The Banking and Finance sector (BFSI) is witnessing one of its most interesting and enriching phases. Apart from the evident shift from traditional methods of banking and payments, technology has started playing a vital role in defining this change.

Mobile apps, plastic money, e-wallets and bots have aided the phenomenal swing from offline payments to online payments over the last two decades. Now, the use of Artificial Intelligence (AI) in BFSI is expediting the evolution of this industry.

But as the proliferation of digital continues, the number of ways one can commit fraud has also increased. Issuers, merchants, and acquirers of credit, debit, and prepaid general purpose and private label payment cards worldwide experienced gross fraud losses of US$11.27 billion in 2012, up 14.6% over the previous year1. Fraud losses on all general purpose and private label, signature and PIN payment cards reached US$5.33 billion in United States in the same period, up 14.5%1. These are truly big numbers, and present the single-biggest challenge to the trust reposed in banks by customers. Besides the risk of losing customers, direct financial impact for banks is also a significant factor.

Upon reporting of a fraudulent transaction by a customer, the bank is liable for the transaction cost, it has to refund merchant chargeback fee, as well as additional fee. Fraud also invites fines from regulatory authorities. The recently-passed Durbin Amendment caps processing fee that can be charged per transaction, and this increases the damage caused by unexpected fraud losses. The rapidly rising use of electronic payment modes has also increased the need for effective, efficient, and real-time methods to detect, deter, and prevent fraud.

Nuances of Banking Fraud Prevention Using AI

AI enables a computer to behave and take decisions like a human being. Coined in 1956 by John McCarthy at MIT, the term AI was little known to the layman and merely a subject of interest to academicians, researchers and technologists. However, over the past few years, it is more commonly seen in our everyday lives; in our smartphones, shopping experiences, hospitals, travel, etc.

Machine Learning, Deep Learning, NLP Platforms, Predictive APIs and Image and Speech Recognition are some core AI technologies used in BFSI today. Machine Learning recognises data patterns and highlights deviations in data observed. Data is analysed and then compared with existing data to look for patterns. This can help in fraud detection, prediction of spending patterns and subsequently, the development of new products.

Key Stroke Dynamics

Key Stroke Dynamics can be used for analysing transactions made by customers. They capture strokes when the key is pressed (dwell time) and released on a keyboard, along with vibration information.

As second factor authentication is mandatory for electronic payments, this can help detect fraud, especially if the user’s credentials are compromised. Deep Learning is a new area in Machine Learning research and consists of multiple linear and non-linear transformations. It is based on learning and improving representations of data. A common application of this can be found in the crypto-currency, Bitcoin.

Adaptive Learning

Adaptive Learning is another form of AI currently used by banks for fraud detection and mitigation. A model is created using existing rules or data in the bank’s system. Incremental learning algorithms are then used to update the models based on changes observed in the data patterns.

AI instances in Insurance for Fraud Prevention

Applying for Insurance

When a customer submits their application for insurance, there is an expectation that the potential policyholder provides honest and truthful information. However, some applicants choose to falsify information to manipulate the quote they receive.

To prevent this, insurers could use AI to analyse an applicant’s social media profiles and activity for confirmation that the information provided is not fraudulent. For example, in life insurance policies, social media pictures and posts may confirm whether an applicant is a smoker, is highly active, drinks a lot or is prone to taking risks. Similarly, social media may be able to indicate whether “fronting” (high-risk driver added as a named driver to a policy when they are in fact the main driver) is present in car insurance applications. This could be achieved by analysing posts to see if the named driver indicates that the car is solely used by them, or by assessing whether the various drivers on the policy live in a situation that would permit the declared sharing of the car.

Claims Management & Fraud Prevention

Insurance carriers can greatly benefit from the recent advances in artificial intelligence and machine learning. A lot of approaches have proven to be successful in solving problems of claims management and fraud detection. Claims management can be augmented using machine learning techniques in different stages of the claim handling process. By leveraging AI and handling massive amounts of data in a short time, insurers can automate much of the handling process, and for example fast-track certain claims, to reduce the overall processing time and in turn the handling costs while enhancing customer experience.

The algorithms can also reliably identify patterns in the data and thus help to recognize fraudulent claims in the process. With their self-learning abilities, AI systems can then adapt to new unseen cases and further improve the detection over time. Furthermore, machine learning models can automatically assess the severity of damages and predict the repair costs from historical data, sensors, and images.

Two companies tackling the management of claims are Shift Technology who offer a solution for claims management and fraud detection and RightIndem with the vision to eliminate friction on claims. Motionscloud offer a mobile solution for the claims handling process, including evidence collection and storage in various data formats, customer interaction and automatic cost estimation. ControlExpert handle claims for the auto insurance, with AI replacing specialized experts in the long-run. Cognotekt optimize business processes using artificial intelligence. Therefore the current business processes are analyzed to find the automation potentials. Applications include claims management, where processes are automated to speed up the circle time and for detecting patterns that would be otherwise invisible to the human eye, underwriting, and fraud detection, among others. AI techniques are potential game changers in the area of fraud. Fraudulent cases may be detected easier, sooner, more reliable and even in cases invisible to the human eye.

Conclusion

Those who wish to defraud insurance companies currently do so by finding ways to “beat” the system. For some uses of AI, fraudsters can simply modify their techniques to “beat” the AI system. In these circumstances, whilst AI creates an extra barrier to prevent and deter fraud, it does not eradicate the ability to commit insurance fraud. However, with other uses of AI, the software is able to create larger blockades through its use of “big data”. It can therefore provide more preventative assistance. As AI continues to develop, this assistance will become of greater use to the insurance industry in their fight against fraud.

Rapidly evolving technology and a digitally focused world have opened the door for a new wave of automation to enter the workforce. Robots already stand side-by-side with their human counterparts on many manufacturing floors, adding efficiency, capacity (robots don’t need to sleep!) and dependability. Add in drones and self-driving vehicles and it’s no wonder many are questioning the role of humans going forward.

Supply chains, although automated to a degree, still face challenges brought about by the amount of slow, manual tasks required, and the daily management of a complex web of interdependent parts. The next generation of process efficiency gains and visibility could be on your doorstep with artificial intelligence in supply chain management, if only you’d let the robots automatically open it for you.

Robotic Process Automation

RPA works by automating the end-to-end supply chain, enabling the management of all tasks and sections in tandem. It allows you to spend less time on low value, high frequency activities like managing day-to-day processes, and provides more time to work on high value, exception-based requirements, which ultimately drives value for the entire business.

PwC estimates businesses could automate up to 45% of current work, saving $2 trillion in annual wages. “In addition to the cost and efficiency advantages, RPA can take a business to the next level of productivity optimization,” the firm says. Those ‘lights out’ factories and warehouses are becoming closer to a reality.

Four key elements need to be in place for you to take full advantage of robotic process automation in your supply chain:

• robots for picking orders and moving them through the facility;
• sensors to ensure product quality and stock;
• cognitive learning systems;
• and, artificial intelligence to turn processes into algorithms to guide the entire operation.

In addition, you’ll need strong collaboration internally and among suppliers and customers to tie all management systems back to order management and enterprise resource planning platforms.

Artificial Intelligence In Supply Chain Automation

AI is changing the traditional way in which companies are operating. Siemens in its “lights out” manufacturing plant, has automated some of its production lines to a point where they are run unsupervised for several weeks.

Siemens is also taking a step towards a larger goal of creating Industrie 4.0 or a fully self-organizing factory which will automate the entire supply chain. Here, the demand and order information would automatically get converted into work orders and be incorporated into the production process.

This would streamline manufacturing of highly customized products.

 Artificial Intelligence In Supplier Management And Customer Service

Organizations are also increasingly leveraging AI for supplier management and customer management. IPsoft’s AI platform, Amelia automates work knowledge and is able to speak to the customers in more than 20 languages. A global oil and gas company has trained Amelia to help provide prompt and more efficient ways of answering invoicing queries from its suppliers. A large US-based media services organization taught Amelia how to support first line agents in order to raise the bar for customer service.

 Artificial Intelligence In Logistics & Warehousing

Logistics function will undergo a fundamental change as artificial intelligence gets deployed to handle domestic and international movement of goods. DHL has stated that its use of autonomous fork lifts is “reaching a level of maturity” in warehouse operations. The next step would be driver less autonomous vehicles undertaking goods delivery operations.

 Artificial Intelligence In Procurement

AI is helping drive cost reduction and compliance agenda through procurement by generating real time visibility of the spend data. The spend data is automatically classified by AI software and is checked for compliance and any exceptions in real time. Singapore government is carrying out trials of using artificial intelligence to identify and prevent cases of procurement fraud.

The AI algorithm analyzes HR and finance data, procurement requests, tender approvals, workflows, non-financial data like government employee’s family details and vendor employee to identify potentially corrupt or negligent practices. AI will also take up basic procurement activities in the near future thereby helping improve the procurement productivity.

Artificial Intelligence in new product development

AI has totally overhauled the new product development process.by reducing the time to market for new products. Instead of developing physical prototypes and testing the same, innovators are now creating 3D digital models of the product. AI facilitates interaction of the product developers in the digital space by recognizing the gestures and position of hand. For example, the act of switching on a button of a digital prototype can be accomplished by a gesture.

 AI In Demand Planning And Forecasting

Getting the demand planning right is a pain point for many companies. A leading health food company leveraged analytics with machine learning capabilities to analyze their demand variations and trends during promotions.

The outcome of this exercise was a reliable, detailed model highlighting expected results of the trade promotion for the sales and marketing department. Gains included a rapid 20 percent reduction in forecast error and a 30 percent reduction in lost sales.

 AI in Smart Logistics

The impact of data-driven and autonomous supply chains provides an opportunity for previously unimaginable levels of optimization in manufacturing, logistics, warehousing and last mile delivery that could become a reality in less than half a decade despite high set-up costs deterring early adoption in logistics.

Changing consumer behavior and the desire for personalization are behind two other top trends Batch Size One and On-demand Delivery: Set to have a big impact on logistics, on-demand delivery will enable consumers to have their purchases delivered where and when they need them by using flexible courier services.

A study by MHI and Deloitte found more than half (51%) of supply chain and logistics professionals believe robotics and automation will provide a competitive advantage. That’s up from 39% last year. While only 35% of the respondents said they’ve already adopted robotics, 74% plan to do so within the next 10 years. And that’s likely in part to keep up with key players like Amazon, who have been leading the robotics charge for the past few years.

 What is the mantra ?

These examples showcase that in today’s dynamic world, AI embedded supply chains offer a competitive advantage. AI armed with predictive analytics can analyze massive amounts of data generated by the supply chains and help organizations move to a more proactive form of supply chain management.

Thus, in this digital age where the mantra is “evolve or be disrupted”, companies are leveraging AI to reinvent themselves and scale their businesses quickly. AI is becoming a key enabler of the changes that businesses need to make and is helping them manage complexity of the constant digital change.

A considerable amount of current conversation in the area of data science and analytics focuses on the virtues of solving all the challenges that organizations face when using this new paradigm in the business world. There is also a lot of discussion around the technology-related issues that impact achieving data science and analytics goals.

What hasn’t gotten the attention that it merits, however, is the role of business leadership and how thought leaders need to raise the stakes to become not only well versed in analytics, but to build data science and analytics literacy throughout their organizations. They need a heightened awareness of analytics if they are going to effectively drive analytics strategies and outcomes for their organizations and become true leaders in this area by all relevant measures.

The following three significant findings from align with this point of view:

• Create a culture for making fact-based decisions.
• Establish a common data science and analytics vision—and strategy—to focus everyone on the outcomes.
• Instill analytics expertise across the entire organization, from the top down.

Senior executives and business managers should aspire to create the core competencies and to develop analytical insights that enable them to become data science and analytics leaders within their industries. Education, mentorship, and consultation with outside advisors should be implemented to gain the knowledge necessary to attain a leadership role. When selecting a consultancy, business leaders should choose one that can advise, mentor, and support based on specific needs and levels of maturity, as opposed to those that may take a force-fit approach that essentially attempts to force a round peg methodology into a square pegenvironment. And a good grasp of numbers in respect to numerical literacy is also important in this age of fact-based decision making based on insights derived from large volumes of data.

Numerical literacy means acceptable levels of working knowledge and experience in decision science in which analytical techniques such as statistical and descriptive analysis, forecasting, and performance management can be applied. In addition, moving from a gut-based decision model to a fact-based one requires both cultural change as well as the tools and know-how required to create and manage the facts themselves. Finance teams can typically be the source of such competencies, and they can be used as a center for fostering and developing these competencies across the enterprise. It is ultimately at the mercy of top level management on how they are going to leverage their business acumen to instil a cultural change to foster data thinking.

Bringing In Data Mentors At The Very Top

Today’s executives and managers are trained primarily in operations, finance, marketing, and sales, along with a bit of strategy thrown in for good measure. While a significant number of senior executives in the US have advanced degrees in their field of expertise, few have been formally trained in information management, analytics, or decision science. Yet, virtually none have been schooled in decision science, information theory, analytics, or risk management. Lack of training in these areas creates a dilemma for those organizations that want to focus on data science and analytics but do not have experienced leaders who can lead from a position of domain expertise. But that wouldn’t mean stacking up data workers at the TLM (top level management). Leadership drive , domain insight and People Skills will still be the most coveted virtues at the very top. But the lack of ground level data-sifting skills need not be only plugged at the lower levels. To achieve these competencies without formal education or hands-on experience requires consultation with outside data mentors and advisors who can work hand in hand with the entire senior executive team. These advisors help ground the team in both the science and the pragmatics required to achieve successful data science and analytics outcomes that can be applied pervasively across the organization. This approach—some call it the “charm school” approach—can be characterized by a close collaboration among all parties involved. It can rapidly accelerate the process of nondisruptively developing the senior executive team’s data science and analytics expertise and competency to maximize strategic outcomes.

Data science and analytics success should be driven by the business, and more importantly from the ranks of its senior executives and managers—not from the bottom up or from the IT function. The inherent accountability for all strategic initiatives is at the very top of the organization and cascades down and across to business managers at various levels who then have responsibilities for its execution within their area of control. Organizations today remain hierarchical in both structure and cultural behavior. To change either of these structures requires engaged and competent senior executive teams that are committed to the outcome and can influence and align behaviors to support it.

Strategic Thinking in Data Monetization

A number of organizations have come to this realization already. They’re now engaging with management consultancies and analytics boutiques to address their shortcomings and accelerate results from their data science and analytics strategies and successfully monetize them. Alongside these mentoring activities, organizational leadership is strongly advised to consider organizational structures and change readiness as complementary endeavors. They can help illuminate the revisions to structure and Organizational Change Management (OCM) activities required to bring the entire organization to a level that they could start assessing the monetizing estimates of data thinking. These accompanying measures can also bring cohesion to the entire data science and analytics strategy and the pursuit of its outcomes.

Firms that are looking to monetize Data and their Data Science strategies, must look beyond the data and into the economic questions that the data can answer. Often the data can help answer questions about the value, use, risk, or future value or risk of a specific asset. Or the data can say something about an overall market and how asset classes perform and how customers behave generally. Such insights are understood to have great economic value to asset owners and market participants. However, not all data will offer these features or value. The temperature readings from inside our refrigerators are unlikely to alter markets. However, the temperature readings of our furnaces and air conditioners could, in aggregate, drive new energy conservation and policy decisions.

Transforming data into economic insights will be the focus of top level executives who will monetize data. This transformation will require the creation of data products. It may be that such data products can be sold or traded to clients. It can also be that giving away data products  will drive other related monetization strategies. Hence creating the data product will not only require technical expertise with manipulating data but a clear vision on how data can be leveraged to answer economical questions and how the future market of the various domains will pan out.

Digital transformation reshapes every aspect of a business. As digital technology continues to evolve, I believe that successful digital transformation will require careful collaboration, thoughtful planning, and the inclusion of every department.

During recent years, we’ve seen shifts in how traditional leadership roles operate, as silos break down and the scopes of various roles widen and change. Digital transformation has morphed from a trend to a central component of modern business strategy. Following are the enlisted major trends that will capture the gist of what is to come in 2017. 

DIGITAL PLATFORM VIEW OF BUSINESS

A platform provides the business with a foundation where resources can come together — sometimes quickly and temporarily, sometimes in a relatively fixed way — to create value. The value comes largely from connecting the resources, and the network effects between them. As digitalization moves from an innovative trend to a core competency, enterprises will understand and exploit platform effects throughout all aspects of their businesses.

• The deepening of digital means that lines are becoming increasingly blurred, and boundaries semi porous — both inside and outside the enterprise — as multiple networks of stakeholders bring value to each other by exploiting and exploring platform dynamics
• CIOs are clearly being given the opportunity to lead a digital transformation that exploits platform effects majorly in managing delivery, talent and executing leadership

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2016/12/18/sameer-dhanrajani-key-win-themes-to-master-in-digital-business/

THE ADVENT OF IMMERSIVE CONTENT:  AUGMENTED REALITY AND VIRTUAL REALITY

The booming success of the Pokémon GO AR app is a wakeup call to any business that hasn’t evaluated the potential of AR and VR. These technologies were once limited to the gaming realm, but they’re now easier to implement than ever before. The mainstream shift toward AR and VR provides new ways to connect with customers and offer unique, memorable interactions.

• The AR and VR resurgence will open up the gates for workplace gamification in a big way into a core business strategy
• 2017 is also going to mark a turning point in the way audiences interact with and consume video content through the releases of the HTC Vive, Oculus Rift, PSVR etc.
• Significant improvements in immersive devices as well as software is anticipated

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2016/05/27/sameer-dhanrajani-retail-industry-redefined-through-data-sciences/

SMART MACHINES AND ARTIFICIAL INTELLIGENCE ARE TAKING OFF IN A BIG WAY

 Our relationships to technology continue to evolve. Soon machines will be able to learn and adapt to their environments. While advanced learning machines may replace low-skill jobs, AIs will be able to work collaboratively with human professionals to solve intensely complex problems.

• Data complexity is the top challenge standing in the way of digital transformation
• AI tools will evolve to read, review and analyze vast quantities of disparate data, providing insight into how customers feel about a company’s products or services and why they feel the way they do
• using AI to expedite knowledge-based activities to improve efficiency and performance will spread from reducing costs through automation, to transforming customer experience

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2016/12/01/sameer-dhanrajani-how-machine-learning-and-ai-will-drive-digital-transformation/

https://sameerdhanrajani.wordpress.com/2016/11/18/sameer-dhanrajani-banking-evolution-using-ai/

GROWING IMPORTANCE OF THE USER EXPERIENCE

The customer experience (including employees) is the ultimate goal of any digital transformation. Customers are more cautious than ever; they’ll turn away from brands that don’t align with their values and needs. A top-notch user experience is a fantastic way to keep customers involved and engaged with your brand.

• Every touch point matters, and those leading the transformation will strive to constantly ask how they are removing friction and enhancing the experience for every customer regardless of where they are in the journey
• Understanding digital consumers’ biases, behaviors and expectations at each point along the customer journey will be at the heart of every successful digital transformation

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2015/07/24/sameer-dhanrajani-how-to-bring-about-a-customer-experience-focused-digital-transformation/

https://sameerdhanrajani.wordpress.com/2016/12/18/sameer-dhanrajani-key-win-themes-to-master-in-digital-business/

BLOCKCHAIN’S DISRUPTIVE GROWTH

What Uber did for on-demand auto transformation, Blockchain will to do for financial transactions. And with $1.4 billion in venture-capital money in the past three years, 24 countries investing in Blockchain technology for government services, 90-plus central banks engaged in related discussions, and 10 percent of global GDP to be traded via Blockchain technology by 2025-2027, it is important that marketers understand the potential implications for their business.

• Blockchain technology will majorly be a part of the next great flattening and removal of middle-layer institutions
• The semi-public nature of some types of Blockchain paves the way for an enhanced level of security and privacy for sensitive data – a new kind of database where the information ‘header’ is public but the data inside is ‘private’
• Data analytics using Blockchain, distributed ledger transactions and smart contracts will become critical in future, creating new challenges and opportunities in the world of data science

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2016/06/21/sameer-dhanrajani-data-sciences-fintech-companies-for-competitive-disruption-advantage/

DIGITAL TRANSFORMATION DRIVEN BY THE INTERNET OF THINGS (IOT).

 Speaking of how invaluable big data is to marketers, the IoT offers immeasurable insight into customer’s mind. Businesses and customers alike will continue to benefit from the IoT. With an estimated 50 billion IoT Sensors by 2020 and more than 200 billion “Things” on the Internet by 2030, it is no question that IoT will be not only transformative, but disruptive to business models.

• IoT will change how daily life operates by helping create more efficient cities and leaner enterprises
• The staple tech for autonomous systems would be the Internet of Things (IoT) which would be the infrastructure, as well as the customers, since they work, interact, negotiate and decide with zero human intervention
• Real-time streaming analytics will collection, integration, analysis, and visualization of IoT data in real-time without disrupting the working of existing sources, storage, and enterprise systems

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2015/09/25/sameer-dhanrajani-real-time-streaming-analytics/

https://sameerdhanrajani.wordpress.com/2014/07/16/making-sense-of-the-iot-phenomenon-extracting-value-transforming-business/

API ECONOMY

We live in an API economy, a set of business models and channels based on secure access of functionality and exchange of data. APIs will continue to make it easier to integrate and connect people, places, systems, data, things and algorithms, create new user experiences, share data and information, authenticate people and things, enable transactions and algorithms, leverage third-party algorithms, and create new product/services and business models.

• An industry vision seeks using APIs to turn a business into a platform involving digital business models
• As the Internet of Things (IoT) gets smarter, things using an application programming interface (API) to communicate, transact and even negotiate with one another will become the norm

Detailed Analysis can be found here:

https://sameerdhanrajani.wordpress.com/2016/02/12/mr-algorithms-the-new-member-in-the-board-room-to-discuss-algorithm-economy/

http://www.gartner.com/smarterwithgartner/welcome-to-the-api-economy/

For more than a decade, Silicon Valley’s technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie.

Robots after the “Like” Button

Now Silicon Valley has found its next shiny new thing. And it does not have a “Like” button.

The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

The shift was evident in a Lowe’s home improvement store here this month, when a prototype inventory checker developed by Bossa Nova Robotics silently glided through the aisles using computer vision to automatically perform a task that humans have done manually for centuries.

The robot, which was skilled enough to autonomously move out of the way of shoppers and avoid unexpected obstacles in the aisles, alerted people to its presence with soft birdsong chirps. Gliding down the middle of an aisle at a leisurely pace, it can recognize bar codes on shelves, and it uses a laser to detect which items are out of stock.

Silicon Valley’s financiers and entrepreneurs are digging into artificial intelligence with remarkable exuberance. The region now has at least 19 companies designing self-driving cars and trucks, up from a handful five years ago. There are also more than a half-dozen types of mobile robots, including robotic bellhops and aerial drones, being commercialized.

The Surge after the Static – The Social Way

There has been a slow trickle in investments in robotics all this while, and suddenly, there seem to be a dozen companies securing large investment rounds focusing on specific robotic niches. Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.

By contrast, funding for social media start-ups peaked in 2011 before plunging. That year, venture capital firms made 66 social media deals and pumped in $2.4 billion. So far this year, there have been just 10 social media investments, totaling $6.9 million, according to CB Insights. Last month, the professional social networking site LinkedIn was sold to Microsoft for $26.2 billion, underscoring that social media has become a mature market sector.

Even Silicon Valley’s biggest social media companies are now getting into artificial intelligence, as are other tech behemoths. Facebook is using A.I. to improve its products. Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on A.I., with a device that listens in the home, answers questions and places e-commerce orders. Satya Nadella, Microsoft’s chief executive, recently appeared at the Aspen Ideas Conference and called for a partnership between humans and artificial intelligence systems in which machines are designed to augment humans.

The auto industry has also set up camp in the valley to learn how to make cars that can do the driving for you. Both technology and car companies are making claims that increasingly powerful sensors and A.I. software will enable cars to drive themselves with the push of a button as soon as the end of this decade — despite recent Tesla crashes that have raised the question of how quickly human drivers will be completely replaced by the technology.

AI Outdoes the Silicon Valley Reset Trend

Silicon Valley’s new A.I. era underscores the region’s ability to opportunistically reinvent itself and quickly follow the latest tech trend. This is at the heart of the region’s culture that goes all the way back to the Gold Rush. The valley is built on the idea that there is always a way to start over and find a new beginning.

The change spurred a rush for talent in A.I. that has become intense. It is unusual that the number of people trying to get the students to drop out of the class halfway through because now they know a little bit of this stuff is crazy. The valley’s tendency toward reinvention dates back to the region’s initial emergence from the ashes of a deep aerospace industry recession as a consumer-electronics manufacturing center producing memory chips, video games and digital watches in the mid-1970s. A malaise in the personal computing market in the early 1990s was followed by the World Wide Web and the global expansion of the consumer internet.

A decade later, in 2007, just as innovation in mobile phones seemed to be on the verge of moving away from Silicon Valley to Europe and Asia, Apple introduced the first iPhone, resetting the mobile communications marketplace and ensuring that the valley would — for at least another generation — remain the world’s innovation center.

In the most recent shift, the A.I. idea emerged first in Canada in the work of cognitive scientists and computer scientists like Geoffrey Hinton, Yoshua Bengio and Yann LeCun during the previous decade. The three helped pioneer a new approach to deep learning, a machine learning method that is highly effective for pattern recognition challenges like vision and speech. Modeled on a general understanding of how the human brain works, it has helped technologists make rapid progress in a wide range of A.I. fields.

The Road Ahead

How far the A.I. boom will go is hotly debated. For some technologists, today’s technical advances are laying the groundwork for truly brilliant machines that will soon have human-level intelligence. Yet Silicon Valley has faced false starts with A.I. before. During the 1980s, an earlier generation of entrepreneurs also believed that artificial intelligence was the wave of the future, leading to a flurry of start-ups. Their products offered little business value at the time, and so the commercial commercial enthusiasm ended in disappointment, leading to a period now referred to as the “A.I. Winter.” The current resurgence will not fall short this time, and the economic potential in terms of new efficiency and new applications is strong.

There has been a great deal of talk about the use of Artificial Intelligence chatbots in the last few weeks, especially given the news that Facebook are looking to implement AI into their Messenger and WhatsApp platforms, which are currently used by more than 1.8 billion people worldwide. However, does this bode well for the relationship between humans and Artificial Intelligence programs? Would you rather speak to an intelligent algorithm rather than a fellow human being?

The Sales and Customer Support Bot-ler ?

Chatbots, done right, are the cutting-edge form of interactive communications that captivate and engage users. But what kind of potential do they have for sales & customer support ?

To answer this, I should emphasize that customer service can be a delicate field. A lot of consumer engagement with a company happens when something goes wrong — such as a recently-purchased broken product or an incorrect bill or invoice.

By nature, these situations can be highly emotional. And as a business, you want to be responsive to potentially problematic customer inquiries like these. So relying on a chatbot to resolve issues that require a human touch might not be the best idea.

This is especially true if you let your bot “learn” from interactions it sees (say, in user forums) with no or minimal supervision. Things can easily go wrong, as the disaster around Microsoft’s Twitter bot “Tay” showed.

On the other hand, with the right supervision and enough training data, machine learning as an A.I. technique can help build very responsive and accurate informational chatbots — for example those that are meant to help surface data from large text collections, such as manuals.

I’d say that machine learning as a technique has been shown to work best on image processing. The advancements that Google, Facebook, and innovative startups such as Moodstocks (just acquired by Google) are showing in that space are truly amazing. Part of the amazement however, comes from the fact that we now see software take on another cognitive task that we thought could only be managed by humans.

What can bots do for the bottom line?

In my opinion, a bot’s primary application lies in customer service since most companies unfortunately continue to rely on an ancient methodology to manage customer interaction. And this is to be expected as most consumers themselves are still “hard-wired” to pick up a phone and dial a number when they want to engage with a company.

Companies haven’t necessarily made it easy for consumers to transition to digital-first interaction. Consumers are forced to either download a mobile app, browse websites, or use voice, the “dumbest” channel the smartphone has to offer, to retrieve information or perform transactions.

This is truly unfortunate because when it comes to paying a bill, checking on an order status, or reviewing account transactions, nothing is easier than sending a simple message. And with 900 million users now on Facebook Messenger, 1 billion on WhatsApp, and hundreds of millions more on basic SMS, companies have a consumer-preferred new medium for engaging with customers.

With messaging, a simple question can be posed in a simple message such as “Where is my order?”

Contrast this to the conventional options of being forced to shepherding that question through a maze of web or mobile app menus, or with IVR systems over the phone. Now imagine how a consumer-adopted, digital and automated interaction for simple questions vs. agent interaction over the phone could impact customer service and its cost. When chatbots handle the most commonly-asked questions, agent labor is reduced or redeployed to manage more complex and time-consuming interactions. Simple and moderate issues are resolved faster, leading to greater customer satisfaction and long-term loyalty. Bots can help deflect calls from the contact center and your IVR, which further reduces speech recognition license and telephony cost.

Could there be Bot-tle-necks?

There is also the question of whether these chatbots will take jobs from humans; a subject of fierce debate for all industries and levels in the last few months. Facebook itself has been quick to clarify that these chatbots are not going to replace the people in their organisation, but instead to work alongside them. For example, Facebook have said that the customer service executives will be required to train the AI bots, and to step in when the AI comes unstuck, which is likely to be fairly frequently in the early stages! Chinese messenger service WeChat has taken the chatbot idea on, with companies having official accounts through which they are able to communicate with their customers. However, the platform is still in its early stages, and is reported to be incredibly frustrating to use, so those in the customer service sector needn’t worry that their jobs are under threat quite yet!

While we might see chatbots starting to appear through the likes of Facebook Messenger and WhatsApp platforms in the coming 12 months, and will be dedicating teams of engineers to train the platforms, rather than relying on the general public. There are three main factors on which their success depends.

The first is with how much freedom AI in general is allowed to be developed, especially given the hesitation that the likes of Elon Musk and Bill Gates have about a potential ‘Singularity’, with Musk recently being quoted as saying that ‘Artificial Intelligence is our biggest existential threat’.

The second is arguably more important; how willing the general public are to help develop the chatbots, by having conversations with them, in the knowledge that they are talking to an autonomous entity.

More important still, are these chatbots going to be safe from cyberattacks? How will you know if your financial information will be secure if you disclose it to a chatbot, especially if there are unlikely to be the same multi-stage security checks that are the hallmark of P2P customer service interactions?

The Road Ahead?

Many companies are already launching bots for customer acquisition or customer service. We will see failures, and in parts, have already seen some. Bots are not trivial to build: you need people with experience in man-machine interface design. But to quote Amara’s Law: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

Bots are here to stay, and will be a great new platform and make things easier for all of us. But bots that try to do too much or set unreasonable expectations will slow consumer confidence and acceptance of them. What might help us now is maybe to calm down a bit with the hype, and focus on building good bots that have value — then share our experiences, and show the world where the true value lies.

In the healthcare industry, what could be more important than having better healthcare outcomes? Each and every day healthcare workers around the globe are striving hard to find more ways of improving our lives. However, the world is changing, and frankly, at a faster rate than most of us can keep up. Intuition alone will no longer be enough for quality patient outcomes. The amount of healthcare data continues to mound every second, making it harder and harder to find any form of helpful information. Big Data is not to be romanticized; it can be a blessing and a curse. It can contribute to both the insight and the fog of visibility.

In truth, data science is proving invaluable to improving outcomes due to its ability to automate so much of the heavy lifting – in fast, scalable, and precise ways. All one has to do is look at our ability to predict epidemics, advance cures, and make patient stays in hospitals safer and more pleasant. In healthcare, data science should be seen as a beneficial intelligence rather than only artificial intelligence, providing an augmentation of services to the healthcare experts already in play.

Hospital Claims Data

In 2010, there were 35.1 million discharges with an average length of stay of 4.8 days according to the National Hospital Discharge Survey. That same survey went on to note that there were 51.4 million procedures performed. The National Hospital Ambulatory Medical Care Survey in 2011 stated the number of outpatient department visits were 125.7 million with 136.3 million emergency department visits. These are some of the basic figures showing the amount of care the U.S. health care system has provided. Using Data Science to annualize this sort of data allows healthcare providers to start building a new intuition built on a data narrative that could possibly help avoid the spread of diseases or address specific health threats. Using a combination of descriptive statistics, exploratory data analysis, and predictive analytics, it becomes relatively easy to identify the most cost-effective treatments for specific ailments and allows for a process to help reduce the number of duplicate or unnecessary treatments. The power in predicting a future state is in using that knowledge to change the behavior patterns of today.

Electronic Health Record (EHR)

Interoperable electronic health records (EHRs) for patient care hold tremendous potential to reduce the growth in costs. EHRs can help healthcare organizations improve chronic disease management, increase operating efficiencies, transform their finances, and improve patient outcomes. However, EHR implementations are in various stages of maturity across the country, and their benefits have not been fully realized. One of the primary challenges healthcare decision-makers face is how to make meaningful use of the data collected, available, and accessible in EHRs.

 By optimizing the use of data accessible in EHRs, we can uncover hidden relationships and identify patterns and trends in this diverse and complex information to improve chronic disease management, increase operating efficiencies, and transform healthcare organizations’ finances.

Patient Behavior and Sentiment Data

A study by AMI Research suggests that “wearables” are expected to reach $52 million by 2019. Wearables monitor heart rates, sleep patterns, walking, and much more while providing new dimensions of context, geolocation, behavioral pattern, and biometrics. Combine this with the unstructured “lifestyle” data that comes across social media and you have a potent combination that is more than just numbers and tweets.

It is obvious that we will experience huge benefits from analyzing the in’s and out’s of healthcare data. In my judgment, we will continue to see a push for prevention over cure which puts predicting outcomes front and center. After all, catching things in the earlier stages is easier to treat and outbreaks can be more easily contained.

It may not resonate as widely today, but in the future we will look back on data science as something significant for healthcare. It is reasonable to expect that we will likely recover more quickly from illness and injury, live longer because of newly discovered drugs, and benefit from more efficient hospital surgeries – and in large part this will be because of how we analyze Big Data.

What makes living in the era of Big Data such a delight is that the healthcare industry is being pressed to find better tools, skills, and techniques to deal competently with the deluge of patient data and its inherent insights? When healthcare makes the choice to fully embrace data science, it will change the future for everyone.

Genomics

Inexpensive DNA sequencing and next-generation genomic technologies are changing the way healthcare providers do business. We now have the ability to map entire DNA sequences and measure tens of thousands of blood components to assess health.

Next-generation genomic technologies allow data scientists to drastically increase the amount of genomic data collected on large study populations. When combined with new informatics approaches that integrate many kinds of data with genomic data in Healthcare applications such as disease research, Prescription Effectiveness etc, we will better understand the genetic bases of drug response and disease. Researchers aim to achieve ultra-personalized healthcare. As a beginning, the FDA has already begun to issue medicine labels that specify different dosages for patients with particular genetic variants.

Predictive Analytics and Preventive Measures

Prevention is always better than cure. For the health-care industry, it also happens to save a lot of money. (The Centers for Medicaid and Medicare Services, for instance, can penalize hospitals that exceed average rates of readmission – indicating that they could be doing more to prevent medical problems.)

Take, for example, the partnership between Mount Sinai Medical Center and former Facebook guru Jeff Hammerbach. Mount Sinai’s problem was how to reduce readmission rates. Hammerbach’s solution was predictive analytics:

• In a pilot study, Hammerbach and his team combined data on disease, past hospital visits and other factors to determine a patient’s risk of readmission.
• These high-risk patients would then receive regular communication from hospital staff to help them avoid getting sick again.

Sinai isn’t alone. In 2008, Texas Health partnered with Healthways to merge and analyze clinical and insurance claims information. Their goal was the same – identify high-risk patients and offer them customized interventions.

Meanwhile, in 2013, data scientists at Methodist Health System are looking at accountable-care organization claims from 14,000 Medicare beneficiaries and 6,000 employees. Their aim? You guessed it. Predict which patients will need high-cost care in the future.

Patient Monitoring and Home Devices

Doctors can do a lot, but they can’t follow a patient around every minute of the day. Wearable body sensors – sensors tracking everything from heart rate to testosterone to body water – can.

Sensors are just one way in which medical technology is moving beyond the hospital bed. Home-use, medical monitoring devices and mobile applications are cropping up daily. A scanner to diagnose melanomas? A personal EEG heart monitor? No problem.

These gadgets are designed to help the patient, naturally, but they’re also busy harvesting data.

For example:

• Asthmapolis’s GPS-enabled tracker, already available by 2011, records inhaler usage by asthmatics. This information is collated, analyzed and merged with data on asthma catalysts from the CDC (e.g., high pollen counts in New England) to help doctors learn how best to prevent attacks.
• With Ginger.io’s mobile application, out in 2012, patients consent to have data about their calls, texts, location and movements monitored. These are combined with data on behavioral health from the NIH and other sources to pinpoint potential problems. Too many late-night phone calls, for instance, might signal a higher risk of anxiety attack.
• To improve patient drug compliance, Eliza, a Boston-based company, monitors which types of reminders work on which types of people. Smarter targeting means more compliance.

The Challenges Ahead

There are plenty of hurdles to creating a data-driven health care industry. Some are technical, some emotional. Health care providers have had decades to accumulate paper records, inefficiencies and entrenched routines. A remedy will not be quick.

And some say it shouldn’t. At least, not without a hard look at patient privacy, data ownership and the overall direction of U.S. health care.

One might be tempted to think we are living in a startup bubble, with investors being largely optimistic about startups and investing millions of dollars in them, with many startups crossing the billion dollar valuation on a regular basis. But managing a startup is tough, with almost unreal targets set in between funding rounds. The founders need to, at all times, be focused on the direction in which they need to head, and be sure of the selective performance indicators that they need to keep watch of. Creating data has become easy at current times. Though acquiring data from multiple sources has its potential benefits, but for a company at its seminal stage, dealing with multiple KPIs is a huge risk. Startups can easily get side-tracked by following the wrong KPI. In an ideal scenario, startups should keep only one performance indicator and keep scaling up in that direction before achieving a milestone and involving others in the development plan. With a large number of startups around, existence of a red ocean, and ample amount of data giving number of insights and scope for strategies, implementing analytics can be a sure shot way to keep startups focused on the optimal way to scale up, and in extension create organic buzz and visibility to scale further. Analytics has the capacity to point out which should be the root nerve of a startup and how to scale further in that direction. The seminal stages of creating a high visibility in the market and expansion of a startup involves a repeated cycle of – Building and improving of the Core Competency, and measuring the effect in KPIs and increase in adoption by customers. Let’s see where and how analytics can be optimally implemented for these scenarios.

Building Core Competency

The starting point for understanding core competences is understanding that businesses need to have something that customers uniquely value if they’re to make good profits. “Me too” businesses (with nothing unique to distinguish them from their competition) are doomed to compete on price: the only thing they can do to make themselves the customer’s top choice is drop price. And as other “me too” businesses do the same, profit margins become thinner and thinner.

This is why there’s such an emphasis on building and selling USPs (Unique Selling Points) in business. If you’re able to offer something uniquely good, customers will want to choose your products and will be willing to pay more for them. Here are three ways to turn analytics insights into actions that make your company more competitive:

Gain control along with visibility of patterns

People often use analytics to understand what’s already happened, but don’t look beyond “what”, to ask “why.” By understanding why certain patterns emerge in your data, you gain greater visibility and control over what’s happening right now.

For example, when you understand why certain factors affect your margins, your sales team is better able to address underperforming products and customers, identify potential revenue opportunities and design more optimal coverage models for your reps.

Put analytics in the right functional areas to drive change

To get results, you need a way to deliver analytical information to sales reps at the product and account level. This empowers reps to negotiate from an informed position and use data to have strategic conversations with customers.

Also, when reps have good access to customer analytics, they’re better able to invest coverage resources in high-quality leads. It helps them to identify opportunities with large value and position sales offers in the context of a dynamic market. For example, if there’s a lot of variability in a commodity and price wars break out, you want to quickly reposition your offer in relation to that dynamic market.

Build an ecosystem

To get the best results from your analytics, you need the ability to monitor what’s happening and use that data to adapt. As you build this process into your company’s DNA, constantly evaluate the criteria you’re using to ensure they stay relevant: Are you looking at the right variables and assessing the marketplace effectively? By maintaining the quality of this information, you’re developing a competitive advantage through pricing and sales sophistication.

Measuring Business Traction

Traction Analytics

Analytics helps your business determine what is working well, and what needs to be improved. We can always go off of a hunch, but the real power comes when we know the hard data behind our marketing or business management efforts, and can make informed decisions that improve our business over and over. Seasoned entrepreneurs know just how important analytics are in growing your business. Without a serious analytics strategy, you are simply relying on hope and luck to grow your company.

In a startup you are constantly under pressure and have way too many distractions. Having a set of metrics that you watch & that you feel are the key drivers of your success helps keep clarity. And the more public you can make your goals for these key metrics the better. Make them widely available inside the company and share your most important goals with your board. Transparency of goals drives performance because it creates both a commitment and a sense of urgency.

If you don’t have a stability goal stated for the company and if you don’t regularly measure how you’re doing against this goal you won’t have your resources focused on the right priorities in the company.

Most companies have some measurements, but I would argue that people often measure the wrong stuff, measure with the wrong precision. The best way is to start by asking yourself at management team level: what are our company objectives and how do we best measure them? Because it can be hard to define or agree company objectives at an early stage I believe most people avoid them.

Customer Acquisition

At the highest level you’ll obviously want to track how many customers you’re adding every month (and for some businesses that have hit scale this is measured on a daily basis). If you can break this down by channel that you’ve acquired them from this is obviously better.

How many additions came through organic SEO? How many through affiliate deals? How many through SEM? Do you have a customer referral program? If so, make sure you can track which leads come from this. Measuring viral adoption is obviously important.

Usually you have a catch-all bucket for “direct” or similar that often came through PR or word-of-mouth.

If you have multiple versions of your product, how many are web vs. mobile? How do the mobile customers break down by device type?

The next step after measuring the customers you’re adding is to add the “cost to acquire” by channel. This is important because it will later tell you whether you have a scalable business or not. In the early phases if you can’t acquire customers cost effectively enough you’ll need to diagnose why and how to fix it.

The Final Question of Scalability

The repeated cycles of Building and re-engineering and Core Competency and Measuring the Market environment effects takes the startups further and further into the final stages of having a scalable model. Like I mentioned at the beginning, there could be n number of directions a startup can head towards, as many as there are number of significant KPIs that need to be improved. But finding the right nerve and chasing the wrong performance indicator is the difference between ending up with a scalable business model and ending up with a marginally incremental model. In this current age of every changing topography of the market with disruptive ideas entering and washing off many hopeful businesses, only having an optimal analytics solution to track their locus can make sure startups sustain and succeed.

In continuation to my last article on Building a Robust Data Strategy, let me meaningfully conclude it by highlighting some of the core issues which need to be addressed before data monetization could really be called our as a success and ROI is achieved.  

Privacy Concerns

Company needs to have the implicit and/or explicit statutory or legal right, or the ethical right, to divulge private consumer data – either personalized or depersonalized, individualized or at an aggregated level. Especially in industries where regulatory bodies have a heavy clout over what data is being used to cull out actionable insights or even the data flow within or beyond the walls of the organizations. Numerous articles, reports & surveys have highlighted how crucial is for businesses to operate within the ethical boundaries of data gathering or dissemination. Leave no stone unturned to see what policies/restrictions/guidelines are in place for the industry you operate in, how easy/difficult is to access data, and what are customer or end user reactions. You definitely do not intend to burn bridges with your existing customer base or repel away new prospects. Legal actions can be fatal to business at times. Be doubly sure what you are up for!

Technology Constraints

Do have a thorough understanding of the technological or hardware-related considerations to implement the strategy chosen to monetize the data. At times, organizations don’t have the requisite resources to execute on their strategy, may be because that’s not their core area of operation or it’s happening in silo’es across the organization which the business unit in question is not privy to. A complete landscaping exercise to understand the current state of business, what’s new in the market & what the competition is up to, what’s the future state & a step-by-step roadmap to mature technological prowess. In many cases, businesses hire external consultants or seek handholding by analytics service providers who have the requisite experience in recommending about the gaps & even executing on filling those. A thorough detailed analysis (but not analysis-paralysis) is crucial to the overall success.

Intellectual Property

At times, organizations sitting on huge pile of valuable data choose to make it available in the market (as another viable revenue model to monetize data). How much data to sell and how to determine costs vs. benefits in putting valuable data on the open market should be thought through. Be privy to the pros & cons of each approach & choose your business model accordingly.

Core Competency

Depending on its core competency, organization needs to identify at which level it wants to monetize the data in the data value chain. Data at each & every touchpoint in the value chain may have its own peculiar problems (missing data, incorrect data etc) and not all of it may be relevant. If your differentiator is “speedy delivery” of goods to your customers, focus on picking the right data sets across the value chain which helps streamlining operations, optimize inventory & transit time. Know what you are best at or what you are known for in the market and harness data capabilities to strengthen your business on that front.

Data Accuracy and/or Liability

Potential problems with inaccurate or directly or indirectly regulated data insights or products hitting the market place. Make sure that data assimilation, aggregation & cleansing exercise is robust enough to ensure the analysis/insights being generated out of it have a high probability of giving the right sense of direction to the business. At times, over-ambitious expectations or poor data quality can directly impact the quality of the outcomes. Garbage-in Garbage-out is the mantra & business managers should perfectly understand the gaps in the data & be cautious before making any solid commitments.

Perceived Market Value

For larger market opportunities, it is likely that an organization would want to play at the higher level in the data value chain. Umpteen times that completely derails the whole Analytics ROI & data monetization exercise. Focus should be specifically on business model(s) used to monetize the data than otherwise.

All the aforementioned considerations should set a good pretext to the data monetization exercise and may be the key to unlocking true value from data strategy initiative. In my subsequent edition, I shall bring to light the “Analytics Centre of Excellence” concept & how can organizations setup a full-fledged Analytics unit to deliver insights to departmants/LOB’s/functions across the business and also serve as a backbone to building a data-driven organization of the future. Stay tuned !

Unarguably, data and technology is truly redefining & rehashing the way companies do business. Organizations have always had data, which they have utilized to run their businesses more efficiently but recent developments have transformed the way data is utilized by such organizations.

In today’s disruptive economic environment, all leaders are vying for identifying new revenue streams and identifying existing value streams inside the organization especially data. This is where the concept of crafting a Robust Data Strategy comes in, how do we make most of the Dark Data ? Data is now being looked as an asset and business models are now being build around this vast value pool which is hidden inside the data being stored. Enterprises are now anticipating future needs based on preference insights culled out from past & present data. They are creating new products and services in tune with what their customers exactly seek. They are lending an ear to all suggestions/recommendations/feedback shared and also responding to queries/concerns in real time. They are doing it all with data and analytics.

While many companies are becoming aware of the opportunities embedded in their enterprise data, only a few have developed active strategies to monetize it successfully. Data Strategy requires companies to not only understand their data, but also to uncover gaps and evaluate suitable business model(s) for appropriately monetizing the enterprise data. To evaluate their respective monetization opportunities in a more informed and results-driven manner, companies need to assess the value of enterprise data, determine how best to maximize its potential and figure out how to get the data to the market efficiently.

Four Stages to Analytics Sophistication

Based on the current state of data affairs, any organization can be categorized as a beginner, developing, matured or leader. In the initial stages of transformation, organization typically
lacks synergies due to silo’ed efforts, is less agile and more prone to errors, with perennial data quality concerns. As they mature to be leaders in the Analytics space, data sits at the heart of business, with increasingly automated, instant, accurate and seamless data driven decision-making.

• Beginner: Basic infrastructure and tools, proliferation of dashboards and reports
• Developing: Building tools and processes for historical as well as deep diving analysis to gain some insights for future actions
• Matured: Organization adoption of advanced analytical capabilities to predict future outcomes
• Business Transformation or Leader: Centralized analytics focus with capabilities to anticipate future and act in a data driven manner

Time’s ripe to ride on the Data & Analytics wave

Enterprises capture a lot of data, most of which is often overlooked. With reducing costs of capturing and storing data, increasing data analysis capabilities and superior analytical technologies available, enterprises have started to recognize data as one of their most valuable assets. In the few years, enterprises who lead the way in reorienting their approach, initiating enterprise wide data-led transformations and effectively monetizing their data are expected to be in the forefront. Typical market forces driving widespread adoption of Analytics are:

Technological Advancement

• Technology advancement has facilitated real time data analysis and personalized communication
• Big data technologies, cloud computing, machine intelligence and other advancements etc. have made analysis simpler & efficient

Rise of Consumerism

• Influx of more demanding consumers will force a wave of change
• Consumer engagement and experience management are key levers to success

Data Explosion

• Daily volume of data being captured increasing rapidly
• Cost of storing data decreasing massively
• Recognition of amount of under-utilized data that can be used to derive additional value

Increasing importance of Analytics & BI

• Business Intelligence and Analytics becoming an integral part of organization’s decision making

Economic Pressures

• Pressure on profit margins are forcing increased focus on efficiency and cost reduction
• Increasing competitive pressure

Is your Data truly worth it?

How much business value can be created via data on which organizations are sitting on depends primarily on the following factors & to an extent determines the success of any Analytics initiative.

Predict Behaviour (Patterns)

Enterprise data should be detailed enough to build a successful data monetization strategy. E.g. Customer data should be detailed enough to be able to predict customer behavior, patterns etc.

Size of the Ecosystem

Businesses with high volume, large breadth of data have the ability to generate highest value from the data. Companies with national or global scale can easily establish market view, which makes it more meaningful and valuable

Accessibility and Actionable

Data becomes valuable only if its rich, actionable and accessible. Structured, & readily scalable data makes the process of monetization simpler and efficient, providing higher potential for data monetization

Customer Identification (Granularity)

Data becomes valuable only if it is granular enough to be able to identify the end user/ customer. Ability to identify/ profile customers helps in expanding the range of products and servives that can be offered

Uniqueness

Uniqueness of the enterprise data is extremely valuable. It makes the products/services offered by the enterprise exclusive to the enterprise, sustainable differentiation which most organization yearn for

Stages to Data Maturity

Based on maturity of organization’s data, it can take a call what kind of a player it wants to be in the market – a “data seller” or a “full services provider”.

Raw Data

• Selling raw unprocessed data to outside stakeholders
• Companies with rich pool of high quality raw data can onsell such data with little investment required

E.g. – Pharma related data or even NASDAQ’s “Data on Demand” service to its ecosystem of partners in the capital markets

Processed Data

• Companies collect and integrate data from multiple sources
• Data is processed, stored and leveraged in summary form
• Secure capture and transport of data
• Proper storage and management of data using a data platform

E.g. Card Advisory companies provide processed data to merchants and/ or use it for improving its operational efficiency

Business Intelligence/ Predictive Insights

• Tools and technologies such as data mining, predictive modeling and analytics convert data into insights
• Insights are made available to the stakeholders (both internal and external) to drive business decisions

E.g. Wal-Mart segments its customers into three primary groups based on purchasing patterns to spur growth

Products & Solutions Implementation

• Data-driven interactions with end users
• APIs and ability for companies to access platform and data to build comprehensive products and solutions
• Companies use the intelligence to improve product and solutions offering portfolio

E.g. Tesco bank uses Clubcard customer data to identify customer needs and creates new personalized offers

Key Elements to Designing a Robust Data Strategy

Unravel Customer Needs

• Continually understand the customer needs to unearth customer requirements and preferences
• Understanding the delivery and integration models that clients require in order to benefit from enhancements
• Create a business model which fits into the core competency and create offerings which fir into client platforms and applications
• Invest in continuous learning and management of customers’ unmet needs ranging from enhancements to new products/ solutions

Decrypting the Enterprise Data

• Understand the enterprise data captured across all business lines and develop an enterprise wide nomenclature for the same
• Identify and map data and analytics services across business units to understand what types of capabilities can be leveraged to build new products and services using the appropriate business model

Gauging the Market Potential

• Calculate the market potential for the various opportunities identified
• Estimate the revenue potential, internal rate of return, investment required, cost reduction, efficiency etc. for the process
• Understand the key competition, factor in macro and micro factor which can affect the marketplace demand
• Seek out opportunities to enhance the core business or develop new products and services.

Deciphering the Value Chain

• Develop insights into partners and competitors across the value chain including upstream suppliers, data partners etc.
• Identify the new opportunities that can be available across the value chain
• Create a comprehensive view of the data ecosystem

Enhance the existing infrastructure

• Develop a sophisticated yet flexible architecture, suitable technology and applications which can help unlock the value that the opportunities might presents
• Put in place a data infrastructure that can provide the necessary foundation to enable the organization to unlock the value of data assets

The crux of the matter is that with the huge amount of data available with the enterprise’s in today’s competitive and converging business environment, they should start looking for market opportunities leveraging the data available with them. Most of the enterprises still do not consider data as an asset which they can monetize if they choose the correct business strategy and build the required capabilities. Enterprises can not only make better use of their internal data to enhance the current product and services portfolio, it can also provide new insights into the value chain and could transform the enterprise, unleashing a whole new set of products and services for the customers.

By utilizing internal data with external data, powerful generation of high margin solutions can be developed which can transform an entire organization which possesses enormous revenue potential. Done properly, data ecosystems can fund the transformation, create value for customers, and build long lasting relationships with other partners firms, 3^rd party vendors & suppliers. But to ensure the true value of data is being monetized by the enterprise, it is essential that it follows a streamlined process to identify the most suitable business model(s) taking into account all constraints which the process might need addressing.