1Building an Analytics CultureIntroductionCommon definition for business analytics:“The practice of iterative, methodical exploration of an organization’s data with anemphasis on statistical analysis.”This common definition doesn’t even hint to the real power of analytics. By applyinganalytics to the seemingly unlimited flow of data available today, organizations canunderstand and address complex business issues in ways never before imagined.Basing decisions on facts rather than on intuition pays off. Companies that use businessanalytics in a strategic way gain a competitive advantage, improve productivity andboost the bottom line.Organizations have gotten this message and have upped their games in recent years.According to a 2011 study by Bloomberg Businessweek Research Services, thepercentage of companies using some form of business analytics rose from 90 percentin 2009 to 97 percent in 2011 (Source: The Current State of Business Analytics: Wheredo we go from here?, Bloomberg Businessweek Research Services, August 2011,sponsored by SAS).However, what some organizations – and even some software vendors – call “businessanalytics” is often little more than sorting, filtering, slicing and dicing information forhindsight reporting or a snapshot view of the present. That is far too limited a definition,considering what is possible. Companies that rely on simplistic forms of analytics aremissing out on the full value of the insights hidden in their databases – and so are thecompanies that don’t establish an environment where analytics can really perform.What do companies have to do to get their money’s worth out of their data assets andanalytic investments? That question was the topic of a companion set of webinars inthe SAS Applying Business Analytics Webinar Series, where three experts from SASaddressed this question as it relates to business managers, IT leaders and executives.Progress Toward the Data-Driven Ideal“Across the board, companies vary in their level of analytical maturity, but the ideais they all want to gain competitive advantage using data-driven decision making,”said Kathy Lange, Senior Director of Consulting at SAS. “They’re trying to improveproductivity, increase revenue, decrease costs, and manage risk and uncertainty in thedecisions they’re making.”The potential benefits warrant attention, and companies are making investments toimprove their analytic strength. According to joint SAS and Accenture research, 45percent of businesses increased their spending on analytics in 2011 and even more ofthem – 65 percent – reported plans to increase spending in 2012.For More InformationTo view this on-demand webinar:sas.com/reg/web/corp/1967891For information on other events inthe Applying Business AnalyticsWebinar Series: sas.com/ABAWSFor a go-to resource for premiumcontent and collaboration withexperts and peers:AllAnalytics.comTo download the SAS white paperGetting Your Money’s WorthWith Analytics:sas.com/reg/wp/corp/44913For more about SAS Analytics:sas.com/technologies/analyticsFollow us on twitter: @sasanalyticsLike us on Facebook: SAS Analytics
2SAS Conclusions PaperThis attention is not just focused on technology. Eight in 10 respondents whoseorganizations were investing in business analytics said they planned to upgrade theiranalytical strengths by improving the skills of existing staff (69 percent) and hiring newanalytical talent (55 percent). This focus on human resources is important, because by2018, the demand for deep analytical talent in the US could be 50 to 60 percent greaterthan the supply (Sources: US Bureau of Labor Statistics, US Census, Dun & Bradstreet:company interviews, McKinsey Global Institute analysis).The gap between analytical potential and reality is already being felt. In the joint SASand Accenture analytics study, only one in four respondents indicated that theirorganization’s use of business analytics was “very effective” in helping them makedecisions. Only one-third reported that they had achieved or exceeded return on theirbusiness analytics investments. However, the leaders have achieved some truly notablereturns, repaying their investment many times over.The Four Dimensions of an Effective Analytics CultureOrganizations with the strongest analytical performance are those that focus onbest practices in four key areas: business analytics skills and resources; informationenvironment and infrastructure; internal processes; and organizational culture. Ourwebinar presenters shared some best practices in all four areas – guidance for creatingan environment where analytics can flourish and deliver on its potential.Focus Area 1: Business Analytics Skills and ResourcesSuccess with business analytics is about more than technology. Organizations mustupgrade their business and technical analytical skills to make full use of the availabletechnology and to apply the results of analytics to the appropriate business issues.Provide the Right Balance of Resources“Of course we need the hardware, the software and the information, but one of the keychallenges is having the right [human] resources in place – analytical resources, domainexpertise and IT resources” said Aiman Zeid, Principal Business Consultant at SAS. “Alot of organizations don’t pay enough attention to this aspect.”Talent upgrades can come by way of training current employees or hiring new analyticalworkers. A key is to attract, retain and continuously refresh the knowledge of teammembers who understand the use of analytics in technical and business contexts.Make Analytics More ApproachableAcknowledging that analytical skills will be in short supply, how do you manage for that?“Organizations can’t get all the resources they need, so they need to figure out a way tomake analytics more approachable, so we can get more business analysts using moreanalytics,” said Lange. For example:Companies making a forayinto business analytics face alearning curve. Moving to fact-based decision making requiresthe right technology, talentand processes – as well asa cultural shift.Companies that want toimprove the effectiveness oftheir analytics should considerfour key focus areas: Find theright talent mix, get the datain order, establish internalprocesses to support analytics,and foster a culture of fact-based decision making.
3Building an Analytics Culture• Business visualization can transform how you see, discover and share insightshidden in your data. Graphical presentations enable nontechnical users toexperience – and share – the aha! moments with an impact and immediacy thatcannot be achieved with static graphs, spreadsheets or reports. “Data visualizations can summarize billions of rows of data into the key informationthat an executive could use on an iPad®,” said Lange. “Unlike a static report, it’sinteractive, so users can drill down into the data detail and see the factors that areinfluencing the outcomes. The idea is to get analytics out of the back room andinto the decision-making process – and to spread it across the organization tomake it more available to the people making the decisions.”Figure 1. Data visualization empowers more users to use more analytics.• Visual programming streamlines and simplifies the process of creating analyticalworkflows. Simply drag and drop options or nodes into the workflow and connectthem to the data as appropriate. In the Gantt-type display, it is easy to add stepsto the workflow and track the steps that have been taken. Wizard-driven analytical processes improve upon traditional coding, empowermore people in the organization to generate analytic insights, free quantitativespecialists to focus on the most complex and critical questions, and improveinformation governance.“By making it intuitive – even wizard-driven – to create analytical process flows andexplore the data, more users are empowered to take advantage of analytics, analystscan focus on tougher analytical questions, and we encourage broader use of analyticswithin the organization,” said Lange.“The idea is to get analyticsout of the back room and intothe decision-making process– and to spread it across theorganization to make it moreavailable to the people makingthe decisions.”Kathy LangeSenior Director of Consulting, SAS
4SAS Conclusions PaperFocus Area 2: Information Environment and InfrastructureAt its core, business analytics is about using data to discover insights that can changethe way an organization operates. Without a strong foundation of accurate and reliabledata, the results of analytics are suspect and likely to be overridden by executives.According to the SAS and Accenture analytics research, intuition is used over analyticsin an average of 39 percent of business decisions. Survey participants cited two mainreasons for opting for intuition over analytically derived results: lack of access to theneeded data and lack of confidence in the data they do have. Both issues can beaddressed through a sound information management strategy.Upgrade the Information ArchitectureMost organizations have data located across a large number of heterogeneous datasources. Analysts spend more time finding, gathering and processing data thananalyzing it. “Data quality, access, security… these are very key issues for mostorganizations,” said Zeid. “Some of these weaknesses really come to the surface whenyou run analytics, especially when working with data from multiple systems, businessunits or regions. Inconsistency in coding and product definitions is common.”The way data is formatted and presented needs to change as well. “IT really needsto step up and understand the data requirements of analytics,” said Mark Troester,former Senior Product Marketing Consultant at SAS. “IT is very well-versed in managingrelational databases in the context of operational systems, but the data requirementsfor predictive analytics, business intelligence and reporting are very different from that.So they need to understand how to structure the data to support different kinds ofanalytics.”There will likely be changes in the infrastructure to accommodate analytic processes.“As analytics becomes more pervasive, organizations are no longer just doing analyticson a project-by-project basis,” said Troester. “So they need an infrastructure thatsupports enterprise-class analytics and multiple projects. IT is uniquely situated to helpwith the data aspects of this evolution. At the very least, they own the infrastructurewhere the data resides, but they also bring a unique blend of business and technicalknowledge that can be key in preparing data for analytics and offloading that work fromthe analysts.“Finally, IT really needs to understand the analytical life cycle. It’s not the same asbuilding applications, where you design, test and put it into production. The analyticalprocess is much more iterative, so IT needs to provide a sandbox environment for theanalysts, and they need to be able to iterate on that, in terms of getting new forms ofinformation, restructuring the data and trying different modeling techniques. And thenIT needs to step in when the analyst is done and take the results of that analytical effortand embed it directly into operational systems, with the rigor for which IT is well-known.”“By making it intuitive – evenwizard-driven – to createanalytical process flows andexplore the data, more users areempowered to take advantageof analytics, analysts can focuson tougher analytical questions,and we can encourage broaderuse of analytics within theorganization.”Kathy LangeSenior Director of Consulting, SAS
5Building an Analytics CultureCapitalize on Advanced Analytics, Not Just Reporting“Companies are turning to business analytics to tackle big issues, but even with the highadoption rate, they are still in the emerging state in their use of advanced analytics,” saidLange. For example, there’s data mining, which is looking at huge data sets – potentiallymillions or billions of rows of data – and uncovering patterns you would not typically beable to see.There’s forecasting, which is also using historical data to make better decisions aboutthe future, but with an added time dimension. For instance, you could use forecasting topredict sales for the next three, six or 12 months.Text analytics is an emerging area of importance, Lange noted. “Organizations areactually collecting more unstructured data (text) than structured data – about three timesas much. We can use linguistics and statistical analysis to incorporate that text into ourmodeling and analytics.” For instance, unstructured data gleaned from social media, callcenter notes, insurance claims and survey tools could uncover insights about consumersentiment, potential fraud, socially connected entities and more.“Finally, there’s optimization, where we’re trying to find out the best answer – eitherminimizing a factor (such as cost or attrition) or maximizing something (such as profit orrevenue) – within constraints such as limited resources, money or time.”Bridge the Gaps Between IT and the Business“Data issues are still the biggest inhibitor to the use of analytics within an organization,but the second biggest constraint is the gap between business and IT,” said Lange.“There’s a huge communication gap; they speak different languages. IT doesn’t understandthat the way they collect the data is not the way the business is going to use the data.The business user might say, ‘I want you to give me the 5,000 best customers thatare going to respond to my campaign,’ and the IT person says, ‘Well, the data is therein the data warehouse’ – but really it isn’t there; there has to be data preparation thatmakes it ready for that kind of analysis. This disconnect requires a third-party interpreter– an analyst or data scientist who can translate between IT and the business.”Focus Area 3: Internal Processes“Organizations need a well-defined set of processes to help the business communitytap into and access analytical resources – and to identify, prioritize and addressanalytical requirements,” said Zeid. “These processes have to be well-defined, acceptedand promoted within the organization.”Manage Analytics as an Ongoing Process, Not a One-Off ProjectNow that predictive models are high-value organizational assets – essential toolsto manage uncertainty and risk – the models and their underlying data must bemanaged for optimal performance throughout the analytical life cycle. It’s not onlyabout developing the models. It is also about deploying them, embedding them into abusiness process and monitoring them over time. With the demand rising for predictivemodels, you want to have an enterprise view on managing them.“The increasing use ofanalytics provides an excellentopportunity for IT to partnerwith the business to makefundamental change to thebusiness by enabling betterdecisions through analytics.By supporting analytics, IT canbreak out of the cost centermentality and adopt a morestrategic role that helps drivetop-line revenue growth.”Mark TroesterFormer Senior Product MarketingConsultant, SAS
6SAS Conclusions PaperWith a formal model management framework – an “ analytics model factory” – itbecomes far easier to document models and collaborate across departments andinternal agencies. An analytics model factory closes the loop in the analytical setup toget your value.IDENTIFY /FORMULATEPROBLEMDATAPREPARATIONDATAEXPLORATIONTRANSFORM& SELECTBUILDMODELVALIDATEMODELDEPLOYMODELEVALUATE /MONITORRESULTSDomain ExpertMakes DecisionsEvaluates Processes and ROIBUSINESSMANAGERModel ValidationModel DeploymentModel MonitoringData PreparationIT SYSTEMS /MANAGEMENTData ExplorationData VisualizationReport CreationBUSINESSANALYSTExploratory AnalysisDescriptive SegmentationPredictive ModelingDATA MINER /STATISTICIANTHE ANALYTICS LIFE CYCLEFigure 2. The analytics life cycle should be managed as an iterative, closed-loop process.Facilitate Collaboration“When you visualize the analytical life cycle, it looks like a cyclical process, but it’sactually very iterative around that circle,” said Lange. There are many people involvedin that process at various stages. For instance, a business manager asks the questionthat requires an analytics-driven answer, provides domain expertise, makes thebusiness decisions based on the analytics, and evaluates processes and return oninvestment from the decision. A business analyst conducts data exploration, works withdata visualization and creates reports. The IT systems/management team is responsiblefor data preparation and model validation, deployment and monitoring. A data miner orstatistician performs more complex exploratory analysis, descriptive segmentation andpredictive modeling.To get the best analytic results, these players with the right skills need to be in place,working collaboratively and empowered to perform these roles.
7Building an Analytics CultureFocus Area 4: Organizational Culture“Creating an analytics culture requires that the organization understand, value anddemand fact-based decisions and strategies,” said Zeid. “The organization needs tocommunicate the value of analytics, fund the appropriate resources and reward properuse. Furthermore, we need to set the right expectations about what we’re doing andwhat analytics is exactly going to do, how it will contribute to the bottom line – andmake sure the culture is tuned and ready to adopt, absorb and use analytics fordecision making, policy validation and so on. If we don’t pay attention to these culturalfactors, the result is usually suboptimal.”Grass-roots analytics efforts can be successful, but the path is shorter and smootherwhen the culture embraces fact-based decision making. Support from executives fromthe outset helps address many of the challenges companies face in trying to move upthe analytics maturity curve. With executive support, talent issues are more likely to beaddressed, collaboration improves, fact-based decision making is more highly valued,and data issues are more readily resolved.How to Get StartedChoose a Business Area that Is Ripe for Success“First, make sure you’re very clear on business priorities and objectives,” said Zeid.“Use those priorities and objectives to assess the analytics environment on the four keydimensions: people, technology, process and culture. Once you take a look at thosefour dimensions, you will quickly form a picture of current capabilities and can make agap analysis and determine what’s missing.”With those gaps addressed and the dimensions in alignment, where should anorganization start using analytics? Zeid recommends choosing a business area that:• Represents a high business priority.• Has integrated and consistent data available.• Has enough historical data to create meaningful insights.• Offers the potential to generate tangible business value.• Has the skills and resources in place to effectively use analytics.“For instance, if we’re trying to forecast potential sales numbers, we need to makesure we have the historical data, and it’s integrated and of a good quality,” said Zeid.The staffing, internal processes, support from stakeholders in the organization – theseelements must be in place. “The first analytic project will be under the microscope, soyou need to get it right.“Focus the effort on something that’s very critical and strategic. Gain consensuson how the results of the project will be evaluated, and be clear and honest aboutcommunicating the results. All of that will be critical to get to the second project.”Integration of analytics acrossthe organization is a markerof analytic success, but it’snot necessarily the cause.When the organization hasembraced analytics as thefoundation for business decisionmaking, integration across theorganization will likely follow.
8SAS Conclusions PaperConsider Establishing an Analytics Center of ExcellenceA center of excellence is a cross-functional team of analytic and domain specialistswho plan and prioritize analytics initiatives, manage and support those initiatives,and promote broader use of information and analytic best practices throughout theorganization. Although it will not have direct ownership of all aspects of the analyticsframework, an analytics center of excellence will provide oversight, guidance andcoordination for technology, process, data stewardship and the overall analyticsprogram – both from an infrastructure and support/governance perspective.“A center of excellence is a very effective way to accelerate an organization’s analyticmaturity,” said Zeid. “It will also produce significant value by finding out where IT, domainand analytical resources exist and making the best use of them.”As an analytics center of excellence mobilizes analytic resources for the good of theorganization – not just for specific business units or one-off projects – it ultimatelychanges the culture of the organization to appreciate the value of analytics-drivendecisions and continuous learning.As an analytics center ofexcellence mobilizes analyticresources for the good ofthe organization – not justfor specific business units orone-off projects – it ultimatelychanges the culture of theorganization to appreciatethe value of analytics-drivendecisions and continuouslearning.The most distinctive difference inanalytical performance betweenthe front-runners and the othersis how they perceive their dataand talent. High-performingcompanies see their data asan asset; they also report thatquality data – and access to theright data – is available.
9Building an Analytics CultureClosing Thoughts“Analytical leaders have very specific attributes,” said Zeid. “First, they value informationas an asset, and that’s not just saying that information is critical to the organization;it is supporting that value statement with the prerequisites for success in all four keydimensions:• The right blend of analytical talent, domain expertise and IT knowledge, infusedwith a collaborative spirit and curiosity.• An agile information infrastructure that offers trusted, analytics-ready data andthe processing power to deliver timely answers to decision makers.• Processes that enable business users and analytics specialists to get theiranswers quickly, and to treat analytics as an iterative, ongoing process rather thanan ad hoc project.• An organizational culture that sets the tone for requiring fact-based decisions andvalidating assumptions with facts.“If analytics doesn’t produce the optimal results, the number one reason is a lack ofstrategy,” said Zeid. “This underscores the need to take a holistic, enterprise approachto the analytical environment.“Allow access to more computing resources. Help business units get the informationthey need – in the way they need it – from multiple functions within the organization.Allow flexibility for analytical experimentation, then apply more rigor to productiondeployment. Foster collaboration between IT and the business. Review and adjust ITgovernance policies to make sure they facilitate the use of analytics. And finally, lookinto establishing an analytics center of excellence to accelerate the organization’smaturity.”The Four Dimensionsof Analytic ExcellenceBusiness Analytics Skills andResources• Analytical, technical andinterpersonal skills.• Training, career advancement toattract and retain talent.Information Environment andInfrastructure• Relevant, accurate, consistent andtimely enterprise information.• Mature and capable enterpriseinformation infrastructure.Internal Processes• Well-defined processes to identify,prioritize and address analyticalrequirements.• Coordinated support fromIT, analytical resources andcomputing power.Organizational Culture• Understanding the value andexpectations for fact-baseddecisions and strategies.• Communicating the value ofanalytics, funding staffing andrewarding proper use.