Bagaimanakah analitik boleh meningkatkan perniagaan? - transkrip episod berturut-turut 2

Nota Editor: Ini adalah transkrip salah satu daripada webcast lalu kami. Episod seterusnya akan datang dengan cepat, klik di sini untuk mendaftar.

Eric Kavanagh: Tuan-tuan dan puan-puan, hello dan selamat datang sekali lagi ke Episod 2 TechWise. Ya, sudah tiba masanya untuk mendapatkan orang bijak! Saya mempunyai banyak orang yang pintar pada hari ini untuk membantu kami dalam usaha ini. Nama saya Eric Kavanagh, tentu saja. Saya akan menjadi tuan rumah, moderator anda, untuk sesi kilat ini. Kami mempunyai banyak kandungan di sini, orang ramai. Kami mempunyai beberapa nama besar dalam perniagaan, yang telah menjadi penganalisis dalam ruang kami dan empat vendor yang paling menarik. Jadi kita akan mempunyai banyak tindakan yang baik pada panggilan hari ini. Dan tentu saja, anda di luar sana dalam penonton memainkan peranan penting dalam bertanya soalan.

Jadi sekali lagi, rancangan itu adalah TechWise dan topik hari ini ialah "Bagaimanakah Analytics Boleh Meningkatkan Perniagaan?" Jelas sekali, ia adalah topik hangat di mana ia akan cuba memahami pelbagai jenis analisis yang boleh anda lakukan dan bagaimana ia dapat meningkatkan operasi anda kerana itulah yang berlaku pada penghujung hari.

Jadi, anda dapat melihat diri saya di sana di atas, itu benar-benar kamu. Dr Kirk Borne, kawan baik dari George Mason University. Beliau adalah seorang saintis data yang mempunyai banyak pengalaman, kepakaran yang sangat mendalam dalam bidang ini dan data perlombongan dan data besar dan semua jenis barangan yang menyeronokkan. Dan, sudah tentu, kami mempunyai Dr Robin Bloor, Ketua Penganalisis di sini di Bloor Group. Siapa terlatih sebagai aktuar banyak, bertahun-tahun yang lalu. Dan dia benar-benar menumpukan perhatian kepada ruang data besar ini dan ruang analitik yang agak serius untuk dekad yang lalu. Sudah lima tahun hampir sejak kami melancarkan Kumpulan Bloor. Jadi masa lalat apabila anda sedang berseronok.

Kami juga akan mendengar dari Will Gorman, Ketua Arkitek Pentaho; Steve Wilkes, CCO of WebAction; Frank Sanders, Pengarah Teknikal di MarkLogic; dan Hannah Smalltree, Pengarah di Data Harta. Jadi seperti yang saya katakan, itu banyak kandungan.

Jadi bagaimanakah analitik boleh membantu perniagaan anda? Nah, bagaimana ia tidak dapat membantu perniagaan anda, terus terang? Terdapat pelbagai cara analitik boleh digunakan untuk melakukan perkara-perkara yang meningkatkan organisasi anda.

Oleh itu, semarakkan operasi. Itulah yang anda tidak banyak mendengar tentang seperti yang anda lakukan mengenai perkara seperti pemasaran atau meningkatkan pendapatan atau bahkan mengenal pasti peluang. Tetapi memperkemas operasi anda adalah perkara yang benar-benar kuat yang boleh anda lakukan untuk organisasi anda kerana anda boleh mengenal pasti tempat di mana anda boleh menggunakan sesuatu atau anda boleh menambah data ke proses tertentu, contohnya. Dan itu boleh menyelaraskannya dengan tidak memerlukan seseorang untuk mengambil telefon untuk memanggil atau seseorang untuk menghantar e-mel. Terdapat begitu banyak cara yang berbeza yang boleh anda menyelaraskan operasi anda. Dan semua itu benar-benar membantu mengurangkan kos anda, bukan? Itulah kunci, ia menurunkan kos. Tetapi ia juga membolehkan anda untuk memberi perkhidmatan kepada pelanggan anda dengan lebih baik.

Dan jika anda berfikir tentang bagaimana orang yang tidak sabar dan saya melihat ini setiap hari dari segi bagaimana orang berinteraksi dalam talian, walaupun dengan persembahan kami, penyedia perkhidmatan yang kami gunakan. Kesabaran yang ada pada orang, rentang perhatian, semakin singkat dan lebih pendek pada hari itu. Dan apa itu bermakna bahawa anda perlu, sebagai sebuah organisasi, bertindak balas dalam masa yang lebih cepat dan cepat untuk dapat memuaskan pelanggan anda.

Jadi, sebagai contoh, jika seseorang berada di laman web web anda atau melayari mencari sesuatu, jika mereka kecewa dan mereka pergi, anda mungkin kehilangan pelanggan. Dan bergantung pada berapa banyak yang anda bayar untuk produk atau perkhidmatan anda, dan mungkin itu masalah besar. Oleh itu, garis bawah adalah operasi merasmikan, saya fikir, adalah salah satu ruang terpanas untuk menggunakan analisis. Dan anda melakukannya dengan melihat angka-angka, dengan mengkritik data, dengan mencari tahu, misalnya, "Hei, kenapa kita kehilangan begitu banyak orang di laman web ini?" "Kenapa kita mendapat beberapa panggilan telefon sekarang?"

Dan masa yang lebih tepat anda boleh bertindak balas terhadap perkara semacam itu, peluang yang lebih baik anda akan mendapat di atas keadaan dan melakukan sesuatu mengenainya sebelum terlambat. Kerana ada tetingkap masa apabila seseorang mendapat kecewa tentang sesuatu, mereka tidak berpuas hati atau mereka cuba mencari sesuatu tetapi mereka kecewa; anda mendapat peluang untuk mendapatkannya, untuk merebutnya, untuk berinteraksi dengan pelanggan itu. Dan jika anda berbuat demikian dengan cara yang betul dengan data yang betul atau gambar pelanggan yang bagus - memahami siapa pelanggan ini, apakah keuntungan mereka, apakah pilihan mereka - jika anda benar-benar dapat mengendalikannya, anda akan lakukan pekerjaan yang hebat untuk memegang kepada pelanggan anda dan mendapatkan pelanggan baru. Dan itulah yang berlaku.

Jadi dengan itu, saya akan menyerahkannya, sebenarnya, kepada Kirk Borne, salah seorang saintis data kami pada panggilan hari ini. Dan mereka sangat jarang hari ini, orang-orang. Kami ada dua daripada mereka sekurang-kurangnya dalam panggilan itu jadi masalah besar. Dengan itu, Kirk, saya akan menyerahkannya kepada anda untuk bercakap tentang analisis dan bagaimana ia membantu perniagaan. Berusaha untuk mendapatkannya.

Dr. Kirk Borne: Baik, terima kasih banyak, Eric. Bolehkah anda mendengar saya?

Eric: Baiklah, teruskan.

Dr. Kirk: Baiklah, baiklah. Saya hanya mahu berkongsi jika saya bercakap selama lima minit, dan orang-orang mengibaskan tangan saya. Oleh itu, ucapan pembukaan, Eric, yang anda buat benar-benar mengikat topik ini, saya akan membincangkan secara ringkas dalam beberapa minit akan datang yang menggunakan data besar dan analisis data untuk keputusan untuk menyokong, di sana. Komen yang anda buat mengenai penyelarasan operasi, kepada saya, jenis ini jatuh ke dalam konsep analitik operasi ini di mana anda dapat melihat hampir setiap aplikasi di dunia sama ada aplikasi sains, perniagaan, keselamatan siber dan penguatkuasaan undang-undang dan kerajaan, penjagaan kesihatan. Sebilangan tempat di mana kami mempunyai aliran data dan kami membuat beberapa jenis tindak balas atau keputusan dalam tindak balas terhadap peristiwa dan peringatan dan tingkah laku yang kami lihat dalam aliran data tersebut.

Dan jadi salah satu perkara yang saya ingin bincangkan tentang hari ini adalah jenis bagaimana anda mengekstrak pengetahuan dan pandangan dari data besar untuk sampai ke tahap di mana kita boleh membuat keputusan untuk mengambil tindakan. Dan kita sering bercakap tentang ini dalam konteks automasi. Dan hari ini saya mahu menggabungkan automasi dengan penganalisis manusia dalam gelung. Jadi dengan ini saya maksudkan manakala penganalisis perniagaan memainkan peranan penting di sini dari segi pertaruhan, kelayakan, mengesahkan tindakan spesifik atau peraturan pembelajaran mesin yang kita keluarkan dari data. Tetapi jika kita sampai pada tahap di mana kita cukup yakin peraturan perniagaan yang kita telah diekstrak dan mekanisme untuk memberi amaran kepada kita adalah sah, maka kita dapat mengubahnya dengan cara yang automatik. Kami sebenarnya melakukan operasi yang memperkemaskan bahawa Eric bercakap tentang.

Oleh itu saya bermain sedikit dengan kata-kata di sini tetapi saya berharap, jika ia berfungsi untuk anda, saya bercakap mengenai cabaran D2D. Dan D2D, bukan hanya data keputusan dalam semua konteks, kita melihat ini di bahagian bawah slaid ini semoga anda dapat melihatnya, membuat penemuan dan meningkatkan dolar pendapatan dari pipeline analitik kami.

Oleh itu dalam konteks ini, saya sebenarnya mempunyai peranan pemasar ini kepada diri sendiri di sini sekarang bahawa saya bekerja dengan dan itu; perkara pertama yang anda ingin lakukan adalah mencirikan data anda, mengekstrak ciri-ciri, mengekstrak ciri-ciri pelanggan anda atau entiti apa sahaja yang anda sedang menjejaki di ruang anda. Mungkin ia pesakit dalam persekitaran analisis kesihatan. Mungkin ia adalah pengguna Web jika anda melihat semacam isu keselamatan siber. Tetapi ciri dan ekstrak ciri-ciri dan kemudian ekstrak beberapa konteks tentang individu itu, mengenai entiti itu. Dan kemudian anda mengumpulkan potongan-potongan yang telah anda buat dan memasukkannya ke dalam beberapa jenis koleksi yang kemudian anda dapat menerapkan algoritma pembelajaran mesin.

Alasan saya mengatakannya dengan cara ini ialah, katakanlah, anda mempunyai kamera pengawasan di lapangan terbang. Video itu sendiri adalah jumlah besar, besar dan ia juga sangat tidak terstruktur. Tetapi anda boleh mengeluarkan dari pengawasan video, biometrik wajah dan mengenal pasti individu dalam kamera pengawasan. Jadi, misalnya di lapangan terbang, anda boleh mengenal pasti individu tertentu, anda boleh menjejaki mereka melalui lapangan terbang dengan mengenal pasti individu yang sama dalam pelbagai kamera pengawasan. Oleh itu, ciri biometrik yang diekstrak yang anda benar-benar perlombongan dan pengesanan bukanlah video terperinci sebenar itu sendiri. Tetapi sebaik sahaja anda mempunyai pengekstrakan tersebut maka anda boleh menggunakan peraturan dan analisis pembelajaran mesin untuk membuat keputusan sama ada anda perlu mengambil tindakan dalam kes tertentu atau sesuatu yang terjadi dengan tidak betul atau sesuatu yang anda mempunyai peluang untuk membuat tawaran. Jika anda, contohnya, jika anda mempunyai kedai di lapangan terbang dan anda melihat pelanggan itu datang dengan cara anda dan anda tahu dari maklumat lain mengenai pelanggan itu, mungkin dia benar-benar berminat membeli barangan di kedai bebas cukai atau sesuatu seperti itu, buat tawaran itu.

Jadi apakah jenis perkara yang saya maksudkan dengan pencirian dan potensi? Dengan pencirian yang saya maksudkan, sekali lagi, mengekstrak ciri-ciri dan ciri-ciri dalam data. Dan ini sama ada dijana mesin, maka algoritmanya sebenarnya boleh mengekstrak, sebagai contoh, tandatangan biometrik dari video atau analisis sentimen. Anda boleh mengekstrak sentimen pelanggan melalui ulasan dalam talian atau media sosial. Sesetengah perkara ini mungkin dihasilkan oleh manusia, supaya manusia, penganalisis perniagaan, dapat mengekstrak ciri tambahan yang akan saya tunjukkan dalam slaid seterusnya.

Sesetengah daripada ini boleh menjadi orang ramai. Dan oleh orang ramai, ada banyak cara yang berbeza untuk anda fikirkan. Tetapi sangat mudah, contohnya, pengguna anda datang ke laman web anda dan mereka memasukkan kata-kata carian, kata kunci, dan mereka berakhir pada halaman tertentu dan benar-benar menghabiskan waktu di sana pada halaman itu. Bahawa mereka sebenarnya, sekurang-kurangnya, memahami bahawa mereka sama ada melihat, menyemak imbas, mengklik perkara di halaman itu. Apa yang dikatakan kepada anda adalah bahawa kata kunci yang mereka taip pada awalnya adalah deskriptor halaman itu kerana ia mendarat pelanggan pada halaman yang mereka harapkan. Oleh itu, anda boleh menambah sekeping maklumat tambahan itu, iaitu pelanggan yang menggunakan kata kunci ini sebenarnya mengidentifikasi laman web ini dalam arsitektur maklumat kami sebagai tempat di mana kandungan itu mencocokkan kata kunci tersebut.

Oleh itu, crowdsourcing adalah satu lagi aspek yang kadang-kadang orang lupa, seperti mengesan keranjang roti para pelanggan, supaya dapat berbicara; bagaimana mereka bergerak melalui ruang mereka, sama ada harta dalam talian atau harta tanah. Dan kemudian gunakan jalan semacam itu, bahawa pelanggan mengambil maklumat tambahan mengenai perkara-perkara yang kita lihat.

Oleh itu, saya ingin mengatakan perkara yang dijanakan oleh manusia, atau mesin yang dijana, akhirnya mempunyai konteks dalam jenis penjelasan atau penandaan butiran data atau entiti tertentu. Sama ada entiti tersebut adalah pesakit dalam suasana hospital, pelanggan atau apa sahaja. Dan sebagainya terdapat pelbagai jenis penandaan dan anotasi. Sebahagian daripadanya adalah mengenai data itu sendiri. Itulah salah satu perkara, maklumat jenis apa, jenis maklumat apa, apakah ciri-ciri, bentuk, mungkin tekstur dan corak, anomali, tingkah laku tidak anomali. Dan kemudian ekstrak beberapa semantik, iaitu, bagaimana ini berkaitan dengan perkara lain yang saya tahu, atau pelanggan ini adalah pelanggan elektronik. Pelanggan ini adalah pelanggan pakaian. Atau pelanggan ini suka membeli muzik.

Oleh itu, mengenal pasti semantik tentang itu, pelanggan-pelanggan yang suka muzik cenderung suka hiburan. Mungkin kita boleh menawarkan mereka beberapa harta hiburan lain. Oleh itu, memahami semantik dan juga beberapa ramuan, yang pada dasarnya mengatakan: di mana asalnya, yang memberikan penegasan ini, apa masa, tarikh apa, di bawah keadaan apa?

Jadi sebaik sahaja anda mempunyai semua anotasi dan ciri-ciri, tambahkan kepada yang kemudian langkah seterusnya, iaitu konteks, jenis siapa, apa, bila, di mana dan mengapa ia. Siapa pengguna? Apakah saluran yang mereka sertai? Apakah sumber maklumat itu? Apa jenis reuses yang kita lihat dalam sekeping maklumat atau data data tertentu ini? Dan apakah, nilai semacam itu dalam proses perniagaan? Dan kemudian mengumpulkan perkara-perkara itu dan mengurusnya, dan sebenarnya membantu membuat pangkalan data, jika anda ingin memikirkannya dengan cara itu. Buat mereka boleh dicari, boleh diguna semula, oleh penganalisis perniagaan lain atau dengan proses automatik yang akan, apabila saya melihat set ciri-ciri ini, sistem boleh mengambil tindakan automatik ini. Oleh itu, kami mendapat kecekapan analitik operasi seperti itu, tetapi semakin banyak kami mengumpul maklumat yang berguna dan komprehensif, dan kemudian mengatasinya untuk kes-kes penggunaan ini.

Kami turun ke perniagaan. Kami melakukan analisis data. Kami mencari corak yang menarik, kejutan, mengatasi masalah baru, anomali. Kami mencari kelas dan segmen baru dalam populasi. Kami mencari persatuan dan korelasi dan pautan di antara pelbagai entiti. Dan kemudian kita menggunakan semua itu untuk mendorong penemuan, keputusan dan proses membuat dolar.

Jadi ada lagi, di sini kita mendapat slaid data terakhir yang saya ada hanya pada asasnya meringkaskan, menjaga penganalisis perniagaan dalam gelung, sekali lagi, anda tidak mengekstrak manusia itu dan itu semua penting untuk memastikan bahawa manusia di sana.

Jadi ciri-ciri ini, mereka semua disediakan oleh mesin atau penganalisis manusia atau bahkan crowdsourcing. Kami menggunakan kombinasi perkara untuk meningkatkan set latihan kami untuk model kami dan berakhir dengan model ramalan yang lebih tepat, kurang positif dan negatif palsu, tingkah laku yang lebih cekap, campur tangan yang lebih cekap dengan pelanggan kami atau sesiapa sahaja.

Jadi, pada penghujung hari, kami benar-benar menggabungkan pembelajaran mesin dan data besar dengan kekuatan kognisi manusia ini, di mana sekeping nota penjelasan menandakan. Dan itu boleh membawa menerusi visualisasi dan visual analitik-jenis alat atau persekitaran data yang mengasyikkan atau crowdsourcing. Dan, pada penghujung hari, apa yang sebenarnya dilakukan adalah menghasilkan penemuan, pandangan dan D2D kami. Dan itu adalah komen saya, jadi terima kasih kerana mendengar.

Eric: Hei yang sangat bagus dan biarkan saya pergi ke depan dan menyerahkan kunci ke Dr. Robin Bloor untuk memberikan perspektifnya juga. Yeah, saya suka mendengar komen tentang memperkemas konsep operasi dan anda bercakap mengenai analisis operasi. Saya rasa itu adalah kawasan besar yang perlu diterokai dengan teliti. Dan saya rasa, sejurus sebelum Robin, saya akan bawa kamu balik, Kirk. Ia memerlukan anda mempunyai beberapa kerjasama yang sangat penting di kalangan pelbagai pemain dalam syarikat itu, kan? Anda perlu bercakap dengan orang-orang operasi; anda perlu mendapatkan orang teknikal anda. Kadang-kadang anda mendapat orang pemasaran anda atau orang-orang antara muka Web anda. Ini biasanya kumpulan yang berbeza. Adakah anda mempunyai apa-apa amalan atau cadangan yang terbaik tentang bagaimana untuk mendapatkan semua orang meletakkan kulit mereka dalam permainan?

Dr. Kirk: Nah, saya fikir ini datang dengan budaya perniagaan kerjasama. Sebenarnya, saya bercakap tentang budaya analisis tiga C itu. Satu adalah kreativiti; yang lain adalah rasa ingin tahu dan yang ketiga adalah kerjasama. Oleh itu, anda mahukan orang yang kreatif, serius, tetapi anda juga perlu mendapatkan orang-orang ini untuk bekerjasama. Dan ia benar-benar bermula dari bahagian atas, semacam membina budaya itu dengan orang-orang yang secara terbuka berkongsi dan bekerjasama ke arah matlamat bersama perniagaan.

Eric: Semuanya masuk akal. Dan anda benar-benar perlu mendapatkan kepimpinan yang baik di bahagian atas untuk membuatnya berlaku. Jadi mari kita teruskan dan serahkan kepada Dr. Bloor. Robin, lantai adalah milik awak.

Dr. Robin Bloor: Baiklah. Terima kasih kerana intro itu, Eric. Okay, cara pan ini keluar, ini menunjukkan, kerana kami mempunyai dua penganalisis; Saya dapat melihat persembahan penganalisis bahawa orang lain tidak. Saya tahu apa yang Kirk akan katakan dan saya hanya pergi sudut yang berbeza supaya kita tidak terlalu banyak bertindih.

Jadi apa yang sebenarnya saya bicarakan atau berniat bercakap di sini adalah peranan penganalisis data berbanding peranan penganalisis perniagaan. Dan cara saya mencirikannya, baik, lidah di pipi pada tahap tertentu, adalah jenis perkara Jekyll dan Hyde. Perbezaannya secara khusus adalah saintis data, secara teori sekurang-kurangnya, tahu apa yang mereka lakukan. Walaupun penganalisis perniagaan tidak begitu, okay dengan cara kerja matematik, apa yang boleh dipercayai dan apa yang tidak boleh dipercayai.

Oleh itu mari kita turunkan alasan mengapa kita melakukan ini, sebab analisis data tiba-tiba menjadi masalah besar selain fakta bahawa kita sebenarnya boleh menganalisis sejumlah besar data dan menarik data dari luar organisasi; Adakah ia membayar. Cara saya melihat ini - dan saya fikir ini hanya menjadi kes tetapi saya pasti fikir ia adalah kes - analisis data adalah benar-benar perniagaan R & D. Apa yang sebenarnya anda lakukan dalam satu atau lebih cara dengan analisis data ialah anda melihat proses perniagaan dalam satu jenis atau sama ada itu interaksi dengan pelanggan, sama ada dengan cara operasi runcit anda, cara yang anda gunakan kedai anda. Ia tidak semestinya masalahnya. Anda sedang melihat proses perniagaan yang diberikan dan anda cuba memperbaikinya.

Hasil penyelidikan dan pembangunan yang berjaya adalah proses perubahan. Dan anda boleh memikirkan pembuatan, jika anda mahu, sebagai contoh biasa ini. Kerana dalam pembuatan, orang mengumpulkan maklumat tentang segala-galanya untuk mencuba dan memperbaiki proses pembuatan. Tetapi saya fikir apa yang berlaku atau apa yang sedang berlaku pada data besar adalah semua ini kini diterapkan kepada semua perniagaan dalam apa jua bentuk dengan cara yang orang boleh fikirkan. Jadi cukup banyak proses perniagaan untuk pemeriksaan jika anda boleh mengumpul data mengenainya.

Jadi itu satu perkara. Jika anda suka, itu akan menjadi soal analisis data. Apa yang boleh dilakukan analitik data untuk perniagaan? Nah, ia boleh mengubah perniagaan sepenuhnya.

Gambar rajah tertentu yang saya tidak akan menerangkan dalam apa jua kedalaman, tetapi ini adalah gambarajah yang kami buat sebagai kemunculan projek penyelidikan yang kami lakukan untuk enam bulan pertama tahun ini. Ini adalah cara mewakili senibina data besar. Dan beberapa perkara yang patut ditunjuk sebelum saya pergi ke slaid seterusnya. Terdapat dua aliran data di sini. Satu ialah arus data masa nyata, yang merangkumi bahagian atas rajah. Yang satunya adalah aliran data yang lebih perlahan yang berjalan di sepanjang bahagian bawah rajah.

Lihat bahagian bawah rajah. Kami mempunyai Hadoop sebagai takungan data. Kami mempunyai pelbagai pangkalan data. Kami mempunyai keseluruhan data di sana dengan banyak aktiviti yang berlaku di atasnya, yang kebanyakannya adalah aktiviti analitik.

Titik yang saya buat di sini dan satu-satunya perkara yang saya ingin buat di sini adalah bahawa teknologi itu sukar. Ia tidak mudah. Ia tidak mudah. Ia bukan sesuatu yang sesiapa yang baru untuk permainan itu sebenarnya boleh disatukan. Ini agak rumit. Dan jika anda pergi ke instrumen perniagaan untuk melakukan analisis yang boleh dipercayai di seluruh proses ini, maka itu bukan sesuatu yang akan berlaku secara khusus dengan cepat. Ia akan memerlukan banyak teknologi untuk ditambah ke campuran.

Baik. Persoalannya ialah saintis data, saya boleh menuntut sebagai seorang saintis data kerana saya telah dilatih dalam statistik sebelum saya pernah dilatih dalam pengkomputeran. Dan saya melakukan kerja aktuari untuk jangka masa jadi saya tahu cara perniagaan menganjurkan, analisis statistik, juga untuk menjalankan sendiri. Ini bukan perkara yang remeh. Dan terdapat banyak amalan terbaik yang melibatkan kedua-dua pihak manusia dan di sisi teknologi.

Oleh itu, dalam soalan "apa saintis data, " saya telah meletakkan pic Frankenstein hanya kerana ia adalah kombinasi antara perkara yang perlu dikaji bersama. Terdapat pengurusan projek yang terlibat. Terdapat pemahaman mendalam dalam statistik. Terdapat kepakaran perniagaan domain, yang lebih merupakan masalah penganalisis perniagaan daripada saintis data, semestinya. Terdapat pengalaman atau keperluan untuk memahami seni bina data dan dapat membina arkitek data dan terdapat kejuruteraan perisian yang terlibat. Dalam erti kata lain, ia mungkin satu pasukan. Ia mungkin bukan individu. Dan ini bermakna bahawa ia mungkin sebuah jabatan yang perlu dianjurkan dan organisasinya perlu difikirkan dengan agak meluas.

Melontar ke dalam campuran fakta pembelajaran mesin. Kita tidak boleh lakukan, maksud saya, pembelajaran mesin bukanlah baru dalam erti kata bahawa kebanyakan teknik statistik yang digunakan dalam pembelajaran mesin telah diketahui selama beberapa dekad. Terdapat beberapa perkara baru, maksudnya rangkaian saraf agak baru, saya fikir mereka berusia sekitar 20 tahun, jadi sebahagiannya agak baru. Tetapi masalah dengan pembelajaran mesin adalah bahawa kita sebenarnya tidak mempunyai kekuatan komputer untuk melakukannya. Dan apa yang berlaku, selain dari apa-apa lagi, adalah kuasa komputer kini di tempat. Dan itu bermakna banyak sekali yang kita katakan, saintis data telah dilakukan sebelum dari segi keadaan pemodelan, data persampelan dan kemudian marshalling itu untuk menghasilkan analisis yang lebih mendalam tentang data. Sebenarnya, kita hanya boleh membuang kuasa komputer di dalamnya. Hanya pilih algoritma mesin pembelajaran, membuangnya pada data dan melihat apa yang keluar. Dan itu sesuatu yang boleh dilakukan penganalisis perniagaan, bukan? Tetapi penganalisis perniagaan perlu memahami apa yang mereka lakukan. Maksud saya, saya fikir itulah masalahnya, lebih daripada apa-apa lagi.

Nah, ini hanya untuk mengetahui lebih lanjut tentang perniagaan daripada datanya daripada dengan cara lain. Einstein tidak mengatakan itu, saya berkata demikian. Saya hanya meletakkan gambarnya untuk kredibiliti. Tetapi keadaan sebenarnya mula berkembang adalah salah satu teknologi, jika digunakan dengan betul, dan matematik, jika digunakan dengan betul, akan dapat menjalankan perniagaan sebagai individu. Kami telah melihat ini dengan IBM. Pertama sekali, ia boleh mengalahkan lelaki terbaik di catur, dan kemudian ia boleh mengalahkan lelaki terbaik di Jeopardy; tetapi akhirnya kita akan dapat mengalahkan orang-orang terbaik dalam menjalankan sebuah syarikat. Statistik akhirnya akan berjaya. Dan sukar untuk melihat bagaimana keadaan itu tidak akan berlaku, ia belum lagi berlaku.

Jadi apa yang saya katakan, dan ini adalah jenis mesej lengkap persembahan saya, adalah dua isu perniagaan ini. Yang pertama adalah, bolehkah anda mendapatkan teknologi yang betul? Bolehkah anda membuat kerja teknologi untuk pasukan yang sebenarnya akan dapat mempengerusikannya dan mendapatkan manfaat untuk perniagaan? Dan kemudian kedua, adakah anda boleh mendapat orang yang betul? Dan kedua-duanya adalah isu. Dan mereka adalah isu-isu yang tidak, pada masa ini, mereka berkata demikian, diselesaikan.

Okay Eric, saya akan lulus kembali kepada anda. Atau saya sepatutnya lulus ke Will.

Eric: Sebenarnya, ya. Terima kasih, Will Gorman. Ya, pergi ke sana, Will. Jadi mari lihat. Izinkan saya memberikan kunci kepada WebEx. Jadi apa yang awak buat? Pentaho, sudah tentu, anda telah berada di sekelilingnya dan jenis BI terbuka di mana anda bermula. Tetapi anda mendapat lebih banyak daripada yang anda miliki, jadi mari lihat apa yang anda ada hari ini untuk analitik.

Akan Gorman: Sudah tentu. Hai semua! Nama saya adalah Will Gorman. Saya Ketua Arkitek di Pentaho. Bagi anda yang belum pernah mendengar tentang kami, saya hanya menyebut Pentaho adalah sebuah syarikat integrasi data dan analitik yang besar. Kami telah berada dalam perniagaan selama sepuluh tahun. Produk kami berkembang bersebelahan dengan komuniti data yang besar, bermula sebagai platform sumber terbuka untuk integrasi data dan analisis, berinovasi dengan teknologi seperti Hadoop dan NoSQL bahkan sebelum entiti komersial dibentuk di sekitar teknologi itu. Dan kini kami mempunyai lebih daripada 1500 pelanggan komersil dan banyak lagi pelantikan pengeluaran hasil daripada inovasi kami di sekitar sumber terbuka.

Seni bina kami adalah sangat mudah dibina dan diperluaskan, dibina dengan tujuan untuk menjadi fleksibel kerana teknologi data yang besar terutamanya berkembang dengan pesat. Pentaho menawarkan tiga bidang utama produk yang berfungsi bersama untuk menangani kes penggunaan analisis data besar.

Produk pertama pada tahap senibina kami ialah Integrasi Data Pentaho yang ditujukan kepada jurutera data dan jurutera data. Produk ini menawarkan pengalaman visual, drag-and-drop untuk menentukan talian paip data dan proses untuk mengatur data dalam persekitaran data besar dan persekitaran tradisional juga. Produk ini adalah platform ringan, metadatabase, integrasi data yang dibina di Jawa dan boleh digunakan sebagai proses dalam MapReduce atau YARN atau Storm dan banyak batch dan platform masa nyata yang lain.

Kawasan produk kedua kami adalah sekitar analitik visual. Dengan teknologi ini, organisasi dan OEM boleh menawarkan visualisasi dan pengalaman analitik drag-and-drop yang kaya untuk penganalisis perniagaan dan pengguna perniagaan oleh penyemak imbas dan tablet moden, yang membolehkan laporan dan papan pemuka iklan hoc. Serta pembentangan dashboarding dan laporan piksel yang sempurna.

Kawasan produk ketiga kami memberi tumpuan kepada analisis ramalan yang disasarkan untuk saintis data, algoritma mesin pembelajaran. Seperti yang dinyatakan sebelum ini, seperti rangkaian saraf dan sebagainya, boleh dimasukkan ke dalam persekitaran transformasi data, membolehkan para saintis data pergi dari pemodelan kepada persekitaran pengeluaran, memberikan akses kepada meramalkan, dan ini boleh memberi kesan kepada proses perniagaan dengan segera, dengan cepat.

Semua produk ini terintegrasi dengan ketat ke dalam pengalaman tangkas tunggal dan memberikan pelanggan perusahaan fleksibiliti yang mereka perlukan untuk menangani masalah perniagaan mereka. Kami melihat landskap data besar dalam teknologi tradisional yang berkembang pesat. Apa yang kita dengar daripada beberapa syarikat dalam ruang data besar yang EDW hampir berakhir. Malah, apa yang kita lihat dalam pelanggan perusahaan kami adalah mereka perlu memperkenalkan data besar ke dalam perniagaan dan proses IT yang sedia ada dan tidak menggantikan proses tersebut.

Gambar rajah mudah ini menunjukkan titik dalam senibina yang kita lihat sering, iaitu jenis senibina EDW-penempatan dengan integrasi data dan kes penggunaan BI. Kini gambarajah ini mirip dengan slaid Robin pada seni bina data besar, ia menggabungkan data masa nyata dan sejarah. Apabila sumber data baru dan keperluan masa nyata muncul, kita melihat data besar sebagai sebahagian tambahan dari keseluruhan seni bina IT. Sumber data baru ini termasuk data yang dihasilkan oleh mesin, data tidak berstruktur, volum dan halaju standard dan pelbagai keperluan yang kita dengar dalam data besar; mereka tidak sesuai dengan proses EDW tradisional. Pentaho bekerja rapat dengan Hadoop dan NoSQL untuk mempermudah pengingesan, pemprosesan data dan visualisasi data ini serta menggabungkan data ini dengan sumber-sumber tradisional untuk memberi pelanggan pandangan penuh ke dalam persekitaran data mereka. Kami melakukan ini dengan cara yang ditadbir supaya IT dapat menawarkan penyelesaian analitik penuh ke barisan perniagaan mereka.

Pada penutup, saya ingin menyerlahkan falsafah kami di sekitar analitik dan integrasi data yang besar; kami percaya bahawa teknologi ini lebih baik bersama dengan bekerjasama dengan satu seni bina bersatu, yang membolehkan beberapa kes penggunaan yang mungkin tidak mungkin. Persekitaran data pelanggan kami adalah lebih daripada sekadar data besar, Hadoop dan NoSQL. Apa-apa data adalah permainan yang adil. Dan sumber data besar perlu disediakan dan bekerjasama untuk memberi impak nilai perniagaan.

Akhirnya, kami percaya bahawa untuk menyelesaikan masalah perniagaan di perusahaan dengan sangat berkesan melalui data, IT dan garis-garis perniagaan perlu bekerjasama dalam pendekatan yang dikendalikan dan dicampur dengan analisis data besar. Baiklah terima kasih kerana memberi kita masa untuk bercakap, Eric.

Eric: Anda bertaruh. Tidak, itu perkara yang baik. Saya ingin kembali ke arsitektur anda ketika kami sampai ke Q & As. Oleh itu, mari kita lancarkan seluruh persembahan dan terima kasih banyak untuk itu. Anda pasti telah bergerak cepat beberapa tahun terakhir, saya perlu mengatakannya pasti.

Jadi Steve, biarkan saya pergi ke depan dan menyerahkannya kepada anda. Dan klik di sana pada anak panah bawah dan pergi ke sana. Jadi Steve, saya memberi anda kunci. Steve Wilkes, hanya klik pada anak panah terjauh itu di papan kekunci anda.

Steve Wilkes: Di sana kita pergi.

Eric: Di sana anda pergi.

Steve: Itu intro hebat yang telah kau beri aku.

Eric: Ya.

Steve: Jadi saya Steve Wilkes. Saya CCO di WebAction. Kami hanya berada di sana selama beberapa tahun terakhir dan kami sudah pasti bergerak pantas juga, sejak itu. WebAction adalah platform analisis data besar masa nyata. Eric menyebut tadi, jenis, betapa pentingnya masa nyata dan bagaimana masa sebenar aplikasi anda mendapat. Platform kami direka untuk membina aplikasi masa nyata. Dan untuk membolehkan aplikasi generasi yang didorong data yang boleh dibina secara berperingkat dan untuk membolehkan orang untuk membina papan pemuka dari data yang dihasilkan dari aplikasi tersebut, tetapi memberi tumpuan kepada masa nyata.

Platform kami sebenarnya platform hujung penuh, melakukan segala-galanya dari pemerolehan data, pemprosesan data, sepanjang jalan ke visualisasi data. Dan membolehkan pelbagai jenis orang di dalam perusahaan kami untuk bekerjasama untuk mencipta aplikasi real-time sebenar, memberi mereka wawasan tentang perkara-perkara yang berlaku dalam perusahaan mereka ketika mereka berlaku.

Dan ini sedikit berbeza daripada apa yang kebanyakan orang melihat dalam data besar, jadi pendekatan tradisi - baik, tradisional beberapa tahun terakhir - pendekatan dengan data besar telah menangkapnya dari sekumpulan sumber yang berlainan dan kemudian tiang itu menjadi takungan besar atau tasik atau apa sahaja yang anda mahu memanggilnya. Dan kemudian memprosesnya apabila anda perlu menjalankan pertanyaan di atasnya; untuk menjalankan analisis bersejarah berskala besar atau bahkan hanya menanyakan kepada banyak data. Sekarang yang berfungsi untuk kes penggunaan tertentu. Tetapi jika anda mahu menjadi proaktif dalam perusahaan anda, jika anda ingin benar-benar diberitahu apa yang berlaku dan bukannya mengetahui apabila ada sesuatu yang salah pada akhir hari atau akhir minggu, maka anda benar-benar perlu bergerak untuk masa sebenar.

Dan itu beralih perkara sekeliling sedikit. Ia bergerak pemprosesan ke tengah. Oleh itu, dengan berkesan, anda menggunakan aliran data yang banyak yang dihasilkan secara berterusan dalam perusahaan dan anda memprosesnya semasa anda mendapatkannya. Dan kerana anda memrosesnya semasa anda mendapatkannya, anda tidak perlu menyimpan segala-galanya. Anda hanya boleh menyimpan maklumat penting atau perkara yang anda perlu ingat yang sebenarnya berlaku. Jadi jika anda menjejaki lokasi GPS kenderaan yang bergerak di jalan raya, anda tidak benar-benar peduli di mana setiap saat, anda tidak perlu menyimpan tempat di mana setiap saat. Anda hanya perlu peduli, adakah mereka meninggalkan tempat ini? Adakah mereka tiba di tempat ini? Adakah mereka memandu, atau tidak, jalan raya?

Oleh itu, sangat penting untuk mempertimbangkan bahawa semakin banyak data dijana, maka ketiga Vs. Velocity pada dasarnya menentukan berapa banyak data menjana setiap hari. Lebih banyak data yang dihasilkan lebih banyak yang anda perlu simpan. Dan semakin banyak yang anda perlu simpan, semakin lama diperlukan untuk diproses. Tetapi jika anda boleh memprosesnya ketika anda mendapatkannya, maka anda mendapat manfaat yang sangat besar dan anda boleh bertindak balas terhadapnya. Anda boleh dimaklumkan bahawa perkara-perkara yang sedang berlaku bukannya perlu mencari mereka kemudian.

Jadi platform kami direka untuk menjadi sangat berskala. Ia mempunyai tiga bahagian utama - sekeping pengambilalihan, sekeping pemprosesan dan kemudian potongan visualisasi penghantaran platform. Di sisi pengambilalihan, kami bukan hanya melihat data log buatan mesin seperti log Web atau aplikasi yang mempunyai semua log lain yang 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. Itupun 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. Okay?

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. Okay?

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. Mengambilnya.

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. Apa pendapat kamu?

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. Thank you so much. We'll catch you next time. Selamat tinggal.