Hello,Welcome to our presentation on Big Data Discovery. This presentation is intended for an audience with a mix of IT and Business users, at a conference or event. It provides an overview of Oracle’s Big Data Discovery solution. Please delete this slide before using the presentation.I’ll now give the presentation as if I were speaking to the audience.
Zatem wiemy już że warto używać BI Świat idzie do przodu i trzeba szukać nowych obszarów danych do zagospodarowania tak aby mieć tego asa w rękawie i być o krok do przodu przed konkurencją lub po prostu pójść o krok dalej w jakości analiz i spostrzeżeniach które nam te analizy dostarczają pewnie słyszeliście Państwo o Big Data, no to usłyszycie jeszcze raz tylko tym razem powiem jak tym rozwiązaniem możemy taki projekt sprawnie uruchomić ponieważ:- możemy to co będe pokazywał wykorzystać do wyznaczenia kierunku prjektu BIG Data- albo wykorzystać go jako projekt sam w sobieBezwątpienia firmy które korzystają z BI osiągają znacznie więcej niż firmy które nie wykorzystują rozwiązań analitycznych.Zatem jeżeli wszyscy korzystają z BI to jak uzyskać przewagę konkurencyją ?Odpowiedzi na to pytanie możemy szukać w nowych źródłach danych i troche innym podejściu które możemy wykorzytać do analiz, podejściu które może uzupełniać istniejącą infratrukturę BI lub może być wykorzystane jako podstawowe rozwiązanie analityczne w firmie. Sporo mówi się o tych nowych obszarach danych ostatnio. Chciałbym Państwu przybliżyć temat nowych możliwości analitycznych z wykorzytaniem podejścia „Information Discovery”.Są dwa podejścia do tego tematu:1) Wykorzystanie ID do tego aby rozpocząć duży projekt BIG Data i wskazać główne kierunki 2) Wykorzystać ID jako projekt BIG Data sam w sobie--------Top companies win on analytics. But now, data from new and very diverse sources are being added to the equation and the analytical environment must be in place to take advantage of that, to stay ahead of the competition. Business Analytics must expand. As you listen to today’s presentation I’d like you to link what you’re seeing and hearing about this new world of information, where that data is coming from and how it’s being used, with where in your organization this would apply. And I want you to do that because being first to use this rich and diverse new data will put you ahead of the competition.There are two ways to apply Discovery in the context of Big Data. First is to use Discovery to understand the data at hand and its value, and use the knowledge gained to design a Big Data project, make decisions on data sources and techniques (such as enrichment) to be used, as well as defining the value proposition to justify the investment. Second is Discovery on Big Data as a project in itself enabling end users to quickly and easily combine and explore a large variety of data. While these two applications are very valuable in different ways to different parts of your organization, the underlying key capabilities are shared and so for our agenda today, in the context of this diversity of data, and supported by several customer stories, we’ll look at what challenges might stand in your way, how to overcome these challenges and what you can do to get started.
Jest organizacja która ma swoich klientów i do tej pory wszystko ok, podejście zróbmy to jak zrobilismy rok temu i nagle czujemy ze sytuajca powoli wymka nam się spod kontroli czasem to co mamy nie da nam odpowiedzi i wtedy warto zerknąc dalej----------Let’s start with a story about the transformative power of using Big Data Discovery in decision making. Our customer is a consumer goods company, who one day saw a sudden spike in sales for a cheese product. Of course the first question that they asked was: why? They pulled their reports pricing, promotions, advertising, none of which had had recent changes. They even researched competitive pricing but nothing unusual had happened. What was going on?They started brainstorming on where else to look and someone came up with the idea of looking at posts from forum sites that are frequented by upscale moms in urban centers. They looked at Social Media data and discovered that there was this explosion of comments about the fact that the CG company had started to use, as voice-over talent for that product, a man with an inspiring hard-luck story, and customers picked up on that story and shared it widely. In fact, customers went out an bought the product in droves because everyone just loves a good story with a happy ending.That discovery gave them ideas about how they might be able to surface more things they were doing as a company that relate to the communities in which they do business; because apparently that has a positive impact on sales. Not just marketing directly to a product, but what they were doing that touched their brand. And so they figured out how to track the sentiment of certain communities, and match that to their product sales to those communities. They built those new metrics back into their regular reports, to help them better run their business. They used discovery to figure out the right question to ask, which was: what can the sentiment in forum posts tell me about cheese sales. [Build]And then they made a leap upward and decided that a question that should be asked on an ongoing basis to their BI reports is: What can sentiment tell me about sales?
Przed: widzieli że coś się dzieje, odnotowali spadek – ok, ale raporty (lub HD) które mieli nie dawała odpowiedzi na pytanie dlaczego ? I co mamy zrobić? Dlatego tak było że raporty pokazywały jak działać bazując na wcześniejszych doświadczeniach!!!!!!A nie zawsze to się sprawdzi. Prawdziwa przyczyna nie była wychwycona w klasycznym raporcie.Nie deprecjonując oczywiście znaczenia tego raportu.Eksploruj: utworzyli nowe metryki, nowe best pracitesLet’s analyze what actually happened. First they saw something different – that spike in sales. They saw it in their BI reports, in the analytics they use to help run the business.Of course they wanted to find out why the spike occurred, but looking at all the BI reports did not uncover the answer. It wasn’t because the reports were wrong. It’s just that the reports were based on the best understanding of how to run the business before this event. But this was a change. The true cause of the spike wasn’t captured in any of the standard reports. So they turned to the analytics that help how to change the business. That’s where they used discovery. And here’s where they did something very different than BI. [Build]They went straight to the data, in particular the social media data, without attempting to model it first. Even more, they went straight to the data with no real pre-determined questions to ask of it. [Build]They took a new data source and combined it in a new way with existing data; [Build]and by navigating, searching and analyzing the data, discovered the root cause of the increase in sales.This discovery on its own was very useful; it explained an otherwise unexplainable change. But then they did something even better. They took that discovery and created a new metric to track, to improve those reports. They took that standalone discovery and turned it into an institutional capability, a new best practice. BI and Discovery together got them to this new best practice faster.
Prowadzenie biznesu: analizujemy bazując na tym co już znamyZmień biznes: analizujemy bazując na tym czego przewrotnie nie wiemy, czego nie przewidzieliśmy, odkrywamyBig data faktycznie jest obszarem który może dostarczyć nam dużo nowego, ale trzeba mieć oczywiście do tego odpowiednie narządkoDwa podejścia razem ok------------This example really illustrates the core topic today: With Big Data, BI and Discovery working together is the fastest way to elevate your business to a new level of best practice.In earlier presentations today you’ve heard that Big Data at Work means that you can take new data sources and combine them together in new ways that you could never even consider before, to solve problems that had previously been unsolvable, and create new value for your organization.You’ve also heard how to get Big Data into the Enterprise: by integrating it into your Enterprise Analytical Environment. And that means both the analytical environment that helps you run your business and the one that helps you change your business. When you integrate Big Data into both aspects of your Enterprise Analytical Environment, and they seamlessly work together, you can, faster than any of your rivals, elevate your business to a new level of best practice.[build]In practical terms, for our time here today, we’ll focus on Business Analytics, with BI as the analytics that help you run your business and Discovery as the analytics that help you change your business. So the key message - and this is the one thing to remember from this session, is that BI and Discovery working together is the fastest way to elevate your business to a new level of best practice.Let’s look at how and where does Big Data have an impact on your organization’s analytical environment?
Jasną sprawą jest to że analityka pomaga firmą i niektóre analizy pokazują nawet 10x zwrot inwestycji do tej pory BI nabierał rozmachu korzystając z HD (modelu) świat się zmienia i pytanie czy możemy zamodelować „wszystko”It’s beyond dispute that today, top companies win with data and analytics. Nucleus Research found a 10x return on analytic investments. Up to now, Business Intelligence has excelled at using data modeled in traditional Enterprise data sources, and has given companies the edge.[build]But the world is changing. More data is being generated from people, from things, from activities and processes; more data and a bigger variety of data.[build]For those companies with an appetite for analytics, this variety of data is mouth-watering. They know it’s how to beat out competitors. So how do companies want to use this data?
First, as always, the business wants to improve processes. For example marketing managers have long known they could make more confident spend decisions if only they were able to enrich the usual reports on campaign performance with survey results and social media comments.[build]Second, the business wants to respond faster when something unexpected occurs. For instance, a quality manager might need to quickly mash up data from call center logs, quality applications and supply chain data to figure out why warranty issues spiked.[build]And third, the business sees all those new combinations of data as an opportunity to innovate. For example a product manager might want to combine data from sensors, weather and customers to invent a new serviceThese three objectives have not changed. They are how you run your business today with data carefully selected and modeled in traditional analytical environments, mostly relational. What has changed is that companies now want to look beyond the warehouse to new and diverse data sources, and use those diverse data in ways that will change and improve the business.
- Dlaczego nie korzystamy z tych wielu źródeł ?So why not use all the data? [build]The truth is that it takes too long to add new data sources to the warehouse: 31 days on average. [build]There’s already too much of it: 6 out of 10 companies already have more data than they can use effectively, [build]and the world’s ability to produce data has outstripped most organizations’ ability to use it.: only 12% of executives feel they understand the impact data will have on their organization. Plus, not all the data will matter, yet knowing in advance which data or combinations of data will be useful, is difficult.To continue to allow top companies to continue winning with data and analytics, Business Analytics must expand beyond the traditional environment you use to run your business to include a way to easily use and explore this big variety of data. Sources:“Mashup Your Way to Better BI”, Aberdeen Group Analyst Insight “Analytics: The New Path to Value”, MIT Sloan Management Review The Economist Intelligence Unit
Wraz z rosnącą ilością danych często słyszymy do klientów że:-- wiele źródeł ale jak to przygotować-- jak tego używać -- jak rozprzestrzenić to na wielu użytkowników (np.bezpieczeństwo)But when we talk to customers, easy exploration on big variety is far from reality…[build]What we hear is that first, customer want access to all of this diverse data. Our marketing manager in the earlier example might want to bring in survey and social media content – very text heavy data – and add it to structured data from CRM applications. Our quality manager might need to combine more than a dozen diverse sources like call center logs, claims verbatim and spreadsheets from OEMs, to get to the bottom of a warranty problem. And our innovators might want to combine machine data from sensors with geological data from a 3rd party and sentiment from customer text.But modeling it all into the current analytical environment is too complex.[build]Next, is how customers want to interact with the data to see what it can tell themThe business might sayI can’t really tell you exactly what report I want, but I’ll know it when I see itIf I ask for this query, is the result set going to miss something important? What if the query is too broad, will I have to do a lot of manual filtering?While IT might say:I have 25 different query and report requests from 5 different departments. How do I prioritize?As far as user experience is concerned, reporting and drill-downs work as designed for their purpose, but don’t give true unfettered exploration.[build]And third, it’s not easy for either Business Users or IT. From the Business we hear comments like:Why do I have to wait a month for this project?I buy this report from Nielsen every month, why can’t I just add it to my sales and marketing data?And from IT we hear:I can’t give access to sales data to everyone because of SEC regulationsHow can I make this available to all 5000 users globally?The lack of a platform which suits the needs of both business and IT is a challengeBut what if you could combine a big variety of data, make it readily available for exploration and make that whole process easy for both Business Users and IT?
And this is especially true when unstructured data is in play. And it almost always will be. For example[Build]Our reports might alert us to a drop in year over year sales. [Build]Querying data sources like a data warehouse or CRM application can tell us even more about our related marketing spend or customers. But the key to understand the “WHY” of the drop in sales can lie elsewhere, often in non traditional sources outside the warehouse.[Build]Maybe we need to try to understand what customers are saying in our online review system with data that is mainly text or dialogue based and not typically integrated in a data warehouse. Maybe we want to understand the morale of our sales team so mining their survey comments might shed some light. Maybe competitors are spreading rumors on their blogs that have hurt sales, what about social media? Has overall consumer sentiment for our products and services declined?Data discovery is about harnessing the power of all the data sources at our fingertips. Not just data in the usual places but information in a wide variety of dialogue based and unstructured internal and external content. Only then can we uncover truly new insights and make decisions that will innovate the business
- Rozwiązaniem na te bolączki może być EIDWell, you can. [build]Oracle Endeca Information Discovery combines a big variety of data by indexing it, not modeling it, so you start right away with the data as is. [build]The consumer-friendly UI provides a unique discovery experience so users explore diverse data with zero training. [build]Plus, it’s an enterprise platform for IT to provide a solution for Business Users, while allowing business users to help themselves and go that last mile on their own, when they need or want to, making any response to new or changing requests fast and easy.
Let’s have a closer look at how Endeca provides this easy exploration on a big variety of data. First is the ability to combine a big variety of data without complicated modeling. This means the users start right away with the data as it is.It means business users can upload their own spreadsheets, let the engine index everything and then look at new data combinations right away, without having to clean up or model the data beforehand. It means creating and modifying personal discovery applications on the fly with your own personal data, blending diverse content from multiple sources like Oracle BI Server, JSON and Excel, combined with trusted connection to enterprise data, without the need for a formal IT project. It means pulling in text-heavy data sources from both inside and outside the organization, extracting not just the words, but also the context, and blending it with existing data.And speaking of easily combining new data sources, Endeca connects to Oracle BI Server as a data source to be used to create new discovery applications on known subject areas, with full confidence that the data and definitions line up exactly between BI and Discovery environmentsOur marketing manager from the earlier example can now start right away analyzing marketing campaign results, with all the data he wants to combine.
Diverse DataHere we can see records from 4 different very different sources systems combined together in the Endeca Server engine. In this example all the sources are considered ‘structured’. We are not conforming the data to fit a target schema. We simply stack records in their native format on top of each other. This way we can get data into the hands of the end-users extremely rapidly, without having to wait for a ‘perfect’ consolidated data model. Similarly, as additional source systems or attributes are indexed over time we can easily accommodate the new or changed data without needing any target schema redesign.Global Attributes Information sources will also contain common or ‘global’ attributes across many records and source systems (e.g. common part numbers, employees, regions and territories, suppliers, etc). Whenever a record contains an attribute that is shared with another record or system a relationship is identified. Records in the Endeca Server Engine are related whenever a common attribute exists. This means that relationships exist at the attribute level – not at the table or record level, but at the attribute level which is the finest granularity possible. Unique AttributesEvery source system will contain its own unique set of attributes. The Endeca Server Engine summarizes and allows navigation on all of these unique attributes without compromising their original structure to fit into a neat box. Jagged DataThe Endeca Server Engine allows information to stay naturally ‘jagged’. Every record may have its own set of unique attributes; even if the records are from the same source system. Accommodating jagged data eliminates issues caused by data sparseness. For example, the Endeca Server Engine eliminates the need to create expensive ‘dummy’ fields that are not relevant to the record on which they are attached, for instance flavor makes sense for a bottled drink or a package of popcorn, but doesn’t for trash bags).This means that the end-users interacting with the Endeca application can use any starting point to enter the data, even a basic search term. The user is not forced down a pre-defined analysis path as with traditional BI ‘drill down’ technologies and as a result they constantly discover new insights in the data as they interact with it. Not only can they drill down, they can drill anywhere and pivot across any type of information in any source system wherever a relationship exists across common attributes or even common values in the data. This offers far more flexibility than relationships pre-defined in a specific data model. To better understand how this works, let’s see how Endeca Server looks at data…
The common pathways, attributes, facets, values through the data are self-exposed as data is navigated and searched. Readily discoverable as the application is used.
Endeca Information Discovery provides agile data discovery capabilities for both business and IT on a robust and managed platform to inspire confidence in decisions and drive innovation across the enterprise.With Oracle Endeca Information Discovery, organizations can..[Build1]Quickly combine diverse content from any source of any type, structured or unstructured, with minimal knowledge of requirements and data up front, creating a dynamic and productive relationship between business and IT[Build2]Explore and combine trusted enterprise data that leverages existing analytic assets with personal and external sources on a powerful managed infrastructure to inspire confidence in decisions[Build3]And expose patterns in unstructured data and text heavy content, including themes, entities and sentiment, for broader perspectives and deeper insights to drive innovation across the enterprise
Let me give you a customer example on the benefits of combining a big variety of data.Land O’Lakes is legendary for its dairy products being at the center of household kitchen tables in North America for nearly a century. Yet Land O’Lakes is also a diversified company and owns WinField Solutions, which is one of the largest wholesale distributors of agricultural seed and crop protection products in the United States. It tests numerous seed hybrids in 200 different farm test plots located around the country, to understand how each variety works in different soil types and different weather. Those tests generate a lot of data. WinField is also able to collect data from farmers in the U.S. about what they’ve planted and their crop yields. When combining publicly available data with WinField’s own sales data and its other data sources, Land O’Lakes saw an opportunity to create a software solution that will help growers buy the best seeds, and help WinField and their farm cooperative partners sell them. Land O’Lakes built its analytics solution using Oracle’s Endeca Information Discovery. BUILDLand O’Lakes said implementing its system using Endeca cut two years and $3 million off the software project.In addition it helped optimize sales cycles to help increase sales performance. Volumes and profits are both up. Turns out combining data on soil type with a lot of other data can indeed help Winfield and its partners sell more seeds.
ProblemDelphi Electronics and Safety , a division of leading global auto parts supplier Delphi Automotive, had a data analysis challenge: they needed to determine if the performance of certain parts were meeting contractual levels and if improvements were neededSeems straightforward, right? Not so much. Delphi receives huge amounts of warranty data generated by its customer. Every month, the automotive manufacturers (OEMs) delivers performance data including verbatim text descriptions of issues related to its 340,000 active parts in service in millions of vehicles worldwide That’s data from over a dozen different OEM systems, each with its own distinct format, as well as data from Delphi’s own parts databases, manufacturing systems, and industry data. Additionally, Delphi had to adhere to strict time guidelines to provide responses to performance issues with parts—including a complete analysis to support their response—or be financially penalized. The real challenge was the diversity of the issues. Delphi’s warranty engineers needed to quickly combine and explore a variety of customer data sets based on the issue under investigation. Warranty Engineers were spending more time manipulating data than getting answers from it. CLICKSolutionIn the first month alone, engineers discovered the root cause of three field performance issues that could have cost them lots of money. Since then, Endeca has paid for itself many times overBut more importantly, their warranty analysts could now spend more time investigating issues and less time manipulating data. The shift in this work was so great that they had a new idea. They realized they could have a warranty strategy for each of 20,000 individual parts they manufacture and ship at a rate of 7 million pieces per month– an unprecedented innovation.This is the power of Discovery with easy exploration on big variety – creating discoveries in a change-the-business environment which are then injected into run-the-business processes and applications to perform at a higher level.
Let me give you a customer example on the benefits of viral adoption of a consumer-style discovery application. Our customer is a global manufacturer of home appliances. Their growth was largely through acquisitions, introducing additional complexity. Traditional cost reduction techniques fell short with that much complexity. So they began an initiative to radically reduce complexity and improve global consistencies at the component level, to reduce costs. However, they had challenges:• Limited visibility to vendor activity between regions, leading to disjointed negotiations and sub optimized supply agreements • Multiple ERP systems leading to multiple vendor records within and between regions• Multiple organizations maintaining vendor and item master data differently leading to inconsistencies in data maintenance and control• Thousands of engineers and specialists globally needing to use the applicationThey unified roughly a dozen diverse operational systems and enterprise apps without having to perfect the data. And because the application is so easy to use, global adoption has been extremely fast and easy. The app is now being used by thousands of engineers and specialists. [Build]It allows the teams to buy only the parts they need across all product lines, worldwide. They‘re targeting a 12% reduction on a multi-billion dollar annual spend. Turns out that a zero training application was key in this cost reduction effort.
Next is the ability to provide a consumer-style discovery experience. This means users explore the data right away with no training. Much like browsing your favorite online store, any user can ask any question, simply by typing in a few words. While behind the scenes, the engine is instantly organizing the data sets; Users get that consumer-style exploration with advanced keyword search, “did you mean…” suggestions and more, along with analytics that go hand in hand with search and navigation.Users also choose from pre-populated page templates to start exploring the data as soon as it’s loaded. Then drag and drop components from a library, like charts and tables and maps, to quickly rearrange dashboards to suit specific needs. They can tag text and extract terms for new insights. They can share applications they build, or use someone else’s or use an IT-built application.And now our quality manager can build her own personal discovery app with the data she needs, share it with colleagues and let them explore the application without having to teach them how to use it.
And third is the ability to empowers business users with self-service discovery while maintaining IT stewardship.When business users can help themselves within the framework of an Enterprise platform, you get quick and easy responses to changing requirements.It means a centrally maintained, IT supported data discovery platform that is always available and scales with data and usage patterns. At the same time, it means business users create their own discovery apps without calling IT.It means users draw on all the IT certified, gold standard data models and business definitions throughout the enterprise. At the same time, they upload their own spreadsheets of personalized data. And mash the two together with an easy-to-use wizard-driven interface. It provides the channel for sharing discoveries from one to many groups and scales to the Enterprise.It values speed, and shuns haste, providing fast prototyping, instant dashboards, in the moment exploration, and it does not cut corners where IT governance, security and scalability are concerned. Now our product manager can quickly iterate prototypes and research potentially valuable combinations of data. He can do this with virtually no IT overhead, yet with IT as a full-fledged partner, balancing the business’ need for independence with IT governance.
Let’s take a moment to further explore why self-service is critical to an efficient IT strategy[Build1]Business Intelligence has ‘cracked the code’ for certain types of analytic apps that provide answers to anticipate or known questions (give example e.g. call center efficiency). These analytics have a pre-defined long-term ROI associated, have a fairly wide audience of varying roles across the enterprise and can be planned and deployed with a solid knowledge of business requirements already defined up frontThese projects are typically IT driven from the bottom upWhat we mean by bottom up is that we understand the source systems of record, how to connect to them and understand the data models required to support the necessary analytics. We also understand the robust infrastructure needed to support the expected heavy usage across the enterprise and we know what dashboards and reports will be needed by which roles across the organization. Technical IT resources, skilled at data warehousing and business intelligence can roll out these solution to the business in a relatively predictable time frame, from the bottom up.[Build]..but there are certain types of projects that don't fit with with an IT driven bottom up process. These are typically unanticipated questions that have an immediate value and are need answers urgently. Users that need answers to these questions are individuals or smaller groups working together. Requirements are ‘fuzzy’ or ill defined and are changing in the moment. Projects cannot be easily planned.The data sources that help answer these unanticipated questions are not well known, not integrated and a combination of both internal data silos and external non-enterprise content. There is typically a huge backlog of data integration requests that IT never seems to be able to get to. Business users have more recently turned to data discovery tools to help users answer these questions for themselves. They typically do this right on their desktops or smaller scale hardware, performing their own basic data mash-ups since they have the best sense of their fluid requirements along with the tribal knowledge of where the important data resides.So what we want is to allow the business users part of the solution themselves. Empower them to drive answer unanticipated questions in a tops-down approach where self-service enables them to go that last mile themselves and not wait on IT.There are significant challenges with this approach however, there are limitations with typical self -service data discovery tools because a) users are typically doing this on their desktop/laptops where compute resources are scarce and more importantly (b) business users data integration skills are limited – basic integration is one thing but what about when they need to tap into some those complex enterprise systems that has taken IT years to integrate with sophisticated data integration technology? [Build]What organizations need, to realize full analytics potential, is to bring these worlds together - let IT focus on what they are good at by enabling self-sufficiency but the also letting the business access to their world - let them leverage gold standard data models already built downstream from complex data integration projects, let them access known metadata definitions by providing secure and trusted connections to the right sets of data in the right places so users can have full confidence in their decisions and leverage solid enterprise grade infrastructure with full IT support, high availability already adopted as an enterprise standard.To bridge the last mile and empower the business user, data discovery must be brought online as a true enterprise level capability - not as a departmental after thought. Users must be given an agile self-service platform on which to answer new questions themselves but do this in a managed and trusted environment that inspires decision confidence.
Voice of Consumer self-service demo
The world of new and diverse data offers even more opportunity to win faster on analytics, by finding which data matter, and using that data to improve processes, respond to change, and innovate. To meet that challenge, Business Analytics must expand.[BUILD]Endeca Information Discovery enables that easy exploration on a big variety of data. As odd as it may sound, our customers - and this is both the business users and IT together - tell us they feel a sense of amazement, when they realize the doors that Discovery opens; especially when they realize where those open doors will lead in ROI, in money saved, in faster time to results.[BUILD]They can be more agile in their investigations; [BUILD]they can back up their decisions with more confidence; [BUILD]they can prototype and invent new products and services faster than before. Not just because they can access new data, when they need it, but because Endeca makes it easy for both the business and IT, as partners in analytics, to combine the data, explore it and evolve the application without limits. Whether you’re using Discovery to understand the data at hand and its value, and use the knowledge gained to design a Big Data project, making decisions on data sources and techniques or enabling end users to quickly and easily combine and explore a large variety of data themselves, the key is that you will be able to easily explore that big variety of data.If you remember at the start of the presentation I asked you to link what you saw with where in your organization wants to apply it – where is that big variety of data your users want to explore, that diversity of questions people want answered. Start there by expanding business analytics with discovery so that the two together can help you win faster with this new wealth of data. Thank you
Endeca is the only Enterprise Self-service discovery platform and provides:The best unstructured data support for themes, concepts, sentimentThe strongest connection between Business Intelligence and DiscoveryThe easiest Big Data Discovery through multiple connectorsThe best security and governance across all data sourcesThe fastest self-service discovery dashboards to make it easy to build, evolve and share
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