Tag Archives: Business Intelligence

Digital Media Platforms: BI Competitive Edge for Businesses & Consumers

This post applies to any digital media platform that distributes news articles, books, music, movies, and more.

As I was looking online at a New York Times article, when I scrolled to the bottom of the screen, a popup appeared and told me I had 9 of 10 free articles left for the month, and I thought that was brilliant.  As digital media becomes more competitive, and the content on the platform varies, regardless if it’s the pay as you go model; trial, with unlimited after trial;  or free until max per month or week as the lure; all companies need to allow their clients or potential clients to see how they are using the digital media platform’s products.

As an example, I would like to see what percentage of Technology articles I am viewing per day, week, or month verses Business articles for a certain periodical, and then I can make an informed decision regarding which periodicals I choose to subscribe to for business and also for Technology.  Maybe digital media companies will evolve to have mixed business models, such as, pay per consumption option for all articles after free until max, then for select sections, such as Business or Technology, they may offer unlimited option for the Business, and eventually even a particular editor of Op-Ed pieces.  It could be a price that is significantly less then getting the whole periodical, but at least you are able to attract consumers that have been less willing to go for the full paper, and don’t want the hassle of a pay per go, or monthly chargeback per use model.

If I want to choose a magazine for photography, and I am into archeology from a specific region, as a perspective buyer, I might want to know from the publisher’s entire content, and not just what I have read, a drill down pie chart of subject matters for all photos, and then after I selected Archeology, what percentage of those articles are from a particular region, a subject, and then a photographer.  This is also a powerful business intelligence tool for existing consumers, and may give you a competitive edge.  Also, alliances, that are able to partner for other content, index, and transform that content, say using NewsMLG2, and then perform sharing margin and chargeback.  The lure to their portal would be the driver for the competition as well as the vast of content, and partnerships.

A Note for Advertisers

There are other forms of Business Intelligence for your digital medial consumption that can be offered, such as indexed content, text, images, and video.  You can not only capture image descriptions, and objects within a video to be indexed, which can be used for advertisers to see what the demographics of consumers are watching videos with the most  sneakers, or smartphones, and descriptions that may include dancing clowns.  This may assist the small to mid side startup digital advertiser to understand the consumers in their target markets, and abstract the data.

Business Intelligence, Analogies, and Articulation of Data on Mediums

As I was reading the article from the New York Times, As Boom Lures App Creators, Tough Part Is Making a Living, the typical doom and gloom story about the get rich quick with creation of applications on Tablets is true of any start-up company, may it be a restaurant, clothing shop, or other.  You have idea, Sally has an idea, and so does Fred, and the likely hood everyone will be elated about every bar, restaurant, clothing store or application is ridiculous.   Simple economics, and opportunity cost, you cannot go to every restaurant in parallel every night.  One USD trades off an opportunity to spend it somewhere else.  One area I would suspect has massive opportunities in the coming weeks, months, and years is Business Intelligence, Analogies, and Articulation of Data on a Tablet medium.  Yes, it is true, there are established players in the marketplace, but being established also makes you less nimble for change.  Being able to look at a clients Data Warehouse, and create mediums for analogies expressing where there customers have been spending their money, why, and help predict trends in a KISS fashion to any level of a business organization is key.  That is why the innate talents of user interface, user interface engineering, or way back it was called industrial design.  In short, part of the appetite for corporate spending will always come from how do I make more money with the product I just bought, Return on Investment (ROI).  Business Intelligence is one area I have been studying for years, and as all people know, we all find it difficult to express, or analogize thoughts, and specifically, dive into ‘data’ and turn it into information a CEO, or business analyst can understand and turn that ‘information’ into a new marketing campaign, hence, business intelligence.  Until we can all read minds, and transfer like for like information, BI, and improving upon this space will be an area to derive income.

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Artificial Intelligence: Tuning with a Content Index And Predictive Models

As I was reading an All Things Digital article, Artificial-Intelligence Professor Makes a Search App to Outsmart Siri, there was a statement that made:

“We memorize the dictionary to read the Library of Congress,” he said. “Siri is trying to memorize the Library of Congress.”

 

A tool more commonly used in the past, in books, at the end of a book, an index of where the words appeared in the book was noted with page numbers.  Classic rules engines is ‘data in a black box’, searchable within the context they appear. The more put into the black box, users can search on ‘rules’ or content, and in precedence, an action occurs, or the content that is searched appears.  If there are cross-references with an associated category or tag with ‘each line of data’ or ‘rule’ that will enable the Artificial Intelligence engine to be more efficient.  Therefore the correlations of ‘data to other data’ with similar or like tags enables an Artificial Intelligence will be more intelligent.  In theory, categorized or tagged content indexed to references of the data points should fine tune the engine.

An addition theory, allows for predictive models to produce refined searches, or rules.  You can make a predictive model, where the intelligence of the user actually refines the engine. The user can ask a question, and as they refine their question, a predictive model,  may allow for refined user output.  If a user is allowed to participate and tag data search output, the search output could be more granular, like a refined Business Intelligence drill down.  The output of a search, for example, can contain a title, brief summary, and tags that can be added or removed (by the user), which allows for a more robust search, and predictive model; however, you are relying on the user to a) not be malicious, and b) have understanding of what information he is search for within the data.  If web crawlers, or if the webmaster submits URLs with tags, the meta data tags of the page, the black box or Artificial Intelligence rules engine will, if properly submitted, or indexed, correlate the data.  To most people, this is AI or Search Engine 101.  Some people cheat, and add pages with false meta data tags because they want their site to appear in a higher order, or precedence and they may make more revenue with advertising dollars.

There are multiple ways around trying to cheat an Artificial Intelligence Content Index:

  • Hit Ratio: People searching on the same question over and over increase the ‘score’ ratio, thus pushing the false results downward on the list, or removing them entirely.
  • Enlist ‘quality’ users, who are known quantities, such as like Twitter ‘certifies’ certain users.  You may apply for ‘relatively’ unbiased, certification status, such as people who have reputations and certifications in the field, are qualified to ‘enhance’ tags, and improve upon your result outputs. e.g Professors, Statisticians,
  • Enlist users who will actually derive revenue, if their ‘hit ratio’ score delta increases exponentially some N number.  These tags are classified as unverified, however, the people are monetarily motivated to increase peoples’ probably of success to find what people are looking for when other users search the tags become qualified as the results of the tags attract users to their content.  If they are using, let’s say, a browser, which the search engine company owns, such as Chrome, a little plug in can appear and say, was this what you were looking to find, yes or no.

Tablet Developers Make Business Intelligence Tools using Google as a Data Warehouse: Completing with Oracle, IBM, and Microsoft SQL Server

And, he shoots, and scores.  I called it, sort of.  Google came out of the closet today as a data warehouse vendor, at least they need a community of developers to connect the dots to help build an amazing Business Intelligence suite.

Google came out with a Google Docs API today, which using languages from Objective-C (iOS), C#, to Java so you can use Google as your Data Warehouse for any size business. All you need to do is write an ETL program which uploads and downloads tables from your local database to Google Docs, and you create your own Business Intelligence User Interface for the creation and viewing of Charts & Graphs.  It looks like they’ve changed strategies, or this was the plan all along.

Initially I thought that Google Fusion was going to be the table editing tool to manipulate your data that was transferred from your transactional database using the Google Docs API.  Today they released a Google Docs API and developers can create their own ETL drivers and a Business Intelligence User Interface that can run on any platform from an Android Tablet, iPad, or Windows Tablet.

A few days ago, I wrote the article, which looked like they were going to use a tool called Google Fusion, which was in Beta at the time to manipulate tabular data, and eventually extend it to create common BI components, such as graphs, charts, edit tables, etc.

A few gotchas: Google Docs on Apple iPad is version 1.1.1 released 9/28/12, so we are talking very early days, and the Google Docs API was released today.   I would imagine since you can also use C#, someone can make a Windows application on the desktop to manipulate the data tables, create and view graphs, so a Windows Tablet can be used.  The API also has Java compatibility, so from any Unix box, or any platform, Java is write once, run anywhere, wherever your transitional database lives, a developer is able to write a driver to transfer the data to Google Docs dynamically, and then use Google Docs API for Business Intelligence.  You can even write an ETL driver which all it does is rapidly transfer data, like an ODBC, or JDBC driver and use any business intelligence tools you have on your desktop, or a nightly ETL.  However, I can see developers creating business intelligence tools on Android, iPad, or Windows tables to modify tables, create and view charts, etc., using custom BI tool sets and their data warehouse now becomes Google Docs.

Please reference an article I wrote a few days back, “Google is Going to be the Next Public and Private Data Warehouse“.

At that time, Google Fusion was marked as Beta on 10/13/2012.  Google has since stripped off the word Beta, but doesn’t matter.  Its even better with the Google API to Google Docs.  Google Fusion could be your starter User Interface, however, if your Android, iOS (Apple iPad), and Windows developers really embrace this API, all of the big database companies like IBM, Oracle, and Microsoft may have their market share eroded to some extent, if not a great extent.

Update 10/19:

Hey Gs (Guys and Gals), I forgot to mention, you can also make your own video or music streaming applications perhaps, using the basic calls of get and receive file other companies are already doing such as AWS, Box, etc. It’s a simple get / send API, so not sure if it’s applicable to ‘streaming’ at this stage, just another storage location in the ‘cloud’, which would be quite boring.  Although thinking of it now, aren’t all the put / send cloud solutions potential data warehouses using ETL and the APIs discussed and published above?  Also, it’s ironic that Google would also be competing with itself, if it was a file share, ‘stream’ videos, and YouTube?

Google is Going to be the Next Public and Private Data Warehouse

In an article I wrote a while back, Google to venture into Cloud, provide Open Source APIs, assist small businesses to be Cloud Solutions Integrators, I was talking in the abstract, but I saw on the Google site, buried way down their menus, under the ‘More’, and then select the ‘Even More’ option, and at the bottom left of the page you will see Innovation, Fusion Tables (Beta).  Google is advanced, ready to compete with the database vendors, with a user friendly UI, better than I thought.  They are currently providing a way to upload data to a Google Drive, then the user imports the data from the Google Drive, and using table views  and Business Intelligence tools, allows the user to manipulate and share the data.  The data allowed to be uploaded into tables seems limitless. Although, they state Google is still in Beta, and publicly are showing users can upload and link to Google data instead of allowing users to connect to external data sources, such as your sales transaction database, there may be an API in the works for 3rd parties to allow for integration using direct connections through drivers such as ODBC or a JDBC driver to integrate with transactional systems to stream data and not just uploaded Google data.  However, this may be their strategy, to host all of the data, and have a migration utility.  At this stage, they would like to house the data and have the cloud storage infrastructure, however, the strategic mid-term goal may be to allow you to house your RDBMS transaction data locally, and we could stream, and/or upload into their data warehouse to apply Business Intelligence to manipulate the data, and then publish it in multiple formats, e.g. they would display the data for public or private consumption, and I can also see you are able to then publish charts with commentary into your Google Plus stream with specific ‘Circles’.  Brilliant.  Hat’s off to you guys.  If Google allows streaming of the data, or what we call data transformations from your e.g. sales transaction system to the Google data warehouse, then they would be competing with IBM, Oracle, and Microsoft.

Update: 12/26/12
After all of that profound scoping, and keen insight, I was chatted by a developer that Google’s BigQuery does the job better.  I am curious why it has not taken off in the Marketplace?  Anti-Trust?  Also, why then create an abstraction layer like these other products like Fusion and call out explicitly Google Docs, maybe that would help them transition into the market space with a different level of user the consumer, or the target user would be different, such as the small business.
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Big Data Creates Opportunities for Small to Midsize Retail Vendors

Big Data Creates Opportunities for Small to Midsize Retail Vendors through Collective Affinity Marketing outside Financial Institutions.

In the Harvard Business Review, there is an article, Will Big Data Kill All but the Biggest Retailers?  One idea to mitigate that risk is to create a collective of independent retailers under affinity programs, such as charities, and offer customers every N part of their purchase applies to the charity to reach specific goals as defined by the consumer.   Merchants, as part of this program, decide their own caps, or monetary participation levels.  Consumers belong to an affinity group, but it’s not limited to a particular credit card.  The key is this transaction data is available to all participating merchants for the affinity.  Transaction data spans all merchants within the affinity and not just the transactions executed with the merchant.

Using trusted, independent marketing data warehouses independent retail vendors share ‘big data’ to enable them to compete and utilize the same pool of consumer [habitual] spending data.

Affinity, marketing data companies can empower their retail clients/vendors with the tools for Business Intelligence and pull from the collection of consumer data.  Trusted, independent marketing data warehouses sprout up to collect consumer data and enable it’s retail vendor clients to mine the data.

These trusted loyalty affinity data warehouses, not affiliated with a single financial institution, as previously implemented with credit cards, but more in line with, or analogous to, supermarket style loyalty programs, however, all independent retail vendors may participate OR may cap these affinity program memberships for retail vendor from small to mid-size companies.

Note: Data obfuscation could be applied so customer identification on fields like social security number will not be transparent, limiting any liabilities for fraud.