Feature for Crystal Gazing

At the dawn of the age of artificial intelligence, when predicting future through data sciences has become a second nature for most of the decision makers. Though we may feel elated on the break-through in the data sciences. Still ground needs to be prepared for seeing the results through.

When we want to peep into the future, our launching station is the past observations. Let’s pause - what’s an observation, isn’t it simply a instance of attribute values. Oh, lets simplify - [Sales Executive] how was my car sales yesterday - fine [ 20 (10 sedans, 6 hatchback & 4 suv) , color mix (white 10, gray 7 & black -3]. Is that enough to predict today’s or let’s say tomorrows sales.

Most of us will say. -No.  So, what we need are the influencers of the predicted value. From gut feel sales manager will tell, following are the subset of attributes which may help --Like the day (is it holiday or working day), what time of the year, is there a promotion going on, how is the overall economy doing etc.

These attribute (influencers) are features which a data scientist will search for, to predict values of certain event. Getting these features in right format is the first step in the journey of prediction.

Then comes the support strength of the features in predicting value, here statistics may help in shaping our features (scaling the features, correlation, visual exploration - list goes on...). Experimenting is the core in honing these features for a near perfect prediction.  It’s all about Features...

Let’s bring the black boxes - yes you heard it right “algorithms”. Scoring and evaluating the algorithm result by using training and test set. In simple words we use past data to train the algorithm and build the model. To check the model’s strength in predicting, we hold back a subset of past data, which acts as test data set, thus providing us the feedback to choose the best predicting model. And the Magic ensues. [let the future come to us...]

Nucleus of Information Genesis

   Business Leaders & Managers demand Information of Business Activities in their respective Area of Concern. Technology Group of Organisation are struggling hard to meet these needs - WHY ?

   Information Solution in most of the Organisations today is Demand Driven. So, pull comes from Business for specific information and then the technology group gears up - itself to fulfill those demands. And there is no end to this cycle of demand and fulfillment, this cycle gets more complex and cumbersome as the day passes by. Information fulfillment pipeline keeps getting longer and longer till the Business becomes weary of the response time of technology group to deliver in time.

   How to reign over Information Demand Fulfillment Cycle? Solution lies in approach - Demand Driven to Business Process Driven

   Business Process Driven approach to Information Demand Fulfillment Cycle starts at the root, and builds infrastructure which takes into consideration almost all current and future information demand.

   Information Solution starts by delineating Business Processes which are contributing towards value generation. Each business process is broken down into Business Events. And each business event is now defined using Actor-Action Analogy -

   Business Event becomes basis of developing the Information Solution. Many business event join in a Bus Matrix to provide comprehensive information framework.

   This is a scientific approach as it encompasses almost all possible permutation and combination, business may ask for.

SOID - Service Oriented Information Dissemination

We have best of the tools and technology at our disposal and these are getting enhanced/matured with each passing day. Ms/Mr Manager still running pillars to post to get the information - why so.

Limited awareness of [Monolithic reporting solutions for each silo/department] leading to compartmentalization of information dissemination mechanics. In the world of Ubers and alike Aggregators there are similis to be drawn while looking out for a solution to the problem.

Let’s focus on anatomy of information -> Data along with Metadata (with business significance defined).

Organisation have multiple applications which generate data. Each application can be easily mapped to its respective Data Owners (personnel's who maintain and understands data flow mechanics of the application). These individuals are the information publishers to the Aggregator.

Decision makers are the Information Consumers interacting with the Aggregator as depicted in the exhibit below -

IOT Touch Analytics

B2C businesses, interfaces with consumers on web either through browser or mobile app.  Interaction of consumer to this interface provides the only opportunity for enterprises to make the experience worth indulging / engaging. 

Capturing all the interaction of consumer with this interface is paramount for continuous improvement and personalization-dynamicity of the interface. Most of the solution in market today around web analytics do provide some head way but it falls short in providing the comprehensive picture and that’s where IOT comes in.

A small detour on what IOT  [ Internet of Things ]  Is.  Any object/entity virtual or real having three properties/attribute should qualify for a thing to be termed as IOT 

Unique ID

Sensitivity

Communication Capability [telemetry]

Basic building block of Interfaces are objects. Enabling these objects as IOT is the first step i.e. object need to identify itself uniquely, have action capturing code and ability to log the activity (telemetry).

As customers interact with interface , IOT's are activated and through telemetry- log is built up of all the activities customer is performing.

Baseline for the next step is ready - amalgamation of the log data with master data such as customer demographics etc. Passing the data through machine learning algorithms provides us exploratory, predictive and pattern analytics.  Which in turn acts as input to the interface for personalization.

Instant personalization of interface content is pinnacle of IOT Touch Analytics.

Operational Business Decision - A Process Of Response to Exception

Business Processes have evolved over time. These process are streamlined to almost military precision. Continuous improvement / Kaizen application over the years have made our business efficient. As the businesses exist in dynamic environment , there is a constant flux in the business ecosystem , which poses new challenges here now and then.

Business decision are rudders keeping the ship on-course to value maximization.  Drift in the course is what exception are. So business decision starts from identifying these exception, prime focus is now on reducing the lead time of reporting these to the decision makers (information dissemination as close as possible to the occurrence of exception) , the better it is.

Kind of business information needed depends on Decision Impact Horizon i.e. long term (strategic) --- short term (operational). Transactional information is the one needed for short term decision process. Accumulated transaction forms the basis for long term decision processes.

Pattern deviation detection is the future of exceptional handling in businesses - thus machine learning is the way to go for organization to apply statistical algorithms to the transactional data and detecting pattern deviation as close as possible to the event occurrence aka real time intelligence.

To correct the course, business decisions are taken. Till now we are half way in accomplishing our goal. Impact of business decision needs to be measured, and on this feedback further action may be warranted.

Information Density In Data

Century of Data has just begun, and we all are overwhelmed with the flow and intensity of data coming to us every day. This is incredible feat human race has achieved in a very short span. 

We had to coin new jargon to represent - 'processing of massive amount of data' - Big Data. Soon with the current rate of acceleration of data accumulation we are moving towards handling super big data. 

The challenge now is finding the right, relevant information from data. 'Like a needle in Haystack'. Millions of dollars are poured by businesses in getting information from data. Classifying sources of data on the basis of information density is next logical step to bring sanity to the process of information extraction from data.

Factors of information density - (Inference Quotient * Data Volume * Data Speed * Structural Quotient)

Inference Quotient: Degree to which direct inference can be extracted from data to reach the relevant information

Data Volume: More the volume, lesser the density

Data Speed: Greater the speed, lesser the density

Structural Quotient: Degree of Structuredness (more structured higher density)

Anatomy Of Intelligence

200 million years ago - Dinosaurs ruled this planet, today its human species. What's changed? Is it intelligence which is the differentiating factor? Not exactly, vital component in the mix is the communication and that's implicit in intelligence.

It all starts from observation - recording event and there results - then making conclusions, improvising as more events of similar nature are encountered. In nutshell pattern recognition match and act. The real difference comes when communication kicks in and amplifies the whole process to a level where innumerable permutation combinations are at disposal of the mankind.

Underling current is the streams of data processed to produce applied information and made available to masses. Same analogy holds true in the porous boundaries of business houses. So a great business organization have one great characteristics, it have smooth uninterrupted communication channels for relevant information to flow through the whole organization.

Where does innovation fits into the picture. Lateral thinkers break the patterns to innovate - it’s so unique to human species. Intelligence then becomes the vehicle for innovation to reach out to every individual of our civilization.