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Interested in a new hat?

Marketing professionals for small to midsize businesses wear a lot of hats; content creation, email campaigns, lead generation, website maintenance, event planning, and more. On top of that, each month we are tasked with compiling all the data from these different areas into a report that shows ROI and trends to help leadership make decisions on future marketing budgets. It is not that we don’t have the data; it’s the opposite. Reports from social media platforms, CRM systems, marketing clouds as well as Google Analytics, leave us swimming in data but pulling everything together is so difficult it feels like now we also need to be data scientists to figure out how to integrate all of it. Today, marketing teams also contend with the challenges of masking PII (Personally Identifiable Information). An email address combined with the demographic information compiled by online search engines must be handled with care, and encrypted before storing. PII data must be masked or anonymized to comply with privacy laws and reduce risk of identity theft, especially if you’re sharing data with others. There must be a better way. How about building your own Data App? Since marketing professionals look so great in hats, let’s try on another – data app builder. Today, marketing teams, not web developers, build websites, create integrations between tools and configure the apps our team’s use on a daily basis. However, data AI apps are still buried in the IT department and out of the reach of the business users who need the information. Velox is the answer. Velox puts the focus on data, content, and use cases, not technology. Not to worry, all the technology is there, and you can read all about it here, but I prefer to think of it as magic. A cloud-based app like everything else you use (hopefully), Velox ensures your data is highly secured and compliant. Velox is powered by machine learning and AI so over time, as you send more data to it, Velox gets smarter and can recommend, nudge, and help you make data-driven decisions. The information produced from these apps can be published to a Velox dashboard, or an API can be created to send it to your company’s preferred location. How does Velox work? Believe it or not, you are four steps away from creating your own secured data app. Much like when you connect your CRM or social media accounts through integrations, Velox works the same way. 1. Ingest A fancy word for connecting or uploading your data sources. You can upload files (excel or csv) or choose the apps you want to pull data from like: · Facebook Ads · LinkedIn Ads · HubSpot · Google Analytics 2. Understand & Transform a. The Automated Profiling feature allows you to understand your data from a completeness and quality perspective. It also provides descriptive statistics such as correlation, mean, median etc. b. You can perform easy transformation steps such as fix date formats, and round off numbers on the data, if needed. 3. KPIs & Visualizations Identify key metrics from your data set and create charts & graphs in a few simple steps from Velox’s chart library. 4. Publish Make your KPIs & Insights available across different users within your organization by publishing it in a single click. An interactive web portal with charts & dashboards can be accessed from any web browser using secure login credentials. And that's it! You have a published, interactive visual app providing real-time insights into your marketing performance. Marketing teams need to make the most out of their data by: Calculating ROI to help guide business decisions, and optimize future marketing efforts Helping marketing teams measure campaign success, justify marketing spends, and allocate budgets for ongoing and future initiatives. Using the right data enables analysis-based marketing decisions, allowing enterprises to track and respond to changing behaviors, try new tools, and anticipate what comes next. Velox does all that, and more; it gives marketers a great new hat to show off!

Data Monetization: Data is Crude

This article originally appeared on Article co-authored by Thiag Loganathan, Founder, Goldfinch Data AI Solutions and Dennis Kettler, Global Head of Data Science & Data Products at Worldpay. By now we have all heard the ubiquitous and somewhat misleading new phrase “Data is the new oil”. Oil derives its value based upon the many useful and critical products created through its refinement. However, in our experience, it is not universally true that data is being leveraged effectively to create value for companies. Thus, data is the “new oil” only for select companies that have been able to appropriately define, refine and monetize. For those that aspire to drive value on their data, how and where should one begin? To continue the analogy, similar to oil, data can be refined in many ways to deliver meaningful insights, decisions and, ultimately, value to a business’ top and bottom line. This process involves science, art, and experience to successfully monetize data. In this series, we demystify and layout a framework to understand and extract value from your data asset. What is Data Monetization? Data Monetization is a fancy way of saying, “making money from your data.” Whether your organization is a data producer, data aggregator or data consumer, you have the potential to generate new revenue streams through data monetization. This value can be achieved across a broad spectrum of analytical activities from descriptive through preemptive. Additionally, data can be leveraged across disparate disciplines: new data-driven products and services, client lifecycle management, internal cost optimization, sales funnel optimization and cross-sell/next best product to name a few. The most successful companies will monetize data in a great range and variety of activities. Why do it? Demand for additional data to understand customer behavior is at all time high across various industries. Historically accumulated data is like a massive reserve of Oil which is not turning into revenue unless mined and refined. Failure to tap could lead to significant opportunity misses. New revenue generated from monetizing the data can be invested to reduce overall expenses or into adding resources to the company to improve insights and revenue. Market pressure…differentiation…market disruption…value chain expansion…industry protection (in saturated markets or at-risk verticals) How? Define, Refine & Market 1. Leader, Strategy & Goals: Companies that succeed in data monetization will have multiple programs using different business models/stakeholders with varying degrees of success. It takes the right leader to manage the successes and learn from the failures. Identify the right leader who possesses strong data skills, business understanding and proven monetization experience. Create a data strategy & roadmap that aligns and supports the strategic initiatives of the business. Define goals on a timeline and make sure the roadmap has incremental goals. 2. People, Process & Technology: We have heard this a lot, “I have old tech, it’s not all in one place, it’s not clean”. Yes, it is a challenge, and it is almost always the case when you start. But right people & process will allow you to get started while dealing with the tech and data quality constraints. So, invest in the right people with light processes before implementing hard processes & technology. But don’t forget: A data monetization team is multi-disciplinary, will include data scientists, functional analysts, engineers, designers, and developers. Processes will continually evolve as solutions progress from innovation to deployed at Scale. Use modular and inter-operable architecture that will allow for technology to mature and scale over a period. Cloud technologies make it easier. 3. Risk, Consent, Privacy & Security: Adherence to ethical, legal and compliance policies, Data protection laws, PII, PCI, HIPAA, and other regulatory compliances, is quite possibly more challenging than the technology skills required to build data apps. We will dive deeper into Risk, Consent, Privacy & Security in our next article but a few important points of note include: Using and sharing data for analysis or monetization will have constraints that need to be overcome. Establish a business & risk council that will continuously balance constraints, risk, and potential value. 4. Business Value and Monetization (Extracting Value):
Understanding your data, as well as its gaps, is a necessity in order to overlay possible value streams with restrictions & constraints to create a business plan that extracts value leveraging your data assets. Other areas to consider include: Layout a plan that incrementally proves the value hypothesis Use MVPs and small scale pilots to prove out business models & adoption before a large scale roll-out that can become an ongoing value stream for the company As you make progress, continuously reevaluate value to address market and business changes. Build out a backlog of use cases and prioritize them to have simple/descriptive use cases early on to complex/preemptive use cases that require increased maturity. 5. Implementation Roadmap & Getting Started This is the most difficult step; it is important, while getting started, to focus on value instead of technology. The roadmap should have incremental business and technology milestones that are achievable while setting you up for long-term success. What's next in our series?
With data privacy and security being a key factor especially with the newer consumer privacy policies such as GDPR and CCPA in place, leaders need to be cognizant of various processes and controls required to ensure compliance. In the next article we will dive into the details of the risk, consent, privacy, and security as they relate to Data Monetization. About the Authors: Dennis Kettler Global Head of Data Sciences, FIS Dennis is currently the Global Head of Data Sciences, Governance and Business Development for FIS.  In his nine years at FIS, he has established Data Science as a core competency enabling transformative capabilities such as advanced data visualization, predictive analytics, and ML/AI. Ultimately, Dennis has played a key leadership role in activating data-driven decisions that have established competitive advantages in market for both FIS and Clients alike. In his current role, Dennis continues to lead data sciences and data product development. He also is responsible for driving governance strategy, capital investment, and business development as a senior leader of the FIS Data Solution Group and the Ethos ecosystem of data solutions. He brings a wealth of experience supporting many of the world’s largest retailers, corporations and payments brands for more than 10 years. Dennis and his team are focused on driving disruption and innovation on multiple fronts, including the payment lifecycle through intelligent real-time decisions (fraud, authorization, cost and dispute), advanced consumer analytics and operational analytics. Dennis currently holds four patents related to payments, attribution and consumer consent. The four patents held by Dennis are: #10706393 - Systems and methods for routing electronic transactions using predicted authorization approval #10621599 - Systems and methods for computer analytics of associations between online and offline purchase events #10528926 - System and method for payment tender steering #10776802 - Systems and methods for capturing account holder consent for transaction data collection Thiag Loganathan Founder, Goldfinch Data AI Solutions A serial entrepreneur, Thiag Loganathan utilizes his deep expertise in enterprise data assets to monetize, drive measurable value and differentiate to improve business outcomes. He has experience in developing and rolling out end-to-end data driven platforms and business solutions including pricing optimization & elasticity, financial modeling, portfolio insights, loyalty programs, customer analytics and segmentation. Both of Thiag’s current ventures focus on leveraging data through purpose-built AI and ML models to deliver better insights leading to data-driven decision making and improved outcomes. is the first true Health and Human Services as a Software company (HHSaaS). Thiag works with agency leaders and caseworkers to create modern applications to streamline workflows, integrate AI to assist caseworkers with decision making and create better outcomes for vulnerable citizens. Goldfinch Group is a data cloud platform company that consumerizes data through products and consulting for businesses ready to streamline data transformation. Thiag leads strategy efforts working with clients who need assistance with employee well-being using our Wellness AI solution and finalizing Velox, a new data app solution launching soon. Prior to his current roles, Thiag led DMI’s Big Data Insights Division. In 2007, Thiag started Kalvin Consulting Inc., a business intelligence solution provider, and an SAP Partner, which was acquired by DMI in May 2013. During his time at Kalvin, he was named the Executive of the Year for 2011 by the Ohio North East Chamber of Commerce, for his long-term commitment to bettering the community.

Using AI to Improve Foster Care Matching & Adoption Outcomes

A version of this article originally appeared on By Thiag Loganathan & Kevin Jones Nick Garza is all smiles at the courtroom on the day he was officially adopted. Pic courtesy: Good Morning America A few months ago, 8-year-old Nike Garza became one happy boy. After spending nearly half of his life (1,553 days to be precise) in the foster care system, he was finally able to join his adoptive family. Extensively captured by the media, the joyous moment was a great display of the State of Texas coming through for its vulnerable citizens. But one cannot ignore the long wait for Nick to find permanent placement in a loving family he could truly call his own. How can state child welfare agencies avoid such indefinite delays in finding permanent homes for their clients? A good first step seen in some states has been getting off decades-old legacy systems, bringing down data barriers between agencies and making a strong commitment toward modernization of IT efforts. We are in a mission to do exactly that, using modern cloud-based AI technologies for the State of Indiana to take steps to adopt a mobile-first, cloud-based approach to leapfrog and go cutting edge, leveraging AI for improved outcomes for children at Indiana Department of Child Services. AI’s Application in Human Services Artificial intelligence (AI) has helped make many consumer experiences seamless, intuitive and effortless; it is time for child welfare systems to benefit from its wide-ranging capabilities: automation of mundane tasks, enhanced decision-making abilities, freeing up workers’ time to engage in more impactful work. Apart from productivity improvements, leveraging chatbots and ease of use examples like NLP/Conversational UI to auto-fill forms, we are looking to operationalize high-impact use cases like "Child at Risk” alerts and "Foster Care Matching". Flagging Children at Risk When should a child be separated from their family? This is one of the most difficult decisions for a caseworker to make. Family is the best setting for a child to develop and grow, but you cannot ignore the risk of further abuse, neglect or, worst case, death. The state’s child welfare system kicks into motion when a referral (say, a neighbor or a relative) indicates a child may be maltreated and is suspected to be abused or neglected. AI can help flag early in the process as when the screening or intake worker takes down details and needs to determine the level of risk a child is in. AI needs training with existing data for training an AI system. The diagram below illustrates some of the data. AI can be more effective in the case of repeat offenders and/or an going support to the family to improve the living conditions for the child, to suggest the next course of action, be it removing the child from the home in light of extended neglect and abuse, or looking at interventions that can tide over child neglect arising from a family’s temporary hardship. Foster Care Matching and Adoption In the unfortunate circumstance of separating a child from the family, at times during late hours, AI can be used in finding the right child-foster care provider fit. Using AI, agencies can select the ideal foster care arrangement based on the attributes of the child and the provider. Further, AI can augment human effort in the recruitment process for adoption by cutting down on time taken to place a child in a loving, permanent home. Advanced ML/AI matching models can help caseworkers with suggestions for the best adoptive family for the child based on their history and application statuses in the past. This seamless, AI-supported case workers and process should improve overall outcomes for vulnerable children in the state of Indiana. Operationalizing AI, Security & Adoption The biggest challenge in operationalizing AI is adoption. It is important to weave the intelligence as recommendations and nudges embedded into the workflows and in context to help case workers take preemptive action. Alerts and notifications can keep caseworkers informed even when they are on the go, while communication between teams can be synced with third-party apps like Gmail, Outlook, Slack and WhatsApp. Moreover, powerful conversational UI like chatbots and natural language processing (NLP) models for search, dictation and creating narratives on the field ensure a seamless workflow that complements, rather than hinders, a caseworker’s day. This is especially important as many states struggle with alarming attrition rates, and at Indiana there is already precedence in leveraging VR technology to train and onboard the workforce. We look to build on top of that by using AI to democratize knowledge for new caseworkers bringing them up to speed with decades of veteran insights. However, seamless workflows and actionable intelligence can’t take away from the fact that privacy and security of all data is critical. We are looking to enable Indiana DCS workers with a secure, smart and easy way to use technology to improve outcomes for the children of Indiana, and set an example for others to follow. About the Authors:

Velox: The solution to your data problem

We live in an age where data is at the core of everything we do. It helps us understand performance, solve problems, improve processes, and comprehend consumers. But there’s a problem: the glut of data often reads like Greek to teams. The real value of data is in the analytics, insights, recommendations, predictions and story that it can help create. The need of the hour for organizations is a holistic and secure data management platform; that’s where Velox, our PaaS offering, comes in. Why Velox? In the last few months, our team spent considerable time thinking and researching our efforts and initiatives around Velox. The name Velox is inspired from a Latin word meaning "swift" or "rapid", and we aim to help businesses make sense of, and manage, data swiftly.

From a product development perspective, our founder Thiag Loganathan says, “Sit, crawl, walk, run and fly is for maturing product organizations. When innovation and excellence becomes a culture and habit, we run and fly.” The ultimate goal is to turn data into information, and information into insight. The more we researched data platforms, data ops, and issues in analytics and visualizations, we became increasingly confident we were headed in the right direction to solve a tough and worthy problem. Businesses today deal with enormous volumes and types of data across business processes. Velox makes data work by allowing users to upload data, execute models to see partterns, then transform data into actionable insights and visualizations. The Opportunity and Market Potential Clearly, the market holds tremendous potential and opportunities, and Velox can be a game-changer in a market full of players. Our team views Velox as a once-in-a-lifetime opportunity to make a difference, create a world-class product, and seize on an opportunity. Why do we feel so strongly about this PaaS product? A platform offering with the potential to strip off functional domain or vertical dependencies and variations has 10X more potential as a platform product. The possibility of using Velox as a base for our own SaaS offerings is another validation. Digitization and the rate at which data is being generated has created a vacuum and scope for value creation. The world has transformed from 'data poor’ to ‘data rich' in less than a decade, and everyone from government agencies to businesses and other entities, wants to unlock the potential of this data. The emergence of new roles, including Chief Data Officer, Data Curator, Data Evangelist etc, supports the market potential. There isn't a simple, easy-to-use tool in the SMB market segment to quickly convert clumsy data to business ready data/analytics. How can we amp up Velox’s velocity? As much as we are data-driven, some key decisions are based on deep introspection and reflection. We believe if there’s one factor that will lead us to success, it is the execution and the velocity at which we build and deliver functionalities and features to our users and customers. “Veloxians” as a team are committed to build one feature/functionality a day. How do we define a feature? It’s anything that makes the work of the end user, be it a data engineer, data analyst, data scientist, or business users, easier or better in a significant way. This is the key metric that we use to measure our effectiveness as a product team. Culturally embodying the “swift/rapid principle” in our product development and release process, and building that as the unique selling point in our product is vital. We have a great market fit, something that has been a challenge over the years. We have seen this with the development of Wellness AI, Redbird AI for Cardinality, and This also came to light with data products we developed for big brands such as WorldPay, Zurich, McKesson, and Victoria’s Secret. How can teamwork help us win this championship? Behind every product stands a great team, and we strongly believe the success of Velox is directly proportional to the success of teamwork. Rightsizing teamwork is what distinguishes Agile product teams from others and we can do this by setting simple goals that help us win. As we go forward, we need to remember legendary NBA coach Phil Jackson’s maxim: “The strength of the team is each individual member. The strength of each member is the team.” Together we can.

A Day in the Life: What does campus life look like in the Covid-age?

How can universities manage learning while protecting faculty and students? Is it possible to balance health, infection, and safety through continued monitoring of changes in key metrics? Wellness AI makes it possible. Read on to learn how seamlessly Wellness AI integrates into university life with little intrusion for students. Covid-19 put a real damper on Peyton’s senior year of high school. She didn’t realize when the Indiana Governor ordered all Hoosiers to stay at home starting March 25, her high school days were finished and all the events associated with the milestone, cancelled. As spring turned to summer, Peyton focused on her next goal, attending college. Peyton enrolled at one of Indiana’s state universities and was thrilled when she found out the University planned to open on schedule. Her mom, Michelle, was excited for her daughter but also nervous to learn more about the university’s plan to keep Peyton and other students safe and healthy. Welcome to the new University ‘Normal’ Peyton moved into her dorm on schedule. Campus officials staggered move-ins, conducted temperature checks, and only roomed together students who had already requested a friend as a roommate. Students were reminded to social distance and wear masks when moving around campus. Both Peyton and her mom felt comfortable with all the new safety measures. The University asked Peyton, and all other students, to install the University’s mobile app on their phones. Over the summer, the University enhanced the existing mobile app to include additional information about Covid-19. Now students can complete health self-assessments through the app, are notified about campus locations with new cases and are directed to health resources if they are not feeling well or struggling with the emotional toll of the disease. In addition to face masks, the other big difference on campus today versus when Peyton visited last fall,are the new kiosks. Mainly in high traffic areas like the Health & Fitness Center, Dining Halls and large lecture halls, student have their temperature taken before entering any of these locations as a precaution. Overall, most students adopt and participate in the changes requested. Bottom line, Peyton and her fellow students feel these requests are a simple way to quickly identify potential issues so the University can stay open, students can stay on campus, and attend class in-person. How does Wellness AI fit into the ‘New Normal’? Wellness AI pulls together all the data collected from the kiosks, inputs from the mobile app and other existing university data sources, to provide a holistic view through intelligent dashboards for University management to review in real-time. It allows university management to out-maneuver uncertainty by course correcting, reviewing presumptions, re-assessing situations, and strengthening the ability to observe and respond. You can view the full Wellness AI University Solution. Are you doing enough to protect your faculty and students? Learn how Wellness AI can enable data-driven decision making for administrators, parents & students. Schedule a Demo Now

Will business ever return to 'normal'?

Last week in our blog post, we posed the question if Wellness AI was the right product to fight Covid-19. Wellness AI is a population health management accelerator that provides assistance to manage operations, employee wellbeing, and brand value. Powered by data and technology, Wellness AI helps decision makers safely operate businesses, drive productivity, and boost revenue. The three-stage process of Wellness AI, which focuses on reopening, limited operations, and the new normal, lets executives and supervisors manage businesses and employees by balancing health, infection, and revenue through continued monitoring of changes in key metrics. We think Wellness AI is the most effective solution to create a safe workspace and uses an ecosystem of advanced sensors, mobile web applications, and an analytical dashboard. Let us show you how Wellness AI delivers outcomes? Wellness AI aims to allow businesses to play the proactive game - to stay ahead of problems instead of reacting after they crop up. If you are interested in seeing a demo of Wellness AI, you can schedule one with our team here.

Is Wellness AI the prescription to fight COVID-19?

COVID-19 has trained the spotlight – yet again – on Charles Darwin’s “survival of the fittest”. According to a McKinsey report, no event since World War II has caused an economic downturn of “quite such scale or scope.” The coronavirus pandemic may now be at, or nearing, its peak in many US states, but the threat persists in the absence of a vaccine, which seems unlikely until the second half of 2021. In the meantime, businesses are suffering. The need for a reset means decision makers must make the right choices and turn the many challenges into lasting change. Charles Darwin’s Origin of Species theory states it is “the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself.” Adapting to changing circumstances is the need of the hour. Could Wellness AI be just what the doctor ordered? Powered by data and technology, the integrated solution helps decision makers safely operate businesses, drive productivity, and boost revenue. Executives and supervisors can manage businesses and employees by balancing health, infection, and revenue through continued monitoring of changes in key metrics like: Operations Employee Wellbeing Business Continuity It’s clear that we haven’t seen the last of COVID-19 yet. With the pandemic peaking in some markets and stabilizing in others, enterprises and decision makers must focus on recovery and reinvention to “survive, revive, and thrive”. Wellness AI uses data sources as inputs to provide reporting and analytics. Take a look at how we are assisting the manufacturing community through Wellness AI. Amid changing circumstances, Wellness AI can help decision makers outmaneuver uncertainty by course correcting - reviewing presumptions, re-assessing situations, and strengthening the ability to observe and respond. Now, more than ever, businesses and leaders need to meld an approach that promotes workforce resilience and plans for a better, brighter future. Wellness AI can do just that: protect employees, businesses and make bottom lines healthier.

Three Takeaways to Manage Personal Risk to COVID-19

“We still do not know one thousandth of one percent of what nature has revealed to us.” – Albert Einstein Evolving developments amid COVID-19 have led to unprecedented levels of disruption and ambiguity on global, business, and personal levels. Research reveals that a significant amount of the world's population will contract the infection. No treatment seems likely soon, and it’s vital to put in place individual risk assessment and management strategies. How much risk do individuals really face? Professor Sir David Spiegelhalter, a risk expert from Cambridge University, stated the world is in a game of risk management and it must get a handle on the magnitude of risk we face. A BBC article, Coronavirus: How scared should we be?, says an average person aged 40 has around a one-in-1,000 risk of not making it to their next birthday and an almost identical risk of not surviving a coronavirus infection. While COVID-19 essentially doubles an individual’s risk of dying, this still represents an average risk. The virus amplifies frailties in the high risk group of every age group so “if your risk of dying was very low in the first place, it still remains very low.” Every individual must do their bit in the fight against COVID-19 and this is where personal risk management comes in. It can help ensure more granular/precision risk measurement, promote preventive and protective actions, and minimize infection risk. How can individuals manage their risk? 1. Know your zone The facts: Overall global case fatality is approximately 6.9%. This varies by location and intensity of transmission, and changes to reflect severity in a particular context, population, and time. COVID-19 cases are present in every state but cumulative incidence varies and depends on population density, demographics, extent of testing/ reporting, and mitigation strategies. States become a ‘hot spot’ when more than 100 new cases per 100,000 people are reported each week. Takeaway: Don’t wait to be told. If you live or work in an area deemed a hot spot, it would be prudent to practice social distancing and wear a mask. 2. How’s your health? The facts: Comorbidities of prevalent illnesses such as hypertension, diabetes, heart diseases, stroke, and cancers can determine the illness course, progression, and outcome. Elderly citizens are more prone to infection due to a reduced ability to fight infection and lower response to medications. Bad lifestyle habits, including smoking and drinking, can impact the body’s reponse. If you have medical complications and comorbidities, bear them in mind when opting to step out or stay in. Takeaway: Understand and acknowledge your overall health. If you have underlying health conditions, keep them in mind before leaving the house. 3. What does ‘interaction’ really mean? The facts: COVID-19 is spread through person-to-person transmission through close-range contact, mainly via respiratory droplets. The risk is determined by type and duration of exposure. Indoor settings are highest risk, with most secondary infections reported among household contacts and settings where individuals reside/work in close quarters. The risk due to indirect contact – passing by/talking to someone with infection on the street - is not well established and may be low. Duration matters, especially in a closed environment, because the viral load increases significantly the longer someone coughs, sneezes or talks. Takeaway: Researchers believe respiratory droplets don’t travel more than six feet. Wear a mask and keep a safe distance when possible. So, what is the way ahead? Professor Devi Sridhar, Chair of global public health at Edinburgh University, has an answer. “There will never be no risk. In a world where Covid-19 remains present in the community it's about how we reduce that risk, just as we do with other kinds of daily dangers, like driving and cycling."

Hunicorns: ‘Monsters’ with a code

In the second article in the series on hunicorns, I look at how going after a win-win strategy makes for a compelling leader, whose measure of success goes beyond towering achievements to take into account work that has lasting impact. Some of the greatest successes in tech upgrades and projects requiring complete overhaul of complex systems have shown us that it is not the technology itself but the leader steering the process. Hunicorns, as I pointed out in my last article on the subject, come with traits that make them forceful leaders and go-getters within and outside their organization. They efficiently navigate the various challenges and conflicting goals in their implementation to emerge successful. Five aspects of your life can determine the speed and level of success: However, while one can argue the nature-versus-nurture debate till the cows come home, one must admit that genetic predispositions, privilege and environmental opportunities are not the only determining factors to one’s success. I firmly believe that leaders can achieve success by plugging gaps that arise from the lack of exposure to people, places and opportunities through sheer effort. And the much coveted luck is often a matter of preparation-meets-opportunity. It is not easy and is risky, but as my ex-boss Romil Bahl would say, “If you don’t take risks, you will always work for someone who does”. While we were on this subject, Andy Russell, Founder and of Empact Collaboration Platform, who is leading a taskforce for COVID-19 pandemic support, reminded me of ‘Think Win-Win’, the Habit #4 in Stephen Covey’s 7 Habits of Highly Effective People. It is not a zero sum game! A recent episode of Billions, one of my favorite American drama series, put forth the idea of ‘Monsters’--high achievers who have no qualms about exploiting others in their path to success and getting what they want. For the uninitiated, set in New York City, Billions pits ruthless hedge fund guru Bobby Axelrod against New York Attorney General Chuck Rhoades in a Battle of the Monsters. The latter reminds me of Dexter, another favorite of mine. Interestingly, ‘monster’ is defined and incorporated into the leading characters’ personas in different ways: Axe subscribes to a more textbook definition of a monstrous person, while Rhoades adopts the mantle of an uncompromising personality with a code. Pop culture, real-life billionaires, state leaders and new-age entrepreneurs are, to a considerable extent, glorified monsters. Many of us have come to believe that one has to be ruthless and have one’s blinders on to achieve greatness. But I think it is time we changed the status quo and embraced leaders who make room for the greater good in their climb to personal glory. I call them hunicorns. Highlighting the ‘win-win’ way of operating will create impactful leaders whose voice and work cannot be ignored. Andrew Vanjani, the ex-COO of State of Maryland’s IT department, describes them as “growth catalysts, who will take the 21st Century to new levels of global transformation using new capitalism to create a fairer world through the power of decentralization of services.'' Perhaps the best person who embodies this renewed definition of a monster is Kevin Jones, who spearheads technology innovation at the Indiana Department of Child Services. Kevin advocates that one has to “be water”. Kevin, like all hunicorns, will prioritize what needs to be done, solving the right problems, the right way. To sum up, whether you call them ‘hunicorns’, ‘monsters with a code’, or ‘growth catalysts’, I think it is important that people who are wired for success should also choose the ‘win-win’ model and work for the greater good. Now we need to figure out how to get more leaders to do just that. Schedule a call with me to learn more!

ML System in Production!!!

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. In general, machine learning algorithms begin with an initial hypothetical model, determine how well this model fits a set of data, and then work on improving the model iteratively. This training process continues until the algorithm can find no additional improvements, or until the user stops the process. A typical machine learning project will include the following high-level steps that will transform a loose data hypothesis into a model that serves predictions. Business needs and desired outcomes drive the purpose of this exercise, so that is the first step in this process. Once this is clear and well defined, steps 2 through 4 depicted above come into play. One of the key facets of the iterative process is to deploy machine learning models as the system learns new aspects/dimensions of data. There are two types of data deployment widely used in the industry- Batch mode and Real time. Batch data processing Batch data processing is an efficient way of processing high volumes of data where a group of transactions is collected over a period of time. Data is collected, entered, processed and then the batch results are produced. Batch processing requires separate programs for input, process and output. An example is payroll and billing systems. Real Time Mode: In contrast, real time data processing involves a continual input, process and output of data. Data must be processed in a small time period (or near real time). Radar systems, customer services and bank ATM's are examples. So the choice of deploying the model in the batch mode vs the real time mode is primarily driven by factors namely, who is using the inferences and how soon do they need them? In order to understand the options available for deploying the models as Batch/ Real time prediction, one needs to have an understanding of Serialization. Model Serialization: Serialization converts the model in the form of a python object into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script. There are two ways to serialize a model in Python: Pickle and Joblib. a. Pickle Python has a module called pickle which helps to save our model and load it later for scoring.The model can be saved using pickle.dump() function with any name or extension and can be loaded using pickle.load() and it is pointed to the name/extension.Here’s a snapshot of the pickle load and dump functionality. b. Joblib Joblib is used for saving the model and works very similar to pickle. This library is available as part of sklearn.externals. Here’s a snapshot of saving and loading the model for prediction using joblib. We can store the pipeline steps in pickle or joblib objects to apply similar pipeline steps on the new data. Now, let’s look at the different options available for deploying the models as Batch/ Real time prediction. Batch Prediction: a. Using Scheduler jobs We can productionize the python model in batch mode using the windows schedulers/cron jobs. The batch prediction on AWS can be done by deploying the model on lambda. These jobs pick up the scripts and execute it during the scheduled time period. As part of deployment we will load the pickle or joblib object ,configure the input data source and the destination for the prediction output. Real Time Prediction: a. Exposing the model as API: We can expose the model as an API to get the prediction using the Flask micro service framework. Flask will allow us to expose business logic as API. As discussed earlier in the article , we will need to Serialize the model (using Pickle or JobLib) before using Flask to expose the model as an API. The exposed API can be used either in a single input mode or a file input (with multiple records) to get the predicted output from the deployed model. b. Docker Containerization Dockerization is another way of deploying the ML models.It is one of the best managed services where you can deploy your application on your own cloud/server or to your client environment. There are no OS issues,environment issues and version conflicts.Dockers helps us with reproducibility, portability and ease of deployment. Docker container has its own set of storage and network systems and it has the underlying defaut LINUX OS.. In the next article let’s delve deeper into the Real Time prediction using API and will provide step by step example by leveraging a Random Forest Model to show it in action. Please let me know your suggestions and questions in the comments section. Stay tuned!!!! Schedule a call with me to learn more!

Why you need a 'hunicorn' to successfully implement complex systems for government agencies

Who is a hunicorn and why is it critical to find one for your company’s modernization efforts and complex system implementations? In the first article in a series on hunicorns, I delve into the traits required in a leader executing modern technology initiatives in Health and Human Services. Modernizing human services systems is a monumental challenge. Entities across the world have found themselves unwittingly making headlines when the task goes wrong. A quick Google search of ‘failed government technology upgrades’ lists countless pages showcasing the disastrous results when taxpayer-funded systems failed to meet the requirements to protect the most vulnerable segments of the population. Why is the task so challenging? It is a multi-faceted problem that requires alignment across stakeholders with conflicting goals. Clearly, there is a need for the technology leader to be able to build a steering committee that is a coalition across stakeholders, and keep them all aligned over a period of 12-24 months while the organization and environment are changing, in order to successfully go live. Then there are the challenges of effectively integrating with legacy systems. Dealing with the lack of knowledge base and experienced staff in implementing cutting-edge technologies like automation and AI. Cybersecurity and mobile access are basic requirements for new systems that didn’t exist during previous platform upgrades. Additionally, understanding and communicating the advantages for a solution built on cloud infrastructure with unlimited integration capability, as opposed to the previous standard of custom solutions, can prove difficult for agency technology leaders. In my years of experience with complex and challenging implementations, watching leaders struggle and do it well, the greatest indicator of success doesn’t lie in the technology but in the agency’s technology leader. This is my attempt to codify the traits that you want when searching for this leader. You want a “hunicorn” - a human unicorn. Hunicorns, by definition, are a rare breed, like a franchise quarterback. If you’re in a “must succeed” situation, you are either going to hire a proven leader, convince them to do a tour of duty, or look for that leader with potential, successes in small stages and give them the big stage. The NFL community has struggled over the years to create a playbook on “how to identify and recruit a franchise quarterback”. My good friend, DMI CEO Sunny Bajaj, and I have talked often of hunicorns. Sunny founded DMI in 2002 with the vision to create a global leader in innovative, next-generation technology solutions. He has successfully built a respected, fast-growth business that is pioneering digital, mobile-first transformation for government agencies and commercial enterprises around the world. Over his 16 years working with government agencies, he knows when he sees a special leader. This is not a conversation I’ve only had with Sunny. I’ve talked with leaders at the biggest consulting firms around the world, like Christopher Merdon, Senior Vice President, Public Sector, for NTT Data Services. Chris has spent his career in the information technology and services industry, working with government agencies to implement change. Here is my attempt at defining what makes a hunicorn, someone who is capable of delivering success, in spite of the conflicting goals of stakeholders and a constantly evolving scenario. The five traits that make a human unicorn: 1.     Complete a task, well! Ability to complete a task well, especially a technical one, helps in understanding what needs to be done at a granular level. This allows the leader to comprehend new technology easier and set appropriate expectations across stakeholders. 2.     Execution: Get it done! Big ideas can impress but a hunicorn can effectively break down big, complex problems into small, understandable tasks, assign to the right team member and leverage the skills of the entire team to get it DONE! 3.     Empathy: Who will do what? Why? Emotional Intelligence is the current buzzword but the bottom line is you need a leader who can empathize with the organization’s community served, provide a calming influence on internal teams nervous about big change, and communicate the program’s message effectively to vendors and other stakeholders to keep them all aligned and motivated. 4.     Business acumen: What to do well and when? Throughout history, many military battles required invading forces to traverse rivers without the use of bridges. Leaders couldn’t build a bridge every time they encountered a body of water to get their forces into position. They would send scouts to swim across the river and report back their findings. Too many questions needed answered before investing the time and resources to build a bridge: is the enemy there? Is there a more strategic location for a bridge? Could a temporary bridge serve the current need and be re-engineered later? Based on the reports of the scouts, leaders could decide a plan of action. Rarely do problems follow the sequence planned to arrive at a solution. When tackling a complex technical program, teams cannot afford to do every task perfect and in the sequence originally planned. There is an art to prioritizing, sequencing, and deciding how well a certain task/component of the solution needs to be done. Hunicorns understand the importance of sending out a scout and adjusting the plan accordingly. 5.     The X factor This is the difficult one. The ‘X’ factor is the set of intangibles: drive, perseverance, grit, positive attitude, leadership, etc. Individuals who are consistently challenging themselves are a great place to start. Ask candidates about their hobbies and interests. What are their goals? How about their passion? Candidates who are challenging themselves in personal pursuits have figured out how to balance their interests. The X factor is hard to pinpoint until you see it in a candidate and then you will immediately know you’ve found it. Based on our close association with the challenges faced and our understanding of the greater good when done right, we are planning a series of articles on our learning and insights for the benefit of the ecosystem. Two of our hunicorns, Kevin Jones and Subi Muniasamy, are hard at work modernizing Indiana Department of Child Services and Maryland Department of Human Services, respectively. With backgrounds in the private sector, both Kevin and Subi bring years of experience and usher in a sense of urgency to their respective large complex programs that are producing great results. Kevin was recently recognized by the National Association of State Chief Information Officers (NASCIO) as the 2020 Technology Champion Award Winner. As the Chief Technology Officer of Maryland’s Department of Human Services, Subi worked at setting up a secure cloud-based technology platform for social service programs for the State. If you are a state leader responsible for finding a hunicorn, you need to follow this series to identify and attract top talent. If you are a human services technology leader, this series can provide insight into how you can grow as a leader while using the playbooks of these hunicorns to learn valuable lessons. Throughout the series, we will break down the complexities and challenges to share a layout of the battlefield through the eyes of the hunicorns we’ve come across. Schedule a call with me to learn more!

Five skills hunicorns use to advocate digital transformation

A look at the traits required in a hunicorn who is looking to win over decision-makers--some often with zero IT knowledge--for investments in tech upgrades. Hunicorns double up as evangelists, convincing people with little tech knowledge, or those with dated ideas of technology, to consider IT upgrades. To successfully champion digital transformation within their organization and external stakeholders, hunicorns must illustrate the efficiencies a tech upgrade can deliver, and the cost benefits for the government agency or organization in the long run. Picture Credit: Unsplash This is not a simple process. Here, old mindsets need to be smartly navigated and new thought processes installed in their place. A tough ask, especially when you are dealing with government agencies rife with political pressures and decades-old legacy systems. Upon accepting the CIO position with the Indiana Department of Child Services, Kevin Jones was given the monumental task of rebooting a much delayed IT overhaul for child support. He not only evaluated the need from a technological perspective but also had to ensure it aligned with the business needs of the department. Clearly, a hunicorn must balance the twin goals of getting in place agile systems and backing them with sound cost-benefit analyses. So, what are the traits required to win over decision-makers--some often with zero IT knowledge--for investments in tech upgrades? Hunicorns deploy a five-pronged approach to advocate digital transformation: Never lose sight of the focus areas Communication/messaging tailored for each stakeholder Create advocates in any department who will be impacted Use business outcomes/values to sell Be realistic about timelines and track progress 1.Focus, focus, focus Before we highlight the unique traits hunicorns bring to digital transformations, we must remember the four areas that will stop a project in its tracks. No matter the project, never lose sight of the budget, user experience, implementation timeline or risk monitoring! We are always amazed when talking to hunicorns how this knowledge is always at the forefront of their minds. For us mere mortals, it’s worth emphasizing this first step. You will never regret extra time spent focused on any of these areas. 2. Communication It is imperative for hunicorns within government agencies, well known for their bureaucracy and manual processes, to finely tune their communication skills to sell their plans for digital transformation. Unlike private organizations, there is often a sense of wariness in government agencies when it comes to technology. Additionally, due to the higher accountability involved, upgrades within these taxpayer-funded agencies are hemmed in by red tape. Many agencies have already sunk massive funds in clunky software, sometimes decades ago, and don’t see the business value in overhauling them. Situations like this will require a hunicorn to channel their communication powers to effectively sell IT to decision-makers with little IT knowledge. Cleverly crafting their message to alleviate the unique concerns of each stakeholder is where a hunicorn excels. This approach results in a hunicorn speaking the language that the listener can understand and relate to. Kevin remembers discussing the high turnover rate of family case managers (FCM) for Child Welfare in Indiana. Previous, well-meaning employee engagement programs failed to adequately address the alarming attrition rate among FCM. Nearly 40 percent of employees stated they did not understand the pressures of the job prior to joining, and had they known earlier, they may not have joined. Kevin knew he could solve this problem with technology. Supplementing this hands-on tech-backed training with a system that kept track of attrition data over the years helped Kevin work out a cost-benefit analysis. The technology implemented now helps the departments save millions of dollars in staff turnover. 3. Developing advocates There is strength in numbers, and a hunicorn will do well in building their credibility and rapport with various personnel across teams. Kevin points out that a CIO or technology leader must start by identifying the advocates in the room. This is a smart way to get others within the organization to support your cause. Hunicorns must invest energy in forging a team of advocates, across multiple departments, to get the agenda for change pushed to the decision-makers. These advocates will pitch the idea to their networks, creating momentum and chatter about what a hunicorn intends to achieve. As a first step, work within your own department, investing time in team-building activities. The tech leader occupies a lonely spot in organizations holding on to paper-based, legacy systems. A hunicorn is not detered from forming a tribe of supporters to expedite selling the new architecture. 4. Sound business acumen Interpreting the business needs of the agency and providing tech solutions to address them is an important trait of a hunicorn. Technology means nothing to people with little IT knowledge unless they see it solving critical issues and plugging spends that bleed the agency. Picture credit: Unsplash A hunicorn knows to talk money and has the ability to translate the need for tech transformation by way of numbers and projections: cost savings for the agency, increase in worker productivity, and enhanced outcomes of programs. Kevin was able to introduce the use of handheld devices for workers on the field as he was able to effectively translate technology adoption by addressing a business need: increasing worker productivity and outcomes. “Their laptops are gone, replaced by Surface tablets. Family case managers are now able to walk around with the tablet, write what they need to and see it immediately system-wide,” said Kevin. 5. Be realistic about timelines A government agency hunicorn understands the challenges that come with the industry, none bigger than the bureaucratic hoops that they need to jump through. Personnel changes, multiple layers of stakeholders, political complications, technology- and change-wary decision-makers and employees all result in considerable delays in getting a technology upgrade off the ground. Due to their unique position, hunicorns tap their ability to build trust and relationships over time, slowly chipping away at obstacles in their way. This process requires patience but it is important that the hunicorn is consistent with efforts to evangelize the need for agile architecture delivered in an asset-light manner and the long-term benefits it will result in. Therefore, a hunicorn needs to be realistic about the timelines and work accordingly to execute their vision to overhaul a government IT system. Ultimately, technology that seamlessly addresses all the problems of the agency and helps facilitate the goals of the program in a cost-effective manner, will win. But, it first takes a hunicorn to sell the idea internally. Schedule a call with me to learn more!

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