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 .
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)