Organizations that have mastered applying analytics to support enterprise decision-making tend to focus on seven analytics principles as they strive to accelerate workforce adoption and capture more value.
Transcript of "Sustaining analytics: Deliver hindsight, insight, and foresight"
Sustaining analyticsDeliver hindsight, insight,and foresightAnalytics: Fad or foundation? The emergence of advanced analytics over the past three toKnowledge is power, says the old axiom. Cliché as it five years has been a great boon for many organizations inmay be, it has never been more true, especially when it their quest to enhance their ability to capture informationcomes to the grueling effort most companies face today that is meaningful and actionable. However, as within gaining and maintaining a competitive advantage. any quickly emerging phenomenon, there are questionsEnhanced access to information can lead to better decision surrounding the staying power of analytics. The biggestmaking, improved performance, and, ultimately, a better- question is: Is analytics a fad or is the mastery of analyticslooking bottom line. Such erudition doesn’t come easy, techniques and technology foundational to success?however; it takes a keen understanding of the businessenvironment coupled with an information technology The answer to that question will depend on howinfrastructure that facilitates the timely, careful acquisition companies implement and maintain analytics solutions.and analysis of information. Analytics doesn’t happen in a vacuum. Companies looking to enhance their analytics capabilities must take a holistic view of the data resident in their information systems. Effective information management is a foundational key to successful analytics. Deriving hindsight from business intelligence and performance management systems is important to understand what has happened, but insight into what may happen is even more crucial. High strategic value — and thus higher return on investment (ROI) on information technology (IT) investments — comes from true analytic insight. Knowing what tools and technologies to apply to yield real business results is critical to an organization’s analytics journey. Deeper analytics pushes into the organization, embedding the capabilities, the outputs and inputs, at the strategic and operational levels. Knowing that, the key question then becomes, “How do we sustain what we’ve built here?” Sustaining analytics and making it foundational to the organization’s continued success is a journey. It is an ongoing effort to excel, to make the most of the IT resources, and to harness the enormous power of analytics to better understand and grow the business. It requires a disciplined approach that will take no less than the best and brightest resources the organization can muster to transform itself and gain the power that comes with true knowledge. Weaving analytics into the fiber of the organization There’s no doubt analytics is on the forefront of the C-Suite’s agenda these days. According to a recent Gartner
Group study, “Leading in Times of Transition: The 2010 CIO Some of the key high-impact processes are: Agenda,” improving analytics capabilities is among their top three priorities, and it’s their number one technical priority.1 • Product management: product pricing, product profitability, portfolio optimization The process of delivering analytics business results is one of • Customer management: lifetime value, customer continuous improvement. Starting anywhere in the analytics segmentation, loyalty, customer experience, cycle, an organization can quickly begin to address its specific profitability, churn needs. Deep analytics pushes insights to frontline decision • Service/operations management: customer makers by embedding capabilities and outputs deep in the experience, capacity planning/demand forecasting, processes of everyday work. However, that process has to be capital expenditure, performance, leakage/shortfall, wrapped with serious change management for it to stick. workforce effectiveness • Enterprise management: fraud, revenue assurance, asset Those organizations governed by a “gut-driven” utilization, security, collections, advanced forecasting information culture must instead embrace a fact-driven • Supplier/partner management: vendor efficiency, mentality. Intuition should be replaced by a fact-driven contract compliance, vendor optimization approach to decision making in which actions are backed • Market/sales management: channel optimization, by data, not instinct. It is also imperative to set and campaign performance, up selling, cross-selling communicate an enterprise analytics strategy and integrate into operations. Predictive models must be integrated into Linking analytics to high-impact processes can push analytics processes, technology, and the workforce. Performance capabilities even deeper into the organization, from should be measured and managed with metrics and business executives to the front lines. No matter where an processes, via analytical applications. Finally, analytics must organization enters the cycle of business analytics — analytics also be linked to high-impact processes. Business processes insight, performance optimization, information management — especially those deemed most critical to business — the challenges of execution and adoption are similar. performance — must be driven by analytics. The principles of sustainable analytics The following figure shows how analytics is interwoven Organizations that have mastered applying analytics to into high-impact business processes: support enterprise decision making tend to focus on seven analytics principles as they strive to accelerate workforce adoption and capture more value. Market/ Product Start where you are sales management The first principle of sustainable analytics is understanding management the current state, and picking a starting point for implementation. An honest self-assessment is the first step in understanding current capabilities — and the gaps that will need to be closed to get more value from analytics Supplier/ Advanced investments. Think in terms of both technical capabilities Customer partner and organizational depth. For example, is data aggregatedmanagement analytics management and standardized? Is there an enterprise data warehouse? How robust are organizational business intelligence capabilities? Is intelligence really embedded into many parts of the business? On the organizational side, is there a data governance structure in place? Has the organization Enterprise Service/ bought into data governance efforts? Is there a culture of management operations management accountability for data quality? Figure 1: High-impact processes that link to analytics 1 “Leading in Times of Transition: The 2010 CIO Agenda.” Gartner Group, January, 2010, p. 5. 2
Figure 2: Assess current capabilities Nonexistent Developing Defined Advanced Leading Advanced • Hindsight driven • Functional level • Competitive • Analytics key • Analytics-driven • No market insight • Across few domains advantage analytics • Analytics incentives strategy analytics • No analytic • Deliver token results • Executive support • Operationalized • Global analytics applications • Analytics isolated • Analysts disparate • Leveraging • Analytics integrated • Analytics isolated analytical tools • Analytics mainstream Performance • Excel based • Market driven • KPIs defined • Integrated PM • PM is analytics driven • Budget analysis • Finance has PM skills • Linked KPIs • Value-lined KPIs • PM COE disseminate management • Process-level • Aspiration for more • Sophisticated skills • Aligned incentives skills decisions • Scorecards tools • Standardizing PM • Integrated PM • Predictive modelling/PM Business • BI limited to reporting • BI skills in IT • BI strategy defined • BI governance • Strategy driven • BI skills basics • Data unreliable • BI-driven strategy • BI/BA skills in IT • BI/BA resources intelligence • BI isolated • Dedicated BI team • BI architecture • Standardized BI tools • DW established • Owners of BI • Full level of BI tools • Information driven Data • No governance • Limited DM skills • DM governance • Full data governance • High-quality data/MDM • Little data quality • Developing DM • EIM architecture • DM team integrated • Skilled DM resources management • No MDM processes • Data quality/MDM • MDM implemented • IBI/DM governance • No IT BI/DM skills • Recognize need • Dedicated DM team • Mature DM tools • Standard DMIt’s not necessary to wait until there is an improved IT Circular as it may sound, asking the right questionsinfrastructure in place to begin to analyze information. starts with asking the right questions. Using their overallBecause of the semantic and in-memory analytics capabilities corporate strategy and business processes as a guide,inherent in of most of the analytics tools today, capabilities organizations first must ask:can be added as needed. The key then becomes to assesswhere an organization is in the sustainable analytics journey • What drives value?and begin to develop those areas that are the most ready. • What questions need to be asked?Once this assessment is complete, the next step is to begin • How do we measure up — against our goals, andto ask the business questions that really matter. against the competition? • What investments will drive the required return?Ask crunchy questions • What information do we need to answer theseShareholder value can be improved when activities that drive important questions?value are recognized and effectively managed with indicatorsthat provide lagging, leading, and predictive information Once these questions are answered, then the real workthat is based on reliable data. Getting that information isn’t starts. That work begins with asking questions withalways easy, though. It’s essential to know which questions answers that matter to the success of the organizationto ask about the business. In fact, knowing which questions — the crunchy questions. Linking the answers to thoseto ask — where the most relevant and valuable insights questions to the overall organizational strategy, thenare likely to be found — is the foundation of any analytics linking the strategy to specific industry pressures, is key.initiative. Asking relevant questions requires collectiveintelligence that spans industry, function, and technology. Most organizations — especially over the past decade or soKnowing which questions to ask will deliver hindsight — have been forced to operate in a volatile environment.into what happened, insight into what is happening, and In such chaos, those crunchy questions may change fromforesight into what may happen. That knowledge can time to time. For example, several years ago, when thefacilitate improved awareness of real business issues, and it capital markets were crashing and the economic situationcan result in the realization of higher value from IT systems, for most companies was precarious at best, one crunchythus producing a higher ROI on IT investments. question that many CEOs had was, “How do I re-forecast my financials to reflect such an unpredictable situation?” Another question was, “How do I adjust my workforce to meet current needs?” Still yet another was, “How is my supply chain, my inventory management affected?” Being ready for those crunchy questions — and ready for those questions to change on a dime — is essential. 3
Listen carefully performed in-depth analysis on reports from the site —Understanding what signals matter most is the next trusting site management to deliver correct information.fundamental principle of sustaining analytics excellence. Early one morning, a critical equipment failure occurred,Business is not conducted in a vacuum. Almost setting off a chain reaction of events that resulted in ainnumerable internal and external forces act to influence massive, deadly explosion.events and decisions based on those events. Each eventthat happens leaves an imprint of some kind, somewhere, All of these seemingly unrelated events combined to createin the business environment. the catastrophe. Could they have been detected sooner and connected? Could the accident have been avoided?Signals are everywhere Perhaps. Each event was a signal that was recorded — orAs a hypothetical example, consider a large global should have been — by one or more of the company’sconglomerate that dabbles in everything from energy information systems.to manufacturing. This company has just experienced acatastrophic incident at one of its remote worksites. A Asset tracking systems should have caught the equipmentmammoth explosion has decimated multiple structures issues. Risk management should have been aware of theat the site, killed several employees, and seriously injured safety issues. Human resources and executive managementscores of others. should have been apprised of — and acted on — morale issues. These are but a few of the information systemsManagement is in crisis mode, scrambling to find out that would have been involved. Unfortunately, there waswhat happened and — more importantly — why. The no capability in place to allow management to combinecompany is excoriated by the media and the international seemingly unrelated events into a cohesive picture that maycommunity. The company’s reputation takes an enormous have signaled a disaster in the making — or at least madehit, and it is exposed to enormous liability. Could this have clear that multiple, critical issues needed to be quickly andbeen prevented, or was it just a random occurrence that effectively addressed to prevent a massive failure.was completely unpredictable? Deciphering the signalsIn most accidents, there is no single cause. Instead, there Admittedly, most organizations rarely face this kind ofare many small events that occur in precisely the right disaster. However, they do face a never-ending stream ofsequence or combination to create a cataclysmic failure. events that affect business performance, both positivelyThis is certainly the case here. These events are signals and and negatively. The ability to decipher and integratethey can come from almost any source. these signals into a coherent reflection of performance is vital. Signals come from myriad sources, both externallyMonths earlier, safety issues at the site were revealed but and internally. External signals can come from marketnot adequately addressed. Worker morale at the site was conditions, news media, social media, wisdom of thelow due to isolation and a harsh management style. Some crowds, etc. Internal signals are captured by transactional,workers had posted comments about conditions on various business intelligence, asset tracking, risk management, andsocial media sites. Work safety procedures were not routinely financial management systems, as well as the ever-growingfollowed, and equipment was not regularly or closely volume of unstructured data from e-mails, customerinspected for damage, although it was under tremendous interactions, surveys, video — the list goes on and on.stress. Inspection reports that were completed were lost. The figure below depicts how events from multiple internalWorse, supervisors had deliberately disabled “overly- and external sources are interrelated and produce signalssensitive” alarm systems so the alarms would not trip at all that must be detected and analyzed in light of other eventshours of the night. Finally, executive management rarely that are occurring, or are expected to occur. 4
Wisdom of crowds Predictive markets Equipment E-mail ur / facilities videos ct ry ed ru ve in As lige te st co energy se nc l un -dis t e E Signal analysis ???? Action st n st Ex tur ctur ru ru Excel Economic ur l te ed ed ct a u c ed ru rn rn a st Inte operational geological al nd systems climate Internet analytics Social Web analyticsFigure 3: Signals come from many sources — external implement an enterprise-wide pricing software tool toand internal support pricing processes and align and integrate the tool with the organization’s ERP systemCritical information from these signals must be organized, • Aligning pricing optimization with compensation.aggregated, standardized, and accessible to answer critical Companies can also develop compensation plans forbusiness questions, provide foresight, insight, and measure the sales organization that are aligned with profitabilitysuccess. Once the information has been processed, the metrics. They can then implement those plans by revisingnext step is to apply business process analytics, asset the sales force commission structure to be based on aintelligence tools, and exploratory data analysis to monitor predetermined, but adjustable mix of volume and margindecisions, strategies, as well as transition optimization • Field execution of pricing strategy. To train theand risk management strategies. Using these tools and sales team, companies can utilize a “high touch”techniques can facilitate the aggregation of seemingly implementation method to explain pricing andindependent events into a coherent picture of what’s really profitability metrics and how the team will be incentedhappening across the business landscape. to meet them. They can then develop a list of performance review activities to allow for measurableAccelerate insights progress in meeting goalsTo accelerate the process of deciphering signals andanswering critical business questions, it is essential to Customer communication — Enhancedautomate information delivery to processes and people, Automation can also play a role in how companies chooseand to automate responses as much as possible, so that the most effective channels for customer communication.decision making is sped up and action can be taken with As an example, consider a large retailer that has ancertainty, at the lowest cost. established online and social media presence and executes most of its marketing communication electronically. ToPricing — Automated facilitate more effective customer communication, theOne key business function that is rapidly being automated retailer has built an automated system that monitors eachby leading companies is pricing. There are a number of type of customer contact and message that it sends. Lately,ways to introduce automation into the pricing function: the retailer has undertaken a project to understand which types of communications are most — and least — preferred• Pricing strategy. Companies can develop price by its customers. Analysts have developed an automated optimization models to allow for more careful and timely tracking system to analyze user actions upon receiving pricing and incorporate data from pricing agreements e-mails, or upon encountering an online advertisement. to allow for more granular price setting. They can then 5
Specifically, the system tracks whether recipients open environment is essential. Well-designed data visualizatione-mail messages or mark them as spam, or whether or not capabilities and interfaces can let business users easily accessthey click through an ad in order to view the advertised actionable information, or drill down for more information.site. The system will help retailer’s marketing function inferthe likelihood of whether a particular communication is To foster a user-friendly analytics environment, it’s crucialhigh value, or whether it is ignored by users. to involve the user community in the design process. They have the field experience to know what information theyFor example, if too many recipients mark a message as spam, need, and further, they most likely have good ideas onthe system would simply stop sending that type of message. how to present that information to facilitate ease of use.In the case of advertisements, the ad developer is notified,also through an automated system, of the low click-through As an example, consider the risk management functionrate, and they can be encouraged to develop an ad that at a major health insurer. Analysts are tasked withusers might respond to more favorably. Utilizing this type of gathering, and presenting information on, provider costsautomation in the marketing function helps management so that management can ferret out potential suspicious,refocus resources to more critical tasks, because they no or fraudulent providers. The choice is to present rows oflonger need to micromanage every customer interaction. numerical data, or to present that data in a graphical format. It’s not difficult to discern which format is more engaging.Enhance visualization capabilities to engage usersAutomation is key, but if users can’t interact well with the The chart below is a scatter plot depicting the number ofsystems that deliver information, they won’t use them, patients across the cost of a claim. There are easily visibleand the analytics effort will ultimately fail. Accordingly, to patterns in the chart, and there are outliers that call forpromote sustainable analytics, an engaging user more analysis. In the analysis of the outlier data, there are also patterns and some clear anomalies as well. 3,500 Clustering and anomaly detection 3,000 identiﬁes health care providers that consistently claim higher charges 2,500 Number of patients Suspect provider 2,000 1,500 1,000 Specialty providers 500 0 $0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 Cost/claimFigure 4: Enhanced user visualization yields clear results 6
The first question to ask of this data would be, “How do we To operate in today’s complex and cut-throat businessexplain the outliers?” A deeper drill-down into those results environment, many leading organizations are building amight reveal that those providers were performing special fact-driven analytics culture to drive streamlined executionprocedures that were very expensive, but necessary and, of business processes and decision making throughout thetherefore, well within treatment guidelines and acceptable enterprise. There are several steps an organization can takeperformance parameters. Examples of this situation could to build a fact-driven culture:include expensive chemotherapy regimens, heart bypassprocedures, or other similar treatments. However, there is • Get leaders who walk the walk. A CFO or CEO who says,still an outlier to the right of those outliers that have been “These are the inputs I use to make decisions.” will go aaccounted for. That outlier is a good candidate for analysis long way to facilitate adoption of those inputs by others.of suspected fraudulent activity. With the data presented • Practice proactive obsolescence. One effective wayin a graphical format, it’s easy to spot both patterns and to make sure outdated ideas and analytics tools areanomalies that call for further analysis. eliminated is to formally forbid their continued use. • Ask tough questions. When reviewing decisions made byThe upshot is that organizations that want to sustain others, ask to see which analytics methods were used inanalytics efforts should understand that the method of the decision making process. Strongly encourage fact-presenting information for analysis is just as important as based inputs as well as assumptions.the information itself. It must be presented in a format that • Get out ahead of change. No matter which approachengages the user and promotes digging deeper to find to change management is deployed, training related toanswers that might otherwise be overlooked. expectations, resources, and benefits will be critical. Plan for them.Build a fact-driven culture • Adopt one version of the truth. One adoption barrierEven the prettiest chart is only as accurate as the facts to analytics is a fear that the “facts” delivered aren’tbehind it, however. Good questions that yield wrong reliable. To counter this perception, it’s often necessaryanswers due to inaccurate information can ruin even the to develop a system of certification to define selectedmost carefully implemented analytics initiative. To sustain reports and findings as “official” and stick with it.an analytics program that provides systematic answers and • Incent adoption. Once there is a stable analyticsfacilitates enhanced decision making and performance infrastructure in place, begin to reward adoption and fact-management, it’s critical to build an organizational based decision making to incentives and compensation.information culture that is fact driven. • Measure what matters. Expand the enterprise data model for reporting KPIs to measure how many decisionsYears ago, it was in vogue to shoot from the hip in people are making, as well as their effectiveness.decision making. Maverick CEO’s were all the rage andtheir successes were touted by pundits as a nod to“trusting your gut” and making the hard decisions basedon experience and knowledge, rather than often unreliablefacts and figures. For some organizations, this approachworked — for a while. Times have certainly changed. Overthe last decade or so, a wave of accounting scandals,external threats, increased regulatory scrutiny, andeconomic adversity has put a new premium on having —and making decisions according to — hard facts. 7
Practice right-fit analytics To get the right fit, it’s essential to take an in-depth look atIn building a sustainable analytics environment, it’s critical the organization as a whole. Examine budget constraints,to drive decision making with fact-based inputs, but staffing levels, and resource availability for analytics efforts.it’s also imperative to get at those facts with analytics Take a deeper look at how well management understandstechnologies that are the right fit for the organization as the business environment. Consider risk tolerance fora whole. The proliferation of analytics tools on the market making decisions. Develop an understanding of datahas already outrun the pool of available talent to use those privacy and regulatory issues regarding data security.tools powerfully in most organizations. This leads to twobig challenges: setting the overall analytics strategy and For example, if an organization is using a data-miningchoosing tools and technologies based on that strategy, tool, data security is a big concern. These tools typicallyand on challenges and constraints faced. touch the database directly. Access to them, and to the information they gather, must be strictly controlled. ThereThe figure below depicts how understanding business are also some countries that forbid the transfer of datachallenges and constraints to the corporate strategy helps outside the country to analyze it. So, if management needsin determining which analytics solutions are the right fit. Business challenges Constraints Solutions Budget CRISP-DM Efﬁciency SAS S/CMM Stafﬁng Linux SQL Server Infrastructure Clustering UNIX Windows Cost Experian SVM CART Solaris Licensing Rapid Oracle SPSS Miner Regression Risk tolerance Risk Urgency Security End usersFigure 5: Analytics solutions based on challenges and constraints 8