Best practices for collecting consumer data

The recent scandal involving Cambridge Analytica and Facebook highlights a major ethical dilemma generated by rapid use of technology in our lives. Companies surrounding us are continually collecting the information about us and using it to drive specific behaviour in us without our knowledge. While most of these activities are limited to pushing offers and promotions on digital channels, it appears like Cambridge Analytica had undoubtedly crossed a line by using it to manipulate voter’s decisions.

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It is true that most major organisations have interest in collecting information about customers and have harnessed it by deploying sophisticated algorithms to generate profits. There are many examples when these activities have made life better for today’s digital consumers. For example:

  1. Amazon uses the buyer’s data to provide the recommendations for next product that they are likely to buy. It is evident that if people buy more products, Amazon benefits from it. But it is equally valid that this approach helps customers find products with ease without having to browse through tediously for hours.
  2. Google uses the email data to classify them as spam or not-spam. Some people would consider it as an invasion of privacy. However, this approach makes our lives much better because a highly accurate and quick machine is pre-screening our emails so that we get only the relevant content.
  3. It is important to point out that even Obama used sophisticated data analytics to predict which voters are at the brink and used his resources to directly talk to these people and convince them about his presidency.

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So if Obama himself used a similar approach, why a similar approach seems scandalous during Trump campaign? There are several reasons:

a. Companies related to the campaign collected the data without the consent of the people. They used the data for an application which was never agreed upon.

b. They abused the default privacy settings of Facebook to collect more information from the network and communities without their explicit permission.

c. Companies related to the campaign distributed the data to the third parties who ran digital ads without attributing it to the origin of the data.

This kind of ill-treatment of the data and blatant carelessness in running marketing campaigns may have worked well for their campaign but is the reason why this approach is so wrong. The data should be collected by fair means and should be used for the specific application for which the consumer has agreed. The consumer is putting their trust in the brands, and it is an ethical responsibility of the brands to protect the interests of consumers, most of whom do not understand the technicalities behind.

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In light of this brewing storm, I have decided to compile a list of best practices that should be followed in collecting, analysing and using the consumer data:

  1. All consumer data should be kept in a private and encrypted database on the servers of brands. This should not be shared between third parties or the public.
  2. All the public data collected should comply with the policies of individual websites from which they are being obtained. For example – Facebook shares specific parameters of the profiles public so that they can appear on the search engines. Any attempt to gather more information may not be ethical.
  3. The data collected should only be used for the application on which it was received. For example, if users are leaving their data on an e-commerce website, it should only be used to push purchases from the e-store.
  4. The terms and conditions for collecting information should be made clear to the consumers, and their consent should be taken to use their data in future.
  5. Any marketing activity done from the data should have a valid and verifiable source.

 

– Anmol Mohan

CEO, Tuple Technologies

P.S.: Tuple provides Big Data Analytics and Artificial Intelligence platform as a service. Our objective is to accelerate and spread the use of data analytics to enable accurate and optimal decision making. Due to the nature of our business, we do help our clients collect, analyse and use the data. However, we have a commitment to protect the consumers from any kind of harassment and manipulation. We never engage in any unethical activity related to data collection or usage for which consumers have not provided an explicit consent.

P.P.S.: All the opinions provided in this article are my own and is not meant to be a proof or guidelines for any specific company or activity. All facts presented in this article are based on publicly available media coverage, and I have tried my best to be factually accurate

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Accelerating Growth with Predictive AI

The big data and analytics company, Tuple Technologies Pte Ltd, put up their first event on accelerating business growth with the help of predictive AI at BASH, the start-up hub of Singapore at One-North, yesterday. The event was attended by around 70 professionals from different industries and from diverse levels of management.

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The event started off with a welcome address by the chief marketing officer of Tuple, Santhosh Nagaiah. It was followed by opening remarks from Mr. Felix Tan, director of The Finlab, which is also an associate sponsor for the event. Felix went on to mention how inevitable it is to adapt to newer technologies to improve business growth. He stressed upon the point that businesses can only go to a certain level in reducing costs and improving productivity. After a point, using technology to grow business would be the only way to truly achieve sustainability. Post Mr. Tan’s speech, the speakers for the night which included Mr. Tan himself, were requested to come forward and take their seats.

The moderator for the panel discussions was Mr. Raymond Lim, an ex-banker and a head hunter for various financial institutions. He called upon the first two speakers for the evening, Mr. Bala Chandrasekaran, Director, Interactive Technology Solutions at JLT interactive Pte Ltd and Mr Mohammed Abdoolcarim, Co-founder and Head of Product at Vahan, an automated messaging-based ride simulator for Uber drivers. The topic for the first panel of speakers was, ‘Is it essential for companies to have an A.I Strategy?’

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Mr. Chandrasekaran started off on the topic by explaining how his organization which is considered to be a middle man in the insurance industry always has to be on top of their game so that insurance companies don’t think the usual way in cutting off the middle men to reach their potential customers directly. “In order to stay that way, it is mandatory to shift our focus towards A.I and not just rely on human intelligence.” said Mr Bala. He added that companies should have clear objectives when they want to start off on the A.I journey as the possibilities are humungous and during the course of this journey many tend to wander into situations which are not their primary intentions. Mr. Mohammed added onto Mr. Bala’s points with how A.I is nothing but supervised learning. He took the example of Gmail filtering out spam messages from the inbox and even segregating private emails to promotions and social media messages.

The second panel constituted of Mr. Amit Singh, Founder of AMLNG Pte Ltd, an LNG trader/consultant with over 20 years of experience in the Oil & Gas industry and Mr. Felix Tan, Director of The Finlab, a start-up accelerator program from the collaboration of UOB & SGInnovate. The topic on them was, “How traditional business can adopt deep
technologies?”. Mr. Amit Singh started off the discussion with a confession on his limited knowledge on technology because his industry has always relied on excel sheets, pen and paper for information on different commodities over the years. He explained how people have done business to the quantum of a trillion dollars per year in the oil & gas industry without any sort of technology intervention.

He also mentioned his desire to bring in blockchain technology into this industry to enable information sharing on a real time basis. He finished off his talk with a tough statement on how technology will help and guide traditional businesses in the near future. Mr. Felix complimented Mr. Singh’s points with his own experience of helping an SME from the traditional way of doing business to the use of technology and scaling it up to a leading land transport engineering solutions company in Singapore. He narrated how traditional or raw businesses like transportation and land engineering also benefit out of technology implementation. Hereditary businesses when passed over to the younger generations find the immediate need to inculcate technology improvements in order to achieve a sustained growth.

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The final panel for the day constituted of two veterans from the technology space, Ms. Lin Hsin Hsin, an IT inventor with tremendous knowledge on technology, with an experience 30 years and Mr. Andy Huang, Founder & CEO of Secure Ark LLP, a cybersecurity company. The last topic of discussion for the day was, “What lies ahead in the future of deep tech revolution?”. Ms. Lin kicked off the discussion with the philosophy that technology has just gone through phases of popularity and topics like machine learning and artificial intelligence have been in existence for as long as 30 years now.

She highlighted on how the first ever conference on AI happened in the year 1988 in Singapore. Since the “tech” space is reaching a state of realization among fellow humans, it is creating this revolution and even something as complicated as language translations and image recognitions has become so easy with the help of technology. She also put the crowd to smiles by predicting how there is a possibility of Olympics for robots in the year 2020. Mr. Andy, on the other hand, concurred with Ms. Lin and gave his own experiences with different phases of technology from his earlier days in the Interpol. Mr. Huang also mentioned how all of this new age technology
was already speculated back in the days.

“The event was just a beginning of more such occasions to come in the future. The idea is to make complex subjects like AI more laymen so that managers would understand the true potential they can bring about to their business and use them wisely” said Ms. Neha Singh, Chief Operations Officer of Tuple Technologies. After the panel discussions, Mr. Raymond Lim went on to giving an exclusive preview of Tuple’s new website to the attendees. The gathering for the evening networked with one other over drinks and pizza before dispersing to their homes.

The costs of not doing data analytics – Part 1

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Implementing an insight-generating system or data-driven strategy for your business is viewed as a large, complex project on par with rolling out a Database management system or ERP system. Since prior systems do not run mission critical processes as the latter for the company, they are often delayed due to other priorities.

However, if we go by the recent developments, it is hard to ignore the progress that Big Data and Artificial Intelligence has made. Not only these technologies are making life easier, but their case studies of adding enormous value to the organisations are also all around us. Research shows that companies with data-driven strategy achieved 6% better profits and 8% better productivity over their not-so-nerdy competitors. So despite the obvious benefits, why do most companies think of data analytics as an optional process? Here are some key challenges:

Get your priority straight

If you are a product manager, you are focussed on improving features of products which will appeal to your customers. If you are a marketing director, you would like to communicate right messages to your customers. Much data is generated through these processes, but that in itself is worthless. Chances are you are already stretched and have so many other priorities, that looking at data sits right at the bottom

Screen Shot 2017-05-02 at 3.28.40 PMAccording to MIT Sloan’s report about Analytics – 50% of the companies are still analytically challenged despite high optimism towards the value driven by analytics.

http://sloanreview.mit.edu/projects/the-hard-work-behind-data-analytics-strategy/

Barriers to Adoption

It takes much hard work to get the required information out of the data. Many companies especially small to midsize firms place emphasis on the price of analytics, like the cost of infrastructure, software pipelines and hiring specific resources. Too often, they stay away from ‘pricey analytics projects’, or that is what they perceive.

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According to a survey by IDG Enterprise, the top reasons to companies’ inability to implement data analytics projects are (a.) Lack of Skilled resources, (b.) Limited Budget and (c.) Legacy issues which make implementation difficult on current setups

https://www.idgenterprise.com/resource/marketing-tools/big-data-and-analytics-the-big-picture-infographic/

Can nature be blamed?

Data Analytics is an aggregated science. It finds recurring patterns with a bird’s eye view, and humans are not good at visualising complex patterns. We would rather rely on our selective judgement than indulging in an overly cryptic, incrementally better and effort consuming system.

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Check out this answer about why data-driven decision making is involved, by Ricardo Vladimiro, Game Analytics and Data Science Lead @ Miniclip

https://www.quora.com/What-are-some-of-the-pros-and-cons-of-a-data-driven-approach-to-decision-making-What-are-some-examples-of-data-driven-decision-making-in-your-answer/answer/Ricardo-Vladimiro-1?srid=omsK

However, the value is apparent

As a result, data takes a backstage for most companies, and decision making becomes a pure art rather than science. It is true that the process of doing analytics is a tedious and costly affair, to begin with. However, surveys have shown that making data-driven decisions can generate substantial value for the companies which is similar to primary functions of the organisations. Even better is the ROI which scales very well and you do not need to invest in data like you constantly have to with other functions.

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According to this report by McKinsey, data and analytics has driven 60%+ increase in net profit margins and 0.5-1% growth in annual productivity for US Retail. Similar observations were made in EU as well.

http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Executive-summary.ashx

Debunking the myth of gut-based decision making

Decision making is nothing but forecasting of events. As in charge of delivering success to your company, you think and decide – What would appeal to your customers? What would drive them crazy? What will give you the best return on costs yet hitting the set targets? I decided to write this article because I think it will drive certain behaviour in the readers. I forecasted.

So now that we have established what decision making is, I want to quote a super book on the super subject called ‘Superforecasting’. The initial parts of the book are mostly about establishing the fact that being good at forecasting is not something that we are born with; it is a skill learned through a painstaking process of gathering information, analysing it and finding something useful (sounds familiar?). In an example they quote that:

“A researcher gathered a big group of experts – academics, pundits, and the like – to make thousands of predictions about the economy, stocks, elections, wars and the other issue of the day. Time passed, and when the researcher checked the accuracy of the predictions, he found that the average expert did about as well as random guessing.”

To achieve the optimal, balance is required where the experience based decisions can be backed by insights from the data. According to this slightly old PwC survey:

Highly data-driven companies are three times more likely to report significant improvement in making big decisions.

http://press.pwc.com/GLOBAL/News-releases/big-decisions-executives-rely-more-on-experience-and-advice-than-data-to-make-business-defining-choi/s/457883b1-db9a-4676-95cc-7f78669c00e6

So how can analytics add intelligence to your decision making? (a.) It can tell you where are you losing money and, (b.) It can find out alternate (often hidden) revenue streams for your business. We will cover both of them in Part 2.

Thoughts?