Bagaimana analitik dapat meningkatkan bisnis? - transkrip techwise episode 2

Catatan Editor: Ini adalah transkrip dari salah satu webcast kami sebelumnya. Episode berikutnya akan datang dengan cepat, klik di sini untuk mendaftar.

Eric Kavanagh: Hadirin sekalian, halo dan selamat datang kembali ke Episode 2 TechWise. Ya, memang, saatnya untuk mendapatkan orang bijak! Saya punya banyak orang yang benar-benar pintar di telepon hari ini untuk membantu kami dalam upaya itu. Nama saya Eric Kavanagh, tentu saja. Saya akan menjadi tuan rumah Anda, moderator Anda, untuk sesi putaran kilat ini. Kami memiliki banyak konten di sini, teman-teman. Kami memiliki beberapa nama besar dalam bisnis ini, yang telah menjadi analis di ruang kami dan empat vendor paling menarik. Jadi kita akan melakukan banyak tindakan bagus saat ini. Dan tentu saja, Anda yang hadir di luar sana memainkan peran penting dalam mengajukan pertanyaan.

Jadi sekali lagi, acaranya TechWise dan topik hari ini adalah "Bagaimana Analytics Meningkatkan Bisnis?" Jelas, ini adalah topik hangat di mana ia akan mencoba untuk memahami berbagai jenis analitik yang dapat Anda lakukan dan bagaimana hal itu dapat meningkatkan operasi Anda karena itulah yang terjadi pada akhirnya.

Jadi Anda bisa melihat diri saya di atas sana, itu milik Anda. Kirk Borne, seorang teman baik dari Universitas George Mason. Dia adalah seorang ilmuwan data dengan jumlah pengalaman yang luar biasa, keahlian yang sangat mendalam dalam ruang ini dan data mining dan data besar dan semua hal menyenangkan lainnya. Dan, tentu saja, kami memiliki Dr. Robin Bloor, Kepala Analis kami di sini di Grup Bloor. Yang dilatih sebagai aktuaris bertahun-tahun yang lalu. Dan dia benar-benar fokus pada seluruh ruang data besar ini dan ruang analitik dengan cukup penuh perhatian selama setengah dekade terakhir. Sudah lima tahun hampir sejak kami meluncurkan Grup Bloor per se. Jadi waktu berlalu ketika Anda bersenang-senang.

Kita juga akan mendengar dari Will Gorman, Kepala Arsitek Pentaho; Steve Wilkes, CCO dari WebAction; Frank Sanders, Direktur Teknis di MarkLogic; dan Hannah Smalltree, Direktur di Treasure Data. Jadi seperti yang saya katakan, itu banyak konten.

Jadi, bagaimana analitik dapat membantu bisnis Anda? Yah, bagaimana itu tidak bisa membantu bisnis Anda, terus terang? Ada berbagai cara analitik dapat digunakan untuk melakukan hal-hal yang meningkatkan organisasi Anda.

Jadi merampingkan operasi. Itu salah satu yang Anda tidak banyak dengar tentang hal-hal seperti pemasaran atau meningkatkan pendapatan atau bahkan mengidentifikasi peluang. Tetapi merampingkan operasi Anda adalah hal yang benar-benar sangat kuat yang dapat Anda lakukan untuk organisasi Anda karena Anda dapat mengidentifikasi tempat-tempat di mana Anda dapat melakukan outsourcing sesuatu atau Anda dapat menambahkan data ke proses tertentu, misalnya. Dan itu dapat merampingkannya dengan tidak mengharuskan seseorang untuk mengangkat telepon untuk menelepon atau seseorang untuk mengirim email. Ada begitu banyak cara yang berbeda sehingga Anda dapat merampingkan operasi Anda. Dan semua itu benar-benar membantu menurunkan biaya Anda, bukan? Itulah kuncinya, menurunkan biaya. Tetapi juga memungkinkan Anda untuk melayani pelanggan dengan lebih baik.

Dan jika Anda berpikir tentang betapa tidak sabarnya orang-orang, dan saya melihat ini setiap hari dalam hal bagaimana orang berinteraksi secara online, bahkan dengan pertunjukan kami, penyedia layanan yang kami gunakan. Kesabaran yang dimiliki orang, rentang perhatian, semakin pendek dan pendek dari hari ke hari. Dan apa artinya itu adalah bahwa Anda perlu, sebagai organisasi, merespons dalam periode waktu yang lebih cepat dan lebih cepat untuk dapat memuaskan pelanggan Anda.

Jadi, misalnya, jika seseorang ada di situs webcast Anda atau menjelajahi sekitar untuk mencari sesuatu, jika mereka frustrasi dan mereka pergi, well, Anda mungkin baru saja kehilangan pelanggan. Dan tergantung pada berapa banyak Anda mengenakan biaya untuk produk atau layanan Anda, dan mungkin itu masalah besar. Jadi intinya adalah merampingkan operasi, saya pikir, adalah salah satu ruang terpanas untuk menerapkan analitik. Dan Anda melakukannya dengan melihat angka-angkanya, dengan menghitung data, dengan mencari tahu, misalnya, "Hei, mengapa kita kehilangan begitu banyak orang di halaman situs web ini?" "Kenapa kita mendapatkan beberapa panggilan telepon ini sekarang?"

Dan semakin nyata Anda merespons hal-hal semacam itu, semakin besar peluang Anda untuk mengatasi situasi dan melakukan sesuatu sebelum terlambat. Karena ada jendela waktu ketika seseorang marah tentang sesuatu, mereka tidak puas atau mereka berusaha menemukan sesuatu tetapi mereka frustrasi; Anda punya jendela peluang di sana untuk menjangkau mereka, untuk meraih mereka, untuk berinteraksi dengan pelanggan itu. Dan jika Anda melakukannya dengan cara yang benar dengan data yang tepat atau gambaran pelanggan yang baik - memahami siapa pelanggan ini, apa keuntungan mereka, apa preferensi mereka - jika Anda benar-benar dapat mengatasinya, Anda akan melakukannya pekerjaan yang bagus untuk mempertahankan pelanggan Anda dan mendapatkan pelanggan baru. Dan itulah masalahnya.

Jadi dengan itu, saya akan menyerahkannya, sebenarnya, kepada Kirk Borne, salah satu ilmuwan data kami yang dipanggil hari ini. Dan mereka cukup langka akhir-akhir ini, kawan. Kami punya dua dari mereka setidaknya di telepon jadi itu masalah besar. Dengan itu, Kirk, saya akan menyerahkannya kepada Anda untuk berbicara tentang analitik dan bagaimana itu membantu bisnis. Lakukan untuk itu.

Kirk Borne: Baiklah, terima kasih banyak, Eric. Bisakah kamu mendengarku?

Eric: Tidak apa-apa, silakan saja.

Kirk: Oke, bagus. Saya hanya ingin berbagi jika saya berbicara selama lima menit, dan orang-orang melambaikan tangan kepada saya. Jadi, kata sambutannya, Eric, bahwa Anda benar-benar terikat dengan topik ini, saya akan membahas secara singkat dalam beberapa menit ke depan yaitu penggunaan data besar dan analisis untuk data untuk keputusan yang didukung, di sana. Komentar yang Anda buat tentang perampingan operasional, bagi saya, itu termasuk dalam konsep analitik operasional di mana Anda dapat melihat hampir di setiap aplikasi di dunia apakah itu aplikasi sains, bisnis, keamanan cyber dan penegakan hukum dan pemerintah, layanan kesehatan. Sejumlah tempat di mana kami memiliki aliran data dan kami membuat semacam respons atau keputusan sebagai reaksi terhadap peristiwa dan peringatan serta perilaku yang kami lihat dalam aliran data itu.

Dan salah satu hal yang ingin saya bicarakan hari ini adalah bagaimana Anda mengekstraksi pengetahuan dan wawasan dari data besar untuk sampai ke titik di mana kita dapat benar-benar membuat keputusan untuk mengambil tindakan. Dan seringkali kita membicarakan hal ini dalam konteks otomatisasi. Dan hari ini saya ingin mencampur otomatisasi dengan analis manusia dalam lingkaran. Jadi yang saya maksudkan sementara analis bisnis memainkan peran penting di sini dalam hal bertaruh, kualifikasi, memvalidasi tindakan spesifik, atau aturan pembelajaran mesin yang kami ekstrak dari data. Tetapi jika kita sampai pada titik di mana kita cukup yakin aturan bisnis yang telah kita ekstraksi dan mekanisme untuk mengingatkan kita valid, maka kita dapat mengubah ini menjadi proses otomatis. Kami benar-benar melakukan perampingan operasional yang dibicarakan Eric.

Jadi saya punya sedikit permainan kata-kata di sini tapi saya harap, jika itu berhasil untuk Anda, saya berbicara tentang tantangan D2D. Dan D2D, bukan hanya data keputusan dalam semua konteks, kami melihat ini di bagian bawah slide ini mudah-mudahan Anda bisa melihatnya, membuat penemuan dan meningkatkan pendapatan dolar dari jaringan pipa analitik kami.

Jadi dalam konteks ini, saya sebenarnya memiliki peran sebagai pemasar bagi diri saya di sini sekarang karena saya bekerja dengannya dan itu adalah; hal pertama yang ingin Anda lakukan adalah mengkarakterisasi data Anda, mengekstrak fitur, mengekstrak karakteristik pelanggan Anda atau entitas apa pun yang Anda lacak di ruang Anda. Mungkin pasien di lingkungan analitik kesehatan. Mungkin itu adalah pengguna Web jika Anda melihat semacam masalah keamanan cyber. Tetapi ciri dan ekstrak karakteristik dan kemudian ekstrak beberapa konteks tentang individu itu, tentang entitas itu. Dan kemudian Anda mengumpulkan potongan-potongan yang baru saja Anda buat dan memasukkannya ke dalam semacam koleksi yang darinya Anda dapat menerapkan algoritma pembelajaran mesin.

Alasan saya mengatakannya dengan cara ini adalah, katakan saja, Anda memiliki kamera pengintai di bandara. Video itu sendiri sangat besar, volume besar dan juga sangat tidak terstruktur. Tetapi Anda dapat mengekstraksi dari pengawasan video, biometrik wajah dan mengidentifikasi orang-orang di kamera pengintai. Jadi misalnya di bandara, Anda dapat mengidentifikasi individu tertentu, Anda dapat melacak mereka melalui bandara dengan mengidentifikasi silang individu yang sama di beberapa kamera pengintai. Dengan demikian fitur biometrik yang diekstraksi yang benar-benar Anda gali dan lacak bukanlah video terperinci sebenarnya. Tetapi begitu Anda memiliki ekstraksi tersebut maka Anda dapat menerapkan aturan pembelajaran mesin dan analitik untuk membuat keputusan apakah Anda perlu mengambil tindakan dalam kasus tertentu atau sesuatu terjadi secara tidak benar atau sesuatu yang Anda memiliki peluang untuk mengajukan penawaran. Jika Anda, misalnya, jika Anda memiliki toko di bandara dan Anda melihat pelanggan datang ke arah Anda dan Anda tahu dari informasi lain tentang pelanggan itu, bahwa mungkin dia benar-benar tertarik untuk membeli barang-barang di toko bebas bea atau sesuatu seperti itu, buat penawaran itu.

Jadi hal-hal apa yang akan saya maksud dengan karakterisasi dan potensiisasi? Dengan karakterisasi yang saya maksud, sekali lagi, mengekstraksi fitur dan karakteristik dalam data. Dan ini dapat berupa mesin, maka algoritmenya dapat mengekstraksi, misalnya, tanda tangan biometrik dari video atau analisis sentimen. Anda dapat mengekstraksi sentimen pelanggan melalui ulasan online atau media sosial. Beberapa dari hal-hal ini mungkin dihasilkan oleh manusia, sehingga manusia, analis bisnis, dapat mengekstrak fitur tambahan yang akan saya tunjukkan di slide berikutnya.

Beberapa di antaranya dapat di-crowdsourced. Dan dengan crowdsourced, ada banyak cara berbeda yang dapat Anda pikirkan tentang itu. Tetapi sangat sederhana, misalnya, pengguna Anda datang ke situs web Anda dan mereka memasukkan kata-kata pencarian, kata kunci, dan mereka berakhir pada halaman tertentu dan benar-benar menghabiskan waktu di sana pada halaman itu. Bahwa mereka sebenarnya, setidaknya, mengerti bahwa mereka melihat, menjelajah, mengklik hal-hal di halaman itu. Apa yang dikatakan kepada Anda adalah bahwa kata kunci yang mereka ketikkan di awal adalah deskriptor halaman tersebut karena kata kunci itu menempatkan pelanggan pada halaman yang mereka antisipasi. Jadi Anda dapat menambahkan informasi tambahan itu, yaitu pelanggan yang menggunakan kata kunci ini benar-benar mengidentifikasi halaman web ini dalam arsitektur informasi kami sebagai tempat konten yang cocok dengan kata kunci itu.

Jadi, crowdsourcing adalah aspek lain yang kadang-kadang orang lupa, semacam pelacakan remah roti pelanggan Anda, bisa dikatakan; bagaimana mereka bergerak melalui ruang mereka, apakah itu properti online atau properti nyata. Dan kemudian gunakan semacam jalur yang mereka, yang pelanggan ambil sebagai informasi tambahan tentang hal-hal yang kita lihat.

Jadi saya ingin mengatakan hal-hal yang dihasilkan manusia, atau mesin yang dihasilkan, akhirnya memiliki konteks semacam penjelasan atau penandaan butiran atau entitas data tertentu. Apakah entitas tersebut adalah pasien di rumah sakit, pelanggan atau apa pun. Dan ada berbagai jenis penandaan dan anotasi. Beberapa di antaranya adalah tentang data itu sendiri. Itu adalah salah satu hal, jenis informasi apa, jenis informasi apa, apa saja fitur-fiturnya, bentuknya, mungkin tekstur dan polanya, anomali, perilaku non-anomali. Dan kemudian ekstrak beberapa semantik, yaitu, bagaimana hal ini berhubungan dengan hal-hal lain yang saya tahu, atau pelanggan ini adalah pelanggan elektronik. Pelanggan ini adalah pelanggan pakaian. Atau pelanggan ini suka membeli musik.

Jadi mengidentifikasi beberapa semantik tentang itu, pelanggan yang suka musik cenderung menyukai hiburan. Mungkin kita bisa menawarkan mereka beberapa properti hiburan lainnya. Jadi memahami semantik dan juga beberapa asalnya, yang pada dasarnya mengatakan: dari mana asalnya, siapa yang memberikan pernyataan ini, jam berapa, tanggal berapa, dalam keadaan apa?

Jadi, sekali Anda memiliki semua anotasi dan penokohan itu, tambahkan ke langkah selanjutnya, yaitu konteks, jenis siapa, apa, kapan, di mana dan mengapa. Siapa pengguna itu? Saluran apa yang mereka tuju? Apa sumber informasinya? Jenis penggunaan kembali apa yang telah kita lihat dalam bagian informasi atau produk data ini? Dan apa, itu semacam, nilai dalam proses bisnis? Dan kemudian mengumpulkan hal-hal itu dan mengelolanya, dan benar-benar membantu membuat basis data, jika Anda ingin memikirkannya seperti itu. Buat mereka dapat dicari, digunakan kembali, oleh analis bisnis lain atau dengan proses otomatis yang akan, pada saat saya melihat set fitur ini, sistem dapat mengambil tindakan otomatis ini. Jadi kita mendapatkan efisiensi analitik operasional semacam itu, tetapi semakin kita mengumpulkan informasi yang berguna, komprehensif, dan kemudian mengkurasi untuk kasus penggunaan ini.

Kami turun ke bisnis. Kami melakukan analisis data. Kami mencari pola yang menarik, kejutan, outlier baru, anomali. Kami mencari kelas dan segmen baru dalam populasi. Kami mencari asosiasi dan korelasi serta tautan di antara berbagai entitas. Dan kemudian kita menggunakan semua itu untuk mendorong penemuan, keputusan, dan proses pembuatan dolar kita.

Jadi di sana lagi, di sini kita mendapatkan slide data terakhir yang saya miliki pada dasarnya meringkas, menjaga analis bisnis dalam lingkaran, sekali lagi, Anda tidak mengekstraksi manusia itu dan itu semua penting untuk menjaga manusia itu di sana.

Jadi fitur-fitur ini, semuanya disediakan oleh mesin atau analis manusia atau bahkan crowdsourcing. Kami menerapkan kombinasi hal-hal tersebut untuk meningkatkan set pelatihan kami untuk model kami dan berakhir dengan model prediksi yang lebih akurat, lebih sedikit positif dan negatif palsu, perilaku yang lebih efisien, intervensi yang lebih efisien dengan pelanggan kami atau siapa pun.

Jadi, pada akhirnya, kami benar-benar hanya menggabungkan pembelajaran mesin dan data besar dengan kekuatan kognisi manusia ini, yang merupakan bagian dari pemberian anotasi penandaan semacam itu. Dan itu dapat mengarah melalui visualisasi dan tipe analitik visual. alat atau lingkungan data mendalam atau crowdsourcing. Dan, pada akhirnya, apa yang sebenarnya dilakukan adalah menghasilkan penemuan, wawasan, dan D2D kami. Dan itu adalah komentar saya, jadi terima kasih telah mendengarkan.

Eric: Hei itu kedengarannya bagus dan biarkan saya pergi ke depan dan menyerahkan kunci kepada Dr. Robin Bloor untuk memberikan perspektifnya juga. Ya, saya suka mendengar Anda berkomentar tentang perampingan konsep operasi itu dan Anda berbicara tentang analitik operasional. Saya pikir itu adalah area besar yang perlu dieksplorasi dengan seksama. Dan kurasa, sangat cepat sebelum Robin, aku akan membawamu kembali, Kirk. Itu mengharuskan Anda memiliki kolaborasi yang cukup signifikan di antara berbagai pemain di perusahaan, bukan? Anda harus berbicara dengan petugas operasi; Anda harus mendapatkan orang teknis Anda. Terkadang Anda mendapatkan orang-orang pemasaran Anda atau orang-orang antarmuka Web Anda. Ini biasanya kelompok yang berbeda. Apakah Anda memiliki praktik terbaik atau saran tentang cara membuat orang lain ikut bermain?

Kirk: Ya, saya pikir ini disertai dengan budaya kolaborasi bisnis. Sebenarnya, saya berbicara tentang tiga C dari jenis budaya analitik. Salah satunya adalah kreativitas; yang lain adalah rasa ingin tahu dan yang ketiga adalah kolaborasi. Jadi Anda ingin orang-orang yang kreatif dan serius, tetapi Anda juga harus membuat orang-orang ini berkolaborasi. Dan itu benar-benar dimulai dari atas, semacam membangun budaya itu dengan orang-orang yang harus secara terbuka berbagi dan bekerja sama menuju tujuan bersama bisnis.

Eric: Semuanya masuk akal. Dan Anda benar-benar harus mendapatkan kepemimpinan yang baik di atas untuk mewujudkannya. Jadi mari kita lanjutkan dan serahkan ke Dr. Bloor. Robin, lantai milikmu.

Robin Bloor: Oke. Terima kasih untuk intro itu, Eric. Oke, cara ini berhasil, ini menunjukkan, karena kami memiliki dua analis; Saya bisa melihat presentasi analis bahwa yang lain tidak. Aku tahu apa yang akan dikatakan Kirk dan aku hanya pergi dari sudut yang sama sekali berbeda sehingga kita tidak terlalu tumpang tindih.

Jadi apa yang sebenarnya saya bicarakan atau ingin bicarakan di sini adalah peran analis data versus peran analis bisnis. Dan cara saya mencirikannya, yah, sedikit banyak, adalah jenis Jekyll dan Hyde. Perbedaannya terutama para ilmuwan data, setidaknya secara teori, tahu apa yang mereka lakukan. Sementara analis bisnis tidak begitu, oke dengan cara matematika bekerja, apa yang bisa dipercaya dan apa yang tidak bisa dipercaya.

Jadi mari kita turun ke alasan bahwa kita melakukan ini, alasan bahwa analisis data tiba-tiba menjadi masalah besar selain dari fakta bahwa kita benar-benar dapat menganalisis jumlah data yang sangat besar dan menarik data dari luar organisasi; apakah itu membayar? Cara saya melihat ini - dan saya pikir ini hanya menjadi kasus tetapi saya benar-benar berpikir itu adalah kasus - analisis data benar-benar bisnis R & D. Apa yang sebenarnya Anda lakukan dengan satu atau lain cara dengan analisis data adalah Anda melihat proses bisnis di satu jenis atau apakah itu interaksi dengan pelanggan, apakah itu dengan cara operasi ritel Anda, cara yang Anda gunakan toko Anda. Tidak masalah apa masalahnya. Anda sedang melihat proses bisnis yang diberikan dan Anda berusaha untuk memperbaikinya.

Hasil dari penelitian dan pengembangan yang sukses adalah proses perubahan. Dan Anda dapat menganggap manufaktur, jika Anda mau, sebagai contoh biasa dari ini. Karena di bidang manufaktur, orang mengumpulkan informasi tentang segala sesuatu untuk mencoba dan meningkatkan proses pembuatan. Tetapi saya pikir apa yang terjadi atau apa yang terjadi pada data besar adalah semua ini sekarang diterapkan pada semua bisnis dalam bentuk apa pun dengan cara yang dapat dipikirkan oleh siapa pun. Jadi hampir semua proses bisnis siap untuk diperiksa jika Anda dapat mengumpulkan data tentang hal itu.

Jadi itu satu hal. Jika Anda suka, itu terjadi pada pertanyaan analisis data. Apa yang dapat dilakukan analitik data untuk bisnis? Ya, itu bisa mengubah bisnis sepenuhnya.

Diagram khusus ini yang tidak akan saya jelaskan secara mendalam, tetapi ini adalah diagram yang kami buat sebagai puncak dari proyek penelitian yang kami lakukan selama enam bulan pertama tahun ini. Ini adalah cara untuk mewakili arsitektur data besar. Dan sejumlah hal yang perlu ditunjukkan sebelum saya melanjutkan ke slide berikutnya. Ada dua aliran data di sini. Salah satunya adalah aliran data real-time, yang berjalan di bagian atas diagram. Yang lainnya adalah aliran data yang lebih lambat yang berjalan di sepanjang bagian bawah diagram.

Lihatlah bagian bawah diagram. Kami punya Hadoop sebagai reservoir data. Kami punya berbagai database. Kami memiliki seluruh data di sana dengan sejumlah aktivitas yang terjadi di sana, yang sebagian besar merupakan aktivitas analitis.

Poin yang saya buat di sini dan satu-satunya hal yang ingin saya sampaikan di sini adalah teknologinya sulit. Itu tidak sederhana. Ini tidak mudah. Itu bukan sesuatu yang bisa disatukan oleh siapa saja yang baru mengenal permainan ini. Ini cukup rumit. Dan jika Anda akan membuat bisnis untuk melakukan analisis yang andal di semua proses ini, maka itu bukan sesuatu yang akan terjadi secara khusus dengan cepat. Ini akan membutuhkan banyak teknologi untuk ditambahkan ke dalam campuran.

Baik. Pertanyaannya apa itu ilmuwan data, saya bisa mengklaim sebagai ilmuwan data karena saya benar-benar dilatih dalam statistik sebelum saya pernah dilatih dalam komputasi. Dan saya melakukan pekerjaan aktuaria untuk jangka waktu tertentu jadi saya tahu cara suatu bisnis mengatur, analisis statistik, juga untuk menjalankannya sendiri. Ini bukan hal sepele. Dan ada banyak praktik terbaik yang terlibat baik di sisi manusia maupun di sisi teknologi.

Jadi dalam mengajukan pertanyaan "apa itu ilmuwan data, " saya telah menempatkan gambar Frankenstein hanya karena itu adalah kombinasi dari hal-hal yang harus disatukan. Ada manajemen proyek yang terlibat. Ada pemahaman mendalam dalam statistik. Ada keahlian bisnis domain, yang lebih merupakan masalah seorang analis bisnis daripada ilmuwan data. Ada pengalaman atau kebutuhan untuk memahami arsitektur data dan untuk dapat membangun arsitek data dan ada rekayasa perangkat lunak yang terlibat. Dengan kata lain, itu mungkin tim. Itu mungkin bukan individu. Dan itu berarti bahwa mungkin itu adalah departemen yang perlu dikelola dan organisasinya perlu dipikirkan secara luas.

Melemparkan ke dalam campuran fakta pembelajaran mesin. Kita tidak bisa melakukan, maksud saya, pembelajaran mesin bukanlah hal baru dalam arti bahwa sebagian besar teknik statistik yang digunakan dalam pembelajaran mesin telah diketahui selama beberapa dekade. Ada beberapa hal baru, maksud saya jaringan saraf relatif baru, saya pikir mereka baru sekitar 20 tahun, jadi beberapa di antaranya relatif baru. Tetapi masalah dengan pembelajaran mesin adalah bahwa kami benar-benar tidak memiliki kekuatan komputer untuk melakukannya. Dan apa yang terjadi, terlepas dari hal lain, adalah bahwa daya komputer sekarang ada. Dan itu berarti banyak sekali dari apa yang kita, katakan, para ilmuwan data telah lakukan sebelumnya dalam hal situasi pemodelan, pengambilan sampel data dan kemudian menyusunnya untuk menghasilkan analisis data yang lebih dalam. Sebenarnya, dalam beberapa kasus kita bisa menggunakan daya komputer. Pilih saja algoritma pembelajaran mesin, lemparkan ke data dan lihat apa yang keluar. Dan itu adalah sesuatu yang dapat dilakukan oleh analis bisnis, bukan? Tetapi analis bisnis perlu memahami apa yang mereka lakukan. Maksudku, kupikir itu masalahnya, lebih dari segalanya.

Nah, ini hanya untuk mengetahui lebih banyak tentang bisnis dari datanya daripada dengan cara lain. Einstein tidak mengatakan itu, saya mengatakan itu. Saya hanya memasang fotonya untuk kredibilitas. Tetapi situasi yang sebenarnya mulai berkembang adalah di mana teknologi, jika digunakan dengan benar, dan matematika, jika digunakan dengan benar, akan dapat menjalankan bisnis sebagai individu. Kami telah menyaksikan ini dengan IBM. Pertama-tama, itu bisa mengalahkan orang-orang terbaik di catur, dan kemudian itu bisa mengalahkan orang-orang terbaik di Jeopardy; tetapi pada akhirnya kita akan mampu mengalahkan orang-orang terbaik dalam menjalankan perusahaan. Statistik akhirnya akan menang. Dan sulit untuk melihat bagaimana itu tidak akan terjadi, itu belum terjadi.

Jadi apa yang saya katakan, dan ini adalah pesan lengkap dari presentasi saya, adalah dua masalah bisnis ini. Yang pertama adalah, bisakah Anda mendapatkan teknologinya dengan benar? Bisakah Anda membuat teknologi bekerja untuk tim yang sebenarnya akan mampu mengatasinya dan mendapatkan manfaat untuk bisnis? Dan yang kedua, bisakah Anda mendapatkan orang yang benar? Dan keduanya adalah masalah. Dan mereka adalah masalah yang tidak, sampai saat ini, kata mereka, diselesaikan.

Oke Eric, saya akan memberikannya kembali kepada Anda. Atau saya mungkin harus memberikannya kepada Will.

Eric: Sebenarnya, ya. Terima kasih, Will Gorman. Ya, ini dia, Will. Jadi mari kita lihat. Biarkan saya memberi Anda kunci ke WebEx. Jadi, apa yang terjadi? Pentaho, jelas, kalian sudah ada untuk sementara waktu dan open-source BI dari mana Anda memulai. Tapi Anda mendapat lebih banyak dari yang Anda miliki, jadi mari kita lihat apa yang Anda dapatkan hari ini untuk analisis.

Will Gorman: Tentu saja. Hai semuanya! Nama saya Will Gorman. Saya Kepala Arsitek di Pentaho. Bagi Anda yang belum pernah mendengar tentang kami, saya baru saja menyebutkan Pentaho adalah perusahaan analitik dan integrasi data besar. Kami sudah berkecimpung dalam bisnis ini selama sepuluh tahun. Produk kami telah berkembang berdampingan dengan komunitas big data, dimulai sebagai platform open-source untuk integrasi dan analitik data, berinovasi dengan teknologi seperti Hadoop dan NoSQL bahkan sebelum entitas komersial terbentuk di sekitar teknologi tersebut. Dan sekarang kami memiliki lebih dari 1500 pelanggan komersial dan lebih banyak lagi janji produksi sebagai hasil dari inovasi kami seputar open source.

Arsitektur kami sangat mudah dikembangkan dan dapat dikembangkan, dirancang khusus untuk menjadi fleksibel karena teknologi data besar khususnya berkembang dengan kecepatan yang sangat cepat. Pentaho menawarkan tiga bidang produk utama yang bekerja sama untuk mengatasi kasus penggunaan analitik data besar.

Produk pertama pada tingkat arsitektur kami adalah Integrasi Data Pentaho yang diarahkan pada teknologi data dan insinyur data. Produk ini menawarkan pengalaman visual, seret-dan-jatuhkan untuk menentukan jalur pipa dan proses data untuk mengatur data dalam lingkungan data besar dan lingkungan tradisional juga. Produk ini adalah platform integrasi data yang ringan, metadatabase, dibangun di Jawa dan dapat digunakan sebagai proses dalam MapReduce atau BENANG atau Badai dan banyak platform batch dan real-time lainnya.

Area produk kedua kami adalah seputar analitik visual. Dengan teknologi ini, organisasi dan OEM dapat menawarkan pengalaman visualisasi dan analisis drag-and-drop yang kaya untuk analis bisnis dan pengguna bisnis oleh browser dan tablet modern, memungkinkan pembuatan laporan dan dasbor ad hoc secara ad hoc. Serta presentasi dashboarding sempurna pixel dan laporan.

Area produk ketiga kami berfokus pada analitik prediktif yang ditargetkan untuk ilmuwan data, algoritma pembelajaran mesin. Seperti disebutkan sebelumnya, seperti jaringan saraf dan semacamnya, dapat dimasukkan ke dalam lingkungan transformasi data, yang memungkinkan para ilmuwan data beralih dari pemodelan ke lingkungan produksi, memberikan akses untuk memprediksi, dan yang dapat memengaruhi proses bisnis dengan segera, sangat cepat.

Semua produk ini terintegrasi erat ke dalam satu pengalaman lincah dan memberikan pelanggan perusahaan kami fleksibilitas yang mereka butuhkan untuk mengatasi masalah bisnis mereka. Kami melihat lanskap big data yang berkembang pesat dalam teknologi tradisional. Semua yang kami dengar dari beberapa perusahaan di ruang data besar bahwa EDW hampir berakhir. Kenyataannya, apa yang kita lihat pada pelanggan perusahaan kita adalah mereka perlu memperkenalkan data besar ke dalam proses bisnis dan TI yang ada dan tidak menggantikan proses itu.

Diagram sederhana ini menunjukkan titik dalam arsitektur yang sering kita lihat, yang merupakan jenis arsitektur penyebaran EDW dengan integrasi data dan kasus penggunaan BI. Sekarang diagram ini mirip dengan slide Robin pada arsitektur data besar, yang menggabungkan data real-time dan historis. Ketika sumber data baru dan persyaratan waktu nyata muncul, kami melihat data besar sebagai bagian tambahan dari keseluruhan arsitektur TI. Sumber data baru ini meliputi data yang dihasilkan mesin, data tidak terstruktur, volume dan kecepatan standar, dan beragam persyaratan yang kami dengar dalam data besar; mereka tidak cocok dengan proses EDW tradisional. Pentaho bekerja sama dengan Hadoop dan NoSQL untuk menyederhanakan konsumsi, pemrosesan data dan visualisasi data ini serta memadukan data ini dengan sumber-sumber tradisional untuk memberikan pelanggan pandangan penuh ke lingkungan data mereka. Kami melakukan ini dengan cara yang diatur sehingga TI dapat menawarkan solusi analitik lengkap untuk lini bisnis mereka.

Sebagai penutup, saya ingin menyoroti filosofi kami seputar analitik dan integrasi data besar; kami percaya bahwa teknologi ini lebih baik bersama-sama bekerja dengan satu arsitektur tunggal, memungkinkan sejumlah kasus penggunaan yang tidak mungkin dilakukan. Lingkungan data pelanggan kami lebih dari sekadar data besar, Hadoop dan NoSQL. Data apa pun adalah permainan yang adil. Dan sumber big data perlu tersedia dan bekerja bersama untuk memengaruhi nilai bisnis.

Akhirnya, kami percaya bahwa untuk menyelesaikan masalah-masalah bisnis ini di perusahaan-perusahaan dengan sangat efektif melalui data, TI dan lini-lini bisnis perlu bekerja bersama dalam suatu pendekatan yang terkendali dan terpadu untuk analitik data besar. Terima kasih banyak karena telah memberi kami waktu untuk berbicara, Eric.

Eric: Anda bertaruh. Tidak, itu bagus. Saya ingin kembali ke sisi arsitektur Anda ketika kita sampai pada Tanya Jawab. Jadi mari kita lanjutkan dengan sisa presentasi dan terima kasih banyak untuk itu. Kalian pasti telah bergerak cepat dalam beberapa tahun terakhir, saya harus mengatakan itu dengan pasti.

Jadi Steve, izinkan saya maju dan menyerahkannya kepada Anda. Dan cukup klik di sana pada panah ke bawah dan pergi untuk itu. Jadi Steve, aku memberimu kuncinya. Steve Wilkes, klik saja panah terjauh yang ada di keyboard Anda.

Steve Wilkes: Ini dia.

Eric: Ini dia.

Steve: Tapi itu intro yang hebat yang kamu berikan padaku.

Eric: Ya.

Steve: Jadi saya Steve Wilkes. Saya CCO di WebAction. Kami hanya ada selama beberapa tahun terakhir dan kami pasti sudah bergerak cepat juga, sejak saat itu. WebAction adalah platform analitik data besar real-time. Eric menyebutkan sebelumnya, semacam, seberapa penting real time dan seberapa real time aplikasi Anda. Platform kami dirancang untuk membangun aplikasi waktu nyata. Dan untuk memungkinkan generasi berikutnya aplikasi berbasis data yang dapat dibangun secara bertahap dan untuk memungkinkan orang membangun dasbor dari data yang dihasilkan dari aplikasi tersebut, tetapi berfokus pada waktu nyata.

Platform kami sebenarnya adalah platform ujung ke ujung, melakukan segalanya mulai dari akuisisi data, pemrosesan data, hingga visualisasi data. Dan memungkinkan berbagai jenis orang di perusahaan kami untuk bekerja bersama untuk membuat aplikasi real-time yang sebenarnya, memberi mereka wawasan tentang hal-hal yang terjadi di perusahaan mereka saat itu terjadi.

Dan ini sedikit berbeda dari apa yang dilihat kebanyakan orang dalam data besar, sehingga pendekatan tradisional - yah, tradisional beberapa tahun terakhir - pendekatan dengan data besar adalah untuk menangkapnya dari sejumlah besar sumber yang berbeda dan kemudian menumpuknya ke dalam reservoir besar atau danau atau apa pun yang Anda ingin menyebutnya. Dan kemudian proses itu ketika Anda perlu menjalankan kueri di atasnya; untuk menjalankan analisis historis skala besar atau bahkan hanya permintaan ad hoc dari sejumlah besar data. Sekarang berfungsi untuk kasus penggunaan tertentu. Tetapi jika Anda ingin menjadi proaktif dalam perusahaan Anda, jika Anda ingin benar-benar diberi tahu apa yang sedang terjadi daripada mencari tahu ketika ada yang salah pada akhir hari atau akhir minggu, maka Anda benar-benar harus pindah ke waktu nyata.

Dan itu sedikit mengubah segalanya. Ini memindahkan pemrosesan ke tengah. Jadi secara efektif Anda mengambil aliran sejumlah besar data yang sedang dihasilkan secara terus-menerus dalam perusahaan dan Anda sedang memprosesnya saat Anda mendapatkannya. Dan karena Anda memprosesnya saat mendapatkannya, Anda tidak perlu menyimpan semuanya. Anda bisa menyimpan informasi penting atau hal-hal yang perlu Anda ingat yang sebenarnya terjadi. Jadi, jika Anda melacak lokasi GPS kendaraan yang bergerak di jalan, Anda tidak benar-benar peduli di mana mereka berada setiap detik, Anda tidak perlu menyimpan di mana mereka berada setiap detik. Anda hanya perlu peduli, apakah mereka meninggalkan tempat ini? Sudahkah mereka tiba di tempat ini? Sudahkah mereka menyetir, atau tidak, jalan bebas hambatan?

Jadi sangat penting untuk mempertimbangkan bahwa semakin banyak data yang dihasilkan, maka ketiga V tersebut. Velocity pada dasarnya menentukan berapa banyak data yang dihasilkan setiap hari. Semakin banyak data yang dihasilkan, semakin banyak Anda harus menyimpan. Dan semakin banyak Anda harus menyimpan, semakin lama waktu yang dibutuhkan untuk memproses. Tetapi jika Anda dapat memprosesnya saat mendapatkannya, maka Anda mendapatkan manfaat yang sangat besar dan Anda dapat bereaksi terhadapnya. Anda dapat diberi tahu bahwa banyak hal terjadi daripada harus mencarinya nanti.

Jadi platform kami dirancang agar sangat terukur. Ini memiliki tiga bagian utama - bagian akuisisi, bagian pemrosesan dan kemudian bagian visualisasi pengiriman platform. Di sisi akuisisi, kami tidak hanya melihat data log yang dibuat mesin seperti log Web atau aplikasi yang memiliki semua log lain yang sedang dihasilkan. We can also go in and do change data capture from databases. So that basically enables us to, we've seen the ETL side that Will presented and traditional ETL you have to run queries against the databases. We can be told when things happen in the database. We change it and we capture it and receive those events. And then there's obviously the social feeds and live device data that's being pumped to you over TCP or ACDP sockets.

There's tons of different ways of getting data. And talking of volume and velocity, we're seeing volumes that are billions of events per day, right? So it's large, large amounts of data that is coming in and needs to be processed.

That is processed by a cluster of our servers. The servers all have the same architecture and are all capable of doing the same things. But you can configure them to, sort of, do different things. And within the servers we have a high-speed query processing layer that enables you to do some real-time analytics on the data, to do enrichments of the data, to do event correlation, to track things happening within time windows, to do predictive analytics based on patterns that are being seen in the data. And that data can then be stored in a variety places - the traditional RDBMS, enterprise data warehouse, Hadoop, big data infrastructure.

And the same live data can also be used to power real-time data-driven apps. Those apps can have a real-time view of what's going on and people can also be alerted when important things happen. So rather than having to go in at the end of the day and find out that something bad really happened earlier on the day, you could be alerted about it the second we spot it and it goes straight to the page draw down to find out what's going on.

So it changes the paradigm completely from having to analyze data after the fact to being told when interesting things are happening. And our platform can then be used to build data-driven applications. And this is really where we're focusing, is building out these applications. For customers, with customers, with a variety of different partners to show true value in real-time data analysis. So that allows people that, or companies that do site applications, for example, to be able track customer usage over time and ensure that the quality of service is being met, to spot real-time fraud or money laundering, to spot multiple logins or hack attempts and those kind of security events, to manage things like set-top boxes or other devices, ATM machines to monitor them in real time for faults, failures that have happened, could happen, will happen in the future based on predictive analysis. And that goes back to the point of streamlining operations that Eric mentioned earlier, to be able to spot when something's going to happen and organize your business to fix those things rather than having to call someone out to actually do something after the fact, which is a lot more expensive.

Consumer analytics is another piece to be able to know when a customer is doing something while they're still there in your store. Data sent to management to be able to in real time monitor resource usage and change where things are running and to be able to know about when things are going to fail in a much more timely fashion.

So that's our products in a nutshell and I'm sure we'll come back to some of these things in the Q&A session. Terima kasih.

Eric: Yes, indeed. Great job. Okay good. And now next stop in our lightning round, we've got Frank Sanders calling in from MarkLogic. I've known about these guys for a number of years, a very, very interesting database technology. So Frank, I'm turning it over to you. Just click anywhere in that. Use the down arrow on your keyboard and you're off to the races. Ini dia.

Frank Sanders: Thank you very much, Eric. So as Eric mentioned, I'm with a company called MarkLogic. And what MarkLogic does is we provide an enterprise NoSQL database. And perhaps, the most important capability that we bring to the table with regards to that is the ability to actually bring all of these disparate sources of information together in order to analyze, search and utilize that information in a system similar to what you're used to with traditional relational systems, right?

And some of the key features that we bring to the table in that regard are all of the enterprise features that you'd expect from a traditional database management system, your security, your HA, your DR, your backup are in store, your asset transactions. As well as the design that allows you to scale out either on the cloud or in the commodity hardware so that you can handle the volume and the velocity of the information that you're going to have to handle in order to build and analyze this sort of information.

And perhaps, the most important capability is that fact that we're scheme agnostic. What that means, practically, is that you don't have to decide what your data is going to look like when you start building your applications or when you start pulling those informations together. But over time, you can incorporate new data sources, pull additional information in and then use leverage and query and analyze that information just as you would with anything that was there from the time that you started the design. Baik?

So how do we do that? How do we actually enable you to load different sorts of information, whether it be text, RDF triples, geospatial data, temporal data, structured data and values, or binaries. And the answer is that we've actually built our server from the ground up to incorporate search technology which allows you to put information in and that information self describes and it allows you to query, retrieve and search that information regardless of its source or format.

And what that means practically is that - and why this is important when you're doing analysis - is that analytics and information is most important ones when it's properly contextualized and targeted, right? So a very important key part of any sort of analytics is search, and the key part is search analytics. You can't really have one without the other and successfully achieve what you set out to achieve. Right?

And I'm going to talk briefly about three and a half different use cases of customers that we have at production that are using MarkLogic to power this sort of analytics. Baik. So the first such customer is Fairfax County. And Fairfax County has actually built two separate applications. One is based around permitting and property management. And the other, which is probably a bit more interesting, is the Fairfax County police events application. What the police events application actually does is it pulls information together like police reports, citizen reports and complaints, Tweets, other information they have such as sex offenders and whatever other information that they have access to from other agencies and sources. Then they allow them to visualize that and present this to the citizens so they can do searches and look at various crime activity, police activity, all through one unified geospatial index, right? So you can ask questions like, "what is the crime rate within five miles" or "what crimes occurred within five miles of my location?" Baik.

Another user that we've got, another customer that we have is OECD. Why OECD is important to this conversation is because in addition to everything that we've enabled for Fairfax County in terms of pulling together information, right; all the information that you would get from all various countries that are members of the OECD that they report on from an economic perspective. We actually laid a target drill into that, right. So you can see on the left-hand side we're taking the view of Denmark specifically and you can kind of see a flower petal above it that rates it on different axes. Right? And that's all well and good. But what the OECD has done is they've gone a step further.

In addition to these beautiful visualizations and pulling all these information together, they're actually allowing you in real time to create your own better life index, right, which you can see on the right-hand side. So what you have there is you have a set of sliders that actually allow you to do things like rank how important housing is to you or income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and your work/life balance. And dynamically based on how you are actually inputting that information and weighting those things, MarkLogic's using its real-time indexing capability and query capability to actually then change how each and every one of these countries is ranked to give you an idea of how well your country or your lifestyle maps through a given country. Baik?

And the final example that I'm going to share is MarkMail. And what MarkMail really tries to demonstrate is that we can provide these capabilities and you can do the sort of analysis not only on structured information or information that's coming in that's numerical but actually on more loosely structured, unstructured information, right? Things like emails. And what we've seen here is we're actually pulling information like geolocation, sender, company, stacks and concepts like Hadoop being mentioned within the context of an email and then visualizing it on the map as well as looking at who those individuals and what list across that, a sent and a date. This where you're looking at things that are traditionally not structured, that may be loosely structured, but are still able to derive some structured analysis from that information without having to go to a great length to actually try and structure it or process it at a time. And that's it.

Eric: Hey, okay good. And we got one more. We've got Hannah Smalltree from Treasure Data, a very interesting company. And this is a lot of great content, folks. Thank you so much for all of you for bringing such good slides and such good detail. So Hannah, I just gave the keys to you, click anywhere and use the down arrow on your keyboard. You got it. Bawa pergi.

Hannah Smalltree: Thank you so much, Eric. This is Hannah Smalltree from Treasure Data. I'm a director with Treasure Data but I have a past as a tech journalist, which means that I appreciate two things. First of all, these can be long to sit through a lot of different descriptions of technology, and it can all sound like it runs together so I really want to focus on our differentiator. And the real-world applications are really important so I appreciate that all of my peers have been great about providing those.

Treasure Data is a new kind of big data service. We're delivered entirely on the cloud in a software as a service or managed-service model. So to Dr. Bloor's point earlier, this technology can be really hard and it can be very time consuming to get up and running. With Treasure Data, you can get all of these kinds of capabilities that you might get in a Hadoop environment or a complicated on-premise environment in the cloud very quickly, which is really helpful for these new big data initiatives.

Now we talk about our service in a few different phases. We offer some very unique collection capabilities for collecting streaming data so particularly event data, other kinds of real-time data. We'll talk a little bit more about those data types. That is a big differentiator for our service. As you get into big data or if you are already in it then you know that collecting this data is not trivial. When you think about a car with 100 sensors sending data every minute, even those 100 sensors sending data every ten minutes, that adds up really quickly as you start to multiply the amount of products that you have out there with sensors and it quickly becomes very difficult to manage. So we are talking with customers who have millions, we have customers who have billions of rows of data a day that they're sending us. And they're doing that as an alternative to try and to manage that themselves in a complicated Amazon infrastructure or even try to bring it into their own environment.

We have our own cloud storage environment. We manage it. We monitor it. We have a team of people that's doing all that tuning for you. And so the data flows in, it goes into our managed storage environment.

Then we have embedded query engines so that your analyst can go in and run queries and do some initial data discovery and exploration against the data. We have a couple of different query engines for it actually now. You can use SQL syntax, which your analysts probably know and love, to do some basic data discovery, to do some more complex analytics that are user-defined functions or even to do things as simple as aggregate that data and make it smaller so that you can bring it into your existing data warehouse environment.

You can also connect your existing BI tools, your Tableau, is a big partner of ours; but really most BIs, visualization or analytics tools can connect via our industry standard JDBC and ODBC drivers. So it gives you this complete set of big data capabilities. You're allowed to export your queries results or data sets anytime for free, so you can easily integrate that data. Treat this as a data refinery. I like to think of it more as a refinery than a lake because you can actually do stuff with it. You can go through, find the valuable information and then bring it into your enterprise processes.

The next slide, we talk about the three Vs of big data - some people say four or five. Our customers tend to struggle with the volume and velocity of the data coming at them. And so to get specific about the data types - Clickstream, Web access logs, mobile data is a big area for us, mobile application logs, application logs from custom Web apps or other applications, event logs. And increasingly, we have a lot of customers dealing with sensor data, so from wearable devices, from products, from automotive, and other types of machine data. So when I say big data, that's the type of big data that I'm talking about.

Now, a few use cases in perspective for you - we work with a retailer, a large retailer. They are very well known in Asia. They're expanding here in the US. You'll start to see stores; they're often called Asian IKEA, so, simple design. They have a loyalty app and a website. And in fact, using Treasure Data, they were able to deploy that loyalty app very quickly. Our customers get up and running within days or weeks because of our software and our service architecture and because we have all of the people doing all of that hard work behind the scenes to give you all of those capabilities as a service.

So they use our service for mobile application analytics looking at the behavior, what people are clicking on in their mobile loyalty application. They look at the website clicks and they combine that with our e-commerce and POS data to design more efficient promotions. They actually wanted to drive people into stores because they found that people, when they go into stores spend more money and I'm like that; to pick up things, you spend more money.

Another use case that we're seeing in digital video games, incredible agility. They want to see exactly what is happening in their game, and make changes to that game even within hours of its release. So for them, that real-time view is incredibly important. We just released a game but we noticed in the first hour that everyone is dropping off at Level 2; how are we going to change that? They might change that within the same day. So real time is very important. They're sending us billions of event logs per day. But that could be any kind of mobile application where you want some kind of real-time view into how somebody's using that.

And finally, a big area for us is our product behavior and sensor analytics. So with sensor data that's in cars, that's in other kinds of machines, utilities, that's another area for us, in wearable devices. We have research and development teams that want to quickly know what the impact of a change to a product is or people interested in the behavior of how people are interacting with the product. And we have a lot more use cases which, of course, we're happy to share with you.

And then finally, just show you how this can fit into your environment, we offer again the capability to collect that data. We have very unique collection technology. So again, if real-time collection is something that you're struggling with or you anticipate struggling with, please come look at the Treasure Data service. We have really made capabilities for collecting streaming data. You can also bulk load your data, store it, analyze it with our embedded query engines and then, as I mentioned, you can export it right to your data warehouse. I think Will mentioned the need to introduce big data into your existing processes. So not go around or create a new silo, but how do you make that data smaller and then move it into your data warehouse and you can connect to your BI, visualization and advanced analytics tools.

But perhaps, the key points I want to leave you with are that we are managed service, that's software as a service; it's very cost effective. A monthly subscription service starting at a few thousand dollars a month and we'll get you up and running in a matter of days or weeks. So compare that with the cost of months and months of building your own infrastructure and hiring those people and finding it and spending all that time on infrastructure. If you're experimenting or if you need something yesterday, you can get up and running really quickly with Treasure Data.

And I'm just pointing you to our website and to our starter service. If you're a hands-on person who likes to play, please check out our starter service. You can get on, no credit card required, just name and email, and you can play with our sample data, load up your own data and really get a sense of what we're talking about. So thanks so much. Also, check our website. We were named the Gartner Cool Vendor in Big Data this year, very proud of that. And you can also get a copy of that report for free on our website as well as many other analyst white papers. So thanks so much.

Eric: Okay, thank you very much. We've got some time for questions here, folks. We'll go a little bit long too because we've got a bunch of folks still on the line here. And I know I've got some questions myself, so let me go ahead and take back control and then I'm going to ask a couple of questions. Robin and Kirk, feel free to dive in as you see fit.

So let me go ahead and jump right to one of these first slides that I checked out from Pentaho. So here, I love this evolving big data architecture, can you kind of talk about how it is that this kind of fits together at a company? Because obviously, you go into some fairly large organization, even a mid-size company, and you're going to have some people who already have some of this stuff; how do you piece this all together? Like what does the application look like that helps you stitch all this stuff together and then what does the interface look like?

Will: Great question. The interfaces are a variety depending on the personas involved. But as an example, we like to tell the story of - one of the panelists mentioned the data refinery use case - we see that a lot in customers.

One of our customer examples that we talk about is Paytronix, where they have that traditional EDW data mart environment. They are also introducing Hadoop, Cloudera in particular, and with various user experiences in that. So first there's an engineering experience, so how do you wire all these things up together? How do you create the glue between the Hadoop environment and EDW?

And then you have the business user experience which we talked about, a number of BI tools out there, right? Pentaho has a more embeddable OEM BI tool but there are great ones out there like Tableau and Excel, for instance, where folks want to explore the data. But usually, we want to make sure that the data is governed, right? One of the questions in the discussions, what about single-version experience, how do you manage that, and without the technology like Pentaho data integration to blend that data together not on the glass but in the IT environments. So it really protects and governs the data and allows for a single experience for the business analyst and business users.

Eric: Okay, good. That's a good answer to a difficult question, quite frankly. And let me just ask the question to each of the presenters and then maybe Robin and Kirk if you guys want to jump in too. So I'd like to go ahead and push this slide for WebAction which I do think is really a very interesting company. Actually, I know Sami Akbay who is one of the co-founders, as well. I remember talking to him a couple years ago and saying, "Hey man, what are you doing? What are you up to? I know you've got to be working on something." And of course, he was. He was working on WebAction, under the covers here.

A question came in for you, Steve, so I'll throw it over to you, of data cleansing, right? Can you talk about these components of this real-time capability? How do you deal with issues like data cleansing or data quality or how does that even work?

Steve: So it really depends on where you're getting your feeds from. Typically, if you're getting your feeds from a database as you change data capture then, again, it depends there on how the data was entered. Data cleansing really becomes a problem when you're getting your data from multiple sources or people are entering it manually or you kind of have arbitrary texts that you have to try and pull things out of. And that could certainly be part of the process, although that type simply doesn't lend itself to true, kind of, high-speed real-time processing. Data cleansing, typically, is an expensive process.

So it may well be that that could be done after the fact in the store site. But the other thing that the platform is really, really good at is correlation, so in correlation and enrichment of data. You can, in real time, correlate the incoming data and check to see whether it matches a certain pattern or it matches data that's being retrieved from a database or Hadoop or some other store. So you can correlate it with historical data, is one thing you could do.

The other thing that you can do is basically do analysis on that data and see whether it kind of matches certain required patterns. And that's something that you can also do in real time. But the traditional kind of data cleansing, where you're correcting company names or you're correcting addresses and all those types of things, those should probably be done in the source or kind of after the fact, which is very expensive and you pray that they won't do those in real time.

Eric: Yeah. And you guys are really trying to address the, of course, the real-time nature of things but also get the people in time. And we talked about, right, I mentioned at the top of the hour, this whole window of opportunity and you're really targeting specific applications at companies where you can pull together data not going the usual route, going this alternate route and do so in such a low latency that you can keep customers. For example, you can keep people satisfied and it's interesting, when I talked to Sami at length about what you guys are doing, he made a really good point. He said, if you look at a lot of the new Web-based applications; let's look at things like Twitter, Bitly or some of these other apps; they're very different than the old applications that we looked at from, say, Microsoft like Microsoft Word.

I often use Microsoft as sort of a whipping boy and specifically Word to talk about the evolution of software. Because Microsoft Word started out as, of course, a word processing program. I'm one of those people who remember Word Perfect. I loved being able to do the reveal keys or the reveal code, basically, which is where you could see the actual code in there. You could clean something up if your bulleted list was wrong, you can clean it up. Well, Word doesn't let you do that. And I can tell you that Word embeds a mountain of code inside every page that you do. If anyone doesn't believe me, then go to Microsoft Word, type "Hello World" and then do "Export as" or "Save as" .html. Then open that document in a text editor and that will be about four pages long of codes just for two words.

So you guys, I thought it was very interesting and it's time we talked about that. And that's where you guys focus on, right, is identifying what you might call cross-platform or cross-enterprise or cross-domain opportunities to pull data together in such quick time that you can change the game, right?

Steve: Yeah, absolutely. And one of the keys that, I think, you did elude to, anyway, is you really want to know about things happening before your customers do or before they really, really become a problem. As an example are the set-top boxes. Cable boxes, they emit telemetry all the time, loads and loads of telemetry. And not just kind of the health of the box but it's what you're watching and all that kind of stuff, right? The typical pattern is you wait till the box fails and then you call your cable provider and they'll say, "Well, we will get to you sometime between the hours of 6am and 11pm in the entire month of November." That isn't a really good customer experience.

But if they could analyze that telemetry in real time then they could start to do things like that we know these boxes are likely to fail in the next week based historical patterns. Therefore we'll schedule our cable repair guy to turn up at this person's house prior to it failing. And we'll do that in a way that suits us rather than having to send him from Santa Cruz up to Sunnyvale. We'll schedule everything in a nice order, traveling salesman pattern, etc., so that we can optimize our business. And so the customer is happy because they don't have a failing cable box. And the cable provider is happy because they have just streamlined things and they don't have to send people all over the place. That's just a very quick example. But there are tons and tons of examples where knowing about things as they happen, before they happen, can save companies a fortune and really, really improve their customer relations.

Eric: Yeah, right. No doubt about it. Let's go ahead and move right on to MarkLogic. As I mentioned before, I've known about these guys for quite some time and so I'll bring you into this, Frank. You guys were far ahead of the whole big data movement in terms of building out your application, it's really database. But building it out and you talked about the importance of search.

So a lot of people who followed the space know that a lot of the NoSQL tools out there are now bolting on search capabilities whether through third parties or they try to do their own. But to have that search already embedded in that, baked-in so to speak, really is a big deal. Because if you think about it, if you don't have SQL, well then how do you go in and search the data? How do you pull from that data resource? And the answer is to typically use search to get to the data that you're looking for, right?

So I think that's one of the key differentiators for you guys aside being able to pull data from all these different sources and store that data and really facilitate this sort of hybrid environment. I'm thinking that search capability is a big deal for you, right?

Frank: Yeah, absolutely. In fact, that's the only way to solve the problem consistently when you don't know what all the data is going to look like, right? If you cannot possibly imagine all the possibilities then the only way to make sure that you can locate all the information that you want, that you can locate it consistently and you can locate it regardless of how you evolve your data model and your data sets is to make sure you give people generic tools that allow them to interrogate that data. And the easiest, most intuitive way to do that is through a search paradigm, right? And through the same approach in search takes where we created an inverted index. You have entries where you can actually look into those and then find records and documents and rows that actually contain the information you're looking for to then return it to the customer and allow them to process it as they see fit.

Eric: Yeah and we talked about this a lot, but you're giving me a really good opportunity to kind of dig into it - the whole search and discovery side of this equation. But first of all, it's a lot of fun. For anyone who likes that stuff, this is the fun part, right? But the other side of the equation or the other side of the coin, I should say, is that it really is an iterative process. And you got to be able to - here I'll be using some of the marketing language - have that conversation with the data, right? In other words, you need to be able to test the hypothesis, play around with it and see how that works. Maybe that's not there, test something else and constantly change things and iterate and search and research and just think about stuff. And that's a process. And if you have big hurdles, meaning long latencies or a difficult user interface or you got to go ask IT; that just kills the whole analytical experience, right?

So it's important to have this kind of flexibility and to be able to use searches. And I like the way that you depicted it here because if we're looking at searching around different, sort of, concepts or keys, if you will, key values and they're different dimensions. You want to be able to mix and match that stuff in order to enable your analyst to find useful stuff, right?

Frank: Yeah, absolutely. I mean, hierarchy is an important thing as well, right? So that when you include something like a title, right, or a specific term or value, that you can actually point to the correct one. So if you're looking for a title of an article, you're not getting titles of books, right? Or you're not getting titles of blog posts. The ability to distinguish between those and through the hierarchy of the information is important as well.

You pointed out earlier the development, absolutely, right? The ability for our customers to actually pull in new data sources in a matter of hours, start to work with them, evaluate whether or not they're useful and then either continue to integrate them or leave them by the wayside is extremely valuable. When you compare it to a more traditional application development approach where what you end up doing is you have to figure out what data you want to ingest, source the data, figure out how you're going to fit it in your existing data model or model that in, change that data model to incorporate it and then actually begin the development, right? Where we kind of turn that on our head and say just bring it to us, allow you to start doing the development with it and then decide later whether or not you want to keep it or almost immediately whether or not it's of value.

Eric: Yeah, it's a really good point. That's a good point. So let me go ahead and bring in our fourth presenter here, Treasure Data. I love these guys. I didn't know much about them so I'm kind of kicking myself. And then Hannah came to us and told us what they were doing. And Hannah mentioned, she was a media person and she went over to the dark side.

Hannah: I did, I defected.

Eric: That's okay, though, because you know what we like in the media world. So it's always nice when a media person goes over to the vendor side because you understand, hey, this stuff is not that easy to articulate and it can be difficult to ascertain from a website exactly what this product does versus what that product does. And what you guys are talking about is really quite interesting. Now, you are a cloud-managed service. So any data that someone wants to use they upload to your cloud, is that right? And then you will ETL or CDC, additional data up to the cloud, is that how that works?

Hannah: Well, yeah. So let me make an important distinction. Most of the data, the big data, that our customers are sending us is already outside the firewall - mobile data, sensor data that's in products. And so we're often used as an interim staging area. So data is not often coming from somebody's enterprise into our service so much as it's flowing from a website, a mobile application, a product with lots of sensors in it - into our cloud environment.

Now if you'd like to enrich that big data in our environment, you can definitely bulk upload some application data or some customer data to enrich that and do more of the analytics directly in the cloud. But a lot of our value is around collecting that data that's already outside the firewall, bringing together into one place. So even if you do intend to bring this up sort of behind your firewall and do more of your advanced analytics or bring it into your existing BI or analytics environment, it's a really good staging point. Because you don't want to bring a billion rows of day into your data warehouse, it's not cost effective. It's even difficult if you're planning to store that somewhere and then batch upload.

So we're often the first point where data is getting collected that's already outside firewall.

Eric: Yeah, that's a really good point, too. Because a lot of companies are going to be nervous about taking their proprietary customer data, putting it up in the cloud and to manage the whole process.

Hannah: Yeah.

Eric: And what you're talking about is really getting people a resource for crunching those heavy duty numbers of, as you suggest, data that's third party like mobile data and the social data and all that kind of fun stuff. That's pretty interesting.

Hannah: Yeah, absolutely. And probably they are nervous about the products because the data are already outside. And so yeah, before bringing it in, and I really like that refinery term, as I mentioned, versus the lake. So can you do some basic refinery? Get the good stuff out and then bring it behind the firewall into your other systems and processes for deeper analysis. So it's really all data scientists can do, real-time data exploration of this new big data that's flowing in.

Eric: Yeah, that's right. Well, let me go ahead and bring in our analysts and we'll kind of go back in reverse order. I'll start with you, Robin, with respect to Treasure Data and then we'll go to Kirk for some of the others. And then back to Robin and back to Kirk just to kind of get some more assessment of this.

And you know the data refinery, Robin, that Hannah is talking about here. I love that concept. I've heard only a few people talking about it that way but I do think that you certainly mentioned that before. And it really does speak to what is actually happening to your data. Because, of course, a refinery, it basically distills stuff down to its root level, if you think about oil refineries. I actually studied this for a while and it's pretty basic, but the engineering that goes into it needs to be exactly correct or you don't get the stuff that you want. So I think it's a great analogy. What do you think about this whole concept of the Treasure Data Cloud Service helping you tackle some of those very specific analytical needs without having to bring stuff in-house?

Robin: Well, I mean, obviously depending on the circumstances to how convenient that is. But anybody that's actually got already made process is already going to put you ahead of the game if you haven't got one yourself. This is the first takeaway for something like that. If somebody assembled something, they've done it, it's proven in the marketplace and therefore there's some kind of value in effect, well, the work is already gone into it. And there's also the very general fact that refining of data is going to be a much bigger issue than it ever was before. I mean, it is not talked about, in my opinion anyway, it's not talked about as much as it should be. Simply apart from the fact that size of the data has grown and the number of sources and the variety of those sources has grown quite considerably. And the reliability of the data in terms of whether it's clean, they need to disambiguate the data, all sorts of issues that rise just in terms of the governance of the data.

So before you actually get around to being able to do reliable analysis on it, you know, if your data's dirty, then your results will be skewed in some way or another. So that is something that has to be addressed, that has to be known about. And the triangulator of providing, as far as I can see, a very viable service to assist in that.

Eric: Yes, indeed. Well, let me go ahead and bring Kirk back into the equation here just real quickly. I wanted to take a look at one of these other slides and just kind of get your impression of things, Kirk. So maybe let's go back to this MarkLogic slide. And by the way, Kirk provided the link, if you didn't see it folks, to some of his class discovery slides because that's a very interesting concept. And I think this is kind of brewing at the back of my mind, Kirk, as I was talking about this a moment ago. This whole question that one of the attendees posed about how do you go about finding new classes. I love this topic because it really does speak to the sort of, the difficult side of categorizing things because I've always had a hard time categorizing stuff. I'm like, "Oh, god, I can fit in five categories, where do I put it?" So I just don't want to categorize anything, right?

And that's why I love search, because you don't have to categorize it, you don't have to put it in the folder. Just search for it and you'll find it if you know how to search. But if you're in that process of trying to segment, because that's basically what categorization is, it's segmenting; finding new classes, that's kind of an interesting thing. Can you kind of speak to the power of search and semantics and hierarchies, for example, as Frank was talking about with respect to MarkLogic and the role that plays in finding new classes, what do you think about that?

Kirk: Well, first of all, I'd say you are reading my mind. Because that was what I was thinking of a question even before you were talking, this whole semantic piece here that MarkLogic presented. And if you come back to my slide, you don't have to do this, but back on the slide five on what I presented this afternoon; I talked about this semantics that the data needs to be captured.

So this whole idea of search, there you go. I firmly believe in that and I've always believed in that with big data, sort of take the analogy of Internet, I mean, just the Web, I mean having the world knowledge and information and data on a Web browser is one thing. But to have it searchable and retrievable efficiently as one of the big search engine companies provide for us, then that's where the real power of discovery is. Because connecting the search terms, sort of the user interests areas to the particular data granule, the particular webpage, if you want to think the Web example or the particular document if you're talking about document library. Or a particular customer type of segment if that's your space.

And semantics gives you that sort of knowledge layering on top of just a word search. If you're searching for a particular type of thing, understanding that a member of a class of such things can have a certain relationship to other things. Even include that sort of relationship information and that's a class hierarchy information to find things that are similar to what you're looking for. Or sometimes even the exact opposite of what you're looking for, because that in a way gives you sort of additional core of understanding. Well, probably something that's opposite of this.

Eric: Yeah.

Kirk: So actually understand this. I can see something that's opposite of this. And so the semantic layer is a valuable component that's frequently missing and it's interesting now that this would come up here in this context. Because I've taught a graduate course in database, data mining, learning from data, data science, whatever you want to call it for over a decade; and one of my units in this semester-long course is on semantics and ontology. And frequently my students would look at me like, what does this have to do with what we're talking about? And of course at the end, I think we do understand that putting that data in some kind of a knowledge framework. So that, just for example, I'm looking for information about a particular customer behavior, understanding that that behavior occurs, that's what the people buy at a sporting event. What kind of products do I offer to my customers when I notice on their social media - on Twitter or Facebook - that they say they're going to a sporting event like football, baseball, hockey, World Cup, whatever it might be.

Okay, so sporting event. So they say they're going to, let's say, a baseball game. Okay, I understand that baseball is a sporting event. I understand that's usually a social and you go with people. I understand that it's usually in an outdoor space. I mean, understanding all those contextual features, it enables sort of, more powerful, sort of, segmentation of the customer involved and your sort of personalization of the experience that you're giving them when, for example, they're interacting with your space through a mobile app while they're sitting in a stadium.

So all that kind of stuff just brings so much more power and discovery potential to the data in that sort of indexing idea of indexing data granules by their semantic place and the knowledge space is really pretty significant. And I was really impressed that came out today. I think it's sort of a fundamental thing to talk.

Eric: Yeah, it sure is. It's very important in the discovery process, it's very important in the classification process. And if you think about it, Java works in classes. It's an object oriented, I guess, more or less, you could say form of programming and Java works in classes. So if you're actually designing software, this whole concept of trying to find new classes is actually pretty important stuff in terms of the functionality you're trying to deliver. Because especially in this new wild, wooly world of big data where you have so much Java out there running so many of these different applications, you know there are 87, 000 ways or more to get anything done with a computer, to get any kind of bit of functionality done.

One of my running jokes when people say, "Oh, you can build a data warehouse using NoSQL." I'm like, "well, you could, yeah, that's true. You could also build a data warehouse using Microsoft Word." It's not the best idea, it's not going to perform very well but you can actually do it. So the key is you have to find the best way to do something.

Go ahead.

Kirk: Let me just respond to that. It's interesting you mentioned the Java class example which didn't come into my mind until you said it. One of the aspects of Java and classes and that sort of object orientation is that there are methods that bind to specific classes. And this is really the sort of a message that I was trying to send in my presentation and that once you understand some of these data granules - these knowledge nuggets, these tags, these annotations and these semantic labels - then you can bind a method to that. They basically have this reaction or this response and have your system provide this sort of automated, proactive response to this thing the next time that we see it in the data stream.

So that concept of binding actions and methods to specific class is really one of the powers of automated real-time analytics. And I think that you sort of hit on something.

Eric: Good, good, good. Well, this is good stuff. So let's see, Will, I want to hand it back to you and actually throw a question to you from the audience. We got a few of those in here too. And folks, we're going long because we want to get some of these great concepts in these good questions.

So let me throw a question over to you from one of the audience numbers who's saying, "I'm not really seeing how business intelligence is distinguishing cause and effect." In other words, as the systems are making decisions based on observable information, how do they develop new models to learn more about the world? It's an interesting point so I'm hearing a cause-and-effect correlation here, root cause analysis, and that's some of that sort of higher-end stuff in the analytics that you guys talk about as opposed to traditional BI, which is really just kind of reporting and kind of understanding what happened. And of course, your whole direction, just looking at your slide here, is moving toward that predictive capability toward making those decisions or at least making those recommendations, right? So the idea is that you guys are trying to service the whole range of what's going on and you're understanding that the key, the real magic, is in the analytical goal component there on the right.

Will: Absolutely. I think that question is somewhat peering into the future, in the sense that data science, as I mentioned before, we saw the slide with the requirements of the data scientist; it's a pretty challenging role for someone to be in. They have to have that rich knowledge of statistics and science. You need to have the domain knowledge to apply your mathematical knowledge to the domains. So what we're seeing today is there aren't these out-of-the-box predictive tools that a business user, like, could pull up in Excel and automatically predict their future, right?

It does require that advanced knowledge in technology at this stage. Now someday in the future, it may be that some of these systems, these scale-out systems become sentient and start doing some wild stuff. But I would say at this stage, you still have to have a data scientist in the middle to continue to build models, not these models. These predictive models around data mining and such are highly tuned in and built by the data scientist. They're not generated on their own, if you know what I mean.

Eric: Yeah, exactly. That's exactly right. And one of my lines is "Machines don't lie, at least not yet."

Will: Not yet, exactly.

Eric: I did read an article - I have to write something about this - about some experiment that was done at a university where they said that these computer programs learned to lie, but I got to tell you, I don't really believe it. We'll do some research on that, folks.

And for the last comment, so Robin I'll bring you back in to take a look at this WebAction platform, because this is very interesting. This is what I love about a whole space is that you get such different perspectives and different angles taken by the various vendors to serve very specific needs. And I love this format for our show because we got four really interesting vendors that are, frankly, not really stepping on each others' toes at all. Because we're all doing different bits and pieces of the same overall need which is to use analytics, to get stuff done.

But I just want to get your perspective on this specific platform and their architecture. How they're going about doing things. I find it pretty compelling. Bagaimana menurut anda?

Robin: Well, I mean, it's pointed at extremely fast results from streaming data and as search, you have to architect for that. I mean, you're not going to get away with doing anything, amateurish, as we got any of that stuff. I hear this is extremely interesting and I think that one of the things that we witnessed over the past; I mean I think you and I, our jaw has been dropping more and more over the past couple of years as we saw more and more stuff emerge that was just like extraordinarily fast, extraordinarily smart and pretty much unprecedented.

This is obviously, WebAction, this isn't its first rodeo, so to speak. It's actually it's been out there taking names to a certain extent. So I don't see but supposed we should be surprised that the architecture is fairly switched but it surely is.

Eric: Well, I'll tell you what, folks. We burned through a solid 82 minutes here. I mean, thank you to all those folks who have been listening the whole time. If you have any questions that were not answered, don't be shy, send an email to yours truly. We should have an email from me lying around somewhere. And a big, big thank you to both our presenters today, to Dr. Kirk Borne and to Dr. Robin Bloor.

Kirk, I'd like to further explore some of that semantic stuff with you, perhaps in a future webcast. Because I do think that we're at the beginning of a very new and interesting stage now. What we're going to be able to leverage a lot of the ideas that the people have and make them happen much more easily because, guess what, the software is getting less expensive, I should say. It's getting more usable and we're just getting all this data from all these different sources. And I think it's going to be a very interesting and fascinating journey over the next few years as we really dig into what this stuff can do and how can it improve our businesses.

So big thank you to Techopedia as well and, of course, to our sponsors - Pentaho, WebAction, MarkLogic and Treasure Data. And folks, wow, with that we're going to conclude, but thank you so much for your time and attention. We'll catch you in about a month and a half for the next show. And of course, the briefing room keeps on going; radio keeps on going; all our other webcast series keep on rocking and rolling, folks. Terima kasih banyak. We'll catch you next time. Sampai jumpa.