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Information Technology Trends 2018 (via comptia)

IT trends, trends, it, IT, information technology, technology



This has to be one of the most detailed lists of information technology trends for this year from Comptia.  I love Comptia!  It has treated me very well when I take classes in computer systems, programming and such.  Anyways, back on subject.  The trends talked about here are focusing on areas where there are higher expectations in business value, security, transparency and equal access.  

3 Key Points 

  1.  The IT sector projections are that it’s to grow 5% this year 
  1. Evolving tech labor market will create both challenges and opportunities 
  1. There’s a balancing act of incrementations and transformations 

12 Trends of Information Technology to Watch 

1.  Open Source Concepts – Allows more people to create in more inventive ways.

  • Able to build more applications using blockchain, natural language, and context-aware algorithms 
  • Use cases include drones, robotics, and 3D printing 

2.  Cloud Keeps on Growing – Presently for non-critical use. 

  • Moving data on to where companies can store and use any program in the Cloud 
  •  The system is rebuilt to maximize cloud characteristics 
  • Companies have to change policies and workflow which will be more difficult than dealing with implementing the Cloud itself.  Something to consider.

3.  Internet of Things – These types of devices are really making an impact in the business world.  

  • There are many benefits for using these devices, but companies aren’t considering system connectivity and optimization, plus IT responsibilities 

4.  Artificial Intelligence –  This is the one to focus on. 

  • The one most likely to change the IT environment 
  • Requires computational resources which found in the Cloud 
  • Algorithms allowing learning through making new products or services 
  • The contextual awareness which can come from IoT devices or big collections of data  

5.  Cyber Security – Incidents are occurring more and more. 

  •  It has to change to meet the challenges that attackers give them 
  • More and more advanced methods of attack to face down 

6.  IT May No Longer be Given the Benefit of the Doubt – Tech is so pervasive that we don’t know where it begins or ends. 

  • Because of how beneficial tech is to us, when something flops we usually give the benefit of the doubt 
  • Signs are showing that we’re now beginning to hold technology accountable for mistakes that happen unlike before
  • Questions dealing with security, privacy and screen time issues come up all the time now 

7.  Insights Economy – Learning about customers wants and needs. 

  • Machine learning and artificial intelligence are driving this and supporting it 
  • Pattern recognition, predictive analytics, natural language processing, and computer vision is what supports the new insight economy  

8.  Upgrading Digital Expertise in the Boardroom – Got to have people who are tech savvy so they can run things smoothly 

  • Tech initiatives include infrastructure, mobile environments, data, and integrations 
  •  Have to have the feel for the tech landscape in the boardroom 
  • Cybersecurity and data governance will be followed, not just known like before

9.  New Collar Jobs Increasing – How does technology work in different industries? 

  • IoT is making new jobs, new categories of learning and technologists 
  • Learn both hard and soft skills, not just a four-year degree  

10.  Online Marketplaces – Friend or Foe of the Traditional Market 

  • Online stores are changing the market in all sectors 
  • Brick and mortar stores just have to make some changes to keep up, along with looking out for new challenges, but it can be done 

11.  Subscription Prices Harder to Figure Out – How will you price your products and/or services? 

  • Make sure to learn Accounting Standards Codification 606 which came out in December 2017 
  • PSA tools like ConnectWise can possibly help with that

12.  As-A-Service World – That’s the tech landscape in a nutshell.   

  • New technologies are beginning to intrude into the as-a-service world 
  • More and more customers are using as-a-service and expect much from it.  Companies have to make sure that they are really up to date and not just talking.   


There are huge changes that are coming and your company needs to keep up or be left.  There are new markets coming to play that can be taken advantage of.  For example, there’s authentication-as-a-service, analytics-as-a-service, artificial-intelligence-as-a-service, drones-as-a-service with many more coming along.  To keep up with all the changes invest in training for new skills, workforce development, and many more areas to be able to take better advantage.  Time to start planning for the future. 


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What is Blockchain? 2 Positives and 2 Negatives

blockchain, ai, machinelearning, computers, IT, iot, internetofthings

Source: Digital Trends


What exactly is blockchain and how can it help your business?  That’s a good question where the answer is changing all the time.  It’s a new technology so many studies and trial and error are going to be completed before that can even begin to fully answer that question.  Here’s what is known right now, which is plenty. 

Positives of Using Blockchain

  1. The blockchain is located in one location – Nobody has control of any of the information going in or out.  This means that all the work that enters and exits is original content that has not been modified.  It’s a public ledger that is extremely difficult to tamper with.  The built-in layer of protection used to encrypt everything is something that standard technology cannot imitate.  
  2. Blockchain has been around for over 40 years – The technology is new and only a decade old, but people have been studying it since at least 1976.  The blockchain is something that will take time before it can become fully useful for what companies really want to use it for.  The technology right now is just so expensive to get as blockchain uses such advanced calculations.  The power these systems need is what has to be taken into consideration.  This is the reason that cryptocurrency was introduced first.  It rewards people for using its technology through monetary means. 

Negatives of Using Blockchain 

  1. The Computing Power Needed (electricity) – Large blockchain systems eat up tons of electricity.   It’s equated to the amount of power a small country uses.  Not very appealing for emerging markets or developed nations. 
  1. The Speed of Transactions – Block in chains that are created takes time to verify in the distributed network.  At worst verification took over 41 hours.   The fastest is 15 seconds.  Still too long if someone wants to buy something.   


The problems will be fixed over time as blockchain is used more.  The question is how long will it take before it’s up to standards for full use in the business environment?  Right now, it’s used for smart contracts and maybe protecting people’s personal information, but that’s about it.  It will take some time before it can handle data more quickly so it can be used for more types of business transactions. Patience is key! 

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3 Benefits Of Combining These Two Trends

trends, artificial intelligence, machine learning, deep learning, security, data security, data, data management, ai, AI, blockchain,

Blockchain and Artificial Intelligence (AI) are two of the biggest technology trends of our time. Here we look at what would happen when you combine these two megatrends. We explore 3 potential benefits from blending AI and blockchain technology.

Source: Forbes

Definition of Artificial Intelligence 

The building of machines that have to think intelligently.  Assisting this through machine learning, artificial neural networks, and deep learning.   

Definition of Blockchain 

This is a sort of file system that stores files in encrypted distributed ledger format.   The data transfers to different computers and devices that are on the company network in tamper-proof, fast databases.  Access is for those who need to know only.   

There are only a few studies done on combining the two trends themselves, but there isn’t much ability to do it yet through the technology that we use presently.   

3 Benefits 

  1. AI and Encryption Work Well Together – This could benefit businesses that have to store a lot of information on the Net.  This will help the company grow, both in influence and customer satisfaction.  Because of all the data gathering occurring when customers log on, there will be a high rate of trust for the customer on how businesses handle their information.  Blockchain helps as everything going through it automatically encrypts.  The only thing that needs any safety measures are the keys giving access to the system, so the cost is cheaper than what organizations have to pay now for data security measures.  Even then security fails but won’t for blockchain.  In this AI works well with blockchain because their information is encrypted due to building algorithms that already read encrypted data.   
  2. Blockchain Explains Decisions Made by AI – AI making decisions and reading the results is hard for people to understand.  This is because of how it weeds out information that it needs.  Auditing of data will be a lot easier as the decisions records are data point by data point with no tampering.  Transparency of everything will help grow trust among the public.   Because of this, they will know that their data is safe with said company. 
  3. AI Manages Blockchain Better – Machine learning powered algorithms can get rid of brute force approaches to finding data.  AI will act more smoothly and not as rough when trying to find a piece of data.   


These two trends when put together can revolutionize the work environment and processes.  They strengthen each other in what they do.  The two also allow for better oversight and hold more accountability to actions completed. 

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4 Things to Know About the Blockchain System

blockchain, innovation, business, competitive advantage

Source: Harvard Business Review


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.   


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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5 Steps for Higher-Quality Data

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


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.


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


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.


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

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


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.


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


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