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How Analytics Make Midsize Companies More Profitable
 

How Analytics Make Midsize Companies More Profitable

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While many large enterprises have adopted analytics technologies such as ...

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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    How Analytics Make Midsize Companies More Profitable How Analytics Make Midsize Companies More Profitable Document Transcript

    • September 2011 Document L100RESEARCH NOTEHOW ANALYTICS MAKES MIDSIZE COMPANIESMORE PROFITABLETHE BOTTOM LINEWhile many large enterprises have adopted analytics technologies such asbusiness intelligence (BI), performance management (PM), and predictiveanalytics, misperceptions about costs and risk have caused small andmidsize companies to embrace these technologies far more slowly.Nucleus has examined hundreds of analytics deployments and found thatwhen senior leaders of small and midsize companies invest in analyticstools, they improve operating results by enabling managers to makedecisions that are more fact based and data driven.MYTHS ABOUT ANALYTICSSmall and midsize companies stand to make significant gains by adopting analyticsapplications such as BI, PM, and predictive analytics. Unfortunately, too manycompanies fail to adopt due to misperceptions and doubts about these tools. Thebiggest concern is costs. With limited IT resources, many small and mid-sizecompanies predict that even a modest analytics adoption is beyond their budgetarycapacity 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. Pessimismis a third problem. Many CEOs and CIOs of small and midsize companies feel thatthe 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 ANALYTICSNucleus Research has examined a significant number of IBM Business Analyticsdeployments at companies of various sizes. Analysts recently compared thedeployments of IBM Business Analytics at large enterprises with those at small andmidsize companies in order to identify the traits, benefits, and best practices thatcharacterize analytics deployments at smaller companies. Nucleus found that thedecision to adopt analytics can enable a company to thrive and be more profitablerather than merely survive and move from one operational or financial crisis toanother. With the truth about analytics being far sunnier than most managersthink, there are three facts about analytics that managers should keep in mind.Even small companies can afford analyticsNucleus regularly performs in-depth examinations of analytics deployments andanalysts have found good news about costs. First, deployment costs, includingsoftware, hardware, personnel, consulting, and training are well within the budgetsCorporate Headquarters Nucleus Research Inc.Nucleus Research Inc. NucleusResearch.com100 State StreetBoston, MA 02109Phone: +1 617.720.2000
    • September 2011 Document L100of even small companies. Nucleus has also found that ongoing support costs aretypically minimal, with the average deployment requiring less than a third of one ITadministrator’s time for support. Costs are also low relative to benefits, leading tohigh returns on investment. Some of the factors making analytics affordableinclude: 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 managedVendors have also found ways to simplify analytics deployments by making it easyto integrate multiple data sources. For example, IBM Business Analytics includesfunctionality that uses metadata, which is data about data, to ensure thatinformation from all data sources is properly interpreted and integrated into assetssuch as reports and dashboards. When analytics tools don’t have built-in dataintegration tools, applications such as DataStage automate the extraction andintegration of information from multiple data sources. In fact, Nucleus found that© 2011 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.com 2
    • September 2011 Document L100data diversity is rarely an obstacle during deployments. At one company thatearned a high ROI on its analytics investment, an IT director said, “Data wasn’t anissue. The consultants helped us build a data cube for each department on thedeployment. Building each cube took less than a few weeks and our consulting billwas less than $30k.” Nucleus has found that with so many cost-effective ways tomanage data during analytics adoption, small and mid-size companies are oftenable to integrate data sources as diverse as ERP systems, contract-relateddatabases, financial applications, and point-of-sales terminals.Analytics improves decision makingAnalytics can improve the decision making of even the smartest and best-trainedworkforces. Although organizations have made significant investments in recruitingand training the best employees, many continue to underperform because of poordecision making. At most companies, employees routinely make importantdecisions with bottom-line impact on the basis of intuition, gut feel, andorganizational folklore. Nucleus has found that when employees instead makedecisions based on facts and data from well-governed analytics applications, theyare far more likely to make decisions that increase revenues, improve grossmargins, and eliminate operating costs.CUSTOMER EXAMPLESNucleus has examined numerous deployments of analytics at small and midsizecompanies. Following are a few examples of deployments which generated highreturns on investment and were well within the financial limitations of a smallcompany. Nucleus found these companies followed a number of best practices thatcompanies can use to maximize the returns on their own investments in analytics.Blue Mountain ResortsBlue Mountain Resorts is a diversified year-round resort located in northernOntario and the third busiest ski resort in Canada. The company adopted IBMCognos TM1 in order to give its managers in a variety of product lines and businessunits more information for making operational decisions. After a rollingdeployment that began with the company’s lodging division, IBM Cognos TM1 wasthen adopted by the skiing, rental, golf, and conference room businesses. For all ofthese businesses, data cubes consisting of historical financial data, current weatherconditions, bookings, and employee schedules were built so that line-of-businessmanagers could use this data when making daily operational decisions.Using IBM Cognos TM1 to enable more data-based decision making by managersand employees led to improvements in three areas. First, managers were betterable 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 IBMCognos TM1 to replace manual report-building processes, the company avoidedhiring new report builders. Revenue was another area of benefit. Sales agentsincreased both sales volumes and margins by using historical bookings data andweather-related information to make better pricing decisions when booking sales.The take awayBuilding databases and datacubes based on business requirements, rather than onexisting 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© 2011 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.com 3
    • September 2011 Document L100to identify the types of reports and decision they wanted to improve. In thelodging department, sales representatives wanted information that would enablethem to make better bidding decisions based on current weather and seasonalbooking patterns. Although the resort had historical booking and revenue data, itdid not have weather-related information. By purchasing information from anational weather agency, the sales department was able to combine seeminglyunrelated data sources, learn more about customer demand, and make betterpricing decisions on the phone with customers.Concept OneConcept One is a provider of licensed accessories for men, women, and children.With an ERP system that had limited reporting capabilities, the company deployedIBM Cognos Express in order to both eliminate manual report building processescompleted by the company’s IS director and provide better operational data tomanagers. The deployment team first built a Microsoft SQL Server databasepopulated with transactional data from the company’s ERP system. After thedatabase was integrated with IBM Cognos Express, existing reports were thenreplicated in order to automate the reporting process.Reduced costs were the primary result of Concept One’s analytics adoption. Byperforming cost and benefit analyses on all of the company’s design projects, thecompany found opportunities to eliminate projects and reduce designer headcount.Sales managers were also able to reduce costs. By using IBM Cognos Express toperform queries on each of the company’s licensing agreements, they were able todetermine which should be renewed and which should expire by obtainingextremely granular cost and revenue data for each agreement. After several yearsof better contract management, the sales department significantly increased boththe company’s revenues and its gross margin.The takeawayReturns on investments in analytics can be increased by maximizing the availabilityof cost-related information. Although the project team was aware of managers’business requirements for analytics during the deployment, the team could notanticipate all the analyses or decision making that would be made with IBM CognosExpress. In order to maximize the range of business decisions that could beimproved with analytics, the team incorporated as many cost-related data sourcesas possible, including some data sets that had not been requested by the lines ofbusiness. The deployment team correctly anticipated that with an abundance ofdata, managers would entrepreneurially seek out and identify cost reductionopportunities, eventually reducing both operating costs and costs of goods sold.Cincinnati ZooThe Cincinnati Zoo is a nonprofit 100-acre facility serving an average of 1.2 millionpeople annually. In late 2009, the organization adopted IBM Cognos BI in order togive managers the information they needed to improve visitation rates, increaseonsite purchases, and reduce costs. The deployment team first created a datawarehouse consisting of point-of-sales data, geographic data, membership lists,and inventory records. The team then built 25 operational reports and dashboardsbased on the organization’s operational and financial objectives.© 2011 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.com 4
    • September 2011 Document L100Adopting analytics led to more data-driven decision making by managers, higherrevenues, and lower operating costs. Revenue increases were the result ofimproving managers’ abilities to examine the preferences and habits of visitors andmembers, leading to increases in membership volumes, ticket sales, and sales offood and merchandise. For example, after examining ice cream sales data, theorganization determined peak sales times for this product and adjusted the openinghours of the ice cream kiosks, leading to higher revenues. By performing highlygranular analyses of the organization’s marketing efforts and results, managersalso identified and eliminated ineffective initiatives and ad campaigns.The takeawayFocusing 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 itwas 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 intwo ways. First, every available customer-related data source was integrated withIBM Cognos BI. Second, the Zoo created a new and extremely rich data source byusing point-of-sales terminals to get the zip code of a visitor during every onsitetransaction. By gathering so much data on its visitors, the organization was able tolearn more about the effectiveness of various ad campaigns and promotions. Italso identified neighborhoods which were underrepresented in the zoo’s visitorbase, enabling the zoo to target its campaigns more effectively.CONCLUSIONNucleus finds that leaders of small and midsize companies can improve theiroperating results by seeing beyond the myths about analytics and investing in toolssuch as BI, PM, and predictive analytics. By delivering reporting and analyticaltools to their managers and employees, leaders who adopt analytics change theircompanies in three ways. First, they enable their employees to make more data-based decisions and rely less on intuition and institutional folklore. Second, byimproving employees’ decision making, they enable those employees toindependently find ways to increase revenues and reduce costs. Third, by usingautomation to eliminate manual report building processes, companies that adoptanalytics are able to improve productivity.© 2011 Nucleus Research, Inc. Reproduction in whole or part without written permission is prohibited.NucleusResearch.com 5