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Should operators be aware and conscious of BigData – as opposed to seeing it as just anotherterm used by the IT department?

Ales Gornjec: Operators should consider deploymentand use of big data analytics very seriously. The term isperhaps new, but the concepts have existed in the gam-ing industry for a long time. In the past only the biggestoperators would invest into infrastructure that allowedthem to perform good analytics, but nowadays thistechnology is available to wider audience as long asthey have a strategy and clear vision which data to col-lect and what to do with it.

Jerry Bowskill: Big Data is more than hype; it’s a usefullabel to help focus organisations on the potential benefitof extracting greater value from data. However, becausethis is an umbrella term with no clear definition, thespecific implementation and strategy is not prescriptiveand up for interpretation. Rather than focus on Big Data,the true value here is the Big Opportunity that is emerg-ing in today’s data rich business environments.

Jeremy Thompson Hill: Yes, operators shouldabsolutely be aware of the opportunities that Big Data

presents to their business – and the threat a lack ofunderstanding and adoption will inevitably bring. Foran operator, Big Data represents the chance to under-stand their customer and act to ensure that their offer-ing is truly relevant and engaging. No matter the trade,the customer should always be placed at the centre ofany business and its strategy .

Big Data is an umbrella term for a whole group ofanalytics tools and technologies – how do youbreakdown the tools that are actually needed forspecific purposes? Many gaming operations areheavily focused on customer-oriented analyticalapplications involving personalisation, ad as wellas email targeting, and search engine optimisa-tion, in addition to quantitative techniques involv-ing data mining, marketing, and experimentaldesign. How do you decide upon the Big Data toolsneeded for specific businesses both now and inthe future?

Jerry Bowskill: A Big Data strategy, first and foremost,needs to be completely aligned with the commercialstrategy of the organisation. Being clear from outset,how Big Data can deliver commercial benefit is a criti-

cal pre-requisite to any technical development or capitalexpenditure. Opportunities will vary across differentbusinesses and therefore the tools, data and skillsrequired will vary accordingly. For Scientific Games, ourapproach has been to focus initially on creating a soliddata architecture on which our gaming platforms arebuilt, feeding a data warehouse that can support the vol-umes of transactions generated by our ‘omni-present’

01 Data Lake - is a massive, easilyaccessible data repository forstoring "big data". Unlike traditionaldata warehouses, which are optimisedfor data analysis by storing only someattributes and dropping data below thelevel aggregation, a data lake isdesigned to retain all attributes,especially when you do not yet knowwhat the scope of data or its use.Currently, Hadoop is the mostcommon technology to create a datalake. It is important to distinguish thedifference between Hadoop and adata lake. A data lake is a concept, andHadoop is a technology to implementthe concept.

TECHNOLOGY - BIG DATAInteractive

Data - the biggerthe better?Everyone collects data - the trick is inusing it to the best advantage. Weinterview the Big Data players in thegaming industry, speaking toComtrade, Playtech, Openbet,Scientific Games Interactive andTapCentive about the importance ofthis technology for the future of theland-based and interactive industry.

Jeremy Thompson Hill, CEO, Openbet

Ales Gornjec, General Manager, Comtrade

Jerry Bowskill, Chief Technology Officer,Scientific Games Interactive

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customers across their landbased and interactive sites.With all data formatted and available, we could thendeploy any toolset, giving us the flexibility to evolve thetypes of tools as our needs change over time. It is impor-tant, as a technology company, not to fall for the trap ofthinking it is cost effective to develop in-house tools.Our advice is to select from the vibrant community oftool suppliers. At Scientific Games, our initial approachhas been to use a small number of analysis toolsetsoffered by companies such as IBM and Google, as theneeds differ between our Social and Real Money Gamingproducts.

Jeremy Thompson Hill: There is not one right answerhere as we are all on a journey towards creating a morecustomer-centric approach to business and one gamingoperator is always going to have a different product setup and set of priorities to the next.

That said, in the main, operators are trying to retro fitnew approaches, tools and technology into existinginfrastructure and operating systems, rather than start-ing with a blank canvas, so the decision as to whichtools to go for tends to be driven by multiple teamsacross the business. For example, a marketing or prod-

uct team might decide to utilise a tool like Maximiser todrive multiple content variants to specific user segmentswhereas a data insight team could implement a specificbusiness intelligence tool that can be used to extract keyinsights into the business.

At OpenBet we know that the velocity of technologicalchange in the market means we need to be flexibleenough to cater for the requirements of our customers,regardless of their chosen Big Data set up. As a result,we are moving towards a truly 'open access' dataapproach where our platform becomes an enabler forgrowing a Big Data solution through APIs rather than ablocker trying to force operators down a specific route.

Ales Gornjec: There are many different areas within

gaming operations that can benefit from a Big Dataapproach, and we would recommend different tools forthem. To start with IT, there are now various tools avail-able that collect all kind of system logs in one place andhelp IT to find potential problems proactively, and totroubleshoot and solve those that have already hap-pened more efficiently. Splunk is the leading commer-cial option, often used in gaming, but we have alsofound an open source combination of Elastic withLogstash, and Kibana very useful.

On the marketing side, there are many tools that canincrease targeting precision, improve relevance, andincrease campaign efficiency. Google Analytics hasbeen used for many years now to improve web sitedesign and to understand user acquisition channels. IBMUnica was quite popular within the gaming industry todrive campaigns. Moreover, quite a lot of operators arenow linking and improving efficiency with results of BigData analytics. These links are often still indirect andnot automated because of an additional integrationinvestment that is necessary. There are many toolsavailable today that help in various areas of marketingactivities. They are usually not integrated into one cohe-sive solution, and substantial manual work is needed togather all the data together, process it, and start the nec-essary actions. Usually this work is split between the ITand marketing departments, where good business ana-lysts and data scientists can have important roles inbridging the gap between business and technologymind-sets.

Big companies with large investments in their datawarehouses have neither the resources nor the willto simply replace an environment that works welldoing what it was designed to do – it’s also notpractical to acquire all the Big Data-enabling tech-nologies needed in one fell swoop. Assuming thebusiness can establish acquisition tiers for key BigData solutions, what are the corresponding budgettiers?

Jeremy Thompson Hill: OpenBet provides multiplelevels of data solutions to meet the needs of our cus-tomers and commercially the business is moving moretowards a shared risk or license fee model. Our datasolutions are costed based on volume of data and alsotangible results. As we make data feeds available to cus-

It is important, as a technologycompany, not to fall for the trapof thinking it is cost effective to

develop in-house tools.

TECHNOLOGY - BIG DATAInteractive

Hadoop cluster

A Hadoop cluster is a special type of computational cluster designed specifically for storing and analysing hugeamounts of unstructured data in a distributed computing environment. Such clusters run Hadoop's open source dis-tributed processing software on low-cost commodity computers. Typically one machine in the cluster is designatedas the NameNode and another machine the as JobTracker; these are the masters. The rest of the machines in thecluster act as both DataNode and TaskTracker; these are the slaves. Hadoop clusters are often referred to as"shared nothing" systems because the only thing that is shared between nodes is the network that connects them.

Hadoop clusters are known for boosting the speed of data analysis applications. They also are highly scalable: If acluster's processing power is overwhelmed by growing volumes of data, additional cluster nodes can be added toincrease throughput. Hadoop clusters also are highly resistant to failure because each piece of data is copied ontoother cluster nodes, which ensures that the data is not lost if one node fails.

As of early 2013, Facebook was recognised as having the largest Hadoop cluster in the world. Other prominent usersinclude Google, Yahoo and IBM.

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tomers the commercial model for the business is pivot-ing towards a more results-oriented approach whereboth parties are motivated to drive success.

Ales Gornjec: Big data solutions are available in vari-ous business models – usually cloud-based solutionshave some volume- or usage-based pricing structures,where the initial investment is kept low and ongoingcosts depend on the volume of data, the number of play-ers, or some similar parameter. Many operators want toprotect their data and are interested in on-premise bigdata installations – in such a case the initial setup cost ishigher, since it also includes hardware and licenses, buthas a lower ongoing component that is usually struc-tured as a license support.

Jerry Bowskill: A Big Data strategy does not require anorganisation to mothball any of their existing datawarehousing or business intelligence infrastructure.Just because an organisation is running a Hadoop clus-ter does not automatically mean that it has an effectiveBig Data strategy. The first tier should be to build onwhat you have for a very well defined goal. With mod-est changes it is possible to get select amounts of dis-parate information connected using simple databasetechnologies and analysed by opensource tools.Running a pilot doesn’t need to break the bank; start bytrying to answer a simple question that adds value tothe business, and get a feel for the tools and resourcingbefore moving forward. At Scientific Games we get anenormous amount of benefit on our Social and free-play games from Google Analytics and this has influ-enced where we have invested in our Real MoneyGaming analytics. Ultimately the power of businessintelligence, over more traditional reporting, is that itallows scenarios to be run and data to be segmented inreal-time so you can get results or spot trends in somecases before you really know how to articulate thequestion.

Firms such as Google, eBay, and Facebook werebuilt around Big Data from the outset. They didn’thave to reconcile or integrate Big Data with moretraditional sources of data. The same isn’t true forthe majority of gaming industry businesses, whichneed Big Data to co-exist with analytics on othertypes of data. Can Big Data fit into the overall dataand analytics environment of the gaming indus-try?

Ales Gornjec: This depends on the data that is collect-ed. IT infrastructure logs are new, but sometimes havesimilar information as server-based gaming systems orcentral monitoring solutions that collect from gamingmachines. These solutions collect structured informa-tion that is defined by Gaming Standards Association’sG2S protocol, while big data logs contain unstructuredinformation. Both can be used to search for potentialfraud, but in a different way.

Player data on the other side can be extended withactivity information that add game play and paymenttransactions that are already stored by existing systems.This could include browsing activity on operator portalsand social sites like Facebook and Twitter. Operatorsalready monitor user activity on the portals with GoogleAnalytics.

Jerry Bowskill: I would take a different perspectivehere and say that Google, ebay, Amazon and Facebookare all great examples of organisations that have fullyembraced a data-driven, evidence-based culturethroughout every part of their organisation. They areobsessed with data and using this to make the mostrobust, accurate and timely decisions possible. In thisrespect, Big Data is more of a philosophy rather than aspecific technology solution. For SG Interactive, havinga big data philosophy is essential to the success of oursocial gaming products. With millions of players per dayand tough competition, we have to AB test our gamesand marketing campaigns; failing fast and building onsuccess in near real-time is an operational necessity.Our Real Money games have a big data mindset, devel-oped from our experiences with Social gaming, whichhas helped us better understand player and productbehaviour. This ultimately allows us to create bettergames for our customers to get a greater ROI from mar-keting them because they are not tied to a set numberof reports. Rather, the games have the tools to runwhatever analysis they like.

Jeremy Thompson Hill: Absolutely. Operators whohave been using more progressive algorithms to under-

stand customer behaviour in greater depth and utilisethis information to define a user-centric front end expe-rience for a customer are winning the battle in the mar-ket. There are operators in the market that have alreadymade this shift.

For businesses that are still on this journey, the majorityin the gaming market, there is always going to be an ele-

01 Hadoop is talked about as if it's onemonolithic thing, but it's actually afamily of open-source products andtechnologies overseen by the ApacheSoftware Foundation (ASF).Theoretically, HDFS (HadoopDistributed File System) can managethe storage and access of any datatype as long as you can put the data ina file and copy that file into HDFS. Asoutrageously simplistic as thatsounds, it's largely true, and it's exactlywhat brings many users to ApacheHDFS and related Hadoop products.After all, many types of big data thatrequire analysis are inherently filebased, such as Web logs, XML files,and personal productivity documents.

TECHNOLOGY - BIG DATAInteractive

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ment of stitching different data sets together to build amore granular picture of the customer. Transactionaldata, front end analytics, payments, demographic data,game paytables and features - the list goes on and whileit is not straightforward to connect these data setstogether the benefits are significant for Operators whodo.

OpenBet has partnered with a business to help solvethese complex problems and to allow the business tomove forward aggressively into predictive analytics andmachine learning. It is a new partnership for OpenBetbut we have high hopes for the output. The reality is,complex backend tools that need a PHD-level of under-standing to use efficiently are going to be obsolete inyears to come. Moving forward, the battleground centresaround automation and machine learning.

Does the collection and storage of increasing vol-umes of data mean we are ultimately betterinformed? And how do you go about turning cus-tomer data into customer loyalty?

Jerry Bowskill: Potentially yes - The collection andstorage of more data does create the opportunity to

make better decisions, however it also introduced a riskof more spurious correlations. Advances in medicalimaging and analysis provide a clear example of one ofthe potential problems. It actually makes it harder torecognize what’s ‘normal,’ as for the first time havingmore data has illustrated how greatly individual param-eters vary between patients. Differentiating betweencorrelation and causation is a potential negative unin-tended consequence of Big Data. In this respect, thetechnology may allow us to more effectively store andanalyse data. However, this places a greater emphasison the interpretation and business implementation ofthe results. Perhaps the ‘AB testing’ of players provides anon-ambiguous example of where analysis can revealthe real underlying preferences for changes betweenproducts variants.

The reality is, complex backendtools that need a PHD-level of

understanding to use efficientlyare going to be obsolete in years

to come.

TECHNOLOGY - BIG DATAInteractive

Even bigger terminology

Terminology like megabytes, gigabytes or perhaps even terabytes already become out-dated. Today we are talkingabout Exabytes, Petabytes, Zettabytes, Yottabytes or even Brontobytes. It is estimated that the current digital uni-verse is 1 Yottabyte in size. This is the equivalent of 250 trillion DVD’s and our digital universe is expanding at anexponential rate.

Jeremy Thompson Hill: This is a key question - datadoes not automatically lead to value for a business, it isthe interpretation of the data, the extraction of action-able insights and the automation of processes that gen-erate value. A business can drown in insight data butremain lost. Give me one actionable insight over amountain of data any day of the week.

The question of how you turn insights into greater cus-tomer loyalty is answered by an efficient product devel-opment methodology. If we listen to our customers theywill show us. In the words of Silicon Valley entrepreneurEric Ries: ”We must learn what customers really want,not what they say they want or what we think theyshould want.” The key to the process is starting fromcustomer insights and putting in place a process thatallows the business to surface key insights and enhancea product iteratively to ensure that it is both relevantand engaging. Once this is achieved then keeping cus-tomers loyal is a much easier process. Price will, ofcourse, always be a factor for someone who likes a betbut I firmly believe that good use of data can drive cus-tomer engagement and loyalty.

Ales Gornjec: We are definitely better informed, butjust collecting data is not enough. Big Data analyticsmight not give us direct answers to all operational ques-tions, but we can complement them with traditionalsystems to improve our decision-making process and toimprove player experience. If IT departments canimprove site performance and stability with Big Datasolutions, this will translate into increased player loyal-ty over time.

In the past, data storage was an issue, but now,with decreasing storage costs, other issues haveemerged, including how to determine relevancewithin large data volumes and how to use analyt-ics to create value from relevant data. At whatjuncture is the gaming industry in relation to theseissues and where should it be heading?

Jeremy Thompson Hill: We have always been facingthese issues, regardless of the operational efficienciesthat have emerged from cheaper and more effectivestorage. The key challenge is surfacing actionableinsights, making changes based on these insights andthen automating processes to ensure that data, or thecustomer, drives the business. The gaming industry hasalways been very data rich, I think we are all headingtowards more efficient use of this data, an increasedlevel of data throughout the customer lifecycle andautomation to ensure that people are focused on newways to improve the customer experience rather thanthe manual facilitation of campaigns and operationalprocedures.

Ales Gornjec: Collecting data is just the first step. Thisone requires some investment into infrastructure, but isnowadays is quite simple from an implementation per-spective. Creating value out of collected data is not triv-ial and requires usage of good analytical and visualiza-tion tools combined with people who understand thebusiness. They also must be able to use the data to makeproper conclusions that translate into improvementactions on the marketing or customer service sides.

Jerry Bowskill: Whilst relevance remains an ongoing

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challenge, the ability of the entire organisation to adopta data driven culture remains a challenge in manyorganisations.

Dealing with torrents of data in real-time is a chal-lenge when the desire is to react quickly enough todeal with data velocity in real-time, and makereactionary changes to the customer experience.How do you make this possible?

Ales Gornjec: There are appliances and on-demandsolutions on the market like HP Vertica that are capableof near real-time analytics. Many Facebook applica-tions providers already use them, and the data gatheringand analytical part would be quite similar also for gam-bling operators. Real-time changes to the customerexperience are, however, much more difficult for gam-bling operators because of the potential risk exposureand regulatory frameworks that demand additional cer-tification steps before new products can be deployed.Real-time gamification features and rewards duringgame play are typical tools that operators are now try-ing to deploy to prolong game play and improve playerexperience. They both depend on real-time information,but don’t require changes in the product. At the moment,

they are triggered by analytics derived from structuredtransaction data but could in the future be extended byadditional big-data analytics.

Jerry Bowskill: Whilst optimal real-time interaction isone potential goal of a Big Data strategy, this needs tobe founded upon a clear understanding of knowingexactly when, how and more crucially why an interac-tion is required. A deep understanding of what has hap-pened historically is important in defining a real-timestrategy. In this respect, predictive models are only asgood as the rules embedded in the underlying algorithmor machine learning methodology. For many organisa-tions, there is huge value in understanding playerbehaviour and what makes a successful or promotionstrategy without needing to develop a costly real-timesolution.

Jeremy Thompson Hill: This is a key priority forOpenBet. We recently released a feature which opensup a data stream of real-time transactional informationfor our customers. OpenBet and our customers are inthe process of building some interesting new featureson top of this data feed that we believe will create amuch more engaging customer experience.

The gambling industry at large can learn from the casualgaming sector here when it comes to creating an engag-ing real-time customer experience. The use of intrinsicmotivators in games like Clash of the Clans whichencourage users to return consistently is a really inter-esting example of how real time data can be used. Thegaming industry has spent too many years focusing onbuilding out the optimum customer lifecyle communica-tions process that is based around a set of industry stan-dard offers. This approach has clearly worked but thereare multiple dimensions to customer retention andbuilding innovative and interesting features that stimu-late customers to return and bet more frequently is a keybattle ground. For OpenBet, opening up a real-time datafeed to enable innovation is a big step forward. Platforms

01 One of the more profounddevelopments in the world of big datais the adoption of so-called datavisualisation. Unlike the specialisedbusiness intelligence technologies andunwieldy spreadsheets of yesterday,data visualization tools allow theaverage business person to viewinformation in an intuitive, graphicalway.

TECHNOLOGY - BIG DATAInteractive

A closed-loop management system

A closed-loop management system is a management system that promotes a controlled base of both preferredoutcomes and feedback from the system. Implementing this management system would mean that business activi-ties such as inventory levels, production schedules, and supply chain functions are guided not just by the sales teamof continued forecasts and orders, but rather includes feedback from ongoing operations from across many businessunits. This common style is different from the open loop management systems, which support zero feedback andhave all inputs driven off of set calculations designed around anticipated outcomes only.Companies who choose to adopt and implement the closed-loop management system facilitate the capacity to avoidmany shortfalls.The closed loop consists of five stages:● Discovery: This involves identifying internal tools, procedures, and ideas such as the mission and vision state-

ments, SWOT analysis, competition analysis, and core capabilities to articulate a strategy statement.● Modeling: The platform above is in turn modeled into objectives and initiatives, utilizing additional tools and pro-

cedures which should include flow charts and key performance indicators.● Deployment: The deployment stage will link the strategy to the physical operation and a third set of tools and

procedures such as quality management, process improvement, engineering, forecasting, planning and budgeting.● Monitoring: As deployment pushes forward, necessary monitoring continues to evaluate and understand data

and the business environment.● Optimisation: In the last stage, teams begin to evaluate strategies and optimize a key component which in turn

begins another loop.

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that do not offer this freedom to innovate are restrictingtheir customers’ ability to challenge and grow.

How do you draw together numeric data in tradi-tional databases, i.e. information created fromline-of-business applications, unstructured textdocuments, email, video, audio, financial transac-tions, etc.? How do you manage, merge, and gov-ern these different varieties of data and determinewhich are the most important statistics at yourfingertips?

Jerry Bowskill: Organisations who are committed todeveloping a data-driven culture often find that a twin-track approach is most effective. An enterprise-wide BIplatform allows a large number of users to access andreceive relevant and timely information. Equally, adata-mining and predictive analytics capability can actas the low-cost and very effective ‘path-finder’ tool toquickly discover the most relevant data types to deliverinsight to the business. Once identified, these new datasources can be built into the enterprise-wide BI toolset.For our B2B Real Money Gaming and Play4Fun socialproducts, the amount of data we can capture is limited.All player data is anonymous to meet the needs of the

regulator, so this constrains both the amount of data westore and has probably helped us focus on unlockingvalue by starting with solving simple questions aroundgame performance across multiple customers and chan-nels.

Ales Gornjec: Combining all this data into one commonformat is a very difficult to even impossible task.Structured data in existing databases will offer betterreporting and analytical possibilities than unstructureddata usually dumped into big data systems. We recom-mend analysing each system with the most appropriateapproach and to combine results into an analyticalsuperset that can then be used to drive the business for-ward.

Jeremy Thompson Hill: The variety of informationcreated and available is one of the key challenges in BigData analysis and one there is rarely a silver bullet fortackling. We don't underestimate both the effort andhuge downstream benefits of good data cleansing. Withthe plethora of data sources at the disposal of our opera-tors, quick exploratory data analysis in traditional toolssuch as R (and even SQL) can give a good initial indica-tion of the potential value of a data source and allowprioritisation of which seams to mine to first.

Data flows can be highly inconsistent with periodicpeaks and daily, seasonal, and event-triggeredoverloads – how do you create an infrastructure tomeet these structured and unstructured chal-lenges?

Jeremy Thompson Hill: The gaming industry hasextreme peaks of both user activity – such as justbefore the football on a Saturday; those final ten min-utes of in-play bets; the Grand National – and offlinetransactional activity including settlement of bets on amajor event. There are also frequent permanent shifts indata volumes from emerging markets and industryinnovation. Event processing infrastructure needs to beboth horizontally scalable to handle the messagethrough-put and also needs to support a variety of con-sumers. Some consumers will be tuned and scaled tohandle the peaks in real-time to interact with customerswhilst they're on the site. Whilst other consumers maybe able to lag behind during the peaks and catch up dur-ing the quieter times. To support the volatility and vari-ety of use cases we designed our messaging platform ontop of a distributed transaction log. The added benefit ofthis approach is that it enables us to replay previousevents, tweak algorithms and see if that change betteridentifies patterns in the data.

Jerry Bowskill: Simple – always build a hardware andsoftware infrastructure that is flexible and scalable. Thevirtualization of servers and being able to use a mix ofpublic and private clouds, based on the types of prod-ucts and jurisdictions being served, allows solutions tobe scaled cost-effectively. The only certainty in any BigData strategy is that your data volume will grow (andprobably at a faster rate than you originally envisaged).

Ales Gornjec: Every system has to be scaled for peaks.If these are extreme this becomes challenging and thereare several strategies possible to address these issues. Incase of Big Data analytics, that is not a mission criticalactivity that has direct impact on revenues, the bestapproach would be to filter our data sources based ontheir priority in case of potential overloads.

How do you link, match, cleanse, and transformdata across systems, before making the connec-tions, correlating the relationships, hierarchies,and multiple data linkages needed to keep thedata under control?

Every system has to be scaledfor peaks. If these are extremethis becomes challenging and

there are several strategiespossible to address these issues.

TECHNOLOGY - BIG DATAInteractive

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How will Big Data, which is having such a profoundeffect on the scale and productivity of major busi-nesses, shape and contribute to the future suc-cess of the gaming sector, both land-based andonline?

Many businesses across different industries arebeginning to understand that data is a productand, if managed properly, can significantly grow abusiness both in terms of efficiencies and rev-enue.

The gaming sector is no different and generateshuge volumes of data. This is increasing at a rapidrate due to the increasing number of channels,products, customers and mobile devices. In recentyears, only high level data was evaluated andacted on, while there was very little technologyavailable to store and analyse data in an effectiveand timely manner and real-time was a mere pipedream. Big Data technologies, however havechanged, and are increasingly changing, the waymany suppliers and operators are able to createmore intelligent processes that can react in real-time in order to shape and navigate the playerexperience in the best way possible.

In preparation for this shift in technology and oneof the most challenging, competitive and com-pelling periods in our industry Playtech hasdeveloped the latest, cutting-edge BusinessIntelligence Technology (BIT). By combining thepower of Playtech’s existing IMS platform and ourfully automated BIT software we are able to lowerplayer churn, extend lifetime value and overallrevenue as well as personalising and significantlyenhancing a customer’s unique gaming experi-ence.

BIT offers the following elements:

The BI Platform – Complete operational overviewEliminates the need to build an expensive,resource-consuming system; serves day-to-dayoperations and high-level management decisions;and allows licensees to compare key metricsagainst competitors.

Data Driven Marketing Tools – The power of personalisationAutomated data driven marketing tools that inte-grate with, and feedback to Playtech’s core plat-form IMS. These tools enhance player experience,lifetime value and licensee revenues by offeringusers a unique personalised offering includinginstant game recommendations, promotions andbonuses, and are based on statistical playerbehaviour analysis and segmentation.

Playtech Analytics – Real-time decision makingReal-time tracking and reporting to maximiseplayer value and brand profitability; instantlyidentify best-performing tools, products, marketsand promotions; rapidly optimise gaming client

In preparation for this shift intechnology and one of the most

challenging, competitive andcompelling periods in our

industry Playtech has developedthe latest, cutting-edge Business

Intelligence Technology (BIT).

and web pages and stay on top of trends andchanges in player behaviour.

Playtech Optimiser - Every click has a valueAutomated, real-time, personalisation and opti-misation engine; focus on player engagement andretention; eliminate guess work and improvecampaign performance – instant marketing suc-cess; measure player engagement with each pageand in-page component.

Smart Installer – Superior acquisition and retention toolPersonalised pre-registration marketing; real-time player re-activation; tailor campaigns to spe-cific user groups.

What actions should operators take to adopt a BigData approach to storing, managing, correlatingand governing their customer data?

Data management can be a highly complex mat-ter, while building big data platforms that are usedto maximise player experience is even harder.Today, rapid data flow and the sheer volume ofdata make it difficult to organize, map and under-stand.

Operators considering their big data optionsshould consult experts from other industries andfollow best practice. Cloud services should also beconsidered particularly because of the flexibilitythey offer during the early stages of deployment.But no matter how much data you gather, in orderto make the best use of it you need a team of datascientists who know to mine, build and extractinformation.

TECHNOLOGY - BIG DATAInteractive

Playtech has announced the hiring of600 additional staff as part of itsOmni-channel technology rollout.Playtech’s use of Big Data tomanage the increase in customerdata as part of this solution is centralto its success.

Optimising thefuture of Big Data

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Ales Gornjec: The first, and very important, step wouldbe to implement a true 360 degree view of each player.This means deploying a single wallet solution or linkingtheir accounts and all activity data behind them within adata warehouse or Big Data system. Once this is done,all players activity and all operators’ communicationwith them (email, chat, phone calls) should be storedthere. Initially, only part of the data would be struc-tured, and some of it only available to No SQL typesearch queries.

Jerry Bowskill: A path-finding tool can act as a greatway to undertake this type of data discovery at relative-ly low cost, compared to building this into an enterprisedata warehouse. These tools also allow for explorationof different tactics to cleanse, transform and enrichsource data.

Jeremy Thompson Hill: Most of the data sources willcontain a unique identifier or digital fingerprint that canuniquely identify the resource. We also use systemsthat make predictive matching based on scoring a num-ber of customer credentials.

Who is currently using Big Data in the gamingindustry right now? How are they using it and howsuccessful have these attempts been to really fullyharvest the benefits of Big Data?

Jerry Bowskill: All major players in the gaming indus-try have a mature data strategy. Many of our RealMoney Gaming customers across landbased andInteractive have data warehousing, with internet opera-tors taking the first steps over 5 years ago. Howeverthere are large variations in levels of expenditure andthe effectiveness of these strategies. What is clear is thatthere isn’t always a direct correlation between levels ofexpenditure and effectiveness!

Ales Gornjec: We see more and more operators andvendors using Splunk to help them collect all IT-relatedlogs in a central BigData repository. Some of them are

publicly listed underhttps://www.splunk.com/en_us/customers.html.

Also Tableau, that is in our opinion one of the best datavisualization tools has already a lot of clients in ourindustry http://www.tableau.com/learn/stories.

Big Data has been credited in being able to affectthe following: a 360 degree customer view; under-standing markets; finding new markets; person-alised website experiences; improved services; co-creation and innovation; reduced risk/fraud; bet-ter organised companies; and a better under-standing of the competition. Do you agree that BigData can achieve all these things, and what are thepriorities for gaming businesses?

Jeremy Thompson Hill: Yes, I agree that good use ofdata can help to answer these questions and manymore. What data allows you to do is get to know andunderstand your customer. The customer expects anddemands a more personalized experience, targeted andtailored communications. Data allows you to build thatinsight and knowledge.

The priority with data will always be the customer,understanding them and keeping them happy so theyremain a customer.

Ales Gornjec: BigData can help in all above mentioned

areas, but many of them can also be solved with tradi-tional structured data gathering and data warehouse-based analytics. A single wallet that processes all playertransactions is a very important element of every mod-ern gaming platform, and in many aspects behave like aBigData system.

Jerry Bowskill: These are all areas where a Big Datastrategy has the potential to deliver significant value.The challenge for any organisation is to have a clearview on where it believes is the greatest opportunity,and to focus on effectively delivering the insight toachieve this. Scientific Games develops industry-lead-ing game titles and products across a range of marketsand channels. Ensuring that we continue to developgreat games and products which offer the best customerexperience is one area where our investment in Big Datahelps us achieve this.

How do you protect your systems from hackingand data breaches, and how do you make surethat the data you gather does not break privacylaws?

Jerry Bowskill: The infrastructure we use to supportour data-warehousing is completely private and ouroperational and security teams, along with trustedthird-party hosting partners, work within the regulatoryand industry best practise security standards acrossphysical, logical, network and hardware levels.Similarly, the data we store is managed based on thejurisdictions and products being served; within RealMoney Gaming, all player data is anonymous and simi-larly for Social Gaming, the platform holder (Facebook,Apple, Android etc) have the lionshare of the personalidentifiable information. This greatly limits the sensitiv-ity of the data we manage.

Jeremy Thompson Hill: There are a number of ele-ments at play here. Firstly, systems employ the principalof least privilege. We also employ industry-best prac-tices around system encryption, hashing and anonymi-

A single wallet that processes allplayer transactions is a veryimportant element of every

modern gaming platform, and inmany aspects behave like a Big

Data system.

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sation, making sure that these systems are regularlyaudited. Furthermore, we work closely with each of ouroperators and the governing bodies in the jurisdictionsin which they operate to ensure privacy laws are notbreached.

Ales Gornjec: A player database is one of the mostvaluable assets of every gaming operator and has beenalways very jealously protected. Traditionally, thisinformation is stored in their on-premise installations,and is protected with strict security measures.

With adoption of white label solutions for online gam-ing, and with more and more service providers (pay-ments, affiliates, risk and fraud, KYC ) operating fromcloud-based solutions this information has spread overdifferent systems that are not under the control of oper-ators of IT departments. These clouds potentially intro-duce risks in terms of data protection and operatorsmust evaluate their maturity in terms of security verythoroughly.

How important is data visualisation in allowing theaverage business person to view information in anintuitive way, as opposed to expecting everyone tobecome Big Data scientists?

Ales Gornjec: Visualization is very important, and alsoquite challenging because it has to help answering busi-ness questions and can’t be generated in some standard-ised way. Nowadays business is changing very quickly,

and this means that also technology will not be thesame for very long time. We are collecting more andmore data, and that constant change and growth para-digm has to be the foundation of any Big Data solution.In order to understand business implications and iden-tify potential improvement actions, the results ofBigData analytics have to be presented in an intuitiveway. For this part big data scientists or data analyticsexperts are needed that understand both the business

side and underlying technology.

Jeremy Thompson Hill: This is key - giving teamsacross a business access to relevant data in a way thatthey can use it is imperative. Visualisation is one piece ofthe jigsaw, as is data mining and interrogation .Empowering individuals to do their job autonomouslyand giving them the access they need to relevant data is abig part of this.

By partnering with a business like Feature Space,OpenBet is making a clear statement that operators needpeople who are trained data scientists to be able to takesignificant steps forward around predictive analytics andthe implementation of algorithms that can drive a busi-ness forward. We wouldn't expect a marketer, for exam-ple, to be able to interpret data as efficiently as a datascientist with a PHD. That said, the marketer will need toable to visualise and act on the insights surfaced for theprocess to bear fruit.

Jerry Bowskill: There is a lot of hype about data visuali-sation at the moment and whilst the presentation ofinformation is important, the accuracy and specificity ofthe insight is more valuable. Data visualisation is just oneelement in being able to craft an effective story and set ofrecommendations based on information. Just becauseyour data presentation looks pretty, doesn’t necessarilymean that it is insightful. Sometimes a few words and asimple “what’s it worth” figure is clearer and more effec-tive than a colourful, animated 3D histogram.

01 According to IBM estimates, morethan 2.5 quintillion bytes of data aregenerated every day. The sheervolume, speed and variety of theinformation make it daunting. Data atone time was in well-defined formatsand fit easily into standard databases,but the advent of social media meantan explosion of unstructured data.Some of the data in the Big Databucket has been available for sometime, but companies had been unableto use it due to a combination ofstorage, processing power, analyticaland timeliness challenges. But thesechallenges have been solved orminimised.

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Does the collection and storage of increasingvolumes of data mean we are ultimately betterinformed? And how do you go about turningcustomer data into customer loyalty?

For all businesses today, becoming betterinformed about your customers is dependent onthe breadth, depth, and sources of customer dataas much as it is on the volume of that data.

Many “big data” systems today rely exclusivelyon collecting and analysing transaction-relateddata. For example, transactional feeds from gam-ing systems provide a rich profile of customergameplay. Food and beverage systems do thesame for purchases in bars, restaurants and enter-tainment venues, and loyalty systems provide yetanother rich source of data. But what about datarelated to customer activities in between each ofthese transactions?

In the physical casino environment, a focus ontransactional data only results in large gaps inunderstanding customer behaviour. It’s as if cus-tomers appear on the radar to transact, and thendisappear. What were they doing between trans-actions? Were they on the property? If so, wherewere they on the property? Did they visit anyfacilities and not spend money and if so, why not?

There’s an underlying assumption that the dataalready being collected by casinos will be suffi-cient to provide the basis for influencing cus-tomers to take a next transactional step. Butinstead of having to guess, wouldn’t it be better ifcasinos had real data clearly showing the connec-tions between non-transactional activities andtransactions?

Mobile data – if done correctly – is an informa-tion gold mine for land-based casinos because itcan provide a much richer profile of customers onthe property on a daily basis. With mobileengagement solutions as a part of a completeland-based casino experience, customers appear“on the radar” much more frequently, providingopportunities to influence their behaviour thatcan lead to a next transaction, and helping casinosdevelop a true 360-degree customer perspective.

The Tapcentive mobile engagement and gamifica-tion platform is one such system that deliverscustomer engagement to influence customerbehaviour and generate a rich set of associateddata for those activities between transactions.This new data creates an entirely new dimensionof mobile data in the “big data” story that comple-ments and increases the value of existing transac-tional data. Rather than make assumptions aboutwhat customers are doing when they’re not trans-acting, casino operators and marketers can dis-cover new connections and behaviours that linktransactions together and lead to improvementsto existing marketing and loyalty program invest-ments.

Big Data has been credited in being able to affectthe following: 360 degree customer view;Understanding markets; finding new markets;personalised website experiences; improved serv-ices; co-creation and innovation; reducedrisk/fraud; better organised companies; betterunderstanding of the competition. Do you agreethat Big Data can achieve all these things andwhat are the priorities for gaming businesses?

In addition to the 360-degree customer view that

Dave Wentker is CEO of Tapcentive, a mobile marketingplatform that is transforming how businesses drive foottraffic and engage their customers by connecting thedigital and physical worlds. Bolstered by more than 25years of experience leading and developing innovative,technology solutions for global brands, Wentkercofounded Tapcentive in 2013 after recognising the needfor more interactive and exciting mobile application toolsfor in-location marketers. Prior to Tapcentive, held seniorleadership titles at Visa, where he led an in-house mobileproduct development team as the company introducedand commercialised NFC payments globally.

Customers who pay in cash forsome or all of their transactions

provide a data trail that is helpfulbut limited. And those visitorswho visit a property and don’t

spend any money at all are evenharder to track and understand.

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can be achieved by adding mobile engagementdata to traditional known-customer transactionaldata, there’s an opportunity to learn more aboutcasino customers that are partially or completely“invisible” to the transactional systems.

Customers who pay in cash for some or all of theirtransactions provide a data trail that is helpful butlimited. And those visitors who visit a propertyand don’t spend any money at all are even harderto track and understand.

With mobile engagement data as part of the “bigdata” stream, casinos have an opportunity to col-lect data on both of these customer groups. Mobileengagement has the potential to “un-mask”anonymous players and visitors by offering theman additional set of offers and opportunities thatthey may be willing to consider during their on-property customer journey. Even if they do not acton offers presented to them, the presentation ofthose offers is the first step in data collection andunderstanding these customer groups.

Not everyone is going to opt into mobile engage-ment opportunities, but with mobile phones inevery pocket and purse, the opportunity to drivemobile engagement and collect mobile customerdata from anyone on-property is very real.

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CONNECTING THE DIGITAL ANDPHYSICAL WORLDS WITH DATA

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How do you employ and task individuals to imple-ment Big Data strategies? Do you outsource suchtasks or build in-house structures? Do gamingbusinesses have the right skills in place to developor customise big data solutions to fit their needs?

Jerry Bowskill: People are the key to any successful bigdata strategy. A significant investment in hardware,software and data will not deliver a guaranteed returnon investment if you have the wrong people using anddeveloping it. It’s a bit like an F1 team; you need a greatcar, great pit crew and mechanics however, if youhaven’t got the right driver, you aren’t going to win anyraces. Recognising and developing what skills you havein-house and then complementing these with externalsupport is an effective way to develop your strategy andretain the technical and commercial knowledge withinthe business.

Ales Gornjec: Initially gaming businesses can acquirethese skills externally, but we would recommend thatwith time they also hire some internal specialists thatwill hold all acquired knowledge together and help indi-vidual departments understand the results of Big Dataanalytics – and to make good decisions based on them.When selecting new platforms or any other middlewarethese should include Big Data functionality, or be at

Caesars (formerly Harrah’s) Entertainment haslong been a leader in the use of analytics, particularly in the area of customer loyalty, marketing, and service. Today, Caesars is augmenting these traditional analytics capabilitieswith some big data technologies and skills.

The primary objective of exploring and implementingbig data tools is to respond in real time for customermarketing and service. For example, the company hasdata about its customers from its Total Rewards loyaltyprogram, web clickstreams, and from real-time play inslot machines. It has traditionally used all those datasources to understand customers, but it has been diffi-cult to integrate and act on them in real time, while thecustomer is still playing at a slot machine or in the resort.

In order to pursue this objective, Caesars has acquiredboth Hadoop clusters and open-source and commercialanalytics software. It has also added some data scien-tists to its analytics group. There are other goals for thebig data capabilities as well. Caesars pays fanaticalattention— typically through human observation—toensuring that its most loyal customers don’t wait in lines.With video analytics on big data tools, it may be able toemploy more automated means for spotting serviceissues involving less frequent customers. Caesars is alsobeginning to analyse mobile data, and is experimentingwith targeted real-time offers to mobile devices.

Caesars Entertainment’s Total Rewards program hasmore than 45 million members. These members aretracked throughout their entire travel journey. From themoment they book until the moment they leave thehotel or casino, everything is tracked and analysed andused to provide supreme services to the members.

Due to the data-drive strategy, Caesars has been able to

trace 58 per cent of all costs spent to the customers in thecompany in 2004 to 85 per cent by 2014. A remarkableachievement and in addition it has given Caesars uniqueinsights into the behaviour of their customers. As JoshuaKanter, vice president of Total Rewards for CaesarsEntertainment, Las Vegas, claims: “Big Data is even moreimportant than a gaming license.”

With all the data, they are able to give loyal visitors verytargeted benefits, while preventing paying too muchmoney without results. The goal is to determine the rightprofile for the guests that arrive at a casino. Camerasrecord everyone’s action in the casinos and these cam-eras even record the choices the gamblers make. A play-er who is guessing, is more likely to lose money than adisciplined player. Caesars combines this data with datathat Caesars collects from customers while bookingstays, travel arrangements, dining, gaming and enjoyingother activities at the company’s properties.

All this information is stored, analysed and used for per-sonal benefits to the guest. Subsequently, a guestreceives a free dinner or hotel room to keep him or her

satisfied. On the other hand, the tracking software is alsoused to prevent any of the 75.000 employees being togenerous in giving away freebies.

WHY ANALYSE SO MUCH INFORMATION?Companies need to look at customers and lifetime valuerather than counting profits simply by product line orgeography, as mentioned by former Gary Loveman, CEOat Caesars Entertainment and Harvard Business Schoolprofessor with a doctorate in economics from MIT.

But there is more. Caesars Entertainment uses the sametype of analytical program to analyze the insuranceclaims of all its employees and their family members.Managers of Caesars are able to track many differentvariables about how medical services are used by theiremployees. This aggregated and anonymous data canhelp the organisation find differences in medical usage.One property for example, Harrah’s in Philadelphia,showed a higher usage the emergency room compared tothe overall organisations. Managers brought this to theattention of their employees and the rate dropped signif-icantly.

least Big Data-ready for potential future extensions.

Jeremy Thompson Hill: Operators across the gamingsector approach this in different ways, some outsource,some have hired big analytical teams but I think thereis an understanding across the board that this is a keycomponent of success. OpenBet takes the view that weneed to combine best of breed data scientists at a busi-ness like Feature Space with our own industry-leadingtechnologists to deliver the optimum service for ourcustomers. Data science is a very serious business andwe all need to be mindful of our own strengths andweaknesses. The creation of a strategic partnershipwith experts is the best way to crack this problem.

Should the primary outcome of a Big Data analy-sis be the automation of the decision-makingprocess, or to make human interaction with thedata more accessible?

Ales Gornjec: We would recommend automation ofthe decision-making process to be based on structureddata within their single wallet data store that is stillmore accurate and reliable. Big Data is still a new con-cept for many operators, and is a very valuable addi-tional source of information. We would recommendmore emphasis on good visualization to make it easily

understandable to humans, that will then prepare cam-paigns and improvement actions based on this informa-tion.

Jeremy Thompson Hill: The primary objective of BigData analysis for the gaming industry should always beto understand the customer better. Getting there requiresa combination of automation and human interaction. It isneither one nor the other, it has to be both. With theprogress that is being made around machine learning,who knows how far automation may go in the future. Inthe words of the pioneering computer scientist AlanTuring: "Machines take me by surprise with great fre-quency".

Jerry Bowskill: Both - some decisions can be automatedonce algorithms have been developed, refined andproven to be superior to an alternative approach. At thesame time, human intelligence is just as important andmore effective depending upon the decision being made.For example, you might use a predictive algorithm tochoose how a web page is presented or what promotionsand bonuses are marketed to an individual player. For alarge strategic investment decision, you will almost cer-tainly use data as a critical input factor. However, it willalso require a significant amount of human input tomake the final decision.

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CAESARS: BIG DATA IN PRACTICE