Technical Story Telling: Oracle Data Cloud

 

I have been used to regular reorganizations (reorgs) as part of Microsoft stint.  After 15 Months into Oracle, just when I was wondering how come there are no reorgs in Oracle Corporation, I got a mail in a weeks time in May, 2014 that we have been reorganized into a new division named Oracle Data Cloud (ODC) headed by Oracle’s latest acquisition Blue Kai head Omar Tawakol.  Telepathy was in works, I suppose Smile.

Omar had a clear business vision on how the ODC should evolve and he has worked tirelessly to acquire another company Datalogix which augmented the product scope very well.  With three companies, namely Blue Kai – Datalogix – Collective Intellect, Oracle Data Cloud has got a solid product scope, huge market share and till today occupies leader quadrant. 

Vijay Amrit Agrawal has been recruited back into Oracle just after an year and has been tasked to setup a team in India under ODC, namely ODC India Team.  Here is a technical story that I have used while recruiting developers into Oracle Data Cloud.  Below, I have given a long story, but usually I have shortened the story appropriately based on the candidate’s experience and exposure.

 

Story – Blue Kai:

Google was a well known brand ~15 years ago.  Everyone who wanted to research about anything, went straight to google.com and searched.  It is this behavior that made Google a leader in digital ad domain.   As people visited the sites via Google search and results, Google knew exactly what the user was looking for and gave a high quality advertisements in the web sites ad panels or areas.  Google has been respected because of their non-intrusive simple-text ads because their quality was so good.  Around ~5 years back, FlipKart, Amazon, SnapDeal became well known brands.  Users who wanted to buy any product, went directly into these sites and searched leaving Google (or any other search engine) clueless on what the user was looking for.   Slowly, the ad quality of Google reduced as more and more sites became well known.  Google also became increasingly clueless, as browsers became advanced and helped the user hit the sites directly because they already have them in search history or have them bookmarked.  Google realized this soon and came-up with a new browser Google Chrome so that it can capture every site visited directly from browser w/o Google search.  While this worked for logged in users, it did not work for others.  Many sites feared sharing the sites search data to Google as they could be eaten by Google the giant.  Blue Kai realized this lack of trust between digital world is a huge opportunity to tap into.  Blue Kai came up with a smart data-sharing win-win proposition and silently worked with many online properties (sites) to buy into this business model.  Before we get into that business model, let’s ask few questions so that we can appreciate what is the unique business model that Blue Kai has offered that many bought into.

Would Amazon, FlipKart, Google share their search data to each other

Your answer would most probably be “No” to above questions.  But, with Blue Kai in the game, the answer is “Yes”!.  How? you may ask!

You share your data to me and I will give you all others data.  In this data sharing, the source site of the data is not maintained.  That is, when I share you others data I can’t tell you from where I got it.  Similarly, when I share your data to the world, I can’t tell them from where it is received.  But, I can tell you which user machine the search data is for.

Soon, Blue Kai has become the online data sharing hub for many online sites (guess, what that “may” means in number – millions of sites!).   OK, how does it work?

You go to Amazon.in and search for a TV.  You open another tab and hit FlipKart.  How would it be, if FlipKart displayed a set of TV Offers?  You open another tab and browsed a technical blog on Apache Kafka, and on a side panel Google displayed TV ads.  How would that be?

That’s the power that Blue Kai brings to all these sites.  It is not some offline data sharing, but real-time web-scale few micro seconds away sharing of the data.  Google has to process only its search data, Bing has to process its own search data, we at Blue Kai has to process every internet sites every search data in real-time and share the data with in few micro seconds to all others when asked.

Thanks to Blue Kai, Google ad quality has problem has been solved!  Once we have so much data, we surely know how to make money out of it.  We do charge the sites in this data-sharing model based on different business scenarios.

 

Story – Datalogix:

Chief Marketing Officers (CMOs) around the world started doubting the whole digital advertisement spend and it’s yield.  In case of TV ads, user’s attention is guaranteed as long as the user stays on the channel.  Where as, with non-intrusive ads in online world, it is not very clear for a CMO if the ad has been exciting for the users.  Google countered it with pay-per-click model of pricing where advertiser has to pay only when the ad is clicked and not just when it is merely displayed.  While this prove that user did see the ad based on click stats, it is still not clear if there is any increase in sales, especially if the advertisements are related to Offline world.  For example an ad like “Reliance Digital offers 35% discount on all Sony TVs”.  Where it is very hard to assess the offline store sales of Sony TVs  CMOs started asking why should we put in so much money if there is no increase in sales? If there is indeed increase in sales, what is the volume?  Is it worth?  How can one compute the net increase in sales as a result of digital ad campaign? 

Welcome to Datalogix, we at Datalogix solve this problem.  Datalogix is an interesting company in that it acts as a bridge between online and offline worlds.  Datalogix buys offline stores sales data in aggregated fashion without any identify of the buyer.  Here is an example record from Reliance Digital Store:

Store Location, Company Name, Product Name, Model, Week Number, Sales Volume

Kondapur, Sony, LED TV, Bravia 1234, 1, 10

Kondapur, Sony, Smart TV, Bravia 5678, 1, 5

Which store would not be happy to make money by giving such data which is not revealing any buyer identity?  Datalogix got this offline sales data from every offline store possible.

Now, it started pitching in to advertisement platforms such as Google, Bing, etc. that you share me your advertisement footprint, I would prove (or disprove) whether your advertisement footprints translates to online + offline sales.  Of course, advertisement platforms would be happy to be proved that digital advertisement works so that they can take this proof to CMOs. 

When a user browses any site where advertisements are displayed.  Assuming, the ads are by Google. Google would share the footprint of the ad such as

Location of Browsing Computer, Advertiser Company, Advertised Product Advertisement Served Time

Kondapur, Reliance Digital, LED TV, 2016-01-01 01:01

Kondapur, Reliance Digital, LED TV, 2016-01-01 02:02

Datalogix does a big-data-join between offline sales data and online advertisement foot print and proves (or disproves) if there is any correlation between online advertisement footprints volume vs. sales volume (by region).  This big-data-join, as you can see, involves aggregating data by region, product, week, etc. 

Advertiser can verify the Datalogix findings by talking to offline stores in the area that Datalogix proves/claims has seen increase in sales volume.    Datalogix being another company not associated with any advertisement platform is regarded well for its non-partiality and advertiser ability to verify the findings, make Datalogix a trust worthy.

We at Oracle Data Cloud are smart in making money.  We make money pre-advertisement by sharing the ad target data (Blue Kai) and we make money post-advertisement by helping prove sales yield and so advertisement quality (Datalogix). 

 

Story – Collective Intellect:

Blue Kai and Datalogix work very well as long as things are searched.  However, all that system fails if there is no searching involved, but just a textual discussion in online world – be it discussion boards, forums, social sites, etc.  That gap is filled by Collective Intellect.  Which I have covered in my previous blog post here.

 

(Disclaimer: Brands used in this post are just an example)

Technical Story Telling: Collective Intellect

 

Collective Intellect is a Boulder, CO, US start-up that Oracle has acquired in June, 2012.  I quit Microsoft and joined Oracle to work for this team in January, 2013.  Here is a technical story that I have used while recruiting developers into the team.

Story:

Bhargavi wanted to buy a Television (TV), so she went into doing a research by searching, reading articles, browsing different sites, different comparisons such as features, technologies.  She being a money-conscious person compared prices between different e-commerce sites such as FlipKart.com, Amazon.in, SnapDeal.com, etc.  She being a thorough researcher also researched which e-commerce sites are better, which seller is better, etc.  Finally, arrived at a TV model Samsung Smart LED TV 1357, e-commerce site, seller and purchased it.  Everyone around her appreciated her decision.

Bhavani also wants to buy TV, but she is not a researcher and trusts friends and family.  She reached out to her friends on Facebook about her desire to buy a TV as “Hi Friends, I want to buy a TV.  Any recommendations?”.  Bhargavi is a friend of Bhavani, saw the post and responded with details of her recent research, and recommendation of TV Samsung Smart LED TV 1357.

Saraswathi is another friend of Bhavani, saw this conversation and she chimed-in and responded about her ordeal  with he TV she has recently purchased.  “Hi Bhavani – I recently bought Sony Smart LED TV 2468 and I strictly recommend that you *not* go for this model”.  

 

Problem(s):

Businesses around the world want to improve their sales.  To improve their sales, they need to improve products.  To improve the products, businesses need feedback on their products.  What’s good and what’s bad about their product.  Businesses also want to reduce the damage due to their “bad” side of the product (of previous version).  Learning what’s not going good about their product in non-internet age was through feedback forms, etc.  In early internet age, businesses have sent e-mails after some time of the purchase (typically a month or two) to fill an online form.   The problem with this feedback is at the instant the form was filled, which might wary due to ongoing usage of product and it’s performance.  For example, a customer may be very happy after 1 month of usage.  But, after 6-months the same customer might be completely upset about the purchase because of other issues that have cropped up.  Knowing these issues and addressing them is very important for a business to succeed.  Reaching out periodically over an e-mail may not work out as it may be regarded as overreaching and or even spamming!.  The unhappy customers are vocal and not only the business has lost him/her as a customer, but because of their vocal nature potential future customers also refrain from their products.  In the current internet age, customers share their feedback in variety of ways – blogs, discussion boards and forums, review sites, ranking sites, social sites, etc. at the very instant they are unhappy about.  If a business can address the unhappy customer at the right time, damage can be controlled by a great margin.  Imagine United Airlines had a way to get notified about this video posted by their customer whose guitar was mishandled plus staff indifference towards the issue before going viral, how powerful would that be?

The problem with online world is there is too much of data for any company to handle.  Lot of that data is not relevant to a business.  Extracting out the relevant data (signal) out of so much data (noise) is a software problem and not a business problem (unless business is also about a software). 

Adding to this, issues of a product may not be global but local, may be due to the local environment or manufacturing site, or some other.  Aggregating and drilling into this information at ease would be a huge plus for any business.  

 

Solution:

Collective Intellects collects the textual data from all the online media such as blogs, news, forums, boards, review sites, social sites, etc.   Analyze all the data (mostly noise) to extract important information (signal).  In this process, the product drops most of the data as the online is full of conversations that are not related to businesses using Natural Language Processing (NLP) domain algorithms. 

In the above story, the discussion is around Entity “TV”.  Bhavani’s post contains “Purchase” language ,  Saraswathi’s response contains a “Support” language, Bhargavi’s response contains “Promotion” language.  It is these meanings that Collective Intellect identifies and then shares the information to TV Companies if they are our customers.   Customer’s can then route this information to appropriate departments with in their company, such as “Support” language events becoming a Support ticket, “Purchase” Language becoming a “Sales” lead, “Promotion” language becoming a “Loyalty” Program.

To give you an idea of this working in real world, have you observed this in Facebook?

Laxmi: I am completely fed-up with my Vodafone connection.  While my SIM Card works well, my wife’s SIM Card does not work in the same house.

Vodafone Customer Care:  Dear Laxmi, We are really sorry for the inconvenience caused.  Can you please share more details of the problem such as which SIM Card is working and which is not,  the locality, etc. so that we can dig deep.

Laxmi: Here are the details.  Working SIM: 1234567890, Failing SIM: 9876543210, Location: Hyderabad

Vodafone Customer Care:  Based on our backend analysis, we think that SIM Card is corrupted.  We dispatched a new SIM Card to you, please try and let us know. 

Laxmi: I received the SIM Card and it works well.  Thanks for resolving the issue.

Wondered, how can Vodafone Customer Care know that Laxmi has posted about them of all the Facebook users and also a particular post that is targeting them of all the posts Laxmi made?  The magic behind that is the products like Collective Intellect.