Blockchain for Dummies!

Many companies are accepting bitcoins, many are not. Here is a list. These include Target, Tesla, Whole Foods, Microsoft, Home Depot, Intuit, Dell, PayPal/EBay, Sears, Bloomberg.com and many others.  With many companies accepting the change and others getting ready to, bitcoins are an extremely fast-spreading currency. The crypto-currencies have multiplied in the market place in recent years. QR codes are the biggest help in real-world bitcoin transfers. Using a smartphone and a Bitcoin wallet app, a user scans a label and presses a small buttoned aptly named “spend.”

Every transaction that happens between a buyer and seller or a transferor and transferee or between 2 members on the network, is verified and validated by “miners” to ensure it is secured and there is no risk of double spending. These miners are similar to VISA or MasterCard or Amex of the credit card world that provides a platform to exchange, validate and authorize. The miner creates a block of records which holds a copied record of all the verified transactions that have occurred in the network over the past ‘n’ minutes. Each transaction in every block is made at specific time and linked to previous block of transactions. Digital records are lumped together into “blocks” then bound together cryptographically and chronologically into a “chain” using complex mathematical algorithms. This encryption process, known as “hashing” is carried out by lots of different computers. If they all agree on the answer, each block receives a unique digital signature. The groups/chains of these blocks of transactions is referred to as Blockchain. The Blockchain is seen as the main technological innovation of Bitcoin, since it stands as proof of all the transactions on the network. Blockchain, or distributed ledger, technology is more secure, transparent, faster and less expensive than current financial systems. The distributed nature of a Blockchain database means that it’s harder for hackers to attack it – they would have to get access to every copy of the database simultaneously to be successful. It also keeps data secure and private because the hash cannot be converted back into the original data – it’s a one-way process.

In short, Blockchain is a method of recording data – a digital ledger of transactions, agreements, contracts – anything that needs to be independently recorded and verified as having happened. The big difference is that this ledger isn’t stored in one place, it’s distributed across several, hundreds or even thousands of computers around the world. In 2015, some of the leading financial institutions such as Visa, Goldman Sachs, Citi and other Wall Street incumbents joined venture capital firms to pour $488 million into the industry. In a World Economic Forum report released in September, “Deep Shift: Technology Tipping Points and Societal Impacts,” 58% of survey respondents said that they expected that by the year 2025, 10% of global gross domestic product will be stored on Blockchain technology. If banks started sharing data using a tailor-made version of Blockchain it could remove the need for middlemen, a lot of manual processing, and speed up transactions. If banks and other financial institutions are able to speed up transactions and take costs out of the system, it should mean cheaper, more efficient services for us.

 

Big Data Analytics – Acquire, Grow and Retain Customers

Start of this year in Jan 2013, I had discussed in my blog Is Customer the King? In Retail, Analytics Say “Yes” about how Retail industry can leverage big data insights to optimize and personalize customer interactions, improve customer lifetime value, improve customer retention and satisfaction, improve accuracy and response to marketing campaigns. In an article by The Wall Street Journal last year, WSJ said that Big Data refers to the idea that companies can extract value from collecting, processing and analyzing vast quantities of data about their customer experience. Businesses that can get a better handle on these data will be more likely to outperform their competitors who do not. Kimberly Collins, Gartner Research vice-president stated that big data, will be the next major “disruptive technology” to affect the way businesses interact with customers.

 

In this new era of big data, companies need to create team of customer relationship management experts that can understand the psychology and buying behavior of their customers, apply their strong analytical skills to internal and external data and provide a personalized and individualized experience to their customers. In addition, companies will also need to apply futuristic insights using predictive and prescriptive models that will help steer innovation in the industry. Steve Jobs and his company created a need. Nobody knew they needed an iPhone or iPad but today it’s a need for millions of users. Companies need to reorient themselves to 21st century thinking, which unequivocally involves applying big data analytics to their customers (clients, employees and other stakeholders).

 

Today, companies have access to data unlike they have ever had before from internal systems and external media. This includes all structured data and unstructured data. And now companies have access to advanced modeling and visualization tools that can provide the insight to understand customers and even more powerfully, predict and prescribe behaviors.

 

Ironically – athough the retail industry is under tremendous pressure to stay competitive – the industry as a whole lags behind other industries in its use of big data analytics. A report from Ventana Research suggests that only 34% of retail companies are satisfied with the processes they use to create analytics. According to a recent infographic from marketing optimization company Monetate, 32% of retailers don’t know how much data their company store. And more than 75% don’t know how much of their data is unstructured data like call center notes, online forum comments and other information-rich customer data that can’t be analyzed in a database.

 

In one of the recent industry case study, CMO of a retail company convened a group of marketing and product development experts to analyze their leading competitor’s practices, and what they had found was the competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments testing their hypothesis before implementing them in real world. At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business. What this customer had witnessed was the fierce market competition with effects of big data.

Retailers that are taking advantage of Big Data’s potential are reaping the rewards.  They’re able to use data to effectively reach consumers through the correct channels and with messages that resonate to a highly targeted audience.  Smart retailers are using advanced revenue attribution and customer-level response modeling to optimize their marketing spends Although there are obvious benefits, many retailers are surprisingly still failing to act on these trends. This delay is largely due to a dependence on siloed information, lack of executive involvement and a general trend among marketers to fail to understand analytics. Without advancing internal structures, gaining executive support or educating internally, jumping on these Big Data trends is nearly impossible.

 

The new IBM/Kantar Retail Global CPG Study of over 350 top CPG executives revealed that 74 percent of leading CPGs use data analytics to improve decision making in sales compared to just 37 percent of lower performing CPGs. By the same token, the new IBM study of 325 senior retail merchandising executives, conducted by IBM Center for Applied Insights in conjunction with Planet Retail, reports that 65 percent of leading retail merchandisers feel big data analytics is critical to their business compared to just 38 percent of other retail companies.

 

The two independently developed studies found interesting trends:

• Sixty-three percent of top retail merchandisers have the data they need to conduct meaningful analytics while 33 percent of other retailers do not.
• Thirty-seven percent of leading CPG companies make decisions predominately on data and sophisticated analytics versus 9 percent of lower performing CPG companies.
• Eighty-three percent of leading retail merchandisers are focusing more on the consumer, compared to just 47 percent of lower performing retailers.
• Forty-three percent of leading CPG company’s sales organizations are highly focused on the consumer versus 28 percent of others.
• Sixty-nine percent of the marketing departments of top retail merchandisers are highly collaborative vs. 39 percent of other retailers.
• Forty-four percent of leading CPG companies report a “robust partnership” between marketing, sales and IT versus only 20 percent of their competitors.

For retailers like Macys, the big data revolution is seen as a key competitive advantage that can bolster razor-thin margins, streamline operations and move more goods off shelves. Kroger CEO David Dillon has called big data analytics his “secret weapon” in fending off other grocery competitors. Retailers are moving quickly into big data, according to Jeff Kelly, lead big data analyst at Wikibon. Big retail chains such as Sears and Target have already invested heavily in reacting to market demand in real time, he said. That means goods can be priced dynamically as they become hot, or not. Similar products can be cross-sold within seconds to a customer paying at the cash register. Data analysis also allows for tighter control of inventory so items aren’t overstocked.

To stay competitive, retailers must understand not only current consumer behavior, but must also be able to predict future consumer behavior. Accurate prediction and an understanding of customer behavior can help retailers keep customers, improve sales, and extend the relationship with their customers. In addition to standard business analytics, retailers need to perform churn analysis to estimate the number of customers in danger of being lost, market analysis to show how customers are distributed between high and low value segments, and market basket analysis to determine those products that customers are more likely to buy together.

 

Retail Banks such as Wells Fargo has gathered electronic data on its customers for decades, but it is only in the past few years that the fourth-largest U.S. bank has learned how to put all that information to work. JPMorgan Chase, Bank of America, Citigroup and Capital One are also taking advantage of the big data opportunity. Big banks are embracing data analysis as a means to pinpoint customer preferences and, as a result, also uncover incremental sources of revenue in a period of stalled revenue growth. Smarter banks will increasingly invest in customer analytics to gain new customer insights and effectively segment their clients. This will help them determine pricing, new products and services, the right customer approaches and marketing methods, which channels customers are most likely to use and how likely customers are to change providers or have more than one provider.

 

Banks, Retailers and CPG companies that are applying big data analytics to better understand consumers and adjust to their needs are outperforming their competitors who don’t, according to a pair of studies released by IBM. Advanced Big Data analytical applications leverage a range of techniques to enable deeper dives into customer data, as well as layering this customer data with sales and product information to help retailers segment and market to customers in the ways they find most compelling and relevant. Historically, retailers have only scratched the surface when it comes to making use of the piles of customer data they already possess. Add social media sentiment to the mix, and they can access a virtual treasure trove of insights into customer behaviors and intentions. The timing couldn’t be better, because these days’ consumers award their tightly held dollars to retailers that best cater to their need for customized offers and better value. The ability to offer just what customers want, when they want it, in the way they want to buy it requires robust customer analytics. The opportunity is now: It’s critical that retailers step up their customer analytics capabilities as they transition to an all-channel approach to business.

 

Making the most of what you have

Many, many companies have built very sophisticated Data Warehouses -They should start using what they’ve got a little more effectively before moving on to tougher things!

So there I was in an ICA store in Stockholm, a huge trolley of goods for the weekend and dead pleased that eventually I got to the front of the queue. It was Saturday, everyone was in a hurry to get home after queuing for ages on the Stockholm motor ways. My partner was diligently packing the goods because it was my turn to pay so imagine my horror when my debit card was rejected – not once, but three times. Crikey, everyone was looking at me as if I was some sort of crook. Well luckily my partners AMEX card came to the rescue but imagine my concern. I kept thinking of the £20k balance in my account and wondering what had happened to it.

In panic on the way home I missed an incoming SMS but got the second when I got back and was horrified to see the number of my bank come up – well I assumed this, as in fact it was actually some random call centre somewhere on planet Earth. I answered it (at my cost as I was roaming) to be told that this was a routine security check because the behavior on my card had proved concerning (to who and why is a mystery as you will see). I was asked to agree the last few transactions of my card to verify that these were correct and not fraudulent: They were:

Currency exchange (at Heathrow)

A purchase at Heathrow of around £30 (two bottles of champers)

Purchase of an airline ticket – UK to Sweden.

Well I confirmed all of this and was simply informed that my card would now start working again – no explanation, no nothing – unbelievable. My card had been refused at a grocery but imagine what could have happened!

Now you might ask yourself a question, why is this guy moaning about this? Well why I’m moaning is that for the two years previous to this incident I had been travelling to Sweden at least once every six weeks – I invariably change money, always buy champagne and always buy an air ticket so why did my bank see this as unusual?  Why weren’t they using some system to check that in fact this was quite a usual style of activity – nothing unusual here? Why has this bank got the authority to arbitrarily stop me using my own money, none the less in such an preposterous manner?

Well, the bank I am talking about was a pioneer in Data Warehousing so I’m just wondering why this event happened when I know that they diligently record all my transactions and store them in a DW whilst apparently failing to understand their meaning. No need for Hadoop here!!!!

Regulation – a class of Big Data apps

There are bad guys out there!

 

Going back to the gist of my last post, one of the pillars that underpinned de-regulation was the idea that companies would work in a ‘correct’ manner and regulate themselves. The truth is that this worked and still does work very well for 95% of companies but there are always bad pennies committing fraud or simply not being careful in accounting practices. Thanks to a few well known financial disasters, even before the global meltdown, the concept of re-regulation loomed large across many industries. There are many sets of rules that are now in place to bring governance to company business – some of the more well known include Sarbanes-Oxley and Basel II and III which have been around for a little while now. We might ask ourselves what do they have in common and the answer is that both and many more such initiatives, demand that very accurate and accountable numbers are produced quickly from very complex underlying data – the need for Business Intelligence rears its head once again and the term ‘Big Data’ can certainly be applied to some of these initiatives.

 

Re-regulation demands that some very complex numbers are delivered:

 

• Quickly
• Accurately
• Transparently

 

 

Throw into the pot that the data needed often as not comes from tens or even hundreds of operational systems distributed across the world and that some of these initiatives need very complex predictive modelling and detailed segmentation and we see a new class of Big Data applications.

Big Data = more diverse data

 

 

Many ‘mega-trends’ are in place today – Globalisation, re-regulation, internet shopping, disengaged customers and more take-overs day by day. The need to have accurate information is paramount simply to survive let alone grow.

 

Too often we use big words without thinking about what they mean and Globalisation is one of them. Now I am not going to write about globalisation here but it is useful to consider it as a phenomenon, is it real, is it important?

 

Let’s consider some facts:

 

• 70% of the world’s shoes come from one town in China – now if you produce shoes in the UK this fact should be very worrisome.
• To all intents and purposes the UK no longer has a car industry – we used to have, but in the end British Leyland amongst others proved a tad slow and not too smart. There’s nothing left anymore.
• In the space of just a few years Vodafone has penetrated nearly the entire know world with its mobile services. Unless you take active steps to prevent it, you are almost guaranteed to end up paying some money one way or another, to Vodafone this year.
• Most holiday companies now make a sizable proportion of their revenue from banking products or shipping cargo.

 

 

Globalisation is the force behind the break-down of trading barriers but globalisation is partly a result of another massive change in business practices over the last twenty years that we call de-regulation. Basically, in the ‘old days’ there were rules about what a company (or type of company) could sell. For example, Building Societies could not lend savers money to borrowing customers directly – you had to have a banking license to do that. Retailers could not sell insurance products. Insurance companies could not provide savings accounts. This all changed in the process of de-regulation and so now retailers can sell banking products, banks can sell insurance products and by and large, anything goes. When you put the two things together, globalisation and de-regulation, we have another world, a world in which the biggest retailer ever seen – Wall-Mart can presumably sell banking services in the UK thus becoming a competitor of Barclays Bank!

 

Note: Wall-Mart own ASDA – I’m not sure if they sell banking products but I guess so.

 

So what does that mean today in terms of Big Data. Well now your average retailer knows a lot more about you than ever before. They used to know what you eat, now they know what you ware, where you go on holiday, how much you spend and get paid a month etc, etc and it’s by combining all of this information that a 360 degree of a consumer can be constructed. By trawling social media feeds they can find who your friends and family are and what you are saying about their products………scary!!

Natural Selection in Business – Does using Big Data provide a sustainable advantage?

 

In nature, when resources are plentiful, species live together quite amicably. Even predator and prey reach a satisfactory balance whereby there is always food for both. However, when resources are scarce, species that were once happy together often turn into bitter enemies. The strong, big guy’s fight each other, determined to completely obliterate their competitor often resulting in mortal damage being inflicted on both. Whilst this is happening, the intelligent guys, who are inevitably smaller and physically weaker, get to work. Firstly, they take advantage of the preoccupation of the others by amassing their basic requirements quickly. They then diversify and find a niche for themselves, knowing that competition will come, but being determined to foresee it and avoid it where possible.

 

Most people accept that this is the way of the natural world and business dynamics tend to follow the same basic rules. Intelligent companies will not measure themselves by numbers of employees, amount of real estate or revenue alone, but will instead increasingly judge themselves on different values:

 

• The average life time value of their key customers
• The elapsed time for a new customer to become profitable
• Public image
• Customer retention
• Knowledge, expertise and willingness of the work force
• Brand awareness and flexibility
• Environmental friendliness
• Efficient and focused work practices
• Customer satisfaction

 

Note: be aware that the little guys don’t always have to take on the big guys directly and in fact it’s usually best not too. Those of you who know the story about David and Goliath should be clear that this was not a simple big guy versus little guy competition in which David shows the world not to be afraid of a ‘larger’ opponent. The fact is that Goliath, although being big, had no noticeable weaponry whilst David however, had the equivalent in those days, of a sawn off shotgun. My guess is that if the two guys had met with equal weapons the result would have been rather less romantic but David showed some real common-sense here. He knew that if he wasn’t prepared for the fight he had no chance so he fought the battle very much on his own terms.

 

I wonder if exploiting Big Data will enable big companies to grow even bigger or whether it will enable smaller companies to compete with them to level the playing field?

I wonder if exploiting Big Data will enable big companies to grow even bigger or whether it will enable smaller companies to compete with them to level the playing field?

 

As companies move forward, whilst it will undoubtedly remain an advantage to be rich and powerful, size in itself, may not be such an important plus point. Most certainly size brings coverage and reach, but it also breeds cost and inflexibility and we will see instead the proliferation of many smaller companies who have replaced the advantages of size, with the advantages of intelligence.

 

What will intelligence bring to a company that might give it sustainable market value?

Well it might enable it to:

 

• Sell more diverse products to its customer base thereby increasing margin and perhaps even loyalty.
• Acquire only those customers who will likely be low risk and high value.
• Only execute marketing campaigns in geographies where the ability to provide service and product actually exists
• Remove the need for inventory completely by direct collaboration with suppliers.
• Reduce the cash to cash cycle by getting customers to pay for goods prior to manufacturing them.
• Eliminate the need for a direct sales force altogether.
• Make fraud so unprofitable for the fraudster that they give up.

 

So what is the major business driver that is set to change our ways of doing business? It can be summed up in one phrase – natural selection.

 

Note: Now I fancy myself as something of a biologist and there are several points in Darwin’s theories of evolution that concern me but maybe we can save that discussion till later?

 

 

Our Favorite 40+ Big Data use-cases. What’s your?

One of the key best practices for successful implementation of a big data analytics solution is to validate the business use case for big data. It will help organization with two important aspects for success:

1. Keeping the scope limited

2. Helping to measure the success of a solution that addresses a key business problem

In case the same data set addresses multiple use cases, an organization may need to prioritize their use case and apply an iterative and phased approach. It’s the theory of getting the biggest bang for the buck, both tactical and strategic. Think Big and Act small!

While there are extensive industry-specific use cases, here are some for handy reference:

EDW Use Cases

• Augment EDW by offloading processing and storage
• Support as preprocessing hub before getting to EDW

Retail/Consumer Use Cases

• Merchandizing and market basket analysis
• Campaign management and customer loyalty programs
• Supply-chain management and analytics
• Event- and behavior-based targeting
• Market and consumer segmentations

Financial Services Use Cases

• Compliance and regulatory reporting
• Risk analysis and management
• Fraud detection and security analytics
• CRM and customer loyalty programs
• Credit risk, scoring and analysis
• High speed arbitrage trading
• Trade surveillance
• Abnormal trading pattern analysis

Web & Digital Media Services Use Cases

• Large-scale clickstream analytics
• Ad targeting, analysis, forecasting and optimization
• Abuse and click-fraud prevention
• Social graph analysis and profile segmentation
• Campaign management and loyalty programs

Health & Life Sciences Use Cases

• Clinical trials data analysis
• Disease pattern analysis
• Campaign and sales program optimization
• Patient care quality and program analysis
• Medical device and pharma supply-chain management
• Drug discovery and development analysis

Telecommunications Use Cases

• Revenue assurance and price optimization
• Customer churn prevention
• Campaign management and customer loyalty
• Call detail record (CDR) analysis
• Network performance and optimization
• Mobile user location analysis

Government Use Cases

• Fraud detection
• Threat detection
• Cybersecurity
• Compliance and regulatory analysis

New Application Use Cases

• Online dating
• Social gaming

Fraud Use-Cases

• Credit and debit payment card fraud
• Deposit account fraud
• Technical fraud and bad debt
• Healthcare fraud
• Medicaid and Medicare fraud
• Property and casualty (P&C) insurance fraud
• Workers’ compensation fraud

E-Commerce and Customer Service Use-Cases

• Cross-channel analytics
• Event analytics
• Recommendation engines using predictive analytics
• Right offer at the right time
• Next best offer or next best action

These are some of my favorites and ones that I have come across. Please add your favorites to the comment section. I would like to know from readers what they are seeing in their organization.

 

Big Data Use-cases – Banking and Financial Services

Big data has become the latest buzz word in Information Technology world and in the business arena from board room to product development and sales & marketing. There is lot of hype and noise in the industry. Many business leaders are curious about the business use case for Big data that can give them competitive edge and head-start in reaching the finish line before others. Many IT vendors from IBM, SAP, Informatica, Microsoft, Oracle, HP, Cloudera, FOSS vendors are investing and pushing their solutions & offerings into the market place. IBM has invested millions of dollars into Smarter Planet initiative and primarily with BigInsights platform. Since 2005, IBM has spent over $14 billion to acquire twenty-five software companies specializing in data analytics, and today it has over 8,500 analytics consultants. During March of this year. H-P said it will reallocate the $3 billion to $3.5 billion between now and 2014 to Big Data analytics, cloud computing and security infrastructure. Moreover, the U.S. government is investing $200 million in big data projects to help the U.S. jump ahead in the next frontier of computing.

So Big Data is here to stay and change our world. MIT economist Erik Brynjolfsson compares the implications of data analytics to an important invention four centuries ago. Brynjolfsson noted that the invention of the microscope resulted in a “revolution in measurement” that enabled scientists to examine objects at the cellular level, objects that were previously invisible to the naked eye. Using this analogy, data analytics is a key enabler for organizations to see previously undetectable patterns in data in order to better understand risk exposure and to better predict decision outcomes (predictive analytics).

Some of the industry in general like ECommerce, Retail, Banking and Financial has made some capital investment in Big Data in past 2 years while Healthcare, Manufacturing and Utility are gaining traction. Here’s few that I have seen as industry use case that’s being considered as Pilot or Proof of Concept/Value projects.

Big Data use-cases in Banking & Financial Services

1. Fraud Detection:

You may not be surprised that Banks and Credit cards are monitoring your spending habits on real-time basis. One of the large credit card issuing bank has implemented fraud detection system that would disable your card if they see suspicious activity based on your past history with spending patters and trends. In addition to the transaction records for authorization and approvals, banks and credit card companies are collecting lot more information from location, your life style, spending patterns. Credit card companies manage huge volume of data from individual Social Security number and income, account balances and employment details, and credit history and transaction history. All this put together helps credit card companies to fight fraud in real-time. Big Data architecture provides that scalability to analyze the incoming transaction against individual history and approve/decline the transaction and alert the account owner.

2. Customer Segmentation

Customer Segmentation applies in every industry from Banking to Retail to Aviation to Utility and others where they deal with end customer who consume their products and services. In Banking & Financial industry, customer segmentation is a key tool in risk scoring analysis and for sales, promotion and marketing campaigns. In addition to existing information that banks and FIs collect from day to day transactions from customers, they are also buying external data like home values, merchant records from hotels, aviation, retailers, etc. The 360 degree view of customer is still a work in progress and Big Data is enabling filling in the gaps by providing the processing power needed to mine for intelligence from underlying data.

The major objectives of segmentation are:

• Customized product offering
• Customized and priority service
• Improve relationship with profitable customers and cut resources spent on loss making customers
• Better offering to new customers based on the intelligence gained from the existing customer segment they belong to
• New product development and bundling as per the customer segment profile

3. Customer Sentiment Analysis

The bank can now respond to negative (or positive) brand perception by focusing its communication strategies on particular Internet sites, countering – or backing up – the most outspoken authors on Twitter, boards and blogs. When a company releases a new product that’s causing problems, analyzing comments in social media sites or product review sites can enable it to quickly remediate.

4. Crowdsourcing

Some of the larger institutions have realized they can use analytics to learn about new lines of business and products, to ask customers what they think, and to get ideas. In a move to expand its utility beyond simply finding better answers to known statistical problems, data-science startup Kaggle is now letting its stable of expert data scientists compete to tell companies how they can improve their businesses using machine learning.

4. Sales and Marketing Campaigns

On the customer experience side, every time you get closer to delighting your customer by showing that you understand what their real needs are, without blindly sending them emails and credit card offers, it makes the customer view their institution as caring about them and understanding what their needs are.

5. Call Center Analysis

For decades, companies have been analyzing call center data for staffing, agent performance, network management. But with big data age, many new interesting software are being implemented today in attempt to take unstructured voice recordings and analyze them for content and sentiment. Banks are applying text and sentiment analysis to this unstructured data, and looking for patterns and trends. Many banks are integrating this call center data with their transactional data warehouse to reduce customer churn, and drive up-sell, cross-sell, customer monitoring alerts and fraud detection.

These are just few of the use cases that I have highlighted here to give a fair idea about how Big Data is being leveraged in this industry. If you have use-case that you are working with, please add it to the comment section.