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(Read Part 1 of this Space Analytics series.) Saudi Arabia has recently given Qatar 13 demands to end a trade and diplomatic embargo – and one of them is pulling the plug on broadcaster Al Jazeera. But it's not the first time that Al Jazeera has been in the crosshairs. In September 2013, when Al Jazeera had its sports broadcast on, the football scores went out uninterrupted. Next, the weather was without any issues. But soon as the news anchor began to talk about the Muslim Brotherhood – the screen froze, fought back to show pixels and finally faded to black. After the report concluded, the screen unfroze and Al Jazeera returned to its normal broadcast. Meanwhile, on the Cairo-Alexandria Desert Highway, west of the capital, several satcom jammers in a non-descript SUV with antennas pointing to the sky, check their television schedule print outs. Like television producers, they flip switches and turn knobs when the clock strikes the exact minute and second the Muslim Brotherhood broadcast is set to begin. And they un-flip the switch when it's set to end. The main characteristic of an intentional interference of satellite communications is the preciseness – almost like clockwork that a signal is jammed and then returned. Al Jazeera suspected the new government of Egypt was behind the jamming. But they needed to prove it. Enter the space cowboys Space cowboys are those that using data science and geolocation techniques to identify possible locations of the source of jamming. They use a mixture of math and Google Maps. Intentional interference or satcom jammers usually keep moving. They never jam in the same place twice for fear of being pinpointed. The key is correlating radio frequency trajectory information to the finding the ellipses of satellite dishes on Google Map images. The catch is – what if the satellite imagery – is too old? Especially if the satellite jamming equipment is attached to a vehicle. However, in the case for Al Jazeera, the space cowboys identified multiple locations: Cairo-Alexandria Desert Highway close to Al-Natrom Valley prison. A large military installation annexed to a building equipped with satellite antennas and a telecommunications tower near to the Cairo-Suez highway. East Cairo, specifically in the densely populated Heliopolis area on the junction between Airport road, El-Thawra street and El Mergheni Street near the Egyptian military intelligence headquarters and the army’s public-relations department. The trick is to predict the jammers next movement. Now taking the coordinates of where the jammers were in order to create a perimeter – a measurement of the distance around something; calculating the length of the boundary where they were found to have jammed previously. Normally, satcom jammers stay in safe-zones where no one will report them to the authorities unless the authorities are paid off. But even then, like creatures of habit, they tend to circle around. So the goal is to anticipate them when they return to a former location. As the jamming locations were given to Al Jazeera and the circle was broken, Al Jazeera is now back on the air uninterrupted. Now the question is if Qatar does not give into the demands – will Saudi Arabia resort to the same tactics? Futhermore, with SpaceX having double-header rocket launches this past month placing telecommunication satellites in the geo orbit, the opportunity grows for more satcom jammers. But so does the demand on space cowboys to geolocate and block the sat blockers. And as satcom jammers get more sophisticated – also armed with data science – the old Waylon Jennings' and Willie Nelson's 1978 cover " Don't Let Your Babies Grow Up To Be Cowboys " seems more pertinent than ever: Mamas' don't let your babies grow up to be (space) cowboys 'Cause they'll never stay home and they're always alone Even with someone they love But if the space cowboys are successful, the goal is for us all to ride happily into the sunset. Article written by Gary Jackson Image credit by Getty Images, Vetta, sharply_done Want more? For Job Seekers | For Employers | For Influencers

If you’re reading this, then you’ve likely heard one of the following terms: blockchain , cryptocurrency or bitcoin . Each of those are related as bitcoin is a cryptocurrency that is built using blockchain technology. Bitcoin really took off when the dark website Silk Road started using it. Before that, it was a nerdy curiosity. You might have heard that most Ransomware asks for payment in bitcoin; this is because it is secure and anonymous. I think we can all pretty easily understand the idea of an electronic transaction, but where it breaks down for most people is understanding what technology is running under bitcoin and how to potentially take advantage of it. This infographic I found is supposed to help explain it, yet this is probably why most regular people don’t understand it: There is around $30 billion in cryptocurrency floating around out there now, so this is a big deal. At its heart, blockchain is simply a ledger, like an old style accounting ledger. However, each transaction contains a key value that links it to the previous block, which means you can’t change an entry in the ledger as it will distort the key value. It will be dropped from the chain, and the other computers in the network won’t allow it. Here's an example of how blockchain technology would detect and prevent a node from hacking the blockchain and changing database transactions: When a node submits a blockchain update that contains an altered block, all other nodes will be able to detect that a change has been made and reject the update. What bitcoin, Ethereum and other cryptocurrencies use to create those blocks is something called Proof of Work . Basically, they chew up a bunch of computer time to generate these hash values to create a new block (that’s what all that mining is you hear about). So over time, this takes longer and longer to do, which means a Proof of Work system isn’t a great way to try and make an application that relies on response time, but you still need to have a level of consensus so you know the transaction is secure. In this article, I’m going to try and uncover some of the mysteries with a small, real world type example that doesn’t have to do with exchanging money. The other accepted method of blockchain consensus is Proof of Stake  and that working can be referred to as “minting” as opposed to “mining” (there is also Proof of Space ) in the PoW model and is several thousand times more cost effective. PoS makes a lot more sense for an application-based system, one popular blockchain platform that supports both methods is Emercoin . There is an emerging system called Dragonchain that came out of Disney (of all places) and embraces a more modular system that allows for virtually any method to be implemented, which is really perfect for building applications. Let’s use a voting system as an example. I found a number of projects built on Ethereum that were unfinished and one on Emercoin that I couldn’t figure out if it worked. This is the biggest problem with all the blockchain projects I saw – no one seems to be able to clearly explain what they are doing and/or make it simple to install and use. They all seem to assume some level of understanding or expertise. But back to the voting system. The 2016 election for President of the United States has been filled with accusations of hacking and influence and various malfeasance, and while we can’t do anything about influence, we can do something about hacking and fraud. We will take a couple liberties with voter registration and assume everyone has a unique voter token for the election they are going to vote in. This could be a stockholders vote, a Homeowners Association election, a school board or a statewide election (we have no direct nationwide votes). Now you would use whatever device has been set up for voting – this could be your computer at home through a webpage, an app for your smartphone or a voting center like we currently use. You use that voter token (a smartcard would be a good application here), log in to the system, do your voting and then that is confirmed through the consensus (proof) system that you are using. This confirms your ID and that your votes are valid for that election. Then your vote is essentially locked in an immutable block, and the next vote block as added in the chain from someone else until the election is done. Each block in the chain has a key to the block in front of it. This is what keeps it from being altered because that key is a hash value of the contents (in theory). At the end of the election, you are able to log in and check your private ID and see that your votes are in the election chain, and you can see the value of all the other votes, but not who did them. In this way, you can confirm that your vote counted and that all votes were counted in the election. If someone tries to use your voter token, then the consensus system will reject that attempt to vote, which prevents fraud or hacking. So you have total visibility and transparency while still having complete anonymity. There are an incredible array of applications for blockchain technology, and if you are a fan of the HBO series “Silicon Valley”, what they were doing with his new distributed next generation internet, is pretty much what blockchain does with data. There is a LOT of information available on this topic. It can get confusing very quickly, but this is the quiet storm that is coming and will transform how we interact with just about everything. It will provide the security that we’ve been missing in so much of today’s modern world. Article written by Shawn Gordon Image credit by Getty Images, Photographer's Choice, William Andrew Want more? For Job Seekers | For Employers | For Influencers

Customers today, whether B2B or B2C, demand a personalized experience. They expect a choice of engagement channels and flexibility to access information or connect with companies at any time, from any place, using any device. But as businesses adopt newer technologies, systems and channels to support the changing needs of customers, the customer data becomes fragmented. For example, customer data may consist of the profile information, demographics, omnichannel interactions, e-commerce transactions and analytical insights. This information is scattered across CRM, marketing automation, financial, logistics, support and business intelligence systems. Additional data sources include social networks to understand customer preferences and sentiments, data from connected devices as well as data purchased from third-party data providers. Companies realize that, in order to deliver the best customer experience and create relevant engagement, they need to process data from various sources and make informed decisions. They recognize the need to understand the complete customer journey and are becoming more customer- rather than product-centric. Customers are becoming more sophisticated and tone-deaf to one-size-fits-all messaging and promotions. They look for messages that are relevant to them. To deliver personalization at scale, a deep customer understanding with accurate and complete customer information is a must. Such digital transformation requires you to have data management capabilities that are responsive to the customer in real time, and help identify the right engagement, with the right message and at the right time. Unfortunately, traditional master data management has not delivered on the promise of meeting the needs of organizations in this new age of the customer. We need to think beyond traditional master data management and into the realm of modern data management where we not only think of “golden records” of customer profiles but also about their relationships, their past interactions and transactions. We need to process these to glean deeper insights and help customer-facing teams with intelligent recommended actions. Building a reliable data foundation Ensuring data reliability involves blending the data from all internal, external and third-party sources to create complete customer profiles. Modern data management platforms provide connectivity to ingest data from all sources in any format. For third-party data sources, that includes Data as a Service capabilities through which data-driven customer 360 applications can subscribe to such sources, search based on any available attribute and enrich the internal customer information. The next step is to blend the data together and create a single-source-of-truth about all B2B accounts and customers. The data from all sources is matched and merged using various survivorship rules. Data owners decide what will be the surviving sources for each profile attribute. Next-generation modern data management platforms also use machine learning to identify hidden match rules. Multiple high-velocity data streams make ensuring data quality hard. It requires ongoing data stewardship including matching, merging and cleansing of the data. Modern data management supports high-volume data management with cleaning, matching, merging, unmerging and verification capabilities while maintaining full audit trails, history and data lineage, where compliance is required. Such consolidated and accurate customer data is then provisioned to all operational and analytics systems. A single source of truth of complete customer data ensures consistent customer experience across channels and functional groups. Understanding relationships Just consolidating customer information is not enough. Discovering relationships between people, products and places is fundamental to understanding customers. For consumers, you want to know the many-to-many relationships they have with products, channels, stores, family members, friends and devices. Retailers, for example, want to bundle customers into households. This is only possible if you know the relationship between various customers and locations. Understanding relationships is important in account-based B2B marketing, as well. Marketers must understand the organizational structure of the account, key influencers, places and products of interest. Modern data management platforms leverage hybrid data stores with graph technology to uncover relationships between all entities like contacts, accounts, products and places. With this information, marketers can design very specific campaigns and offers for the accounts. Furthermore, using an advanced analytics environment, like Apache Spark, data teams can uncover hidden relationships or gather other interesting insights about relationships. Business owners can find the key influencers, understand product penetration and get to the key member of procurement committee. This information helps them get to the right person at the right time, with the right offer. Navigating the complex organizational hierarchies in large accounts can be tricky. It takes a lot of effort and resources to figure out the business units, their locations, key contacts and product penetration across the account. Companies use third-party data sets such as Dun & Bradstreet data to acquire hierarchy information, but that information may be limited to the legal structure. Sales need hierarchies that can provide information about product penetration, competitive coverage, credit risk roll ups and business value across the account. Graph capabilities can help in creating personalized hierarchies that provide a roll-up of such contextual information for account planning and execution. Building data-driven customer 360 applications Once you have all customer information, complete with relationships, you can visualize the information as contextual data-driven applications. A reliable data and relationship foundation allows data-driven applications to provide relevant and consistent information for sales, marketing or support. The information is utilized for accurate segmentation, campaign design or to run analytical models like customer value or churn propensity and includes such insights in the data-driven application. Relevant insights, delivered in the context of a user’s role and objective is essential for marketing success. Modern data management provides capabilities for near-real-time analytics and recommends next-best actions using predictive analytics and machine learning. You can determine trends in customer preferences, the effectiveness of marketing programs, the business value of an account and the influence of a contact. Armed with this information, marketers can provide personalized information and offers to the customer, using the right channel of engagement, at the right time, delivering superior customer experience. Enabling collaborative data curation Data clean-up is not a one-time job. It’s an ongoing undertaking that requires collaboration from all operational and customer-facing teams. Data-driven customer applications must incorporate an easy way to collaborate via tools like discussion threads and voting to gain more information about a client, and keep it current. Business users may also need to use structured processes to source additional information from a data stewarding group, flag suspected bad data or request data updates. Bringing all teams together in a collaborative workspace helps build a sound customer engagement strategy and supports high data quality. As customer data volume, variety, and sources increase, managing data will continue to challenge business and IT teams. To make sense of vast customer information and ensure that all operational and analytical systems are using accurate data, you need to establish a reliable customer data foundation. Blending data from all sources, cleansing and curating it, and discovering relationships provides a better customer understanding and enables personalized engagement. Agile modern data management allows organizations to build reliable customer views and ensures that, as more information sources, systems and channels are introduced, those can quickly be included to create a fuller customer picture. Article written by Ajay Khanna Image credit by Getty Images, DigitalVision Vectors, gobyg Want more? For Job Seekers | For Employers | For Influencers

(This article is a sponsored placement written by Capella University, and republished from the Capella blog .) Shawna Thayer never intended to make a career in charts and spreadsheets. So how did she become Capella University’s vice president of data strategy and institutional analytics? The answer, Thayer admits, still surprises even her. After finishing her undergraduate degree at a brick-and-mortar school, Thayer enrolled in a traditional graduate program, planning to earn a PhD and embark on a career as a university professor. As part of her program, she studied statistics and learned to crunch numbers – an essential skill in academic research. She discovered she had an aptitude for managing data, but her real interest still lay in analyzing and drawing conclusions from large collections of it. Midway through her graduate studies, however, Thayer came to the realization that most university professors rely on graduate students to analyze data and write up the conclusions. The heart of the process – analysis – was something instructors delegated so they could spend time teaching and writing, among other things. “It kind of threw me into a panic,” Thayer recalls. “I thought, ‘I don’t know if being a professor is really what I want to do.’” A mentor suggested she consider an alternative career in business analytics – which initially perplexed Thayer. “The idea sounded completely absurd to me. I’d never considered a corporate career at all,” she recalls. “But I thought, ‘Well, there may be something here.’” Data Crunching in the Corporate World Thayer went on to finish a PhD in Family Science, with a specialty in quantitative analysis and research design. Rather than seeking a post at a university, she leveraged her skills to land a position as a quantitative analyst at American Girl, a subsidiary of toy maker Mattel, where she worked on retail strategies, catalog tests, loyalty programs, and digital advertising metrics. “It was definitely a crash course in business,” Thayer says. “At the beginning, I didn’t even know what a P&L statement was. It was a tough first year. I had to learn a whole new world.” Over the next decade, she went on to jobs at the data company Nielsen, the grocery retailer Supervalu, and a consulting firm, Analytic Partners. Her skill at managing and assessing data, along with her background in the social sciences and her communication skills, propelled her up the career track quickly. A Return to Education A little over a year ago, Thayer began considering a change. She missed being in higher ed. Was it time to go back to teaching? She discussed the matter with her husband and the next day, to her surprise, got a call from a recruiter asking if she might be interested in working for Capella. In March of 2016, she began her current job, assessing university data in the hopes of maximizing student success. Thayer says, “I’m not teaching, but I can see how the data analysis my team does helps our students achieve their goals.” Advice for Starting a Career in Data Thayer is encouraging of other women who want to enter the field and notes that Capella has a great track record in educating women who want to make a career in data. And she has some good advice. “You need to know how to handle both structured and unstructured data; know how to learn coding languages; and have savvy technical skills,” Thayer says. “But you also need the ability to pull insights from big data. How do we interpret, communicate, and visualize data? You have to be able to communicate your insights to an audience.” Perhaps not surprisingly, she also encourages people to take career risks. She made a switch that ultimately paid off – even if at first it seemed like a crazy turn on her life path. “If you’d have asked me then if I ever thought that my future career was going to be in statistics or analytics, I would’ve laughed,” Thayer says. Capella offers certificate, bachelor’s, master’s, and doctoral degree programs in data and analytics. Visit www.capella.edu/analytics to learn more > Article published by Anna Hill Image credit by Capella University Want more? For Job Seekers | For Employers | For Influencers

A few weeks ago, I met an executive from a major Hollywood Studio. One thing that I heard from him that I did not expect was studios are regulated and, more importantly, very concerned about protecting our personal privacy. Honestly, I have grown to expect to hear these words in healthcare, banking and insurance, but not from a Hollywood executive. As with most things like this, there is an interesting inception point for this regulation. What I learned was that during Robert Bork’s Supreme Court confirmation hearings, his video rental history was published without his authorization. While nothing on the list apparently would create a scandal even in that time, most felt that Bork’s privacy had been violated by the release. Given this, The Video Privacy Protection Act (VPPA) was passed in 1988. Its aim was to prevent a "wrongful disclosure of video rentals or sales.” It established, as well, damages for "video service providers" that disclose rental information outside their ordinary course of business. The per person penalties are not a lot, but in the age of cyber hacking, the damages from a mass release could really add up. Interestingly, as well, there is even case law that has applied the act to Facebook, Netflix and Hulu. With this said, those that collect information on me and my video purchases have an interesting dilemma. They need to use my purchase data to intelligently suggest videos based upon my previous purchase history, but at the same time, they need to make sure that this data is not released or misused internally. This means that they need to anonymize data in two directions. First, they need to ensure that connecting me to my video purchases history is protected. Second, they cannot gather information on all the purchasers of a video. Information releases in both these scenarios are protected. Doing this well is difficult unless you create separate databases for purchases and users, but this makes it difficult to automate suggestions — a key element in providing a winning digital experience. Is there a better way? If you govern and de-identify a purchaser’s identifying information, you can protect purchase history data from internal users without authorized permissions. This can even include the computer scientists developing movie suggestion algorithms. At the same time, you can prevent a mass release of user video purchases and viewing histories. All that will be shown to those without authorization is a scrambled display of information whether it be for purchases or accesses for a video. Clearly, in an appropriate customer support situation, purchase history for a user can be unscrambled to reveal customer specifics. Imagine a customer service rep needing this information when a client is arguing that they did not purchase or rent a video. So how would this work? In intelligent, dynamic, data-centric and person-centric protection (we call this data-centric security), data protection is established for databases containing customer information, with centralized governance and control for this data and de-identification to protect the identity of customers as it relates to their video rentals and purchases. De-identification leveraging tokenization uses consistent tokens as substitutes for identifying information so that identities of purchasers are always protected, even during processing. With centralized governance of de-identification, data remains protected wherever it goes. The power of this approach can be understood by considering a healthcare example: doctors should see entire medical records but not financial information, while researchers studying how to derive better care should see entire medical records but not who they belong to. Your brand’s data matters – are you protecting it? Customer data matters to all businesses including studios and organizations renting and selling videos. However, the ability to connect a customer with their video history needs to only be available to authorized data stakeholders. Without these safeguards, the bad guys only need to acquire or compromise one privileged person’s credentials to get into your complete video library purchase history. Meeting compliance and privacy expectations means raising the bar to make it harder to access sensitive information. In traditional methods like encryption, if a privileged administrator’s credentials are compromised, they have everything. Centralized control of data policies, governance and de-identification allows firms to overcome the limitations of encryption alone. Further reading How should you protect enterprise data?  5 Takeaways from The Privacy Engineer’s Manifesto  Is your EDW a target for hackers? Talk to me on Twitter: @MylesSuer Article written by Myles Suer Want more? For Job Seekers | For Employers | For Influencers

Minneapolis Federal Reserve Bank President Neel Kashkari said recently that blockchain technology has more potential for being adopted in the future than bitcoin itself. Blockchain is a subset of bitcoin which acts as a transaction ledger driven by computer networks. It has garnered attention worldwide for its efficiency in tracking and recording of assets. Don Tapscott, co-author or “ Blockchain Revolution ,” said, “The technology likely to have the greatest impact on the next few decades has arrived. And it’s not social media. It’s not big data. It’s not robotics. It’s not even AI. You’ll be surprised to learn that it’s the underlying technology of digital currencies like bitcoin. It’s called the blockchain.” Blockchain contains applications with far-reaching impacts that defy the basic elements of big data by surpassing basic activities such as transfers of money and electronic currencies. Blockchain has the capability to manage everything from medical records to property deeds to musical ownership rights. This concept will defy traditional approaches of having arbitrators handing and processing transactions and will bypass processes which slow down business processes. In other words, blockchaining will allow for instantaneous results which serve to profit all. In the era of big data and the Internet of Things, blockchain has applications that go beyond the tangible, intangible and monetary things like digital currencies and money transfers. From electronic voting, financial services, health care, supply chain, oil and gas, smart contracts and digitally recorded property assets to patient health records management advertising, publishing, media, energy, government and proof of ownership for digital content – among many others. Blockchain will profoundly disrupt hundreds of industries that rely on intermediaries – think banking, finance, trading stocks, bonds and commodities, academia, real estate, insurance, legal, health care and the public sector. Business journalist Laura Shin mentions that there are numerous firms “pursuing blockchain technology including IBM, Microsoft, Walmart, JPMorgan Chase, Nasdaq, Foxconn, Visa and shipping giant Maersk. Venture capitalists have so far poured $1.5 billion into the space, with storied firms such as Andreessen Horowitz, Kleiner Perkins Caufield and Byers, and Khosla Ventures making bets on startups.” Existing technology Algorithms which are indigenous to blockchain eliminate the dependency of the human factor to validate business activities. Through these measures, the cost of overhead for companies will become greatly reduced. The healthcare system will benefit greatly from blockchains as it will open up once closed doors for the efficient exchange of electronic information between the individual and medical providers. Smart contracts will be standard operating procedures as business practices will be written into software, which will allow for streamlined practices in operations. In turn, businesses will deliver the most cost-effective product to the general public requiring such services. Katherine Shaffer from Social Service Administration adds that a vast amount of storage is required for blockchaining. Many activities go on behind the scenes in the implementation of blockchain activity, such as network encryption, generation of offline data and open sourcing and narrowing of cloud storage. The security of blockchaining is not compromised as sequential hashing in tandem with scalability is implemented in order to maintain the integrity of the data. There is also an extensive log of etiology of any type of item which is a valuable tool in the quest for transparency of data. This process will reveal to the individual the history of the data, from its inception to the users who have viewed or altered the data through processing. The online ledger, which is blockchain, bypasses third-party interaction and remains anonymous while remaining secure. As such, it is the safest and most important method to implement any form of business transaction. In the article “ The Truth About Blockchain ,” Professors Marco Iansiti and Karim R. Lakhani of Harvard Business School make relevant points. “For most, the easiest place to start is single-use applications, which minimize risk because they aren’t new and involve little coordination with third parties. One strategy is to add bitcoin as a payment mechanism. The infrastructure and market for bitcoin are already well developed, and adopting the virtual currency will force a variety of functions, including IT, finance, accounting, sales and marketing, to build blockchain capabilities. Another low-risk approach is to use blockchain internally as a database for applications like managing physical and digital assets, recording internal transactions and verifying identities. This may be an especially useful solution for companies struggling to reconcile multiple internal databases. Testing out single-use applications will help organizations develop the skills they need for more-advanced applications. And thanks to the emergence of cloud-based blockchain services from both startups and large platforms like Amazon and Microsoft, experimentation is getting easier all the time.” Bitcoin was relatively small Iansiti and Lakhani further note, “The parallels between blockchain and TCP/IP are clear. Just as email enabled bilateral messaging, bitcoin enables bilateral financial transactions. The development and maintenance of blockchain is open, distributed and shared – just like TCP/IP’s. A team of volunteers around the world maintains the core software. And just like email, bitcoin first caught on with an enthusiastic but relatively small community.” “Once this basic infrastructure gained critical mass, a new generation of companies took advantage of low-cost connectivity by creating internet services that were compelling substitutes for existing businesses. CNET moved news online. Amazon offered more books for sale than any bookshop. Priceline and Expedia made it easier to buy airline tickets and brought unprecedented transparency to the process. The ability of these newcomers to get extensive reach at relatively low cost put significant pressure on traditional businesses like newspapers and brick-and-mortar retailers.” Built on a new peer-to-peer architecture Iansiti and Lakhani wrote, “Relying on broad internet connectivity, the next wave of companies created novel, transformative applications that fundamentally changed the way businesses created and captured value. These companies were built on a new peer-to-peer architecture and generated value by coordinating distributed networks of users. Think of how eBay changed online retail through auctions, Napster changed the music industry, Skype changed telecommunications and Google, which exploited user-generated links to provide more relevant results, changed web search.” “Ultimately, it took more than 30 years for TCP/IP to move through all the phases—single use, localized use, substitution and transformation—and reshape the economy. Today more than half the world’s most valuable public companies have internet-driven, platform-based business models.” Futuristic growth envisaged Gartner projects that devices or things using blockchains to transact will comprise 30 percent of the global customer base by 2030. Shin reports that, “One of the more popular futuristic scenarios is that we may someday tell our self-driving car that we're in a rush and to send a micropayment to any car that is willing to be passed on the highway. The money will be transmitted via a combination of blockchain and IoT technologies.” Since blockchain is a highly integrated and interdependent system, there is a low potential for security breaches, as infiltrating one block would have a detrimental effect on the other block, etc. The network system must have capabilities of extensive bandwidth in order to process and hold vast amounts of data. Overall, in the realm of government bureaucracy with the proverbial “red-tape” which it is known for, blockchain would be the answer to more efficient and timely government agency processing, especially in assisting those who come to the federal government looking for assistance with medical issues. If blockchain would be implemented, we all benefit from creating a better work product and most importantly, by assisting citizens in their time of need. Article written by Raj Kosaraju Image credit by Getty Images, E+, Vertigo3d Want more? For Job Seekers | For Employers | For Influencers

Create a winning consumer experience Digital disruption over the last decade has significantly impacted how retailers communicate with their customers. Most retailers acknowledge a shift to more technology-enabled consumers. As a result, retailers are adjusting their business strategies and refocusing their marketing so it is more consumer-centric. Roxanne Austin, a member of Target’s Board of Directors, says their decision to appoint Brian Cornell as Chairman of the Board and CEO was driven by the growing importance of data and analytics and for their brand to catch up digitally. “As we sought to aggressively move Target forward and establish the company as a top omnichannel retailer, we focused on identifying a leader who could bring vision, focus and a wealth of experience to Target’s digital transformation.” Austin said that the Target board had determined that “Target would not grow if they weren’t relevant to our customers.” As part of this change, she says they now view smartphones as their storefront. This step has liberated them to increasingly turn stores into fulfillment centers. Target’s CIO, Mike McNamara, clearly agrees with his board member. He claims the trick now is to make their physical stores add value to a digital strategy and not the other way around. Part of delivering this in the digital era is creating, “an adaptable architecture and agile processes.” Doing this, McNamara says, involves creating a business philosophy of “test and learn.” McNamara says that he has for this reason brought all software development back in house, asking “how can you outsource your IP?” Retailers like Target understand that it is critical to use data to build direct relationships with customers. Their goal is to create great experiences with their brands and products by empowering consumers with knowledge. This requires a better understanding of consumer needs as well as the removal of purchasing barriers. Strategically this means managing customer, product and supply chain data, and proactively identifying the relationships among them. Doing this in a way that does not damage customer trust and goodwill is essential. Integrating fragmented customer data is critical Consumer, product, partner, supply chain and external data should be proactively managed to ensure a complete picture of consumers. Without accurate and complete data on next-generation customers, neither the systems nor the resulting marketing campaigns will function as intended. Retailers with inaccurate targeting will end up frustrating consumers with irrelevant messages and offers which will be ignored or lead to a complete shutdown in communications. Typically, the above data is highly fragmented which creates problems for the sales, marketing and even service teams who use data to develop approaches for local markets, products, brands or campaigns. Marketing costs are driven upward by the inability of these teams to leverage common resources and the duplication of their efforts across programs. A patchwork approach is at odds with corporate initiatives to create a single view of customers and products, reduce costs and increase profit margins. Becoming a better customer data custodian Fragmented consumer information leads to data privacy and security concerns, and companies collecting data about customers need to ensure this sensitive and valuable data is protected holistically. Data protection regulations, including Payment Card Industry Data Security Standards (PCI DSS), carry hefty costs for data breaches. Privacy International counts over 100 countries with comprehensive data protection legislation and several other countries in the process of passing such laws. Data protection is not just about risk aversion and avoiding fines, it is about protecting your corporate brand. A Deloitte study found that the primary reason consumers choose to purchase higher-priced products is because they are from a trusted brand. Importantly, the study found that for 59 percent of consumers just a single data breach would negatively impact the likelihood of them purchasing from a particular company. Deloitte found that with confidence in an organization’s ability to protect their data, consumers are more inclined to share information. Without data protection assurance, retailers will be limited in the information they can collect – and allow competitors (or trading partners) to win their customers’ loyalty instead. Information security drives customer experience University CIO Stephen Landry argues information security is an essential part of customer experience, as he says Target now knows this. Healthcare CIO David Chou says that unfortunately, “once the trust is gone, forget about all of the digital and innovative experience you have put forth.” Isaac Sacolick , a former financial services CIO, adds “you can compete on overall customer experience, innovation, performance, service, design and a big yes to security and trust.” Cynthia Stoddard , CIO of Adobe, says that “data is the new currency for organizations driving insights, customer engagement and ultimately financials.” Ann Cavoukian , Director of the Privacy and Big Data Institute, agrees with them and says, “you can't afford to leave security out of the equation: must have both privacy and security, ideally by design!” Your CIO gets it. The question is what are you doing to help bring data stewardship to the forefront? Parting thoughts As we have said throughout this post, it is essential that retailers get their data act together. This means that they put together a complete picture of consumers and at the same time become better data stewards. Without the latter all the intimacy sought can be lost in an instant. To give you more information, please take a look at more detailed brief on this topic: "Customer-Centric Retailers Win Based Upon Customer Data" Article written by Myles Suer Image credit by Getty Images, Gallo Images, Anthony Bannister Want more? For Job Seekers | For Employers | For Influencers

Organizations are slow at adopting progressive methods. This is true for CFOs, CPAs and accountants. The accounting profession needs to prepare for change and threats to competitive advantage because there is an accelerating and disruptive digital technologies transformation in progress called the “digital revolution”. We are witnessing significant changes in the nature of technologies available for today’s managers and employee teams with regard to infrastructure, availability and capacity. These elements have accumulated in four key technologies often referred as SMAC – Social, Mobile, Analytics and Cloud. Venture capital investors have recently shifted towards big data and artificial intelligence that combine these technologies. These investments are accelerating the impact of this revolution. Examples of digitization disrupting traditional industries such as Uber for car passenger transportation are just the tip of the iceberg. While the term artificial intelligence (AI) originated in the 1950s, the world turned its back to the promises of AI. For decades, research in this space continued by a handful of researchers in Canada. Their continuing research has recently contributed to the rebirth of interest in artificial intelligence. It’s no surprise that some of the relevant startups for professional services firms emerged out of Canada. As examples: Ross Intelligence can streamline the manual search that junior attorneys in law firms labor at to research past cases. MindBridge AI is capturing and augmenting assurance associates’ knowledge into an AI, enhancing professional judgment and simplifying work around data and analytics. Both examples will create new capacities or remove obsolete job titles and will redefine workflows in these professions. Embracing “digital transformation” is the recourse for protection and preservation. This doesn’t mean that accountants should seek to become data scientists or build mobile apps. They need the competence to choose the technologies fitting them and be demanding and aggressive in adopting them. In some areas, low cognitive tasks, such as the manual and tedious tasks performed by accountants, can be augmented and monitored, down to key strokes, by an AI engine. The AI will never take a vacation or get tired. It can operate 24/7. 5 accounting functions that will be impacted As automation displaces a traditional accountant’s work, it is important for those affected to have a positive and an optimistic attitude and consider the newly-created upside potential for them to perform fulfilling work and higher cognitive tasks. Despite science fiction movies that present an apocalyptic view of robots, the future should not be feared. This is because robotic software can, for now, only handle low-cognitive tasks and does not have a sense of self-preservation like humans. Regardless, we need to clearly identify where they will impact work the most. Here are five accounting functions that we believe will be highly impacted: 1. Transactional accounting processes Clerical accountants are the most vulnerable to digitalization and automation because their roles involve routine tasks like bookkeeping and data entry. Primary examples are customer order processing, invoicing, credit, accounts receivable, payment collection, vendor purchase order processing and accounts payable, payroll processing, and travel and expense processing. 2. Fiscal period-end accounting closes The risk of digitalization for accountants is due to the increasing application of affordable commercial software that automates the workflow processes of the monthly, quarterly and fiscal year-end accounting close. Software can quickly access source data and apply tax calculation rules. Small businesses, similar to individual households, can now use commercial tax preparation software instead of hiring tax professionals from a third-party service. 3. Auditing The purpose of an audit is to obtain reasonable assurance about whether financial statements are free of material misstatements and irregularities due to error or fraud. Digitalization improves the quality of an audit in many different ways. For example, using an AI-expert system capable of scanning through 100% of the data and applying advanced analytics and anomaly detection in the audit can lead to better-informed risk assessments. It leads to a far more focused and relevant (higher quality based on risk) sample which increases the speed of engagements and decreases liability. 4. Business process outsourcing (BPO) of accounting tasks The general term for third parties who perform outsourced accounting tasks is business process outsourcing (BPO). The BPO business model is typically based on fee-for-service pricing. With centralization and economies of scale from having multiple customers, a BPO provider can often perform both front and back office accounting tasks more efficiently. 5. Regulatory filings Automation and technology have already begun to revolutionize regulatory compliance reporting. The implications are that rather than accountants requiring only mathematical acumen, mastery of tax laws or bookkeeping proficiency, accountants can devote more time with increased skill to interpreting and analyzing financial information. For example, they can use XBRL, a format that can now digitally transmit its financial statement filings to government regulatory agencies. Mitigating risk due to digitalization within accounting Automation is bound to impact accounting tasks and jobs. In some tasks where complexity is substantial and the volume, variety and velocity of data are all high, computer software may outthink a human analyst. Automation is also capable of applying what was learned from previously solved problems to new problems. For example, automated analysis to evaluate the financial return from varying capital investments, such as for different assets such as machines, can be used to evaluate acquiring different types of new customers. Accountants need to face the reality that low-cognitive tasks will soon be performed by a combination of brute computer processing power, big data and algorithms. The most severe risk an accountant faces due to digitalization and automation is the elimination of their job. Other risks are downward pressure on salaries for some accounting positions, with potential increases in workload and work hours. Different people have different reactions to change. Some people may deny the change, while others may embrace it. There are several ways that accountants can mitigate the impact on themselves: • Increasing skills with education and training As automation increases examining the output of automation, including reports and analysis, will be emphasized. As this emphasis changes, accountants can convert their feared risks into opportunities. They can do this by acquiring new skills and capabilities such as with planning, strategizing and analysis which contribute higher value to the organization than simply reporting data. This can be accomplished via education and training. For example, The Institute of Management Accountants reports that members who pass its Certified Management Accountant (CMA) exam earn on average a 35% higher salary relative to comparable accountants without the CMA degree. • Augmenting digital automation In certain cases, accountants will find that robotic and analytic software does not fully replace a job function. Instead, it will automate the repetitive tasks of a workflow process, and the accountant can then augment the automation with value-adding work. For example, as automation reduces errors and generates information more quickly, the accountant can shift from producing reports to investigating discrepancies. In effect, the accountant becomes the machine’s supervisor. As automation occurs many jobs will be redefined rather than eliminated. • New business models from digital disruption Entrepreneurial accountants will recognize the opportunities that digitalization, automation and AI can bring for expanding existing business models such as business process outsourcing and tax processing services. Additional opportunities are to pursue new business models, such as financial software implementation services, including providing the analysis generated from the information produced from the software. As one begins to more fully understand the impact of software automation and the speed at which it will affect accounting jobs, accountants have two broad choices on how to react. The first is fearfully, wondering if they chose the wrong profession and should pursue a different career. The second is to seize the opportunity for change and embrace the positive and imminent impacts from automation. This includes preparing themselves for less tedious and more fulfilling work that will bring increasing value to their organizations and their clients – as well as themselves. The choice will be their own. No one has a crystal ball, but our bet is they will make the latter choice. Article co-written by Gary Cokins and Solon Angel Image credit by Getty Images, Photographer's Choice, Biddiboo Want more? For Job Seekers | For Employers | For Influencers

Apparently, Uber has a company tool called “God View” that reveals the location of Uber vehicles and customers who request a car. This tool can allow a wide number of Uber employees to view customers’ locations. So this is “my data” and now Uber has it! So what's the trend? Having differentiated customer data will be a source of competitive advantage from now on. And for this, gaining consumers’ confidence will be key. Companies that transparently inform customers about the information they gather, give customers control of their personal data and offer fair value in return for it will be trusted. Consumers will even give such companies larger access and a disproportionate share of their wallet. Soon, investors will value this, and it will reflect in stock prices, too! Over time customers would demand that their personal telecom, credit cards and banking data be available in a personal data warehouse. Yet many industries still take a short-term view of this trend. Big banks have even tried to choke the flow of data to popular websites that help consumers manage their finances. In India, I am not sure how leading banks like HDFC and ICICI would respond to requests for electronic receipt of my data. Banks, facing increasing competition from these companies, are becoming more protective of their customer information and are limiting how much data they pass on. And yet, by allowing third-party software access to data, a bank could act as a platform for third-party innovation, just like Apple acts as a platform for developers through its App Store. In fact, this becomes even more critical in the healthcare arena. Shouldn’t patients be the owners of their own medical data? The U.S. government has an interesting Blue Button Initiative . This protocol is already providing a secure way for veterans and Medicare beneficiaries to share their medical history with healthcare providers they trust. You can use your health data to improve your health and to have more control over your personal health information and your family’s healthcare. Power of APIs Many businesses are still not getting the fact that using APIs can be a powerful force multiplier for them. Put simply, an API is a set of instructions that allows one piece of software to interact with another. In general, as the array of API-enabled devices and services grow, so does the range of ways that they can be connected. An interesting UK government report suggested that: "You can even connect the lights in your living room to ESPN so that they flash when your football team scores, or to your calendar so that they blink on your birthday." The growth in the use of public APIs reflects the fact that there are a number of ways in which organizations can benefit from allowing their software and data to interact with third parties. For some companies, their APIs are their core business model. Twillio, for instance, provides a service that allows partners to send and receive voice and SMS communications.When a customer receives an SMS message telling them that their Uber driver has arrived, this is powered by the Twillio API. An interesting proposition Richard Thaylor, a professor of economics and behavioral science at the Booth School of Business at the University of Chicago has an interesting proposition. This is what Thaylor provocatively suggests: “If a business collects data on consumers electronically, it should provide them with a version of that data that is easy to download and export to another website. Think of it this way: you have lent the company your data, and you’d like a copy for your own use.” I think this is a powerful idea and can have a huge impact on consumers and also create a huge number of intermediary companies who help consumers make sense of their data. The idea of using analytics as “personal power” will definitely be resisted by companies and governments, but it has the power to allow the customer to control and manage her relationships with telecom, retail and myriad other companies. Here is how Thaylor explains this can happen: “Mydata” is the term we use in the United Kingdom; in the United States it’s called “smart disclosure.” The idea is that in many cases we can help consumers simply by making their own usage data available to them. Here’s an example: You’re searching for a new smartphone calling plan. What you’d like is access to all the ways you use your smartphone – in a machine-readable format. That would create a business opportunity for online services that I call “choice engines.” With one click you could upload all of your usage data, and the choice engine would recommend plans that suit your needs. We can help people make smarter decisions across many areas of their lives just by giving them access to their data." My experience of working in large banks and retail companies has been that often thoughts like these can be seen as fluffy and not hard “revenue producing” ideas. This really is about becoming more customer centric. In many companies, such as banks, business silos make customer-centric initiatives far more difficult. As marketers in service companies, often the power does not lie with the CMO to drive such thinking forward. Yet as industries are getting disrupted, maybe this is the time to make CMO voices heard and take steps to become more customer centric. How does all this matter to companies? 1. Business is no longer the gatekeeper to data. Customers will demand their data and intermediaries will build a solution that offers “customer value” on top of it. Banks, retailers and telecom companies will need to follow this trend and partner their customers for making money. 2. Marketers can help customers lead a better life. Whatever business you are in, customers will want you to add value to their lives. Helping customers use their own data in creative new ways can be a great differentiator. Customer data can be used to benchmark customers. Customers would love to know how their telecom spends compare with someone of a similar profile. Am I spending too much time on the phone lately as compared to others? Or how many hours of kids’ television programming does my household watch as compared to others? Customers may willingly provide more data (information about their family or interests) in return for getting additional value. 3. Creating a personal data product business. Most millennials are data natives. A data native is someone who expects their world to not just be digital, but to be smart and to be able to personalize to their taste and habits. For example, a bank should not only be digital and interactive – it should be personalized. It should tell you what you need to know based on your interests, location and preferences. Data products provide context and personalization. If your brand has the customer's trust and if you can help them improve their lives, they may even be open to giving you their data from other service providers. The expectations have shifted. Companies need to focus on creating a product based on data and making it valuable for their customers. Read more on my blog about customers, analytics and how data can transform us. Article written by Ajay Kelkar Image credit by Getty Images, E+, Vertigo3d Want more? For Job Seekers | For Employers | For Influencers

"Chatbots are computer programs that mimic conversation with people using artificial intelligence. They can transform the way you interact with the internet from a series of self-initiated tasks to a quasi-conversation," according to Julie Carrie Wong of The Huffington Post. They can now recognize objects in images and video and transcribe speech to text better than humans can. A bot might be able to process your credit card faster than a human (though humans can still do things bots can't even comprehend). There’s Siri in our iPhones, Alexa in Amazon’s Echo and Facebook Messenger’s PSL (Pumpkin Spice Latte) bot. And David Marcus, vice president of Facebook Messenger, says it can’t escape the fact that bots are still in early staging and require ongoing testing, measuring and improvement. Yet the tools and platforms for creating and hosting bots are becoming more widespread and so easy to use that you may be making your own bots within a year or two. Versatility of bots and AI "AI is all around us, from searching on Google to what news you see on social media to using Siri," said Babak Hodjat , co-founder and chief scientist of Sentient. "And with the momentum around AI growing every day, it’s not surprising that some of the most innovative retail sites have recently been experimenting with the use of AI, as well." This reminds me of the popular TV show “Undercover Boss” where company owners disguise themselves as regular staff members and work with everybody else. Some of the discoveries they make are real eye-openers – leading to changes that make the company more efficient, profitable and enjoyable for everybody to work in. Likewise, "both chatbots and AI together form a small but significant step in revolutionizing the way enterprise solutions are supposed to be – simple, intuitive, and engaging," said Siddharth Shekhawat , CEO and Co-Founder of Engazify. What makes bots noteworthy and special is that they can easily be integrated into some of the existing communication platforms used by businesses in order to give an in-app experience to its users. Bots are user-friendly for no programming required Take San Francisco-based Motion AI , for instance. No programming skills required. Regardless of how simple or complex your use case is, Motion AI streamlines the entire process. While there are thousands of chatbots everywhere, if you can simply draw a flowchart, you can create a chatbot. This is how it works – diagram your conversation flow, connect your bot to a messaging service and go. Motion AI allows you to deploy Node.js code directly from their interface. This makes for an excellent product when integrating your bot with third-party APIs, databases and services. Bots versus chats Kemal El Moujahid , lead product manager for the Messenger team at Facebook, puts it this way: "Typically, a bot would be useful to retrieve information for you, alert you at the right time or play a game with you. Very basic, very simple. When we opened the Messenger platform, it was natural to refer to these new experiences that developers could build for our users as “bots.” Some would tell you the weather, others would send you news, all of this automated and through chat. If you prefer the feel of sending a text to filling out a field, you’ll prefer the chat bot experience. Given such scenarios, one can conclude that there is much more to bots and machine translation. According to Mariya Yao , head of design and engagement at Topbots.com, "The practical applications are mind-blowing, as well. Computers can predict crop yield better than the USDA and indeed diagnose cancer more accurately than elite physicians." Google replaced Google Translate’s architecture with neural networks, and now machine translation is closing in on human performance. But as developers build more and more valuable and delightful experiences for consumers and businesses, leveraging all the tools in a platform, it’s become clear that “bots” are about much more than chat. Chatbots and AI landscape As it stands, the chatbot ecosystem is already robust, encompassing many different third-party chat bots, native bots, distribution channels and enabling technology companies. ( Laurie Beaver, Business Insider ) There’s a global market for messaging and mobile interactions with different features. Facebook Messenger and Whatsapp combined see more than 60 billion messages sent each day on the two platforms. Traditional text messaging clocks in at approximately 20 billion sent globally. Another example is Chinese messaging platform WeChat. Its P2P feature is accessed by 600 million of its users in China. There are, of course, many chatbot platforms that already exist. These tend to be bot-as-a-service platforms, where you can build, adapt and deploy your service in the cloud. For instance, WeChat uncovers how adding payments to the chat experience can have vast implications on user engagement. A look at the system According to Dr. Peter Norvig and Professor Stuart Russell : In order to pass the Turing Test, the computer system would need to possess the following capabilities: natural language processing knowledge representation automated reasoning, and machine learning "Natural language processing is possibly the hardest area of artificial intelligence to crack," said Paul Boutin , senior reporter of Chatbots Magazine. "A conversation held by a human customer service trainee who barely speaks the customer’s language is still beyond the reach of today’s NLP systems, or that rep would be out of a job. As it turns out, natural language processing is so complex that it isn’t just one field of research, it’s at least four." Chatbots often require a database backend. Your options range from the standard SQL databases, whose structure is often not compatible with natural language, or the more accessible NoSQL databases. You can also choose to use a graph backend. Graph database query languages can often easily mimic the connections present in natural language, which makes them ideal for building chatbots. ( Alexandra OrthFollow of Grakn.AI ) Grakn.AI is a database of AI with a reasoning query language (Graql) that enables you to query for explicitly stored data and implicitly derived information. It uses machine reasoning to simplify data processing for AI applications with less human intervention. Other options to look at include the Microsoft Bot Framework, WIT.AI, Pandorabots and API.AI. All provide a different set of features, integrations and a different level of usability. For any A.I. system that involves communicating with a human as a main part of its agenda, it is important for it to be able to use NLP as much as possible. So, NLP is not only useful but also necessary. That’s something we observe a lot nowadays with chatbots – for example, A.I. systems geared at emulating human communication through a web API, in order to convey useful information or facilitate a certain action. ( Zacharias Voulgaris, data science author ) As the lesser-known components of AI, Knowledge Representation and Automated Reasoning aren’t as commonly spoken about in the press but nonetheless play a key role in the creation of intelligent systems. Knowledge Representation allows us to make sense of the complexity in information. Automated Reasoning capabilities allows a system to “fill in the blanks” as there is no such thing as complete information or data with no gaps. ( Precy Kwan of Grakn.AI ) Another supportive element of the ecosystem is the actual user interface with which the end user engages with the bot – and this may take place across various platforms. For example, Facebook Messenger, a website’s chat facility, inside an app on a device or native (built into an operating system) or greater equivalent. ( Glenn Miller, digital strategist ) Best bots for businesses Adelyn Zhou, head of marketing at TOPBOTS  outlined recently the 100 best bots for brands and businesses leading their industries in messaging innovation. She said: "You’d never expect Mark Zuckerberg, king of social networks, to publicly admit that “messaging is one of the few things that people do more than social networking,” but the evidence is now indisputable. In 2015, Business Insider reported that messaging apps overtook social networks in popularity. Since then, every major messaging product – from Messenger to iMessage, Slack to Skype, Echo to Allo – opened up their platforms for “bots” and “micro-apps” which enable users to interact with brands and business without leaving the app. Leading companies from every sector jumped at the opportunity to use conversation and voice to engage customers in a scalable, personalized way." In closing Chatbots are well-suited for mobile (possibly more so than apps) as messaging is at the heart of the mobile experience. Wide consumer adoption of AI and machine learning-based bots is rapidly growing, as well. Creating your own bot has never been easier or more affordable than it is now with a variety of developer tools available from different platforms. Looks like chatbots are here to stay for a long time. Article written by Raj Kosaraju Image credit by Getty Images, E+, AndrewJohnson Want more? For Job Seekers | For Employers | For Influencers