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Top 5 Trends From Mobile World Congress 2018

mobile, trends, mobile trends, ai, iot, industrial iot, devices, ai

Source: Forbes

In March this year, there was a mobile trade show that talked about many new trends.  Everything shown there, from AI to 5G.   Here’s a list of them and why they might affect us all… 

  1. 5G – This implements faster connectivity.  Because of improved speeds, the user experience will go up.  In turn, this allows for a better experience between consumers and brands that they might be following.   What’s in reach is 100x better speed than 4G and 10x better than broadband speed.  Because of this IoT, AR, VR and Edge Computing are becoming closer in reach than before.   
  2. Artificial Intelligence – 5G will be able to handle AI for a change.  This is good as AI can be used to plan and manage networks.  Consumer demand predicting is going to become easier.  Easing tariffs will become the norm.   AI and telecommunication are just scratching the surface of what’s to come. 
  3. Augmented Reality – Apparently Google is planning on putting AR in every new smart phone by December 2018.  That will be something to see and will be extremely interesting to see how they will make it work on a phone.  On top of that AR can be used in supply chain management and allow customers to test products almost before buying.  Because of this innovators will be able to better meet demand. 
  4. Industrial IoT – Industrial IoT uses technology differently than if it’s a consumer.  Things needing consideration is moving business data securely from one site to another, sorting it out, and how it’s going to be used. Mostly they were talking about using IoT for tracking vehicles and robotics. 
  5. New Devices Coming to Market – New mobile features will possibly include virtual reality and biometrics.  That will be interesting to see how they can enable those features.   New types of cooling systems and having almost no bezels might just be coming next! 

Summary 

It’s going to be an interesting time, with many new innovations coming to our smartphones.  Because of these changes, it’s going to make everything so much easier to complete without even having to go to PC or Mac it looks.  Just how much customer improvement is there going to be with all these potential implementations?  That’s the big question.    

 

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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.   

 Summary

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

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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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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9 BI Software Upgrade Mistakes

BI, upgrading, upgrade, buiness intelligence, business

Types of BI

Business Intelligence (BI) is extremely popular and is a top focus. There is the traditional software, cloud services, mobile apps and social media. What type do you need for your business that will help and not hinder progress? Choosing the wrong software to fix your business problem(s) or having end users who don’t understand the new programming will cause failures.

BI Mistakes

  • Not defining the business problem(s) – Don’t jump the gun until you know exactly what you need. Buying for general capability is the worst mistake you can make. Look for defined problems that need solving.
  • Not getting commitment from end users before choosing the BI solution – Make sure to get people’s opinions of the selections available as they’re the ones who will ultimately use it every day. Otherwise, the tools will be ineffective as no one will use the upgrades. They have to approve of what might become the new initiative, or they will never use it. Don’t tell them they have to use it, help them to understand why they’ll want to use it when it’s up and running.
  • Not considering security or legal requirements – Follow data governance when selecting new solutions. It protects both your company and customers.
  • Don’t get swayed by features and forget legacy systems and integration – Most companies look and rank BI software by the features available. This is wrong. You need to look and see which app will be able to integrate with what system you use already. There has to be an ability to work with all your other business systems or it won’t work correctly.

More BI Mistakes

  • Not choosing a program that can scale and adapt to change – Choosing a solution that isn’t flexible is one of the worst things you can do. Self-service analytics are now the norm, where they can work well with new data sources. The ability to scale is necessary is as the system grows, the software has to keep up. You don’t want to have to buy new software every time the business grows.
  • Not considering the mobile workforce – You have to consider mobility. Being able to use the new BI solution on a smart phone is a great advantage and allows for more productivity.
  • Rushing implementation – Never rush the upgrade. Mistakes are going to be made with possible cost increases.
  • Insufficient training and underestimating costs of training – A few weeks of training isn’t going to cut it with today’s BI systems. The systems in place now are very complex. End users need a lot more training to work them. There’s also the need for ongoing training to keep the end users in the loop for changes to the programs.
  • Not leveraging intelligence and reporting – If you’re going to collect all this data, then make sure to share, analyze and act on what is found. It’s a waste to buy the new BI solution if not used properly.

Uses for Upgrades

This software is used in many ways. It is used to build reports, find risks and opportunities and forecast trends. Don’t become complacent with pre-defined sets of reports. You’ll miss changes that are occurring in the business world.

 

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3 Challenges to Business Intelligence and Analytics Adoption

big data, challenges, business intelligence, BI, analytics

Business Intelligence and Analytics

What is the biggest challenge today for upgrading your business? You’ll find the answer through using tools enabling business intelligence (BI) and analytics strategies. It seems that businesses are looking to adopt BI and more advanced analytics to achieve bigger gains. Upper management needs to explore more information to gather key areas that they feel need improvement.

The Biggest Challenges:

  • Data Preparation – The ability to complete data management in putting together and cleaning data before compiling reports and analytics.
  • Skills and Leadership – Is there a structure focused on needs of both BI and analytics along with more complex methods?
  • Ease of Use – Can those who are less technically inclined be able to use the system?

Preparing Data

The biggest gaps are in data prep. It is more difficult and more complex than it seems. The size of the company and how they are using BI and analytics doesn’t matter. They all see big gaps in how they filter, transform and prepare their data. Good data gathering and compiling is completed. It helps find both relationships and potential outcomes quickly. The information found here will help improve your company’s productivity and build competitive advantages. Consider automation and self-service also for those in the agency who are less technically inclined. Analytics are behind BI. Improvements are needed to be able to prepare, augment and explore the data gathered. This will help your agency to find root causes and trends. You can then build and update predictive models through using machine learning.

Skills and Leadership

Skills and leadership are key too. Agencies strong in these areas still say that there are many challenges in innovation, creativity, and leadership. Shown executive support enforces the need for upgrades or change just will not happen. If there’s no strong vision and support at this level, the initiatives will fail or underperform. A leader needs to make sure that they openly show the value of success from the new initiatives.

Ease of Use

Overworked data scientists or analysts already in your company because of changes being implemented need help. In this case, it’s better to create self-service and interactive tools where everyone can find them. Nontechnical users will need the guidance, and instead of overworking your data scientists and analysts, this can help. There’s software built to aid building systems that can detect relationships, correlations, segments, and outliers. It could use natural language to create queries. It could present context-based narratives of important findings. All sorts of things could be created with the new software to help the organization.

Increasing adoption of BI and Analytics

  • Invest in data and data prep
  • Nurture culture, skills, and leadership
  • Keep self-service and Ease of Use in the Forefront

Conclusion

If the challenges of gathering and preparing data are not addressed, it will become more difficult to find a use for BI and analytics. If strong leadership isn’t used to make the employees excited to use the new technologies, the initiative will be a failure. And if ease of use with smart/automated capabilities isn’t used, it’ll hinder adoption of BI and more advanced analytics. Consider these things before upgrading your systems.

 

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10 ERP Upgrade Mistakes to Avoid

ERP, strategic planning, business, upgrade, IT, information technology, information systems, competitive advantage

What Is ERP?

Enterprise Resource Planning (ERP) has to be considered part of the plan when upgrading your system. In this information technology (IT) has to be combined with the business process to help bring a competitive advantage to others in the same industry. It is anything from reading an e-mail to updating records of procedures. An ERP is an application that uses a centralized database to run the entire company. This medium allows data from one department for viewing in other branches of the same company. It can be bought either as modules for different parts of the agency, or a subset. ERP systems are there to enforce processes that your business will adopt.

Upgrade Mistakes

  1. Not explaining what new systems means to users before starting the project – If the users of the upgrades don’t know why it’s happening and are in agreement, the update will fail.
  2. Not load testing systems with scripts and end users – How do you know if your file loads are typical? Load testing with scripts and users will complete real tests in this area to make sure that the process will work or not.
  3. Not performing tests of the new process to see if it works or not – Will everything work as planned?
  4. Not taking change management or testing sincerely – Know everything you need to know about the change and regression tests beforehand, so you aren’t surprised by “new opportunities.”
  5. Assigning internal personnel as project managers – Get a consultant project manager as they will focus on the upgrade only. They’ll catch the mistakes made and keep things on schedule and budget.
  6. Not telling others of changes before they happen – End users don’t like change so make sure to communicate before so they aren’t caught off guard.
  7. Giving classroom training only – Allow for video training that the users can find and use if they come across a problem they can’t fix. Create a Knowledge area that they can access to see before elevating.
  8. Not moving components to open business standards – This speeds up future upgrades that will happen. Try to change reports and interfaces to open business standards. It will help a lot in the future, and possibly save money with more completed upgrades.
  9. Not archiving before upgrading and keeping up security during – Archiving before upgrading will save you time and money. It speeds up queries on large tables, and table conversions as they are will run quicker. Security wise, upgrades are to be need-to-know only. There’s no use in upgrading if spies find a way to get the information out to your competitors.
  10. Assuming internal tech personnel will pick up years of experience in weeks – Keep the consultants around for a while after the upgrade is complete. Enhancements aren’t easy to learn, and someone has to be there who knows how to run the system. Or at least until the first workers understand the system as well.

Impact of IT on Business

IT has a significant impact on how companies design, build and support their business processes. Information systems have a considerable effect on how these methods work. Agencies improve effectiveness and quality of products and services through empowering their employees. Something to consider.