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.

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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?

Welcome to Tuple

 

It’s a beautiful Monday morning in Singapore with people bustling in buses and MRT stations making their way to work. Somewhere in Bugis, the owner of a Sago shop (a F&B outlet specializing in a delicious Chinese desert) looks at her last week’s business reports and wonders, “I wish I had the power to know my customers’ liking even before they ordered…”. At the same moment, on the other side of the town, a mango seller is loading up his truck hoping to find the right customers where he could get more value for his specially imported mangoes.

As the owner of the Sago shop opens her shop’s Facebook page the mango seller is checking out the orders received for the day. Although the number of likes have gone up in the FB page, she is not aware how a sentiment analysis could be done on the people who have liked the page. Also, if only she knew that her customers are already talking about the delicious mango sago they had last night over social media, she could come out with an offer on the particular to attract more footfall.

Similarly, the mango seller takes note of the fruit shop and juice shop outlets that have ordered. He is worried about the excess stock which he might have to undersell. There is no one to suggest him that he should consider the Sago shop as a premium customer and deliver an exclusive batch of mangoes to increase his value proposition. The solution to these problems might not be of data but, analyzing the already available information and finding the value in it.

Ladies & gentlemen, the above story is based on true incidents happened over 3 years. Now, both the characters of the story are involved in a strong business relationship and till date serve the best Mango Sago in town. Such common business issues have made us wonder whether holding information and not seeing a pattern in it to improve business is innocence or sheer negligence.

As the amount of data from the Global IP traffic branches out to exabytes and zettabytes, so is their value. Companies gain an edge over their competitors solely due to data sourcing and cleaning. Companies employing analysts to make sense of their data are moving ahead aggressively. Soon, there will be a world where data would be the universal currency and businesses would transact with their knowledge stored as data.

At this juncture, Tuple Technologies would love to share the opinions of people from a variety of industries and markets on what they believe is the value for making sense of the so called Big Data. So, moving forward, we will interact with statisticians, data scientists and analytics teams of various multi national organisations. This will be purely to understand the supply side, i.e. the analysts, on how they see this industry’s growth and how each market is responding to this game changing business segment.

Alternatively, we shall discuss with C-level executives of a plethora of industries to identify what is that they are looking from the information collected on their customers, how they are leveraging that information to gain an upper hand over their competition. We hope that the blog will bring out adrenaline pumping, mind boggling revelations.

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