Bring your investment firm into today’s Data Economy

THE EXECUTIVE’S GUIDE TO ALTERNATIVE DATA ANALYTICS

3 Seeking alpha in data

4 From Baruch to quants

5 Theageofalternativedata

6 Movingtothecloud

7 Snowflakeforfinance

8 Whyalternativedataisbestatrest

9 Find alpha in the Data Sharing Economy

10 AboutSnowflake

The search for a profitable edge is as old as financial markets. And there is no bigger edge than valuable proprietary information, coupled with the capacity to act before it gets priced into the market.

In the late 1800s, resourceful financiers made fortunes with timely proprietary knowledge. Decades later, new generations of investing trailblazers took to scouring obscure company financials to identify profit-making opportunities. And in the 1980s, quantitative analysts deployed their new found computing power, mining trading statistics for recurring patterns to inform profitable trades.

Today, the industry seeks alpha in alternative data. Alternative data sets offer a myriad of new ways to find an edge: social media trends and transaction records, private company data and satellite imagery, public website scrapes and geolocation tags, and more. Investment managers spent $400 million on alternative data sets in 2017 and that figure is expected to more than quadruple by 2020.1

By 2020, the world will be generating 1.7Mb of data per person per second, or roughly 1.1 zettabytes each day.2 The sheer volume of data produced will necessitate a change in how businesses acquire, process, and use that data.

SEEKING ALPHA IN DATA

Identifying and acquiring data sets is only the beginning of an investment firm’s data strategy. The key will be the ability to integrate a broad range of custom data sets, to share them flexibly and to extract key insights in time. To that end, alternative data sets will reside in the cloud, where they can be guarded, accessed and shared much more quickly and cheaply than anywhere else.

With a fast-growing customer base in every industry, Snowflake already has a critical mass of alternative data source inventory. Much like seeking opportunities in developing markets, companies that take advantage of modern data sharing early on will get an edge as the Data Economy continues to grow and mature.

1AlternativeDatabytheNumbers:https://alternativedata.org/stats/2DataNeverSleeps6.0.Domo:https://www.domo.com/learn/data-never-sleeps-63SEC:https://www.adviserinfo.sec.gov/IAPD/content/ViewForm/crd_iapd_stream_pdf.aspx?ORG_PK=106661

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Alternative data is information gathered from non-traditional sources. In the asset management and trading industry, those can include social media posts, satellite images, credit card transactions, geolocation data, product reviews, and more. Alternative data can be structured, semi-structured, or unstructured, and the sheer volume of it may require companies to dedicate significant resources to store, process, and analyze it in order to extract valuable information.

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FROM BARUCH TO QUANTS

“In the stock market, one quickly learns how important it is to act swiftly,” wrote legendary financier Bernard Baruch. After getting early word of a decisive American victory in the War of 1898, Baruch made a midnight dash to Wall Street by ferry and private train to buy up U.S. stocks in London for his first big score.

Butnoonegetsthatluckyoftenenough.Later,inhisimmenselysuccessfulcareerasPresidentWoodrowWilson’stopeconomicadviser,Baruchbegantodistrust“insideinformation,”warningthatittendsto“paralyzeaman’sreasoningpowers.”Hecametopreferobjectivedata.Wilsonwouldnicknamehim“Dr.Facts.”

Decadeslater,anothersmartandambitiousyoungmanwouldfindhisedgeinthelittle-readcorporatefactsofMoody’sBankandFinanceManual.HisnameisWarrenBuffettand,morerecently,Buffett

hasfreelyacknowledgedthatopportunitiessuchas those he found in his early days are no longer around.Forexample,qualityinsurancecompaniessellingat1xearnings(Geico)arelonggone.Instead,thefinancialsofthemostobscurestocksareregularlyscouredbyalgorithm.

Inthe1980s,mathematiciansarmedwithnewlydevelopedcomputingpowerfoundadifferentedge,miningtradingstatisticsforrecurringpatterns.SuccesshasbeenverylucrativeforquantitativetrailblazerssuchasRenaissanceTechnologies,whichmanagesassetsworthnearly$131billionasofAugust2018.3 But with algorithmic trading

andmachinelearningmodelsnowaccountingforanestimated60to75percentoftotalU.S.tradingvolume,machinesareincreasinglytradingagainstmachines.AndthatmeansRenaissance’smanyimitatorswillfindithardertomakequantitativetradingpay.

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THE AGE OF ALTERNATIVE DATA

The largest financial institutions are already leading the charge into the alternative data space. J.P. Morgan Chase has set up its own think tank4 to delve into proprietary data. For example, it now knows that 4.5% from a sample of 39 million checking accounts received income from an online platform in the course of a year, and that ride hailing platforms such as Uber and Lyft have driven much of these online platforms’ recent growth despite steep declines in average driver incomes.5 Chase also knows that Columbus, Ohio, saw the strongest overall consumer spending growth in May 2018 among the 14 metropolitan areas tracked by its Local Consumer Commerce Index.6

Creditcardtransactionsareamongthemostpopularandsought-afteralternativedatasetsamonginvestmentfundsforthereal-timeinsights

theyprovideintomacroconsumerspendingtrends.ThechiefmarketintelligenceofficerforPoint72, thefamilyofficeofbillionairefundmanager StevenCohen,reportedlytoldaconferencein 2017thathisfirmscrutinizes80millioncardtransactionsdaily.7

Thissortofvolumeillustratesoneofthemainchallengesofworkingwithalternativedata:acquir-ingitisonlythebeginningofacomplexjourneythroughlegacyprocessessuchasdatatransfers,reportgeneration,distribution,andtheneedtocontinuallyrinseandrepeatwhenthenewdatabecomesavailable.

Withmorethan350mostlyproviders8 selling anevergrowingvolumeofdata,firmsmustalsoevaluatethevaluepropositionofeachprovider. Asthenumberofbuyersandsellersgrows,sodoesthedifficultyofmaintainingandintegratingmultipledatasets,feeds,andplatforms.

4TheJPMorganChase&Co.Institute:https://www.jpmorganchase.com/corporate/institute/institute.htm5JPMorganChase&Co.Institute,TheOnlinePlatformEconomyin2018:Drivers,Workers,Sellers,andLessors6JPMorganChase&Co.LocalConsumerCommerce,May20187FinancialTimes,HedgeFundsSeeaGoldRushinDataMining,August27,20188AlternativeDatabytheNumbers:https://alternativedata.org/stats/

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MOVING TO THE CLOUD

That quickly proved to be the case for MUFG Investor Services Group, a U.S. subsidiary of the top Japanese bank that provides accounting and administration services for pension, real estate and private equity funds. After entering the business via acquisitions

several years ago, MUFG Investor Services faced the formidable task of reconciling and upgrading legacy reporting systems.

“AsaCTO,thebiggestchallengeIfacedwasIhadmultipledatasourcescominginacrossmultipleassetclasses,feedingmultiplegeneralledgersystems,”MUFG’sChiefTechnologyOfficer,EricNorbergsaid.Whenclientswantedtocrunchtheirownhistoricaldata,MUFGInvestorServiceswouldprovideastaticreport,asmanyintheindustrystilldo,byemailorfiletransferprotocol(FTP).

MUFGInvestorServicespartneredwithSnowflaketobringitsledgersintotheonlycloud-builtdatawarehouse.Itwasthefirststepineliminatingdatasilosandcomplex,time-consuminglegacydatatransferprocesses.“Withintwoweeks,wewentfromcreatingstaticreportswithdatasprawltoanon-demandcapabilitytosliceanddiceclientinformation,”Norbergsaid.“Theeaseofadoptionwasjustamazing.”

MUFGInvestorServicesnowoffersitscustomersinstant and secure access to a wealth of their own historicaldata,alongwithvalue-addedanalysis.“Wenowhavetheabilitytomonetizeandcreateanewserviceofferinginthemarketplace,”Norbergsaid.“Webelievethataccesstoreal-timedataisakeydriverforusfromabusinessperspective.”

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9HarvardBusinessReviewAnalyticServices.AnInflectionPointfortheData-DrivenEnterprise.https://www.snowflake.com/resource/an-inflection-point-for-the-data-driven-enterprise/

SNOWFLAKE FOR FINANCIAL SERVICES

The financial industry identified achieving better insight into customer needs and expectations as its top goal in a recent report by Harvard Business Review Analytic Services (84 percent of respondents).9 Snowflake, the only data warehouse built for the cloud, is uniquely equipped to enable financial services to overcome legacy processes and organizational silos, with the most powerful, flexible, scalable and secure data warehouse for modern data analytics:

• Unify all your data. A cloud-built data warehouse is a centralized location for all your data, from all sources, structured and semi-structured, offering the centralized computing power to analyze and extract insights from the data.

• Achieve a single source of truth for all users. All workgroups can concurrently operate against the same data, with transactional integrity, including shared data with external data consumers.

• Easily onboard new data sources. Natively load any type of diverse data, without the need for preprocessing or transformations.

• Implement advanced data protection with ease. Data protection, processing failover, end-to-end data encryption and more are baked into Snowflake, on a HIPAA and SOC 2 Type 2, PCI DSS-compliant and FedRAMP Ready platform.

• Achieve ultimate security. For the highest level of security, choose Snowflake’s most secure product edition, Virtual Private Snowflake (VPS). VPS provides all the benefits of Snowflake, within a separate, dedicated pod.

“As a CTO, the biggest challenge I faced was

multiple data sources coming in across multiple asset

classes, feeding multiple general ledger systems.

[With Snowflake], within two weeks we went from

creating static reports with data sprawl to on-demand capability to slice and dice

client information.”

Eric Norberg

Chief Technology Officer, MUFG Investor Services Group

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WHY ALTERNATIVE DATA IS BEST AT REST

Legacy data transfers simply cannot match the speed at which industries generate alternative data, nor the pace at which investment firms must consume and process it as they seek to gain an edge over competitors. The traditional data trek from provider to repository, to staging area for transfer, and then those same steps in reverse on the receiving end, carries too high a risk to the security, integrity, and timeliness of the transmitted data.

AtSnowflake,we’vedevelopedasolutionwithin ourcloud-builtdatawarehousethateliminates thoserisks.WecallitSnowflakeDataSharing,asitallowsourcustomerstosharedatawitheachother, withoutmovingit.

Throughdatasharing,Snowflakecustomerscansecurelysharespecificdatabases,datatables,oreventablerows,whileretainingfullcontroloverwhohasaccesstothatdata,andforhowlong.SnowflakeData Sharing reduces or altogether eliminates the costsassociatedwithdatapackagingandtransfer.Moreimportantly,italsoeliminatestheneedtoquestiontimelinessasallpartieshaveaccesstothesame,livedata.

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FIND ALPHA IN THE DATA SHARING ECONOMY

The trouble with edges is that, inevitably, they erode. The trouble with alternative data sets for financial markets is that the most popular ones will eventually lose a lot of their value as markets absorb and price in their signals. But data grows bigger all the time, so there will be no shortage of alternatives. Companies that already actively participate in the data sharing economy will already have the connections and infrastructure in place to get their hands on those alternatives first.

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ABOUT SNOWFLAKE

©2019Snowflake.Allrightsreserved.

Snowflake’sclouddataplatformshattersbarriersthathavepreventedorganizationsofallsizesfromunleashingthetruevaluefromtheirdata.ThousandsofcustomersdeploySnowflaketoadvancetheirbusinessesbeyondwhatwasoncepossiblebyderivinginsightsfromtheirdatabyalltheirbusiness

users.Snowflakeequipsorganizationswithasingle,integratedplatformthatoffersthedatawarehousebuiltforthecloud;instant,secureandgovernedaccesstotheirnetworkofdata;andacorearchitecture

toenablemanytypesofdataworkloads,includingasingleplatformfordevelopingmoderndataapplications.Snowflake:Datawithoutlimits.Findoutmoreatsnowflake.com