Srinivasan G

Srinivasan G

Today, employer’s primary interest is to find the best person for the job.  They have time constraints to put right effort to hire talent and the educational institutions are not providing them with precise metrics to explore. They lack the advantage of data and depend on the intuition of the decision makers.

The corporate recruitment team asks the decision maker to help them in recruitment and the quality bar of completed student output. The decision maker has time constraints and  observes that best accountants and engineers came from a specific institution (brand) and flock to that institution. They know this might not be the best approach. The decision maker recommends the recruitment team  to follow minimal risk approach, fire from branded institutions and institutions where they studied.

Following the same approach over years, the power of the alumni increases and the institutions visited by corporates become branded ones.  Students wants to get jobs paying higher salaries and  do not care for institution’s learning methods and join the institution that has placement. The historical perception of branded institutions has made whole thing a mess. There is clear lack of transparency and that is problem.

An educational institution offers a primary outcome and secondary outcome. The primary outcome is to facilitate learning for student and enable students to understand requirements of career post the learning period and also make them matured for life. The secondary outcome is to research, networking, extracurricular activities, and job placement. Today there are a lot of initiatives to bring a change in the secondary outcome including social networking channels, self-learning on-line programs, MOOC, online career assistance and remote learning.

A good start indeed. The initiatives  also needs to focus on the primary outcome of employ-ability and  cover both fresh jobs at graduation of a student and help  student to increase their ability to chart their careers with evolving industry needs.  To solve the problem, learning approaches need to measures learning parameters and provide transparency in measurement  to remove the bias and create comfort for  the stake holders. Start developing metrics and mature methods to capture metrics is one strategy to solve the problem.

  • Focus on what can be measured. Do you want to focus on  the skill that can be measured or  just soft skills that is not easy to measure?
  • Start the process where it is easier to measure and where even minor differences matters to employers. Computers can be enabler in this process.
  • Focus on areas where learning outcomes are measurable. Already computer science learning is measured.  Identify other entry level jobs that are performed on computers. Here comes  finance, accounting, banking and insurance.
  • To make the data collection  strong, the data set needs to be correlated with the data sets of the corresponding skills required for most entry level jobs in the minds of employers. The stronger the correlation, the importance of students trained in this approach increase.

While alumni brand matters to people, recruitment teams will consider another candidate on being convinced that another candidate is stronger. The another candidate needs an alternative credentials to demonstrate that   he could perform the necessary work exceptionally well.  When the data driven educational institution comes, the biases will break down. Employers should be able to view the metrics and also trust the metrics and the metrics need to lead the recruiter to think of hiring the applicant from the non-elite brand.

If educational institution is newly started one and lack strong placements, go on-line, take control of the data and get control of your situation.

  • Create accurate, real-time data sets around learning outcomes. Make sure the outcomes are inevitable.
  • Be transparent with the approach to measure the learning outcome
  • Start measuring learning outcomes and record them.
  •  Make the learning outcome available on-line  and create transparency by providing access to corporates.

Can Big Data lead to customer-centric practises? – Final

Let us assume that you walk into your favorite store. The cameras identify who you are and you are identified and the identity is passed on to  the app carries by the  in-store sales associates.  All  your shopping characteristics such as loyalty, past purchases, cross-channel preferences, service incidents, and social media footprint  are at the hands of the sales associate.  With this information, can the sales associate walk up to you and greet by the name and also  inquires about the most recent interaction rather than just pushing the new product. How will the customer feel in this scenario? Will he feel personalized? Does it alters the in-store shopping experience of customer?

  • Some of you might point that  a subset of this experience is available in e-commerce websites? How to get more from e-commerce websites? How to make in-store sales associates provide you will this experience. How can the same experience also come the physical stores?
  • What does it mean to business for collected Big Data to be in fingertips of sales associate to deliver a better customer experience?  How will business look at Big Data that provides information in a short window of time to sales associate  to make  your customers’ interactions better?  Helping your customers  is value delivered, indeed.

Without pushing Big Data down throats of business decision makers, Can we demonstrate to business stakeholders how truly Big Data can deliver the value as smart and intelligent actions for their in-store sales associates to understand customer context better?

In the above example, what should sales associate perform when he is aware that the  customer is grappling with the issues in product purchases earlier this month and there is no fault with the customer? In some of these scenarios,  the store associate cannot solve the issue. Being aware of the customer issues, empowers sales associate to respond more empathetic to the customer and customer can be made feel better.

Let Big Data to help business to make customer more happy and satisfied and also equip business to position products to customers they are genuinely  interested.

  • Discover what organizations can perform to win the loyalty of customer and make them come back again and again.
  • Understand beyond basic customer profile of who is customer, what they want and their contact preferences. Help business to come with customer programs to collect the information from the customer for dual benefit of to serve the customer better and also to plan strategy to get maximum value from the customer.
  • Arrive at effective promotional campaigns that is not general message and is more targeted towards individuals or  a category of individuals. For example if the customer has history of purchasing health conscious foods, a campaign that offers coupon to get discounts for junk foods has no meaning and makes him feel irritated
  • Influence or join new product manufacturers to target their samples to the right set of customers who are more likely to experiment with product at launch.
  • Enable Collaboration between two business that are in complimentary category from a customer stand-point. For example, what Big Data needs to perform for business to provide the customer availing parking ticket reimbursement with alternate option of getting a discount coupon to dine the nearby restaurants and the customer can choose what he wants to take?

Which business would say No if  Big data helps them to get  more business value from existing customers? While they might want to say Yes, lot of SMB business do not have luxury to store and manage Big Data. They cannot upgrade to complex software that  is expensive and not suited to their context. For example, Hadoop can be overkill for the size of their business. What are their needs?

  • Cloud based service to offer big data support in their business context(size and value) in terms of customer acquisition.
  • Support service( prefer in person)  to help them hook on to the Cloud service.
  • Analyst service( prefer in person) to understand their business and help them implement the actions across their organizations.

Can Big Data lead to customer-centric practises? – Part2

In enterprise, every employee does not recognize different customer segments across the entire organization.As customer, we might expect all employees to be aware of customer specific information and that does not happen in real sense in existing systems as the enterprises work in silos to achieve organizational their efficiency. They do not measure the impact of silos on customer efficiency.

A customer-centric approach requires a seamless and positive customer experience at every touch point of the customer life cycle.

Today approach towards customer  is more of  process centric / product centric / channel centric / service centric. Data is available in silos across an organization and this  makes it difficult to really achieve customer-centric approach. So therefore, as long as there is no truly data-centric approach within your organization, truly customer-centric is difficult to achieve.

Every department has a different view of who the customer is. This should be prevented at all times. For data-driven, information-centric no department should ‘own’ the customer, or the data, and all should have the same view of who the customer is. This will ensure better customer interactions across all channels and departments.

While CMO is more interested with big data implementation to understand customer, are other enterprise stakeholders view Big Data critical to the ability to develop and execute customer-centric programs.

Enterprises need to create one customer view across the organization. The understanding of what data impacts the customer experience is first step to build customer-centric company. Integration of different business data involve different stakeholders across the organization and can lead to cross-organizational discussions about that customer.

Organizations need to strive for such an alignment and integration of data, technology, processes and people towards customer needs to become the main focus point for companies.  Is leadership ready to develop a culture that places the customers at the heart of decision-making?


Can Big Data lead to customer-centric practises?

Let look at approach of operation of bank or mobile operator today to resolve issues faced by customers.

If you’re at an ATM and it is out of money, it would be nice if the bank will know that and issue some sort of apology by SMS or the next time you meet the banker. Better if the bank identifies that ATM is out of money and pro-actively refill cash.

Well, you are talking to a friend on your mobile. The call drops in-between and this happens at a specific location while travelling to your home. Can your carrier track this and make sure that the tower coverage in that location is made better? Better the next time your carrier drops a call, the carrier applies an immediate credit to your bill.

Today companies look at incoming data of all types, analyze it, and then tweak their applications or user experiences according to what the data tells them; This creates a good fast-response development for customer challenges. This is a good start point towards being customer-centric.

Today enterprises make use of social media to enable customers to interact easier, better, faster and more-often with organization’s and enable the organization’s to know more about their customers. For me a truly customer-centric organizations will be one that would identify the customer had issue and make the process to resolve the same simpler.

I also see that the call centers handling customers lack capability of combining and analysing all data created during interactions(email, phone, physical, social network and chat) with a customer and to link it with other data within your organization. if the phone call drops during customer call to call center,the customer is put the onus of responsibility to identify and repeat all the information to new call center representative and this is frustrating.

As customer, my expectations to start with are

  • Receive a seamless integration across the entire value chain.
  • Organizations have no excuse any-more to put the customer at the centre of all decisions and the organization should be able to connect relevant details in every medium where they connect with that customer 1:1.

Such a customer-centric organization should build an operating model around a deep understanding of its customers, what they value, and the contribution, or the customer life-time-value, that each customer makes to the profitability of the organization.

How to validate the insights of a data scientist?

There is a debate about the details of the data scientist role, but it’s all about business value from big data.  Do we really need to call them data scientist? How close if their jobs compared to scientist?  What does the work scientist meant to business person? To understand data scientist, let us start understanding the  role of scientist.
Scientists predict ill effects if  global warming and genetic foods are good and also that vaccines are safe. How do we know if they are right?Why do we believe in them?  One is by observation of their prediction over a long time.

Let me look at the main reason we  believe science follows scientific method. What is the scientific method?  Scientists follow this method and the method validates the truth of their claims.This is how it goes, the scientist develops a hypothesis based on assumptions, they find consequences lead by the hypothesis, they observe those consequences over a longer gestation period to check whether  consequences validates hypothesis. Great. Are there no risks/pitfalls in the scientific method?

  • Start hypothesis definition based on wrong assumptions
  • By observing all the consequences happening, can we logically prove that the theory is correct?  Observed consequences were limited by a subset  of observation using a specific tool and some observations have been ignored.  
  • Start to continuously  observe what is happening around and collect data. After collecting lot of data, propose hypothesis and  claim validation.

Scientists mitigate the risk? Who is the judge to decide what goes right and wrong?  Other scientists. They  judge hypothesis based on seeing evidence and also scrutinizing the evidence and some time questioning the evidence. Scientists also collaborate to judge the evidence like a jury committee and has wide number of choices Any validation as above starts from the place of distrust and it is  difficult to get “Yes” to new things from a scientists. The authority to accepts evidence as true and valid is a community of scientists who worked on a problem and some times there work is more than 100 years.

Hmm. Are there no risks/pitfalls in the collaborative method? This is not not sufficient condition to claim no risks as all of people who collaborated fallen trap to one of the above risks.

Not Good news. What else can be done? . First, Observe consequences happening for longer durations to confirm that evidence is real on sustainable basis for a long period. Second, document and share the method to replicate implementation  independently and continue to observe the evidence. Third check whether the observation of  evidence continues to stand up to the scrutiny verification of hypothesis.

For years, People earlier wanted to find reasons for infant mortality rate. All hypothesis was not leading to reduction in infant mortality rate for different reason. Based on  intuition and observation of a person, there arrived the hypothesis that “Getting hands sterilized after attending on one patient and moving to next patient helped infant to be alive”.   He took efforts to implement the hypothesis in his hospital in action and observed the consequence of infant mortality rate falling. This evidence stood easy scrutiny and the same evidence was found true in other hospitals also.

Will business wait for this long to implement data insight provided by a data scientist?  The data scientists need to explain what they know and also how they know it.  Is there a  risk for business to takes actions based on insight and lead to bad customer experience.? Yes. In firms with one data scientist, who is  going to scrutinize?  The question ” who will scrutinize the evidence” remains open.

Does your application support external APIs?

Prakash Article “Digital Businesses and APIs” made me write this article hearing the term API on different occasions  from  non-technical folks. Few years earlier the term API was used to describe win32 APIs understood by developer in the depth of technology and today we are in era where  business talks of API.  A significant change.

With cloud provider, a large amount of data is stored across extended networks. It is neither cost-effective and not easy to download data overnight to perform analysis.  People want to access summary on their mobile and mobile is not suited to download large amount of data. Hence the most practical thing  is to expose an API that, at a bare minimum, gives application developer access to a summary of that data.

In addition, 80 percent of application needs seem to be already developed and  available as apps and cloud services.  Should we need to recreate the same or can we leverage from the ecosystem  so that we can focus on the  20 percent that is key value proposition to our end users and customers? For this to happen,  the functionality needs to be available as APIs.  APIs  provide the following value proposition to  business.

  • Focus more on core value proposition :-  By outsourcing secondary operations to other domain expert companies, the focus is on primary value proposition.
  • Gain knowledge : You can learn from using others APIs
  • Make ecosystem provide value: When web application authentication integrated with Facebook, the responsibility to track vulnerability is shared with Facebook and other applications leveraging Facebook authentication.
  • Create and validate value given by new offerings: when you offer APIs, ecosystem of  partners and existing customers can choose to leverage API  based on the value created. This can bring in new opportunities.

API developed shall be consumed by mobile application accessed  by end users and web applications accessed by end users and also corporate users from within the enterprise.  Hence there is demand on applications offering similar functionality targeting different customers to provide the respective customer experience to suit the customer  and a demand to integrate all the underlying services as an application platform. The end user in the external world needs to view a one-stop solutions that abstracts all underlying complexity.

To design APIs and application platform, we need smart upfront planning and more effort. To start with, the software developed should be broken in to smaller and smaller discrete APIs. This design approach helps to implement analytic on the top of the data running through the APIs created by us.  One also needs to think how will the API be consumed by other services.

  1. Provide a service to transform input data in to something else. If you send them data, the service processes the data and sends back  a response . Payment APIs fall here.
  2. Become Data market place where  providers come to sell sets of data and publish metadata of the data-set they can offer. Government data sharing falls here.
  3. Give information about who, what, when and where a user is. Facebook, Gmail and twitter fall in this category.

The infrastructure to support API design needs to think aloud about assurance on reliability and availability of APIs,  how APIs handle data privacy and integrity of data, how the APIs support  access Control( think of OAuth) for API data and how APIs can be integrated with customer’s existing security federation.

If your application already comprises of developed APIs for external usage,  your  business teams needs to gear to market your APIs. If your application has not been designed for APIs for external usage,  ask your business team to put API offering in the product roadmap.

Can common man handle data privacy issues?

Analytics done by business can be necessary to provide better value to the customer. Problem is with the intent of  business to collect different data about me, the customer.   I am happy when they use the data about me to  server me better, report complaint on-line and propose a time for the support to call and the support staff  full understand the problem and initiate call with me at stipulated time.  Will they use my data to exploit me by trying to blame for their mistake or make get a new behaviour or liking towards product and spend more?

Our non-profit campaign had made me realize that people shop products due to marketing and on the instinct and never used the product and give away the product for charity, some time the package is not even opened. Will the government start using the business data to track my activities?

In the last few months, I have started to purchase books from book portal. When I visit books shops, browse books and find one to purchase, I check for the price in book portal and end up coming home and ordering the book.  I have no urgency to purchase the book and being in start-up mode saving money is important and have moved across to on-line purchase mode. Kids continue to choose and buy their books in the shop.

With kids growing  and less place at home for my books, I am contemplating in my mind when I should purchase Kindle? Let me keep intent to move on-line aside and look what happens to the already collected data bout me and put in the database. Do I have any effective controls over how it is used or secured? Already I observed the marketing emails about special campaigns and books have become my default search option and similar books are recommended.  The e-commerce portal is learning more about me and would continue to learn and use the same to expand its business.

Most successful corporations find it difficult to  protect their customers’ information from security breaches and something needed to be done to protect them. Your medical data can be sold to marketers without knowledge or consent of the people it belonged to. Your tablets and mobiles come with lot of encryption capabilities switched off by default too

Some people make statements like ” if you’re not paying for it, you’re the product” and  “I am okay to share data as long as I get something back.”. We are trying to adopt the thought ” As  long as I get ROI in the form of relevant content, or customised goods or services, I am not bothered”. 

Most of the customer are not clear how all this big data will be used in the near or long-term future. The rate at which personal data is collected is accelerating from our on-line activities for much different purposes. There are black operators matching disparate data sets across the web to help identify people who might be suitable targets for a scam.  Are we volunteering  to share our personal data on-line simply as the price we pay for free services,  without understanding the future consequences and without debate over the implications?

Who is going to take care of protecting people from their own ignorance when it comes to privacy and security? Who will educate the common man arrive at a  price for his personal data or privacy information? Check the articles How Much Surveillance Can Democracy Withstand? and  care.data. to learn more about privacy.

Does Big Data lead to better understanding of customer ?

Big data provides a way of predicting behaviour. Does it provide a means of understanding behaviour pattern? You have large amount of data, technology and algorithm know-how to apply on the data and predicted some insights.  when the prediction goes out of way from actuals, the business owner is interested to know and where is the error and how and what needs to done to mitigate the same in future. How to identify origin of the error? data or technology or algorithm? On  success of the prediction, the business owner wants to know what is customer behaviour leading to the success and also would want to validate success with real data points from real customers.

A billion things can correlate with each other,  depending on how the data is structured and what are getting compared as part of the analysis.  While correlation helps to derive meaningful interpretation from  meaningless ones, it needs a analyst with the understanding of the domain to come with causal hypothesis about what is leading to what. Humans has their own limits and correlation needs human inputs and there is limit of correlation.

“Data is Bad”  means that the data has not been collected accurately or consistently or the data has been defined differently. The huge amount of “bad” data that arrives as input for  analysis makes it difficult for system to independently observe  linkages. There comes efforts of the analyst to focus in on what is important to the business and  identify relevance of data islands and dismiss  spurious relationships in the data. Humans come with their bias and preferences.

In retail space, the analysis of Big Data helps to maximize profits by predicting what people will buy and retailer wants to focus on  profits (and therefore shareholder profits and owner profits). They might also want analyst to  identify customer segments to enable cross selling of  new products to specific  customer segments.  Will they also focus on customer service?  When you look in pharma space and the company want to establish causes for flu outbreaks, diagnosis. We need to examine whether the question is ” How to profit from misery of a disease?” or “How to eradicate the disease? How to reduce the cost of drugs to cure the disease?”   The problem does not lie  in Big Data or act of analysis of big data. It lies in the question to frame problem and find the solution.

When you look at the fastest route to go from home to office, Big Data can help a straightforward decision. For subjective ones like who to get married and what career to purse and what college to join involves to find things that are in harmony with person. If you are relying just on data, there is a  tendency to trust preferences and anticipate a continuation of what is happening right now. if the reality is misinterpreted wrongly, there might be a vicious cycle of self-enforcing feedback. Are you looking for patterns of error in addition to patterns of preferences?When

when faculty is provided with report of which students are likely to fall behind. To actually help the student, you might need to personally intervene to help that student from the world of responsibility. Let us acknowledge that in regular world, when humans are asked to perform x, they perform y.While big data can help the players to see patterns that they miss otherwise, the intuition of the players and the  coach continue to play a  larger role.  Big Data might be good at telling what to pay attention to.

While Big Data focus on numbers, it is important to focus on the needs of the business  and to convey story that conveys benefits to the business owner. Where is the talent to come with the story and share the same is a entirely different topic.

What NOS and QPs mean in education space ?

Most of us working in corporates are aware  that  college students joining the job today are not employable and there exists a  gap. Students and college universities have questioned and asked the corporates to specify their needs in terms of job positions, so that they can develop required course-ware to handle needs of the job. They  want to know what qualification is required  to perform a specific job position in a particular industry sector.

The industry under the leadership of NSDC has started defining the National Occupation Standards for job positions. NOS  is the standard of performance that needs to be achieved as part of working in a job in the work place in an individual. It also expects that the individual knowledge and understanding meet the standard consistently.

NSDC wants Sector Skill Councils(SSC) to represent each sector. Sector Skill Council represents the companies that work in the particular industry sector and they come together to define occupation standards for job positions in their sector. SSC have the objective to complement the existing vocational education system  in meeting the entire value chain’s requirements. They bring the industry, employees and academia together.

A set of NOS, aligned to a job role, called Qualification Pack. While NOS is more definition from the corporate side, Qualification Pack(QP) is to drive the creation of curriculum, and assessments. By defining NOS for all the entry level positions, the  industry wants to drive competency based training for every job role in industry. All the current vocational courses need to be aligned to job roles.

There are Sector Skill council for different sectors like Gems and Jewellery, BFSI and IT/ITES. You can view all Sector Skill Councils in this web page. Drilling in to IT/ITES sector,  there are  four sub-sectors and for each of the sub-sector, job roles are defined at the Entry, Middle and Leadership Level, respectively.

  • IT Services (ITS)
  • Business Process Management (BPM)
  • Engineering and R&D (ER&D)
  • Software Products (SPD)

If you are a faculty with university or training organization, please get aware of the new guidelines and be geared to up-grade the course-ware to map with QP.  If you are a corporate recruiter, please upgrade the job profiles to also include NOS for the job position and a good way to get attention of the people working in industry to validate the NOS in practical sense and also collect inputs for improvements and upgrades to QPs and NOS.

  1. What is the way to communicate already defined NOS and QPs to all stakeholders? The employers are part of sector Skill council and the academia may be engaged by the industry. Who represent student’s aspirations in the formulations of QP and NOS?
  2. Where are the sufficient number of competent people to help to arrive at course-ware to satisfy the defined QPs and also map aspiration of students at different learning maturity, rural and urban students? Will the academia have bandwidth to upgrade course-ware on frequent basis?
  3. Who is responsible for assessments to validate the maturity level of the student after undergoing training for a specific job position? Can the training provider be also the judge as assessment provider?. is the same set of persons running industry in sector, also run a training company and assessment company as three separate legal entities?
  4. For people who already are working in a job position, how to evaluate the existing skills and match their maturity to a same job position or higher or lower. Will the person would agree to the evaluation as fair? Will there be cry of unfairness?
  5. This question is more suited to the IT/ITES industry. as it continues to evolve at a faster rate and training providers are not able to keep pace with industry. some times there is hype in demand of job position and then the demand dies in short period. How to make sure that what affects stakeholders are based on long term requirements of the industry and not short term?
  6. Who would handle the coordination and collaboration needed between industry, training provider and assessment provider?

Some open questions

  • Would too much standardization in job roles hinder innovation? Would the  learners be forced to think in a particular way.
  • interesting observation is that  there is no sector skill council for education and neither for art, music and dance.  What about subjects like my major mathematics that are applied in different sectors? Will this skew students towards industry needs and people exploring arts , music and dance die down?
  • Will advertisement of NOS and QPs make parents to start driving children in approach similar to pushing them for becoming  doctors and engineers?

I wish  the objective of our efforts would lead to change  the education systems  focused to produce clerks and coolies as in British era.

Tools used in analytics

Having learnt statistics and operations research in college days,, I have started to explore analytics for application development. While in college, the faculty provided a a small problem data , I learn that I need to construct the data, generally large in real life. While in college. faculty constructed/designed operation research or statistics/probability scenario and provided(some time copied from a book) the problem for us to solve, in industry, I need to understand the business problem and formulate the problem and then arrive with solution.

It is quite possible that arrived solutions might not be feasible for  implementation  due to people constraints or data cannot be collected due to regulatory  reasons, leading to modifying  the problem definition jointly with business owner and also updating the solution. Good news is that tools are available to  perform complex calculations and come with algorithms implemented and tested already.  The new challenge is to identify the algorithm or technique in the context of  the current problem and to supply  input data for the  algorithm or procedure defined by the tool.   Exploring how to perform application development that leverage the tool.

  • [Free] R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.  R is available as Free Software under the terms of the Free Software Foundation‘s GNU General Public License in source code form.
  • [Paid] AmiBroker is a  trading system and development platform, with two primary modes of operation – charting and formula evaluation. AmiBroker is a technical analysis tool to manage your stock portfolio and gives you real time quotes taking the information from on-line services. In its charting mode, historical price and volume data are displayed along with technical indicators to help analyst to look  for patterns and conditions. In the formula evaluation mode,  patterns, conditions, and rules are described using a programming language. The program analyses the price and volume data and reports on the profitability of the rules. When profitable trading systems have been found, it scans the group of stocks that are of interest to the trader and lists the current buy and sell signals. The signals can optionally be send directly to a broker for execution. Any system that runs Windows efficiently will run AmiBroker efficiently.

Explored R and Amibroker and learnt that both tools are  extensible and useful in the  respective scenarios.

R Project for Statistical Computing

  1. One needs to spend time learning R  in an interactive environment using RStudio and the tutorials at Quick-R and Learn-R got me going very fast.
  2. Python has packages that allow you to do most of the things that you want to do in R, from data wrangling to plotting. One can access R objects from Python. Check Stack Overflow  link for more information.
  3. PHP be can be leveraged to work with R.  Check GitHub link  ability to run R scripts from PHP and   How to integrate and execute with R from PHP 
  4. Vim-R-plugin : Plugin to work with R


  1. AmiBroker can be extended writing new scripts. AFL code is used to represent both user interface and calculations.
  2. Programmers can package the AFL scripts to work as part of DLL library and the DLL library can be deployed in to existing AmiBroker client environment.There is Net SDK that can be used to help in developing DLLs that would work integrated with AmbiBroker. More details at link. There are plugins to convert AFL scripts to C++ plug-ins.
  3. AFL scripts can be easily and quickly converted to .NET plugins leaving no public trading logic in AFL files. .NET plugins can be protected and licensed. The protected plugins can be made public and  only customers with license for their machines can use the protected .NET Plug-Ins.




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