they improve operating results by enabling managers to make
decisions that are more fact based and data driven.
MYTHS ABOUT ANALYTICS
Small and midsize companies stand to make significant gains by adopting analytics
applications such as BI, PM, and predictive analytics. Unfortunately, too many
companies fail to adopt due to misperceptions and doubts about these tools. The
biggest concern is costs. With limited IT resources, many small and mid-size
companies predict that even a modest analytics adoption is beyond their budgetary
capacity and fail to even investigate their options. Complexity is another factor.
Companies often fear that they’ll be unable to integrate their various applications,
data sources, and spreadsheets into a centrally managed analytics tool. Pessimism
is a third problem. Many CEOs and CIOs of small and midsize companies feel that
the collective wisdom and intuition of their employees is strong enough that data-
driven analytics won’t meaningfully improve day-to-day operations.
THE TRUTH ABOUT ANALYTICS
Nucleus Research has examined a significant number of IBM Business Analytics
deployments at companies of various sizes. Analysts recently compared the
deployments of IBM Business Analytics at large enterprises with those at small and
midsize companies in order to identify the traits, benefits, and best practices that
characterize analytics deployments at smaller companies. Nucleus found that the
decision to adopt analytics can enable a company to thrive and be more profitable
rather than merely survive and move from one operational or financial crisis to
another. With the truth about analytics being far sunnier than most managers
think, there are three facts about analytics that managers should keep in mind.
Even small companies can afford analytics
Nucleus regularly performs in-depth examinations of analytics deployments and
analysts have found good news about costs. First, deployment costs, including
software, hardware, personnel, consulting, and training are well within the budgets
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of even small companies. Nucleus has also found that ongoing support costs are
typically minimal, with the average deployment requiring less than a third of one IT
administrator’s time for support. Costs are also low relative to benefits, leading to
high returns on investment. Some of the factors making analytics affordable
include:
 Lower per-seat costs. Applications such as IBM Cognos Express are based on
tools designed for large enterprises, but have been streamlined to provide a
narrower scope and packaged with lower per-seat costs to make these tools
more affordable. Although these applications lack some of the breadth and
complexity of tools for larger enterprises, they require less configuration and,
in many cases, they can grow in complexity if the operating environment
changes. Nucleus has found that the average deal size for IBM Cognos Express
is between $25,000 and $30,000 and spoke with one IBM Cognos Express user
who said, “When we shopped around, we were surprised that IBM Cognos
Express came in at well under $50K.”
 Flexible scopes. In examining deployments, Nucleus has found two ways that
vendors and users typically use to minimize software costs. First, not all
analytics functionality needs to deployed at once. Small and midsize
organizations can choose to adopt any combination of BI, PM, reporting,
dashboards, or predictive analytics and keep the functionality as narrow as
they want in order to lower the costs. Second, costs can be lowered by
deploying in stages. Nucleus has analyzed many deployments which began as
narrow proof-of-concept projects that were followed by a series of increasingly
larger deployments. At one company examined by Nucleus, the deployment
team leader said, “We caused analytics to catch on like a fad. We helped our
sales people make better decisions and when folks from the purchasing
observed this, they wanted analytics too.” By managing costs and publicizing
deployment wins, analytics champions were able convince internal decision
makers of the value of analytics, enabling them to broaden their deployments.
 User friendliness. In examining analytics applications and deployments,
Nucleus has found vendors have minimized their customers’ training costs in
several ways. First, the applications have become so intuitive that end users
typically require little or no training in order to use the application. An
analytics champion at a company examined by Nucleus stated that cost was
not an obstacle to adoption and said, “Once we deployed, people learned how
to use the applications on their own, so we didn’t need to spend any money on
formal training.” Second, by enabling end users to build their own dashboards
and reports, vendors have minimized the amount of support required by IT
departments.
 Financing. To make it easier to adopt analytics, vendors such as IBM offer
financing of both software and services so that upfront costs are not a barrier
to adoption.
Complexity can be managed
Vendors have also found ways to simplify analytics deployments by making it easy
to integrate multiple data sources. For example, IBM Business Analytics includes
functionality that uses metadata, which is data about data, to ensure that
information from all data sources is properly interpreted and integrated into assets
such as reports and dashboards. When analytics tools don’t have built-in data
integration tools, applications such as DataStage automate the extraction and
integration of information from multiple data sources. In fact, Nucleus found that
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data diversity is rarely an obstacle during deployments. At one company that
earned a high ROI on its analytics investment, an IT director said, “Data wasn’t an
issue. The consultants helped us build a data cube for each department on the
deployment. Building each cube took less than a few weeks and our consulting bill
was less than $30k.” Nucleus has found that with so many cost-effective ways to
manage data during analytics adoption, small and mid-size companies are often
able to integrate data sources as diverse as ERP systems, contract-related
databases, financial applications, and point-of-sales terminals.
Analytics improves decision making
Analytics can improve the decision making of even the smartest and best-trained
workforces. Although organizations have made significant investments in recruiting
and training the best employees, many continue to underperform because of poor
decision making. At most companies, employees routinely make important
decisions with bottom-line impact on the basis of intuition, gut feel, and
organizational folklore. Nucleus has found that when employees instead make
decisions based on facts and data from well-governed analytics applications, they
are far more likely to make decisions that increase revenues, improve gross
margins, and eliminate operating costs.
CUSTOMER EXAMPLES
Nucleus has examined numerous deployments of analytics at small and midsize
companies. Following are a few examples of deployments which generated high
returns on investment and were well within the financial limitations of a small
company. Nucleus found these companies followed a number of best practices that
companies can use to maximize the returns on their own investments in analytics.
Blue Mountain Resorts
Blue Mountain Resorts is a diversified year-round resort located in northern
Ontario and the third busiest ski resort in Canada. The company adopted IBM
Cognos TM1 in order to give its managers in a variety of product lines and business
units more information for making operational decisions. After a rolling
deployment that began with the company’s lodging division, IBM Cognos TM1 was
then adopted by the skiing, rental, golf, and conference room businesses. For all of
these businesses, data cubes consisting of historical financial data, current weather
conditions, bookings, and employee schedules were built so that line-of-business
managers could use this data when making daily operational decisions.
Using IBM Cognos TM1 to enable more data-based decision making by managers
and employees led to improvements in three areas. First, managers were better
able to rapidly redeploy workers across the organization’s different operations,
resulting in a reduction to annual labor costs of $2.5 million. Second, by using IBM
Cognos TM1 to replace manual report-building processes, the company avoided
hiring new report builders. Revenue was another area of benefit. Sales agents
increased both sales volumes and margins by using historical bookings data and
weather-related information to make better pricing decisions when booking sales.
The take away
Building databases and datacubes based on business requirements, rather than on
existing on-premise data sources, can be a critical success factor for a deployment.
Prior to the deployment, the project team spent time with business-unit managers
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to identify the types of reports and decision they wanted to improve. In the
lodging department, sales representatives wanted information that would enable
them to make better bidding decisions based on current weather and seasonal
booking patterns. Although the resort had historical booking and revenue data, it
did not have weather-related information. By purchasing information from a
national weather agency, the sales department was able to combine seemingly
unrelated data sources, learn more about customer demand, and make better
pricing decisions on the phone with customers.
Concept One
Concept One is a provider of licensed accessories for men, women, and children.
With an ERP system that had limited reporting capabilities, the company deployed
IBM Cognos Express in order to both eliminate manual report building processes
completed by the company’s IS director and provide better operational data to
managers. The deployment team first built a Microsoft SQL Server database
populated with transactional data from the company’s ERP system. After the
database was integrated with IBM Cognos Express, existing reports were then
replicated in order to automate the reporting process.
Reduced costs were the primary result of Concept One’s analytics adoption. By
performing cost and benefit analyses on all of the company’s design projects, the
company found opportunities to eliminate projects and reduce designer headcount.
Sales managers were also able to reduce costs. By using IBM Cognos Express to
perform queries on each of the company’s licensing agreements, they were able to
determine which should be renewed and which should expire by obtaining
extremely granular cost and revenue data for each agreement. After several years
of better contract management, the sales department significantly increased both
the company’s revenues and its gross margin.
The takeaway
Returns on investments in analytics can be increased by maximizing the availability
of cost-related information. Although the project team was aware of managers’
business requirements for analytics during the deployment, the team could not
anticipate all the analyses or decision making that would be made with IBM Cognos
Express. In order to maximize the range of business decisions that could be
improved with analytics, the team incorporated as many cost-related data sources
as possible, including some data sets that had not been requested by the lines of
business. The deployment team correctly anticipated that with an abundance of
data, managers would entrepreneurially seek out and identify cost reduction
opportunities, eventually reducing both operating costs and costs of goods sold.
Cincinnati Zoo
The Cincinnati Zoo is a nonprofit 100-acre facility serving an average of 1.2 million
people annually. In late 2009, the organization adopted IBM Cognos BI in order to
give managers the information they needed to improve visitation rates, increase
onsite purchases, and reduce costs. The deployment team first created a data
warehouse consisting of point-of-sales data, geographic data, membership lists,
and inventory records. The team then built 25 operational reports and dashboards
based on the organization’s operational and financial objectives.
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Adopting analytics led to more data-driven decision making by managers, higher
revenues, and lower operating costs. Revenue increases were the result of
improving managers’ abilities to examine the preferences and habits of visitors and
members, leading to increases in membership volumes, ticket sales, and sales of
food and merchandise. For example, after examining ice cream sales data, the
organization determined peak sales times for this product and adjusted the opening
hours of the ice cream kiosks, leading to higher revenues. By performing highly
granular analyses of the organization’s marketing efforts and results, managers
also identified and eliminated ineffective initiatives and ad campaigns.
The takeaway
Focusing on customers is one way to earn high returns on an analytics deployment.
One reason the Cincinnati Zoo’s adoption of analytics was so successful is that it
was focused on helping managers learn about the zoo’s customers: who they were,
where they lived, why they visited, and what they purchased. This was achieved in
two ways. First, every available customer-related data source was integrated with
IBM Cognos BI. Second, the Zoo created a new and extremely rich data source by
using point-of-sales terminals to get the zip code of a visitor during every onsite
transaction. By gathering so much data on its visitors, the organization was able to
learn more about the effectiveness of various ad campaigns and promotions. It
also identified neighborhoods which were underrepresented in the zoo’s visitor
base, enabling the zoo to target its campaigns more effectively.
CONCLUSION
Nucleus finds that leaders of small and midsize companies can improve their
operating results by seeing beyond the myths about analytics and investing in tools
such as BI, PM, and predictive analytics. By delivering reporting and analytical
tools to their managers and employees, leaders who adopt analytics change their
companies in three ways. First, they enable their employees to make more data-
based decisions and rely less on intuition and institutional folklore. Second, by
improving employees’ decision making, they enable those employees to
independently find ways to increase revenues and reduce costs. Third, by using
automation to eliminate manual report building processes, companies that adopt
analytics are able to improve productivity.
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