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digital transformation (65)

How Covacsis is changing the Manufacturing?

Internet of Things (IoT) began as an emerging trend and has now become one of the key elements of Digital Transformation that is driving the world in many respects. we are evolving to a more connected, digitized world. Leveraging Industry 4.0 technologies is a necessity if you are going to meet consumer’s demands and maximize efficiencies. Now is the time to redefine how we look at gathering and analyzing data across machines and the supply chain to enable fast flexible, and more efficient processes.

General Electric coined the term Industrial Internet of Things (IIoT) in late 2012.

While many of us are familiar with the Internet of Things used by Nike FuelBand, FitBits, Nest and Samsung as connected devices, there’s much more going on in connecting industrial devices in the world of IIoT.

The Industrial Internet is still at an early stage, similar to where the Internet was in the late 1990s. The IIoT, through the use of sensors, advanced analytics and intelligent decision making, will profoundly transform the way plants & factories connect and communicate with the enterprise.

Industries impacted by IIoT are Manufacturing, Aviation, Utility, Agriculture, Oil & Gas, Transportation, Energy, Mining & Healthcare.

One of the key opportunity that early adopters of the Industrial Internet are pursuing is the improvement of worker and equipment productivity, safety and working conditions in the factories.

The IIoT will revolutionize manufacturing by enabling the acquisition and accessibility of tons of data, at lightning speeds, and far more efficiently than before.

There are several challenges factories are facing:

  • Manual data collected by floor person in a shift has human delays, errors

  • It is not continuous and also not real time

  • Data is not comprehensive enough to do analysis and provide insights to senior management

One such framework available to factories is Intelligent Plant Framework provided by Covacsis.

The benefits are tremendous:

  • It collects real-time data from all the factory machines

  • It is completely automatic so no human errors

  • The data collected is comprehensive to provide actionable insights to the factory in charge

  • With predefined algorithms, the productivity and costs are calculated automatically and recommendations are made for improvement

While systems like MES can only synchronize the operations of the factory, IPF does the performance measurement and management.

Business benefits by implementing IPF:

  • Conversion costs are reduced by 20-30% from raw materials to finished goods

  • Production productivity is improved by up to 30%

  • Plug-n-play with minimal or no customization hence no impact on running factories

  • Implemented in 3-4 weeks compared to months and years of competitive products in the market

With solid experience of implementation in over 70+ factories and 15+ sectors across manufacturing such as Pharma, Chemical, Textile, FMCG, etc; IPF is a clear winner and the need of an hour for factories of future.

The path to Industry 4.0 is via Industrial Internet of Things IIoT and implementation of automation via IPF.

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A new wave of technologies, such as the Internet of Things (IoT), blockchain and artificial intelligence (AI), is transforming cities into smart cities. Many of these cities are building innovation labs and zones as part of their new civic landscape. Smart city innovation labs are vital components of the smart city ecosystem (Figure One). They provide an organized structure for cities, communities, experts, and vendors to come together to create solutions. Successful solutions piloted in smart city innovation labs are then scaled and deployed into a city’s operations and infrastructure.

Figure One. Strategy of Things Smart City Ecosystem Framework.

 
Many municipalities are considering and planning smart city innovation labs today. Over the past year, we helped to create, launch and operationalize San Mateo County’s Smart Region innovation lab (SMC Labs). From this experience, we share ten best practices for civic innovation leaders and smart city planners.
 
 
Ten Smart City Innovation Lab Best Practices
 

Develop a well defined innovation sandbox. Every smart city innovation lab has an unique mission. That mission is specific to its community, capabilities, priorities, and surrounding ecosystem. However, it is easy to get distracted and work on the “next shiny object”, vanity projects and “me too” innovation pilots. These projects don’t add value, but take resources and focus away from the problems the lab was created to address.

Build innovation discipline and focus by defining a “sandbox” from the start and updating it annually. The innovation sandbox defines clearly what types of projects are in-scope and which ones are not. The criteria includes alignment with city or department priorities, problem set type, problem owner(s) or sponsors, budget availability, cost, resource requirements, and organizational jurisdiction.

 

Create procurement policies and processes for innovation projects. Innovation pilots fall outside the “sandbox” municipal procurement processes and policies operate in. These pilots may work with start-ups with limited operating history, use immature and evolving technology, or bought in non-traditional ways (“as a service”, loans, etc.). This mismatch leads to higher risks, extra work and long sourcing times. Due to this, many vendors choose not to work with cities.

Effective smart city innovation labs are agile and responsive. They employ new procurement policies and practices designed specifically for the unique needs of innovation projects. This includes simplified processes and compliance requirements, new risk management approaches, faster payment cycles and onboarding models.

 

Build a well defined plan for every innovation project. Many innovation pilots are “successful” during the pilot phase, but fail during the scaling phase. This is because the pilots were not fully thought out at the start. Some test a specific technology or solution, and not the approaches. Others test the wrong things (or not enough of the right things). Some are tested in conditions that are not truly reflective of the environment it will be deployed into. Still others don’t test extensively enough, or over a sufficient range of conditions.

Successful projects in smart city innovation labs involve extensive planning, cross-department collaboration, and a comprehensive review process throughout its lifecycle. They have well defined problem statements. They define a targeted and measurable outcome, a detailed set of test requirements and specific success criteria. While innovation projects contain uncertainty, minimize project execution uncertainties with “tried and true” project management plans and processes.

 

Continuously drive broad support for the lab. A successful civic innovation lab thrives on active support, collaboration and engagement from stakeholders across the civic ecosystem. However, many city departments and agencies operate in silos. Launching and having an innovation lab doesn’t mean that everyone knows about it, actively funnel projects to it, or support and engage with it.

Successful smart city innovation labs proactively drive awareness, interest and support from city leaders, agencies, and the community. This includes success stories, progress updates, technology briefings and demonstrations, project solicitations, and trainings. They engage with city and agency leaders regularly, host lab open houses and community tours. They conduct press and social media awareness campaigns. Regardless of the “who, how and what” of the outreach, the key is to do it regularly internally and externally.

 

Measure the things that matter - outcomes. There are many metrics that an innovation lab can be measured on. These range from the number of projects completed, organizations engaged, number of partnerships, investments and expenses, and so on. Ultimately, the only innovation lab metric that truly matters is to be able to answer the following question - “what real world difference has the lab made that justifies its continuing existence and funding?”.

All innovation lab projects focus on solving the problem at hand. It must quantify the impact of any solutions created. For example, many cities are monitoring air quality. A people counting sensor, mounted alongside an air quality sensor, quantifies the number of people impacted. Any corrective measures developed as a result of this project can now point to a quantifiable outcome.

 

Build an innovation partner ecosystem. A smart city innovation lab cannot address the city’s innovation needs by itself. A city is a complex ecosystem comprising multiple and diverse domains. Technologies are emerging and evolving rapidly. New digital skills, from software programming to data science, are required to build and operate the new smart city.

Successful smart city innovation labs complement their internal capabilities and resources by building an ecosystem of strategic and specialist partners and solutions providers, and subject matter experts. These partners are identified ahead of time, onboarded and then brought in on an as-needed basis to support projects and activities as needed. This model requires the lab to build strong partnership competence, processes, policies and the appropriate contract vehicles. In addition, the lab must continuously scan the innovation ecosystem, identify and recruit new partners ahead of the need.

 

Test approaches, not vendors or solutions. Real world city problems are complex. There is no magic “one size fits all” solution. For example, smart parking systems use sensor based and camera based approaches. In some cases, both approaches work equally well. In other cases, one or the other will work better. A common innovation mistake is to only test one approach or fall in love with a specific vendor solution and draw a generalized conclusion.

Effective innovation lab projects focus on testing various approaches (not vendors) in order to solve problems effectively. Given the rapid pace of technology evolution, take the time to identify, test and characterize the various solution approaches instead.

 

Employ a multi-connectivity smart city strategy. There are many options for smart city connectivity. These include, but not limited to cellular 3G/4G, Wi-Fi, LoRaWAN, SigFox, NB-IoT and Bluetooth, and so on. Use cases and solutions are now emerging to support these options. However, some smart city technologies in the marketplace work on one, while others work on more. There is no “one size fits all” connectivity method that works everywhere, every time, with everything.

To be effective, smart city innovation labs need to support several of these options. The reality is that there is not enough information to know which options work best for what applications, and when. What works in one city or region, may not work in another. Pilot projects test a possible solution, as well as the connectivity approach to that solution.

 

Make small innovation investments and spread them around. Open an innovation lab and a long line of solutions vendors will show up. Everyone has a potential solution that will solve a particular problem. Some of these solutions may even work. Unfortunately, there is not enough budget to look at every solution and solve every problem.

Focus on making smaller, but more investments around several areas. Overinvesting in one vendor or one approach, in a market where technologies are immature and still evolving, is not wise. Invest enough to confirm the pilot outcomes. A more detailed evaluation of the various solutions and vendors should be made when the pilot moves out of the innovation lab and into a formal procurement and RFP phase.

 

Simplify administrative and non-innovation workloads. While innovation pilot projects are challenging, interesting and even fun, administering and managing the projects are not. These unavoidable tasks range include managing inbound requests, proposals and ongoing projects. These tasks increasingly consume time and resources away from the core innovation activities.

Effective smart city innovation labs get ahead of this by organizing, simplifying and automating administrative activities right from the start. For example, SMC Labs reviews inbound proposals once a week and organizes follow up calls and meetings on a specific day once every two weeks. In addition, the lab uses a tracking and pilot management tool (Urban Leap) to track innovation projects. Administrative and management activities are unavoidable. However, advanced planning and tools help reduce the burden to keep the lab's focus on innovation.

 

Benson Chan is an innovation catalyst at Strategy of Things, helping cities become smarter and more responsive through its innovation laboratory, research and intelligence, consulting and acceleration (execution) services. He has over 25 years of scaling innovative businesses and bringing innovations to market for Fortune 500 and start-up companies. Benson shares his deep experiences in strategy, business development, marketing, product management, engineering and operations management to help IoTCentral readers address strategic and practical IoT issues.

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According to global management consulting firm Bain & Company, long-term prospects for the industrial Internet of Things remain ambitious. However, many executives are resetting timeline expectations for reaching scale due to early adoption struggles. Notably, certain “darlings of IoT” like predictive maintenance have not lived up to the hype. And while Bain’s survey of 600 industrial customers shows increasing traction with ‘workhorse’ scenarios like remote monitoring and asset tracking, it exposes areas where many teams and vendors are struggling to deliver the goods. In the end, an iterative strategy focused on specific business outcomes remains critical.

Notably, Bain’s survey finds increasing concerns around integration with existing enterprise systems and data portability. Executives worry their visions for digital transformation will be restricted by internal skill gaps and proprietary vendor services. Understandably, they fear losing control of any data not managed by their own enterprise IT departments. Despite this, confidence remains high that an estimated 20 billion devices will be successfully connected by 2020.

Many executives feel the value proposition for industrial IoT is still emerging. For them, the ability to capitalize on this value and achieve better business results remains elusive. To address these challenges, Bain calls for organizations to build a new operating model and position themselves for long-term success in a connected world.

Recommendations for accelerating IoT adoption in the enterprise

First, Bain recommends industrial organizations choose specific, high-value use cases to tackle upfront. Prove out your ability to address security and other valid IT concerns. Then, adopt an iterative approach for demonstrating ROI and ease of enterprise integration.

Second, use experienced partners to address your gaps. Don’t try building everything yourself. Differentiation comes from the combination of acquired data with your industry-specific domain knowledge. We’ve seen manufacturing digital transformation initiatives stall out when internal engineering teams try to build their own IoT infrastructure. Software for collecting data (and system integration services) can be bought. Build your value, not your tools.  

Third, don’t expect overnight success. You’re building up organizational capabilities and working with a new set of specialized partners. Commit to a realistic investment timeline and prepare for change. You’ll likely need to bring in new, more entrepreneurial talent to drive your connected business model. At a minimum, empower your existing teams to think differently. Remember, you’re not rolling out a new CRM application. You’re transforming your enterprise. Act accordingly.

Fourth, industrial IoT revenue starts at the top. Executives must ensure the entire organization is aligned for transition to the new operating model. This requires both vision and clear communication. Unsurprisingly, those responsible for existing products and revenue streams fear cannibalization. Furthermore, IoT initiatives take time to meet traditional P&L requirements. If executives don’t create an environment where the new operating model can take root, prevailing forces will prevent its maturation while competitors move ahead.      

Prepare to scale the business

Eventually companies reach the point on their digital transformation journey where they’ve proven out their connected product technology and business concepts. Now what? Bain concludes with a method for assessing readiness to scale up your industrial IoT efforts.

To begin, how well do you understand the full potential of industrial IoT to your enterprise? IoT can dramatically impact the quality of manufactured products, service offerings, maintenance  procedures, and other areas of your enterprise. But what will this cost, and what will revenue look like once the system is deployed to production and fully commercialized?

Never forget, your competitors aren’t standing still. You can be sure they’re working on their own industrial IoT initiatives. What is your plan to win in this new arena?

Additionally, scaling IoT requires incentives alignment and coordinated execution across the enterprise. Engineering, IT, service, sales, and business teams must work together for organizations to realize the benefits of digital transformation. Make sure everyone understands their part and is rowing in the same direction.

Bain summarizes their last recommendation with a sentiment that we refer to as “strategy over software.” By strategy, we mean not just a plan, but a comprehensive roadmap, organization structure, and business model across the enterprise to support the success of your industrial IoT initiative.

Digital transformation is a journey

As you start your journey, you’re going to need an industrial IoT platform. Whether it makes sense to build your own or buy one depends on a variety of factors. But digital transformation isn’t just about technology. As Bain notes repeatedly, it’s about so much more. Business models and sales strategies, along with clear user stories, team roles, and responsibilities are equally critical to successful IoT initiatives. Beyond a platform, an experienced digital transformation partner can accelerate planning, implementation, and successful commercialization of your connected systems.

 
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Digital Transformation is now a number one priority for many businesses. Over the past two years, businesses have put increased focus on digitally transforming their brands from the inside out.

 It is an ongoing process of change based on the market and the needs of the customers. To deliver this change successfully, there is a need to establish a clear vision with objectives & expected outcomes.

 Simply put vision is a picture of how the organization will look like after stipulated time.

 Importance of Vision:

 ·      Provides the big picture and clearly describes what your organization will be like in several years

·      Clarifies the right direction of change to ensure that everyone is moving forward

·      Inspires everyone to take action in the set direction

·      Synchronizes the action of different people. It provides self-sufficiency to individuals and teams while reducing conflicts.

 

There are some do’s & don’ts for setting up a vision:

 

Do’s:

·      Develop a Vision that is in line with the company growth strategy.

·      Connect with partners who support your vision, not only third-party technology vendors but your own customers and employees

·      It should create the sense of urgency

·      Link vision to specific goals in future

·      Describe how the company will actually change

·      How will you engage differently with customers?

 

Don’ts:

·      It remains only as floor branding and marketing

·      Restricting the employees with set vision & its boundaries

·      Vision is way too complicated, vague and lacking actionable initiatives

·      Poor communication of the vision beyond the involved few stakeholders

·      Setup the vision before analyzing current systems and operations

 

Vision brings in the cultural change that is required for Digital Transformation. People are extremely important in this roller-coaster ride. 

  

When the digital vision is not clear, that affects the speed of adoption of both senior management and middle management. People will not act just because technology is ready. 

 Some successful vision statements, which helped companies in their digital transformation:

 Google - To provide access to the world’s information in one click

 Amazon - To be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online

 Walmart - To be the best retailer in the hearts and minds of consumers and employees

 GE - To become the world’s premier digital industrial company, transforming the industry with software-defined machines and solutions that are connected, responsive and predictive

 Ikea – To create a better everyday life for the many people

 Southwest Airlines - To become the world’s most loved, most flown, and most profitable airline

 

A top-down vision is a cornerstone & catalyst for digital transformation. These and many companies have created great vision statements to survive in this digital age.

 
 
 
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Digital Transformation is moving any company into the future. Well, this statement is true for Disney, Nestle, Apple, Amazon and other leaders as they focused mostly on the customer/user experience.
 
Digital Transformation is in almost every c-level magazine, blog and whitepaper and executives do not want someone comes in and disrupt their business.
 
If you want to reap the rewards of the digital revolution, a smooth, easy and positive user experience is vital.
 
People may not be getting technically proficient, but they have become more comfortable using their smartphones to download music, find the nearest movie theatre or pay for purchases. This raises the bar for user experience.
 
Today organizations are adopting MobilityAnalytics, and the Internet of Things for Digital. However, solutions, which are not intuitive, responsive and social, does not have a place in the customer’s list. So it becomes appropriate for companies to get into the user experience space.
 
A UX design can supercharge productivity, add immense value to customer interactions, and help employees love their jobs.
 
It is extremely important to do an accurate analysis of users, their needs by conducting surveys, workshops to design the UX.
 
Here are six major criteria for UX design:
  • Easy to find: How easy it is to find a site or application
  • Ease of use: How easy it is to use the site or application, how easy to learn
  • Easy to access: How easy it is to access the site or application, easy to understand, easy to reach
  • Usefulness: How useful the features and functions are and they meet my needs
  • Elements of desirability: Will make users like the product’s looks and feel and visual appeal
  • Credibility: How much users trust the site or application, creating the overall brand experience
 
Overall UX design should have short navigation steps so users require very few clicks to get the information they want. It should be based on responsive web designs so application maintain full functionality, usability and appeal on different browsers and screens from desktop to laptops to tablets to smartphones.
 
Once you have applied these criteria it is also important to measure the results, for future course correction. One initial KPI is user adoption rates, which should be higher than 60%.
 
The great user interface makes the experience better, transactions easier, decision making simple and cuts down the operational costs. 
 
It is the deciding factor for keeping/loosing or successfully engaging a customer.
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A field guide describing the 5 approaches to industrial IoT platform development and how to know which approach is the right one for your enterprise based on your goals, requirements, constraints, and where you are today in your digital transformation journey.
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What is a smart city? The answer depends on who you ask. Solutions providers will tell you it’s smart parking, smart lighting or anything to do with technology. City officials may tell you it’s about conducting city business online, such as searching records or applying for permits. City residents may tell you it’s the ease of getting around, or about crime reduction. Everyone is right. A smart city, built properly, will have different value for different stakeholders. They may not think of their city as a “smart”city. They know it only as a place they want to live in, work in, and be a part of. To build this type of city, you have to first build the smart city ecosystem.

 

A smart city is built on technology, but focused on outcomes

A scan of the various smart city definitions found that technology is a common element. For example, TechTarget defines a smart city as “a municipality that uses information and communication technologies to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare”. The Institute of Electrical and Electronics Engineers (IEEE) envisions a smart city as one that brings together technology, government and society to enable the following characteristics: a smart economy, smart mobility, a smart environment, smart people, smart living, smart governance.

But what does a smart city really do? Our scan of smart city projects worldwide showed that initiatives fell into one or more smart city “outcomes” (Figure One).

Figure One. Smart city projects are aligned to one of seven outcomes.

 

As a starting point, we define a smart city is one that uses technology extensively to achieve key outcomes for its various stakeholders, including residents, businesses, municipal organizations and visitors.

 

The smart city ecosystem framework

Figure Two shows our framework for a smart city ecosystem. A vibrant and sustainable city is an ecosystem comprised of people, organizations and businesses, policies, laws and processes integrated together to create the desired outcomes shown in Figure One. This city is adaptive, responsive and always relevant to all those who live, work in and visit the city. A smart city integrates technology to accelerate, facilitate, and transform this ecosystem.

Figure Two. The smart city ecosystem framework.

 

Four types of value creators

There are four types of value creators in the smart city ecosystem. They create and consume value around one of the outcomes listed in Figure One.

When people think of a smart city, they automatically think of services provided by municipal and quasi-government agencies, such as smart parking, smart water management, smart lighting, and so on. In fact, there are three other value providers and users that co-exist in the smart city – businesses and organizations, communities, and residents.

Businesses and organizations may create services that use and create information to create outcomes for its stakeholders. Some examples of “smart” businesses include Uber and Lyft for personal mobility, NextDoor for information sharing, and Waze/Google for traffic and commute planning.

Communities are miniature smart cities, but with very localized needs. Some examples of potential smart communities include university campuses, office parks, airports, cargo ports, multi-dwelling unit (MDU) or apartment complexes, housing developments/neighborhoods, business districts and even individual “smart” buildings. They have needs for smart services that may be tailored specifically for their stakeholders.

Residents or individual citizens are also smart services providers in the smart city. A resident living near a dangerous street intersection can point a camera at the intersection and stream that information live to traffic planners and police. Residents place air quality measurement sensors on their properties to monitor pollution and pollen levels during certain times of the year, and make that information available to other community members. Residents can choose to make these smart services temporary or permanent, and free or fee based.

 

The Smart City is built on layers

A smart city is an ecosystem comprised of multiple “capability layers”. While technology is a critical enabler, it is just one of many foundational capabilities that every smart city must have. No one capability is more important than the rest. Each capabilities plays a different role in the smart city. These capabilities must integrate and coordinate with each other to carry out its mission.

 

Value layer. This is the most visible layer for city residents, businesses, visitors, workers, students, tourists and others. This layer is the catalog of smart city services or “use cases”, centered around the outcomes (Figure One), and offered by value creators and consumed by the city stakeholders.

Innovation layer. To stay relevant, value creators in the smart city must continuously innovate and update its services for its stakeholders. Smart cities proactively facilitate this through a variety of innovation programs, including labs, innovation zones, training, ideation workshops, skills development and partnerships with universities and businesses.

Governance, management and operations layer. The smart city creates disruption and results in digital transformation of existing processes and services. Smart city management models must integrate a new ecosystem of value creators and innovators. They must plan, support and monetize new business models, processes and services. They must upgrade their existing infrastructure and management processes to support “smart” services. Finally, they must measure the performance of the city with a new set of metrics.

Policy, processes, and public-private partnerships, and financing layer. The smart city doesn’t just magically appear one day. An entirely new set of engagement models, rules, financing sources, and partners are required to build, operate and maintain the smart city. Cities must develop a new set of “smart” competencies in order to get and stay in the “smart city game”.

Information and data layer. The lifeblood of the smart city is information. The smart city must facilitate this in several ways, including open data initiatives, data marketplaces, analytics services, and monetization policies. Equally important, they must have programs that encourage data sharing and privacy policies to protect what and how data is gathered.

Connectivity, accessibility and security layer. People, things and systems are interconnected in the smart city. The ability to seamlessly connect all three, manage and verify who and what is connected and shared, while protecting the information and users is crucial. The highest priorities for smart cities are to provide a seamless layer of trusted connections.

Smart city technology infrastructure layer. Most people automatically think of technology when talking about smart cities. The smart city technology infrastructure must scale beyond the traditional municipal users and support a new class of value creators, and city/user stakeholders.

 

Leveraging the smart city ecosystem framework

The smart city is a complex ecosystem of people, processes, policies, technology and other enablers working together to deliver a set of outcomes. The smart city is not “owned” exclusively by the city. Other value creators are also involved, sometimes working in collaboration and sometimes by themselves. Successful and sustainable smart cities take a programmatic approach to engage its stakeholders across the ecosystem.

Our research has found that many cities are not taking an ecosystem approach to smart city projects. This is due in part to smart city projects being managed by the Information Technology (IT) organization where their charter is on systems development and deployment. In contrast, more experienced smart cities manage their smart city programs through internal cross functional “Transformation” or “Innovation” organizations.

Regardless of where cities are in their smart city journey, they must get ahead of the “curve” with smart city projects. They begin by thinking in terms of building the broader ecosystem in order to create a sustainable and scalable smart city. Key next steps include:

  1. Understand the smart city ecosystem framework and tailor it to the realities of their specific city. Incorporate this model into the development of their smart city vision, strategy and execution plans.
  2. Relative to the smart city ecosystem framework, identify current capabilities and gaps across the various layers. Understand what is needed to support the four types of value creators.
  3. Evaluate existing and new smart city projects and initiatives against the ecosystem framework. Use this framework to identify what is missing from the project plans and what is needed to make the projects fully successful.
  4. Prioritize and develop competencies across the various ecosystem layers. A smart city requires new skills and competencies. Augment existing capabilities through strategic partnerships and contracting with service providers, as required.

 

About:

Benson Chan is an innovation catalyst at Strategy of Things, helping companies transform the Internet of Things into the Innovation of Things through its innovation laboratory, research analyst, consulting and acceleration (execution) services. He has over 25 years of scaling innovative businesses and bringing innovations to market for Fortune 500 and start-up companies. Benson shares his deep experiences in strategy, business development, marketing, product management, engineering and operations management to help IoTCentral readers address strategic and practical IoT issues.

This post was co-authored with Renil Paramel, an IoT Innovation Catalyst, Strategist and Senior Partner at Strategy of Things.

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Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective. 

While IoT may be seen as another buzzword at the moment, companies like SAP, Cloud Sigma, Net Atlantic and Amazon Web Services are working to make sure that for businesses, IoT is a reality. It’s companies with this willingness to change, adopt and invent that will win the new economy. Mobile phones, online shopping, social networks, electronic communication, GPS and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Most companies want their platform to be the foundation of everything it does, whether it is with big data, data analytics, IoT or app development. The same  rub off phenomenon was emulated in Latin American countries  like Brazil, Argentina, Mexico and European countries like Brussels, Italy,  Germany, Denmark , Poland and Prague in recent times.

It is important to realize that technology is exploding before our very eyes, generating unprecedented opportunities. With easy access to cheap cloud services, smarter people came up with these platforms, and it has fundamentally changed businesses and created new ways of working. Mobile cannot be an afterthought. It needs to be integrated in everything you do and positioned at the forefront of your strategy. You have no valid reason to avoid migrating to the cloud. Cloud provides a ubiquitous, on-demand, broad network with elastic resource pooling. It’s a self-configurable, cost-effective computing and measured service. On the application side, cloud computing helps in adopting new capabilities, meeting the costs to deploy, employing viable software, and maintaining and training people on enterprise software. If enterprises want to keep pace, they need to emulate the architectures, processes and practices of these exemplary cloud providers.

One of the main factors of contributing value additions is the concept of a Smart City which is described as one that uses digital technologies or information and communication technologies to enhance the quality and performance of urban services, to reduce costs and resource consumption and to engage more effectively and actively with its citizens. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other.The idea is to embed the advances in technology and data collection which are making the Internet of Things (IoT) a reality into the infrastructures of the environments where we live. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other. Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective. The current model adopted for IoT is to attract businesses to develop software and hardware applications in this domain. The model also encourages businesses to put their creativity to use for the greater good, making cities safer, smarter and more sustainable.

A few years ago like many others  I predicted  that Business models will be shaped by analytics, data and the cloud. Moreover, the IoT is deeply tied in with data, analytics and cloud to enable them and to improve solutions. The key goal is to ensure there is value to both customers and businesses. You can effectively put this strategy into action and build a modern data ecosystem that will transform your data into actionable insights.  

Till we meet next time...

Best,

Raj Kosaraju

CIO 

 

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Digital transformation is no longer a buzzword. No industry is left behind. 
We’re currently in a new wave of  digital transformation with new technologies, processes, business models and opportunities popping up in the market faster than we can blink & think.
All the companies are using  big datamachine learningcloud, smart devices and  Internet of Things to achieve digital. But these are just means or vehicle to achieve it whereas you need a human to drive it.
It is easy to get wrapped by technology but without considering human element the transformation process will fail.
CEOs are taking a digital-first approach to change the  culture of organizations. This shift starts at the top and requires complete employee buy-in to achieve success.
Digital transformation can’t thrive unless your organization has a culture that’s willing and able to embrace it. Organization-wide adoption requires teams to change their attitude, automate the processes, shift their thinking and reject the status quo.
People are engaged by people. Productive and satisfied employees who like their work, go out of the way to satisfy customers. 
How to get this human element on your side in Digital Transformation?
·        Know your customers – customers are not just records but they are also humans, know their  behaviors, their motivations, what they like, dislike and their desires
·        Engage with employees – elaborate on what is in it for me, people need to know what is the change and how it will benefit them
·        Focus on human collaboration, learning, and innovation for digital which yields better ideas, better results
Digitization is by no means de-humanization. It is 20% technology but 80% human touch. Without a strong involvement and without taking the human element into account on all levels, digital projects are going to fail.

The best results will occur when technology and humans collaborate to create an entire ecosystem, which technology alone cannot achieve.

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Rise of the Intelligent Revenue Machines

An early theme of digital transformation was the notion of selling services rather than products. A contract with the “thing maker” to circulate cooling fluid throughout my factory rather than a purchase order for me to buy the pumps and filters needed to do it myself, for example. The contract lets me focus on creating products for my customers rather than maintaining the machines making this possible. I don’t want to spend time on the process (pumps and filters), I just need the outcome (properly cooled machines) in the least distracting way possible to my core business of producing goods, medicine, energy, etc. The contract lets you, purveyor of the connected pumps and filters, build a closer relationship with me, streamline your business, and avoid competing in an increasingly commoditized space.

The fundamental shift happening today goes beyond providing guaranteed services rather than just hardware. Ensuring my lights stay on rather than selling me light bulbs solved your commodity hardware problem, but over time service offerings will face similar pressure as your competitors follow your connected product path and undergo digital transformations of their own. Your long term return on investment in IoT depends on more than keeping my lights on and water flowing. The value your IoT system creates for you depends on your IoT system’s ability to generate more business for me. There’s no such thing as a cheaper “good enough” replacement part when it comes to generating new revenue.

In healthcare for example, when your IoT system enables me to perform procedures in 24% less time, my clinics can perform 24% more procedures each day, increasing my revenue by 24% and delivering a 24% better patient experience. That’s what I’m looking for when I’m buying medical equipment. Depending on my corporate agility, the adoption and rollout of your connected machines may be a phased approach, following a progression of business outcomes. Asset Management means knowing the status of each device at all times and controlling them accordingly. This first step helps me see the potential value of incoming data and better understand my current utilization. Workflow Integration is connecting this information with my enterprise systems, which enables Predictive Maintenance and automatically alerts service technicians when a machine shows signs of impending failure. Where everything comes together and bonds me securely to your connected product service is Yield Optimization.

At this point your IoT system is collecting data from machines in my facilities as well as external data like weather and information from my other enterprise systems, correlating this information and uncovering patterns and ways for me to achieve more with less. Your “things” are now more than hardware installed in my facility performing physical tasks. They’re active components in a new System of Intelligence engaged in a loop of continuous learning and improvement.

This is true digital transformation, the creation of business value out of data collected and processed by your IoT solution.

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Digital has brought in so many technological advances to this age and one of them is Robotic Process Automation (RPA).
A simple definition of RPA is, automation of business processes across the enterprise using software robots. Any repetitive task which requires some decision making is an ideal candidate for RPA. Automation has become an integral part of  Digital Transformation. Implementing these software robots to perform routine business processes and eliminate inefficiencies is the key for business leaders.
Today’s organizations often need to execute millions of repetitive and time-intensive business processes each day. Using RPA they can automate administrative functions such as customer address changes, registrations and other high-volume tasks and transactions. This helps avoids human errors & also allows employees to spend more time & focus on customer-related functions for better  customer experience.
RPA is well suited for processes that are clearly defined, repeatable and rules-based. Any company that uses workforce on a large scale for general knowledge process work, where people are performing high-volume, high transactional process functions, will boost their capabilities and save money and time with RPA software.
Process automation can expedite back-office tasks in finance, procurement, supply chain management, accounting, customer service and human resources, including data entry, purchase order issuing, a creation of online access credentials.
By adding the  cognitive computing power of  Natural language processing, speech recognition, and  machine learning, businesses can achieve high-end tasks which require human interventions.
Automation of front-end operations typically involves  chatbots or conversational agents. RPA can provide answers to employees or customers in natural language rather than in software code. This can help to conserve resources for large call centers and for customer interaction centers.
As RPA brings more technologically-advanced solutions to businesses around the world, they bring a multitude of benefits as below.
·       Increased Speed: routine tasks are carried out swiftly by RPA without any intervention, thereby faster time to resolution
·       Reduced labor costs due to software robots than humans
·       Enhanced employee experience: since repetitive tasks as taken care by RPA, employees can spend quality time for strategic work and enjoy their work life
·       Higher quality: better consistency & accuracy due to minimized variations and better customer service
·       Enhanced insights: by automating the data collected and applying Big Data analytics for improved efficiency
·       Scalability: robotic workforce can be scaled to any level required
BPO industry is the most benefited sector due to RPA. Insurance, Banking industries use RPA for KYC, claims processing, policy admin, statement reconciliation, credit card application processing etc.
Automation Anywhere, Blue Prism, UiPath & Verint are some of the few top vendors in the market today.

Be ready for RPA storm coming in near future with the addition of artificial intelligence capabilities. 

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To paraphrase Geoffrey Moore, smart “thing makers” are investing in IoT solutions for their customers today in order to generate more revenue for themselves tomorrow. Traditional hardware vendors are being commoditized and replaced whenever a cheaper “good enough” option comes along. To thrive in the long run, your value must be “sticky”, embedded in your customer’s business, providing benefit to their customers as well. The “things” you sell now simply enable your customers to run their basic operations. Whenever a part breaks, customers make a decision to order a new one either from you or a competitor. How differentiated is your equipment from the rest of the market? Your business is constantly at risk.

What we’re seeing as a result are “thing makers” creating smart systems that empower their customers to not just operate, but to *optimize* their operations. These devices still perform their physical functions as before, but also collect and share a stream of data about their status and conditions in the world around them. It’s the data they produce, and the insights your system derives from this data, that enable your organization to offer far more valuable products and services to your customers that are not so easily replaced.

If you know the state of your machines at all times, you can build predictive maintenance and service models enabling guaranteed uptime and automatic replenishment. If your equipment never breaks or runs empty, your customer is unlikely to replace it with a competitor’s version.

If your products provide not just lighting and temperature control but also insights correlating usage patterns with time, weather, and utility data that reduce your customer’s costs, you can sell them this information for a percentage of these savings.

It’s the future. Your connected product system is part of your customer’s operating procedures, continuously generating insights for maximizing productivity. Improved asset utilization, faster turnarounds, synchronized workflows, and more. Smoother operations and reliable performance deliver better experiences for their customers, further expanding your customer’s business, because of your IoT solution. You don’t just sell “things.” You sell outcomes, which is what your customers really wanted in the first place.

That’s pretty smart.

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Digital disruption is omnipresent, get on board or get thrown off the track.

Digital is no longer a purview of only BankingInsuranceHealthcare or Retail. The Restaurant industry is having pressure from multiple directions. 

Today’s consumer expects fresh food, whether it is in season or not, with an exotic dining experience.

Successful restaurants recognize that the easy path to their customers' stomachs begins in their minds. They need to grab customer's attention and entice them with a memorable experience in order to trigger repeat visits.

Here are some of the applications of Digital disruption in the restaurants & food service industry:



  • Digital Signage to deliver eye-catching graphics to engage customers the moment they walk through the door
  • Online reservations using mobile app & flexibility of customization of menu as per customer taste
  • Chatbots: Restaurants are using virtual assistants to respond to customer inquiries and to process and customize customer orders. Taco Bell, Pizza hut have adopted chatbots to automate ordering process from a social media platform.
  • Robots – Restaurants are using AI-driven robots to increase capacity and speed of food preparation and delivery.
  • Recommendation engines – Developers are designing applications which use AI to help consumers choose meals & suggest foods based on their eating preferences.
  • Wi-Fi enabled dining spaces for truly engaging customer experience
  • Kiosks – Restaurants are integrating AI-driven self-service Kiosks to reduce customer waiting time and enhance the customer ordering experience.
  • Pay by phone and flexible paying options
  • Loyalty programs based on frequent visit
  • Digital supply chains to accurate demand forecasting, inventory optimization, and cost reduction.

Restaurants generate vast quantities of data through software that controls everything from scheduling food delivery and shift staffing to taking reservations to managing vendors and inventory to paying bills.

Today almost every consumer is making dining decision on their smartphone. They have tried new menu item based on the mobile ad.  Mobile payments have become the norm now in this industry. Customers would like to order quick meals via mobile and want to use mobile payments.

McDonald’s was the first store to accept Apple Pay.

Starbucks is a leader in digital transformation. Using more than 50mm Facebook fans & over 15mm Instagram followers at their disposal they mastered the social media engagement. First, they created an app to pay for coffee and food in their restaurants. Then they added the loyalty program, starting to craft hyper-personalized offers and experiences for their 24-hour connected customers. The company also developed new digital services to be enjoyed in their physical stores, achieving a highly praised omnichannel approach.

Pizza Hut has started an order and payment-enabled pepper robot. Customers can now have a personalized ordering, reduce wait time for carryout, and have a fun with the frictionless user experience.

TGI Fridays, Wendy’s and other big names have all adopted digital technology to lure their customers.

OpenTable, GrubHub, and Zomato are some of the latest apps showcasing nearby restaurants with high-quality pics, presenting a menu with exotic pictures, price, ratings etc. you can also get offers, deals instantly.

The digital technology available to restaurants has streamlined the lives of restaurant owners much like smartphones have bettered our daily lives.

Digital has entered the restaurants and food industry through the front door and brings many exciting trends.

As consumers expect Apple to come up with a new iPhone every year that makes the earlier model obsolete, similarly they want fresh ways of serving food with fantastic dining experience which is made possible by Digital disruption.

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Digital Transformation in the Fashion Industry

Gone are the days when brand communication was mostly made up of ads that appeared on billboards, in magazines and/or on television. Today, all of this is augmented with Digital revolution.
The fashion industry is engaging with digital technology in new and different ways, in order to stay competitive and to engage with the ways that consumers are searching for jewelry, clothes, and accessories.
Technology is turning the fashion industry inside out. Today, consumers are most active as digital shoppers in the Fashion industry and are demanding a heartening digital experience across channels. People love the brick-and-mortar stores but also exploit online channels through social media, while on the go and online. These  Omni-channel experiences should provide customers with a “wow” factor and  Digital Transformation is the way to achieve this objective.
In today’s fashion world, competition is fiercer than ever, giving consumers’ far greater power & they demand only the very best customer service. Most of the fashion brands now have a  social media presence on Pinterest, Instagram presence, tapping into our heightened engagement with imagery.
It can take many years to build a successful brand, but only a short time to destroy it. Fashion brands have always needed to be ready and able to respond to issues of uncertainty, risk, and reputation, all at varying times.
Burberry is the poster child in digital for fashion that started with live streaming runshows. Then came iPads and mobile apps for consumers to try out different outfits.
In Paris, a window front invites passers-by to download the Louis Vuitton Pass app in order to interact with the window and explore.
L’Oréal has put up a 'social wall' on its main website so consumers can share posts while shopping.
Harrods is the latest luxury retailer to transform its in-store experience with digital technology. They have many new super-high resolution stairwell displays at the flagship Knightsbridge, London store
Adidas has a store wall which shows shoe collections in 3D to see shoe designs from all angles.
With this availability of streaming  big data and resultant analytics, fashion brands use the insights for hyper-personalization, align consumer experience and to track customer trends. The customer’s data is the core component of digital transformation in the fashion industry. So, hyper-personalization of mobile retail experiences will be huge in the near future.
Today, dressing rooms enhanced with  augmented reality and social media features have transformed the shopping experience altogether. L’Oreal, Maybelline have already started testing special kiosks that enable shoppers to virtually try on makeup by simply taking a picture.
Even the most successful digital  retail experiences are built from desktop experiences but the future is in mobile with a predicted 80% of sales traffic coming via this medium.

With digital at a side, fashion weeks across London, Paris, Milan & New York witness runway shows streamed online, Instagram & snapchat stories in real time, creating a close connection between consumers and brands. 
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In this  Digital age, every organization is trying to apply machine learning and  artificial intelligence to their internal and external data to get actionable insights which will help them to be closer to today’s customer.

A few years back it was the field only for  data scientists and statisticians, who used to analyze the data, apply several techniques and provide results.

Today many of the organizations are using  APIs to access the ready-made algorithms available in the market as they make it easy to develop predictive applications. In fact, you don’t even need to have an in-depth knowledge of coding or computer science to introduce them into your apps.

APIs provide the abstraction layers for developers to integrate  machine learning into real world applications without worrying about which technique to use or how to scale the algorithm to their infrastructure.

These APIs can be categorized broadly into 5 groups:

  • Image and Face Recognition: It understands the content of the image, classifies the image into various categories, detects individual objects and faces, detects labels and logos from the images.
  • Language Translation: Translate text between thousands of languages, allows you to identify in which language any text that you need to analyze was written. Some APIs allows organizations to communicate with the customer in their language.
  • Speech Recognition and Conversion: Today most of the customer service is handled by Chatbots with underlying APIs helping simple question and answer. Speech to text APIs are used to convert call center voice calls into text for further analysis.
  • Text /Sentiment Analytics using NLP: With the rise of Social Media, consumers easily express and share their opinions about companies, products, services, events etc. Companies are interested in monitoring what people say about their brands in order to get feedback or enhance their marketing efforts. These APIs can identify, analyze, and extract the main content and sections from any web page. They further help in to analyze unstructured text for sentiment analysis, key phrase extraction, language detection and topic detection. There are some tools also helps in spam detection.
  • Prediction: These APIs, as the name suggests helps to predict and find out patterns in the data. Typical examples are Fraud detection, customer churn, predictive maintenance, recommender systems and forecasting etc.

Google Cloud, Microsoft Cognitive Services, Amazon Machine Learning APIs & IBM Watson APIs are the leaders in the market.

With growing number of free/reasonably priced APIs and tsunami of data generated every day, the race is on as to which is the best Machine Learning API.
These machine learning APIs are not yet perfect or matured and they will take some time to learn and act accurately. But they allow faster time to market-based on ready availability, rather than asking data scientist to code the algorithms.

In future, machine learning will lead to revolutions that will intensify human capabilities, assist people in making good choices and help navigate through the world in powerful ways, like Iron Man's Jarvis.
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Digitization is disrupting every business and is spreading like a wild fire across every sector such as Banking, Financial Services, Insurance, Retail, and Manufacturing.

Digital Transformation does not happen overnight. It is a continuous process. That is why it is very hard to plan too far ahead in a digital transformation program. The technology is evolving so rapidly that your plans will certainly change.
How do you measure return on digital transformation in order to make the timely course correction and improve its success rate? It is even more important that people who will measure the progress know the actual meaning of digital and customer behavior. You will be surprised to know that how many employees/leaders take the  customer journey themselves – buying an online policy on their own website, purchasing a merchandise or calling their own customer center to complain.
One of the ways is to break the long term plan into small doable projects with specific KPI’s. These should not last more than six months.
While traditional metrics of revenue, costs, customer satisfaction should be measured, companies should move beyond these quarterly revenue and margin guidance as they keep pulling them back to short term tactical focus. The new metrics have to be added to get the right control and visibility of progress.
Some of the new metrics which can be considered are:
·       % of  marketing spend that is digital
·       Brand value in market
·       Reach of organization in the market
·       Digital  maturity quotient of the employees including board and senior leaders 
·       % revenue through digital  channels
·       Contribution to digital initiatives from each department like purchasing, finance, HR, IT, Sales & Marketing
Customer Focus:
·       Net promoter score
·       Rate of new customer acquisition
·       Number of customer touch points addressed to improve  customer experience positively
·       % increase in customer engagement in digital channels
·       Reduction in time to market new products to customers
·       Change in customer behavior over time across channels
Return on innovation:
·       % of revenue from new products/services introduced
·       % of the profit from new ideas implemented
·       Number of innovative ideas reach concept to implementation
·       Number of new products or services launched in the market
·       Number of new  business models adopted for different class of customers
·       Rate of new apps and APIs to offer new products/services inside and outside the company
Always keep these metrics simple and measure right things and celebrate even the small successes so employees are motivated.
A digital transformation is a big  culture change so there is plenty of fear which leads to resistance. Such inertia has to be changed with clear communication, as to why it is needed to change and what benefits it will bring to each department and employee.

A lack of urgency is the greatest obstacle businesses face when considering the value of digital transformation. Proper planning is important but more than that execution as per the KPIs you select, is what take you through.

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We are all familiar with machine learning in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better shopping and movie experience.
Artificial intelligence (AI) has stormed the world today. It is an umbrella term that includes multiple technologies, such as machine learning,  deep learning, and computer vision,  natural language processing (NLP), machine reasoning, and strong AI.
Organizations are using machine learning for various insights they want to know about consumers, products, vendors and take actions which will help grow the business, increase the consumer satisfaction or decrease the costs.
Here are some top use cases for machine learning:
·     Predicting & preventing  cyber-attacks: With WannaCry making havoc in many organizations, machine learning algorithms have become extremely important to look for patterns in how the data is accessed, and report anomalies that could predict security breaches.
·     Algorithmic Trading: Today many of financial trading decisions are made using algorithmic trading at higher speed, to make huge profits.
·      Fraud Detection: This is still one of the key issues in all the financial transactions. With the help of deep learning/artificial intelligence, the identification and prediction of frauds have become more accurate.
·      Recommendation Engines: In this digital age, every business is trying  hyper-personalization using recommendation engines to give you a right offer at right time.
·      Predictive Maintenance: With embedded sensors of  Internet of Things, many of the heavy industrial machinery manufacturers are applying machine learning to predict the failures in advance, to avoid the costly downtime and improve efficiency.
·     Text Classification: Machine Learning with NLP is used to detect spam, define the topic of a news article or document categorization.
·     Predict patient’s  readmission rates: By taking into consideration patient’s history, length of stay in hospitals, lab results, doctor’s notes, hospitals now can predict readmission to avoid penalties and improve patient care.
·     Imaging Analytics: Machine learning can supplement the skills of doctors by identifying subtle changes in imaging scans more quickly, which can lead to earlier and more accurate diagnoses.
·      Sentiment Analysis: Today, it is important to know consumer emotions while they are interacting with your business and use that for improving customer satisfaction. Nestle, Toyota is spending huge money and efforts on keeping their customer’s happy.
·     Detecting drug reactions: With Association analysis on healthcare data like-the drugs taken by patients, history & vitals of each patient, good or bad drug effects etc; drug manufacturers identify the combination of patient characteristics and medications that lead to adverse side effects of the drugs.
·      Credit Scoring & Risk Analytics: Using machine learning to score the credit worthiness of card holders, defaulters, and risk analytics.
·     Recruitment for Clinical Trials: Patients are identified to enroll into clinical trials based on history, drug effects
With today’s advanced  cognitive computing capabilities, image/speech recognition, language translation using NLP has become a reality which is used in very innovative use cases.
Machine learning is nothing new to us but today it has become the brain of  digital transformation. In future, machine learning will be like air and water as an essential part of our lives.
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Are you drowning in Data Lake?

Today more than even, every business is focusing on collecting the data and applying analytics to be competitive. Big Data Analytics has passed the hype stage and has become the essential part of business plans.

Data Lake is the latest buzzword for dumping every element of data you can find internally or externally. If you Google the term data lake, you will get more than 14 million results. With entry of  Hadoop, everyone wants to dump their siloes of data warehouses, data marts and create data lake.
The idea behind a data lake is to have one central platform to store and analyze every kind of data relevant to the enterprise. With the  digital transformation, the data generated every day has multiplied by several times and business are collecting this  consumer data, Internet of Things data and other data for further analysis. 
As the storage has become cheaper, more data is being stored in its raw format in the hopes of finding nuggets of information but eventually it becomes difficult. It is like using your  smartphone to click photographs left, right and center, but when you want to show some specific photograph to someone it’s very difficult.
Data Lakes, if not maintained properly, have the potential to grow aimlessly consuming all the budget. Some companies have their data lakes overflowing on premise systems into the  cloud.
Most data lakes lack governance, lack the tools and skills to handle large volumes of disparate data, and many lack a compelling business case. But, this water (the data) from your data lake has to be crystal clear and drinkable, else it will become a swamp.
Before getting into bandwagon of creating the data lake that may cost thousands of dollars and months to implement, you should start asking these questions.
·        What data we want to store in Data Lake?
·        How much data to be stored?
·        How will we access this massive amounts of data and get value from it easily?
Here are some guidelines to avoid drowning into data lakes.
·        First and foremost - create one or more business use cases that lay out exactly what will be done with the data that gets collected. With that exercise you will avoid dumping data, which is meaningless.
·        Determine the Returns you want to get out of Data Lake. Developing a data lake is not a casual thing. You need good business benefits coming out of it.
·        Make sure your overall big data and  analytics initiatives are designed to exploit the data lake fully & help achieve business goals
·        Instead of getting into vendor traps and their buzzwords, focus on your needs, and determine the best way to get there.
·        Deliver the data to wide audience to check and revert with feedback while creating value
There are many cloud vendors to help you out building data lakes – Microsoft Azure, Amazon S3 etc.
By making data available to  Data Scientists & anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.
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Do you want to hire a Data Scientist?

As mentioned by Tom Davenport few years back, Data Scientist is still a hottest job of century.
Data scientists are those elite people who solve business problems by analyzing tons of data and communicate the results in a very compelling way to senior leadership and persuade them to take action.
They have the critical responsibility to understand the data and help business get more knowledgeable about their customers.
The importance of Data Scientists has rose to top due to two key issues:
·     Increased need & desire among businesses to gain greater value from their data to be competitive
·     Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment
So it is extremely important to hire a right person for the job.Requirements for being a data scientist are pretty rigorous, and truly qualified candidates are few and far between.
Data Scientists are very high in demand, hard to attract, come at a very high cost so if there is a wrong hire then it’s really more frustrating. 
Here are some guidelines for checking them:
·     Check the logical reasoning ability
·     Problem solving skills
·     Ability to collaborate & communicate with business folks
·     Practical experience on collaborating  Big Data tools
·     Statistical and  machine learning experience
·     Should be able to describe their projects very clearly where they have solved business problems
·     Should be able to tell story from the data
·     Should know the latest of  cognitive computingdeep learning
I have seen smartest data scientists in my career who do the best job best but cannot communicate the results to senior leaders effectively. Ideally they should know the data in depth and can explain its significance properly. Data visualizations comes very handy at this stage.
Today with  digital disrupting every field it has an impact on data science also.
Gartner has called this new breed as citizen data scientists. Their primary job function is outside  analytics, they don’t know much about statistics but can work on ready to use algorithms available in APIs like Watson, Tensor flow, Azure and other well-known tools.
The good data scientist can make use of them to spread the awareness and expand their influence.
It has become more important to hire a right data scientist as they will show you the results which may make or break the company.
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How Customer Analytics has evolved...

Customer analytics has been one of hottest buzzwords for years. Few years back it was only  marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza.
SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services.
In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics.
By the late 2000s, Facebook, Twitter and all the other  socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.
With the  digital age things have changed drastically. Customer is superman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience.
This tsunami of data has changed the customer analytics forever.
Today customer analytics is not only restricted to marketing for churn and retention but more focus is going on how to improve the customer experience and is done by every department of the organization.
A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating  customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.
From the technology perspective, the biggest change is the introduction of  big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.
Then came  Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.
Predictive models of customer churn, Retention,  Cross-Sell do exist today as well, but they run against more data than ever before.
Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical.
There are various ways customer analytics is carried out:
·       Acquiring all the customer data
·       Understanding the customer journey
·       Applying big data concepts to customer relationships
·       Finding high propensity prospects
·       Upselling by identifying related products and interests
·       Generating customer loyalty by discovering response patterns
·       Predicting customer lifetime value (CLV)
·       Identifying dissatisfied customers & churn patterns
·       Applying predictive analytics
·       Implementing continuous improvement
Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time
Now via  Cognitive computing and  Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their  deep learning neural network algorithms provide a game changing aspect.
Tomorrow there may not be just plain simple customer  sentiment analyticsbased on feedbacks or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time.
There’s no doubt that customer analytics is absolutely essential for brand  survival.
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