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6 hot digital transformation trends — and 4 going cold (via CIO)

interactive voice, digital transformation, ai, machine learning, cloud

Now considered essential to driving profits, digital transformations are shifting from platform-first strategies to initiatives that leverage emerging technologies in service of clear customer and operational goals.

Source: CIO

  1. Location Services – Helps remove friction and smooths the process of talking to a company.  It’s hot now.  Medical services already use it for remote care and telehealth.  It can help people find out where there’s an open parking spot in a crowded lot.  Location services can also help customers to find offices that they need to get to for meetings and appointments.  Video conferencing, receiving text message updates to remind people of appointments and wearable devices are all considered a part of digital transformation too.
  2. Cloud First Strategy – It’s going cold.  When upgrading infrastructures now companies tend to move everything to the cloud.  The feeling isn’t the same as when the cloud became popular to use.  Now people go whichever way is the best for what their company wants.  Some go for on-premises while others go for cloud.  The main thing companies want is flexibility, security, affordability, and speed.   
  3. Atomic Pricing of the Cloud – It’s going hot.  Digital transformation needs to have a reliable cloud solution that agencies can use.  Atomic pricing is when agencies can buy infrastructure per-second use.  In other words, when a subject becomes more popular the site being visited can scale automatically to be able to handle the requests better.
  4. Build it Yourself IoT – Is going cold.  Ready-to-use platforms are more in demand instead of building your own.  This is because successful implementation of IoT platforms is more in demand now across all industries.
  5. Blockchain for Business – Is going hot.  There’s not much blockchain implementation in businesses now, but more and more organizations are looking into it as it’s much safer than other modes of transferring information between work devices.   Agencies are interested in using blockchain for smart contracts instead of cryptocurrency for which it became known for.  It can also give some control back to the customer too.   
  6. Mobile-first and Tablets – Is going cold.  The trend of people using their smartphones and tablets for everything, therefore cutting out PCs and laptops completely never happened. It’s reversing instead, focusing more on computers than smartphones and tablets.
  7. Identity Management in the Cloud – Is going hot. Single identity accounts for using services so users don’t have to verify themselves every time.  It will help to streamline the process of signing on. 
  8. Using Many Tools for Collaboration – Is going cold. Having too many options causes too many problems.  Companies are getting frustrated with having all these different tools.  Choosing one that can do multiple things like Skype or Google Talk is what’s becoming popular now.  It speeds up the process and makes doing business so much easier for everyone. 
  9. AI and Machine Learning – Is going hot.  AI is becoming very popular and due to this, there’s more interest in machine learning which is a subset of AI. Machine learning allows software algorithms to improve reliability and effectiveness.  It’s a new form of statistical analysis that companies can use.  Artificial Intelligence is moving from central processing to IoT (local devices) on the edge and in concert.  It’s used in customer care, improving interactions between the customer and the company through automated interaction.  Text analysis, facial recognition, network analysis, and threat detection are just some of the things it does. 
  10. Interactive Voice – Is going hot.  Alexa and Siri are examples of this.  There’s a huge push to embed healthcare information into these types of devices too. The trend is that traditional means of computing are becoming less popular while more interactive voice methods are becoming more popular. 
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6 internet of things trends that will dominate 2018

internet of things, IoT, IIoT, IT security, security, IT

Many organizations are already incorporating IoT technologies into their products, processes, and workflows, but 2018 is shaping up to be a breakout year for IoT deployments. Here are six IoT trends to watch in 2018.

Source: CIO

6 Internet of Things Trends

  1. Everything Will Connect – Consumer IoT will increase.  Because of this industrial IoT (IIoT) will learn to take advantage of real-time analytics, feedback from the customer and so forth.  Even with the proof of the power of consumer IoT, using it in industrial environments is slower.
  2. New data infrastructures –  Companies will increase the use of IoT in the field due to more innovative technologies.  These agencies use sensors for reading data, sending it through IoT in real-time.  The infrastructures need updates to accept all the formats.  In order to really be able to use the information coming in they need to be able to handle constant streams and better ways to analyze the data, either through deep and/or machine learning. 
  3. Processing and IoT edge – Everything will expand into the cloud.  But in this case, everything will also expand to the edge, almost into and sometimes directly into the devices used.  The need will be to create tiers of sensitivity to better partition and create latency.  Some devices need a constant connection to the Internet while others do not.  Big data control devices in higher tiers than others, in order to better control them.  Edge analytics and intelligence are increasing in speed too.   
  4. IT and OT will collaborate more – Information technology and operational technology will work together more this year.  It applies to IIoT as it needs them to work together correctly.  Analytic tools being introduced to the end users makes operational decisions real-time now.  Deploying IoT projects to business operation teams instead of IT teams is more popular now.  69% either say that they have adopted or plan to adopt IoT solutions in 2018, so demand is increasing.   
  5. The motivation for IoT attacks will be financial – IoT is extremely open to attack.  Because of this, it will become a part of the business-critical infrastructure.  Presently the attitude is to wait and see until further notice as IoT attacks is seen as futuristic.    
  6. Pilot Projects to Business Value – 2017 was about pilot programs and experimenting with IoT.  It continues into this year also, but edge technology will help it to pick up speed and implementation.  
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4 Things to Know About the Blockchain System

blockchain, innovation, business, competitive advantage

Source: Harvard Business Review

Introduction

Why is blockchain becoming a trend for businesses?  There’s one area for improvement that needs help in systems today.  It’s contracts, transactions, and records of actions completed.  Paperwork all over the place.  The system in place hasn’t kept up with the rest of the digital age.  Blockchain can help in this arena if companies started to take advantage.   

The blockchain is open source and takes care of virtual currencies. It records transactions between two people buying and selling currency in permanent ways. If blockchain is in the business world, imagine the possibilities.  Storing contracts without worrying about tampering.  All agreements, process, task, and payment go into the records.  People, agencies, machines, and algorithms would be able to contact and communicate with each other with little trouble.   

The problem here is looking after security and breaking down barriers already in place.  The blockchain isn’t considered a disruptive technology because it will create new foundations.  The ramifications are huge.  

The 4 Adaptations for Blockchain

  1. Single Use – Low coordination applications to make better, cheaper, highly focused solutions. 
  2. Localization – High in innovation but don’t need many users to create usefulness. It makes it easier to promote to the rest of the agency. 
  3. Substitution – These build on what’s already in place.  And here there will be high resistance.  It requires coordinating and replacing systems already fully integrated into systems. It could take years to put in place and start using.   
  4. Transforming – These are new items that will be placed into use, creating fast change to economic, social and political systems.  Smart contracts are the best options to start with now.  It automates payments when conditions are met.   

Summary 

Start the blockchain in single-use applications.  There won’t be as much risk taken during changes as they aren’t new and don’t involve much coordination with third parties. Blockchain can help to find problems quickly through tracking processes agencies have in place already.  Another cool thing is that it could possibly cut costs of transactions.  The big thing to consider is this, if blockchain becomes big in business, it will affect your company in some manner.   

 

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Key Digital Trends For 2018

digital trends, trends, security, cyber security, augmented reality, digital innovation

The yearly trend report outlining both where the digital and social landscape is headed and what brands and agency partners should do about it, from Marshall Manson and James Whatley.

Source: ogilvy.com

5 Trends to Look for

  1. Augmented Reality – Proliferation of mobile, AR platforms and mass-user penetration have all improved immensely as it becomes more and more popular.  Brands have started to take part in it too.   
  2. Typing as a thing of the past – User interfaces are turning away from hands-on to voice and images.  Users are using technology in a more natural and instinctive way.  Technology will learn to understand people and natural language.  Brands have to prepare for the coming changes.
  3.  Changes in influencers Marketing – The present method of marketing through using influencers is not good enough.  Giving money to people who might not really know the brand can actually lessen the significance of what the brand stands for.  In time this can lower the credibility of your brand.  Influencer marketing continues to grow, but cracks are beginning to show. 
  4. Using Amazon as a Sales Funnel – Amazon is growing all the time as a digital marketing platform.  It’s no longer about product pages but using Amazon as an advertising platform instead.   
  5. Digital Security – There are many weaknesses in the digital arena.  There are also a lot of improvements that are coming along this year to counter the threats or mitigate possible problems that might come up.  There is GDPR starting at the end of May.  There are new initiatives to help counter fraud and addressing customers who have issues with buying online.  Transparency is the new word of the day, making it easier to show customers what brands are doing to market to them.  State-sponsored hacking is still a problem, and the security industry is busy trying to keep up with all the possible new techniques of crime through the Internet.   

Summary

There have been many improvements already, but there is much that still has to be seen.  Amazon really is the way to go to get items shown to more people.  It’s so popular that it is about to take over Google and Facebook as the place to advertise.  I’ll be using it myself in the future as I love Amazon.   Security will always be an issue.  New threats come all the time, both from domestic and international threats.  It would be smart to find a platform that is known for good security that reacts quickly.  The faster the response time, the more trustworthy the store will be known as.  Very important in today’s world.   

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5 Steps for Higher-Quality Data

Higher-Quality Data, data, data science, data, data scientist, data management, data modeling, data model, smart data

Introduction

Source: Harvard Business Review

There’s one thing that you as a business owner really has to look out for, and that’s poor data quality.  Machine learning (ML) especially can get harmed by using data that’s bad, as higher-quality data is a high demand part of ML.  Bad data can appear in historical data that’s used to train the predictive model, causing new data contamination, therefore giving bad solutions to future decisions that might be made for the business.

In order to train the predictive model correctly, the data’s correct, properly marked, formatted and be the right data.  You can’t make a predictive model if by mistake the data scientist is given the wrong information to sort out.  Presently most data fails to meet standards.  Causes include that the data creators don’t understand what’s expected of the data, measurements are poorly calibrated, processes are to complex and just plain human error.

It can in turn take up to 80% of data scientists time just to clean up the data given to them, even though it isn’t guaranteed that everything’s repaired before putting it into the predictive model.  This can cause further problems as more and more ML technology becomes popular.  The output from one model feeds another and another, all the way down the line, crossing department lines.  So if there’s even a small error it will cascade, causing more and more errors.

5 Steps of Higher Quality Data

  1. Clarify Objectives and Assess if you Have the Right Data to Support the Objectives – If it doesn’t meet goals, find new data or scale back goals, or both.
  2. Build Plenty of Time to Execute Data Quality Fundamentals into the Overall Project Plan – Start 6 months’ out
  3. Maintain Audit Trail Preparing Training Data – Helps to understand biases and limitations in the model. The audit trail helps to sort it out.
  4. Charge Specific Person or Team with Responsibility for Data Quality When Releasing the Data Model – They need to have some strong knowledge of the data, set and enforce standards, and are in charge of finding and getting rid of the root causes of any errors found.
  5. Have Independent, Exact Quality Assurance – The key word here is independent.

Summary

These steps won’t fully guarantee that your data is completely error free.  But it’ll be better than using data that hasn’t gone through these 5 steps.  This in turn makes for use of an extremely powerful tool in ML.  Think of everything that done if the data is of a higher quality, and how much more you can learn about your business, the competition, and your customers.

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5 ways companies are using big data to help their customers (via VentureBeat)

big data, enterprise data, analytics, data analytics, data modeling, data science, data modeling, data model, data, data science, data scientist, data management,

Five ways companies are using big data to treat customers more like individuals — and build better long-term relationships so those customers happily buy more and more

Source: VentureBeat

Review

As we all remember, back in the day you could go to the store and the clerk would know you personally.  They would ask you how you are and how your family is. It was a very personal relationship you would have, therefore creating loyalty between you and the store.  It has been lost for a while when stores started to sell online.  There were no programs to make your shopping experience more personal or enjoyable.  You just went online to search and buy.  Big data helps to build relationships again as it can help companies offer better service to customers if used.   Here are the five ways that big data helps online stores to treat their customers more like people instead of just numbers.

5 Methods to Use Big Data

  1. Prediction – Big data can help analyze past behaviors of customers to build a more personalized experience for them. This in turn creates satisfaction for the person and increases purchases.
  2. Excitement – This is more for wearable technology. FitBit and other companies spew out the data they gather to their clients, which makes the client more interested and excited to see improvements.  This is completed in other industries too, not just the health industry.  There are apps to help track finances too and make people excited to invest more.  Showing the data makes the client happier.  It can show them where they need to work to improve themselves too.  It’s a good tool for the customer to use.
  3. Improvement – Customer service is just as important as effective marketing and product development. Big data can help in all these areas too.  Representatives can answer questions more quickly and effectively when the correct data is in front of them.  This way the customer doesn’t feel like they are being badgered.  The data helps as the customer has so many ways to get a hold of companies now than before.
  4. Identify – Find the difficulties customers are having to improve their experience. It’ll make for happier and more loyal customers.
  5. Reduce – This deals with the health care industry for improving quality of patient care. It helps to cut cost and improve treatments.

Summary

Big data helps companies now to understand their customers better.  This helps agencies give better services and build relationships again, in a more modern way.  Just consider all the possibilities.  I would think about switching over myself if I had a bigger company and could afford it.

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84% Of Enterprises See Big Data Analytics Changing Their Industries’ Competitive Landscapes In The Next Year (via Forbes)

big data, data science, big data analytics, analytics, data modeling, data management, smart data, data mining

87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% believe that companies that do not adopt a Big Data analytics strategy in the next year risk losing market share and momentum. These and other key findings are from an […]

Source: Forbes

I just thought that I would share this article.  It has some great statistics on why Big Data is now considered essential for any type of competitive growth.  For example, only 13% use Big Data analytics in predictive modeling, while only 16% are using the information that they find to improve processes.  If you were to use Big Data analytics, image what kind of growth your business could have…

I love studies as they always show the numbers to help strengthen their arguments.  Just wanted to share this with you all.

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Steps to create Data Model (via learndatamodeling.com)

Big Data, big data, data modeling, data, data science, data scientist, data management, analysis, data analyzing, technology, tech

Source: learndatamodeling.com

Review of Article

These is general guidance for creating standard data models.  I’m not going to include all the steps as it’s over 24 separate steps, but depending on what your business requires you might not need to have all the steps anyways.  The link to the article is above if you’d like to see the entire list of what you can include.

Steps for Building Logical Data Models

  1. Gather up the business requirements
  2. Analyze business requirements
  3. Select target database – the data modeling tool will build the scripts to create reports
  4. Assign data type to attributes created to find data
  5. When analysis complete create columns to sort data
  6. Build subject areas to add the data
  7. Validate data model
  8. Create reports

Steps for Building Physical Data Models

  1. Get the logical data model and build a physical one from it
  2. Add properties to sort data
  3. Create SQL scripts
  4. Compare the database from the data model
  5. Create change log to document changes that have occurred

 

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Data Modeling Trends in 2018 – DATAVERSITY (via DATAVERSITY)

data modeling, data science, data scientist, data, data security, ai, machine learning, deep learning, big data, Big Data, enterprise data, data management, internet of things, iot, smart data

The Database Management and Data Modeling landscapes have evolved much in the past few years, from the traditional relational model to now include non-relational models as well.

Source: DATAVERSITY

Review of Article

Advantages of Data Modeling

  1. Provides clear framework for development projects
  2. Enables high performance
  3. Corrupt datasets are found quickly and cleaned before using
  4. Offers tested models for building software
  5. Outlines scope and risks during development
  6. Includes detailed documentation which helps with future maintenance

New Trends

  • Wide variety of machine facts to include Internet of Things (IoT)
  • Scale and speed of data increasing along by machine learning
  • Demand of aggregated data is increasing
  • Public, private on-site Cloud storage
  • Huge amounts of data collected into what is Data Lakes (unchanged data) for data scientists to analyze later
  • Automated data modeling (algorithms)
  • Predictive modeling with advanced machine learning
  • Semantic data models

SQL Database Trends 2018

  • Adoption rates will differ as companies due to security concerns
  • Cloud adoption
  • AI capabilities are increasing
  • SQL Servers for Linux
  • There are new schedules for software updates (handled by the vendors themselves, not Microsoft like before)
  • Use of Data Vaults

Summary

It sounds pretty exciting doesn’t it?  So many changes to look forward to trying out for your business.

 

 

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10 Key Big Data Trends That Drove 2017 (via Datanami)

hadoop, big data trends, big data, analytics, data modeling, data science, machine learning, ai, deep learing

2017 has come and (almost) gone. It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it’s worth taking some time to ponder about what happened in 2017 and put…

Source: Datanami

10 Big Data Trends

  1. The re-emergence of AI, deep learning and machine learning
  2. Hadoop becomes less popular
  3. Graph databases grow in use
  4. Apache Spark is keeping up with the competition
  5. The Cloud is super popular for storing Big Data
  6. Big Data fabric bypasses integration problems
  7. Big Data swamps are becoming a problem with too much data saved
  8. Big Data company IPOs are becoming popular
  9. Data Science platforms and vendor choices are growing
  10. Look out for GDPR (General Data Protection Regulation) that goes into effect on May 25, 2018