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14 Key Methods of SISP

SISP, sisp, strategic information systems planning, strategic business systems planning, business planning, planning, business

Source: Brian Fergerson

Introduction

I’m in the middle of rewriting this post, and the link I used before doesn’t work.  Because of this, I decided to give you another source that’s extremely detailed.  It’s an annotated bibliography that answers 5 questions on methodologies of SISP.  I think you guys would enjoy reading it.

SISP  defined is the process of determining computer applications helping to meet business goals. Consider it a critical management tool because it focuses on strategic goals. You need it for completing strategic movements anywhere in the company.  Consequently, things could get sloppy really fast. SISP keeps things organized when implementing change in agencies, especially when dealing with information technology (IT).

Why Use Any Methodology of SISP

Different methodologies SISP use are:

  1. Business System Planning
  2. Strategic Systems Planning
  3. Information Engineering
  4. Information Quality Analysis
  5. Business Information Analysis

When choosing the changes you have to realize that these choices will influence the implementing of the method. It may also be to your benefit to choose a bunch of different ways to complete it. This will help to keep a balancing act together when planning for the future.

Why Use SISP At All?

It seems that top management still doesn’t fully support using SISP methods to better compile and carry out new changes. Research suggests agencies cannot reach success if there isn’t a proper aligning of business and information systems strategies. You need to have a good mix of changes for implementing the changes in order for there to be no complications.  If it doesn’t fit, most likely there won’t be a proper aligning between the information technology (IT) department and the rest of your company.

14  Methodologies

The different methodologies are:

  1. Business planning
  2. Competitive impact
  3. Computer-based applications
  4. Conceptual analysis
  5. Information systems planning
  6. Information technology resource planning
  7. Methodology
  8. Strategic alignment
  9. Strategic information systems planning (SISP)
  10. Strategic management
  11. SISP approach
  12. SISP method
  13. Strategic management planning
  14. Strategic planning

Summary

Go ahead and switch around methods.  Doing this will help you find better solutions to your problems.  And make sure to do it correctly.  If not it can cause huge losses of money due to investing in solutions that won’t work.  The benefits can only be seen when alignments occur between IT and strategic business strategies. Due to this, IT resources have to target areas in your business considered most critical to success. This has to be done with upper management, to include CEOs, CIOs, and managers.  Upper management is super critical in SISP. If they are not willing to change initiatives and programs, there will be no change in the agency.

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4 Steps in Strategic Management Processes

processes, strategic management planning, strategic, management, planning

Source:  Management Study Guide

Introduction

What exactly are strategic management processes?  It’s the defining of the company’s strategy.  This is all about gaining a competitive advantage over your competition in the industry that you belong in.  Your managers and yourself choose types of strategies you plan on using or think would work best to reach higher performance levels. It’s a continuous process that never stops.  Here are four steps to help the process along.

4 Steps of Strategic Management Processes

  1. Scanning the Environment – Collect, analyze and pass along information for strategic means. Analyze internal and external factors that influence the company. As a result of completing this process periodically, adding on improvements are completed continuously.
  2. Formulating Strategy – Choose the best course of action. Create corporate, business and functional strategies.
  3. Implementing Strategy – Work the new strategy. As a result, creating processes to improve systems in addition to implementing is done around the clock.  Consequently, this will increase performance and standings.
  4. Evaluating Strategy – This is the final step.  There will be a reviewing of internal and external factors.  Due to these reviews, a measuring of performance can be done.  As a result of these actions, you will be able to better fix issues identified.

Why Use It?

Always use these four steps in the order above when creating a new strategic management plan. There’s no stopping when you realize that all the steps work together and in a chorus. You can and will evaluate and control your business placing in the industry you belong in.   The most important thing that you need to remember is that it evaluates competitors and sets goals and strategies to better compete with them.

Summary

It seems like there’s two questions that need asking.   Is the process already successful as is it the opposite and need fixing?  The role of strategic management processes is to create functional areas and make sure they work well together. Furthermore, it is to keep an eye on goals and objectives of what you want as the business owner.  Consequently, it helps to make sure that new processes are followed and used correctly.  Especially relevant is that if it isn’t used correctly damage can be done to the company in question.  Just take your time and don’t jump the gun so to speak.

 

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Big Data Classification and Architecture

classification, big data, analyze, data analysis, data modeling

Source:  IBM developerWorks

Introduction 

Big data classification is not a new concept.  It’s been around for a while, people just didn’t realize it for the most part. What changed is that now companies know what it is and use the results to find new clients.  The question then becomes, how is the architecture created to handle all the different information out there?  First, you have to classify the data, then look for the right architecture that you’ll need to use.  Classifications are actually big data problems that your company has.  That’s why you need to classify the data first, then decide on what kind of architecture you’re going to need to fix the problem. 

Big Data Classifications 

 Utilities:  Predicting Power ConsumptionData creation completed by machines.  Uses smart meters to measure consumption and power grids.  Big Data solutions need the ability to analyze supply and demand. 

Telecommunications:  Customer Churn – Data creation done by reviewing social media, Web and transaction data.  You need to create detailed churn models to keep up with the competition.  Big Data solutions can help by using predictive analytics. 

Marketing: Sentiment Analysis – Completing data gathering through using social media and the Web.  Sentiment and profile data needs integration to find any useful results. 

Customer Service: Call Monitoring – Human generation creates the data needed here.  IT departments need the ability to analyze application logs in standardized formats to create Big Data files.   

FSS/Healthcare: Detecting Fraud –  Creating data files here is through using machine, transaction and human generation.  This needs real-time or near real-time monitoring to be effective.  There’s no other way to react quickly if there are reporting of unusual activity.   

Make sure that when you’re classifying the data that you look for characteristics.  This will help you to figure out what kind of architecture you’re going to need to create a Big Data platform for your agency.    

Big Data Architectures 

Analysis Type – Is analysis completed real-time or saved for later?  Consequently, choices here will decide types of tools, hardware and data sources to name a few.  Because of this, it can affect how you want to analyze the data.    

Choosing a Processing Methodology – This is where you’ll choose the techniques that will be used to process the data.  Your business requirements will decide which technique is going to be used.  Choose wisely.  It can be either predictive or analytical models, ad-hoc query or report building.  Which do you need more for your business?   

Data Frequency and Size – Knowing these two things will help you to decide which storage unit, formats, and tools you need.   Consequently, the frequency and size depend on data types also.   

The types of data that need gathering, plus content formats which are key to choosing which tools and techniques are going to be used are extremely important.  Considering what the source is of the data is important too.   

Data Consumers

Business processes/users 

Applications 

People in different business roles 

Process Flows 

Different data repositories and/or applications 

Hardware – Make sure to choose the right hardware. Because you understand limitations of any hardware that you decide to buy to support this, it so helps you decide on the solution for the company. 

Summary 

A lot goes into choosing which big data architecture is best for your company.  Don’t rush into it, take your time when figuring this out as it will cost money.  Think about which area you need the most help with, then decide on which architecture you’ll use.  It’ll help in the long run to save money and gain more customers.    

 

 

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8 Success Factors to Use Business Intelligence

business intelligence, business intelligence systems, intelligence systems, business

Source: Cloud Data Integration Software | Matillion

Introduction  

Business intelligence can be completely transformational if done correctly.  Users can get access to self-service reports which in turn creates a faster and more efficient timetable.  Well-designed data warehouses can reduce errors and possible conflicts in the information found.  All that needs to be done to make sure that this happens is to build a good business intelligence system.   

4 Factors of Successful Business Intelligence   

  1. Don’t Focus on Every Type of Data – Focus on one subject.  Which area would benefit the most from getting better reports?  Work on that area until fully improved, then move onto the next area.  It’ll help to build your reputation among the competition.  It seems like will help to lower your costs and shorten timelines also because of the more streamlined setup. 
  2. Ease of Use – The front end needs to be with no complicated, less understood technical terms.  Along with this creating ease of use, tables help make finding data easier.  And then reports can be built this way if not using self-service options.  In other words, have a search option available so that people can find data through a browser instead of through programming alone.    
  3. High Performance – Try using modern technology when building your business intelligence system.  in-memory analytics, columnar databases, SSD disks and advanced caching will help to speed up your system, therefore making all users happier to do the work. 
  4. Choose Technology Carefully – Don’t just go for the one that’s bundled to what you have already.  Make sure you check out everything and all your options first before deciding to go for the easiest.  Consequently, you might get more bang for your buck and save at the same time. 

4 More Factors to Consider

  1. Understand the Cost – When you see the license it’s really only about 20% of the cost itself.  The other 80% includes hardware, consultancy, and software to run the databases and operating systems in some cases.  It’s not always this way, but it’s something to consider when shopping around. 
  2. Advocates – Who are your domain experts?  They can become your advocates as they really have a full understanding of what your reporting requirements are. They can test the numbers and turn in the reports correctly.  Because of this, they are your best bet for getting the rest of the workers to accept implementing the new system more quickly in the work area. 
  3. Reconcile – You have to make sure that your numbers add up correctly, or you’ll have your users losing faith in you and your company.  Ensure the reconciling of the numbers back to trusted sources in order to make sure everything adds up correctly.  If including calculations allow a way for the user to find out what they mean through something like tool-tips.   
  4. Involving the Executives – Business intelligence works much better with involving executives then not.  Because of this, the executives can make decisions easier and more quickly, along with allocating resources to the right places. 

Summary 

Involving everyone in the process of implementing a new business intelligence system is a priority.  It’s like anywhere else.  It the worker doesn’t see interest in the executives and managers, then they aren’t going to want to work with it either.  Consequently, it could cause you problems in the future and a loss of money.  To deter this from happening, make sure all are on board to support implementing the new system.  Focus on small areas first to improve, then move to other areas.  This will help you to not spread yourself too thin.  Make sure that the system is put into action is easy for everyone to use and is high performance.  Otherwise, people will really hate to work with because it’s too slow.  It won’t be cheap to build either, so make sure you shop around for what you really need instead of what’s there.  If you do this, your business intelligence system could become one of the best.   

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10 Privacy Problems of Big Data Analytics

privacy, data security, security, data

Source: Rebecca Herold

Introduction  

With all the data leaking, hacking episodes, and finding out that some data agencies use people’s emotions against them to change their mind on a subject pertaining to elections, it’s no surprise that data privacy is such a concern.  I know it’s a big concern of mine.  I don’t want my information to be abused or sold to third agencies to use as they will.  Who wants that?  Tonight, I decided to share 10 big privacy concerns we all have to look out for. 

10 Privacy Problems

  1. Privacy Breaches and Embarrassments –  We all know about the problem Facebook has now due to this.  Instead, let’s look at how companies might use data that they captured on their sites of pregnant women who didn’t tell their families.  Said family finds out through flyers in the mail, the wife is then embarrassed as she didn’t want to tell anyone yet.  These situations happen a lot.
  2. The Possible Impossibility of Remaining Anonymous –  With so much data being shared and the powerful analytics being used to decipher it, makes sharing information anonymously almost impossible.  The customer needs to be able to have a way to make rules on how to use anonymous data.   
  3. Masking Data Might Become ObsoleteData masking needs to be done correctly.  If not, it’s very possible that Big Data Analytics might just break open the mask set in place.  You must set up policies, procedures, and processes effectively in order for the user to keep their masks.   
  4. Unethical Use Based on Interpretation – This is the big one on everyone’s radar today.  Trying to influence behaviors and decision-making processes is a super huge threat presently.
  5. Big Data Analysis isn’t Always 100% Accurate –  The data gathered isn’t always on point, which means results aren’t always going to be right.  This is a problem.  It could also be flawed algorithms or using incorrect data models.  The more complex the algorithm or data set, the more chances for mistakes to occur.  People, in turn, can be denied services, be falsely accused of something or even be misdiagnosed as an illness that they don’t have.   

If That Isn’t Enough…

  1. Discriminating People – Analytics has to be totally objective here or it’s very possible that people can be turned down for opportunities.  This includes promotions, hiring job candidates, and getting loans.   
  2. A Gap in Laws to Protect Involved People –  This one pertains to today especially.  It’s amazing how it always takes a situation before companies have to look at their business models to see how they can better protect their users.  Most still only tell the user about privacy risks thinking that it’s enough, but it isn’t enough, not anymore.   
  3. It’s Most Likely that Big Data Will Last Forever – There’s not much indication that any company will ever delete all the data that they’ve gathered about customers over time.  It’s too valuable for them to consider giving up.  So, the repositories just keep on growing as the insights are invaluable.    
  4. E-discovery Problems – Companies, for the most part, have to provide paperwork for litigation.  Now that most all documents are stored in repositories, analytics has to be used through what’s called predictive coding.  This helps to find and review papers needed in the litigation.  The concern here is that the code might be faulty, not finding all the data and documents for litigation proceedings. 
  5. Patents and Copyrights Might Become Obsolete – The big concern here is that when patents are submitted the patent offices might have a hard time determining if the patent is unique or not.  There’s so much data that it might just be too difficult to verify.  This is affecting copyrights also as it’s so hard to control information, which in turn affects royalties too. 

Summary 

Don’t get me wrong.  Big Data is great for business and can really help improve processes.  A lot of technology upgrades and inventions are occurring every day in order to keep up with all the new trends.  What needs considering is the 10 problems above to make your business a leader in data privacy along with many others.   New accountability policies and procedures need to be created to cover all these changes.  Make sure to implement privacy and security controls before putting anything to use.  Keep vigilant and don’t do what other companies have done in the past, by either ignoring the problems brought up or brushing them under the rug.  That’s my suggestion.  Customers will like you a lot more for it. 

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4 Best Practices for Big Data Privacy

big data, data, data privacy, privacy, security, data security, cloud, cloud storage

Source: TechTarget

Introduction 

Big data is becoming a more popular method of gathering data for business purposes.  It seems like it isn’t just for storing data anymore.  As a result, more companies are using the data to gather useful information via business events.  This can be anything from reviewing contracts to finding new ways to entice potential customers to your store.  Because of this it doesn’t have the old way of doing things like passing information from the company server to data storage.  Consequently, it uses virtualization architecture to draw from large content stores and archives; as a result of finding this information, it becomes a global resource.  In turn this allows for better forecasting and predictions that might actually work. 

Sources of Privacy Concerns 

  • Quality and Accuracy of Data – How will it possibly negatively affect people in decisions being made?  How does the Internet affect data through possible bad Internet searches?  Is it possible that the scientist looking up the information might be using unverified information without realizing it? 

Best Practices in Big Data Privacy 

  1. Developing High Competency – You need to become extremely proficient in finding, buying and managing cloud services which are considered an intragyral part of big data for keeping costs down.    There are also companies that prefer not to make the investment and in its place use cloud-based applications, infrastructure, and processing power.  Anyways around it, to ensure privacy there has to be constant monitoring and audits of cloud services that your company is using.  Checking on data integrity, confidentiality and availability are all a must. 
  2. Implementing Converged Storage – It’s much more efficient and reduces possible errors.  Because of this, it increases data quality and accuracy.  There’s going to be a reducing of duplicate data being stored in the same locations and increase cost efficiency too. 
  3. Properly Sanitizing Data –  Make sure to analyze, filter, join, diagnose data at the earliest possible touch points.  It’ll make work much easier without having to go back fixing errors while saving you money in the long run. 
  4. Encourage and Invite – Make some sort of process for consumers to be able to gain access to, review and correct information already collected on them, being at no cost and user-friendly.  Ensure finding privacy policies are easy to reach.  Most of all, make sure to have an easy way for people to contact you with questions or concerns that they have.   Transparency and ease of access to be able to talk to you is key. 

Summary 

Asking for the consent of gathering information is not enough now.   In conclusion, there’s so much gathering of data from others that it isn’t really a question to ask.  More on point is something like telling customers how they can restrict the use of their information or delete it.  Consequently, it’s not something that all companies would offer to their customers, therefore you should try it.  This is something that most likely is going to become a requirement for companies to tell customers in the future.  It seems that enabling privacy using best practices is going to be your best bet.  Most noteworthy it will help to increase the levels of trust and transparency that you and your customers will have in the long run, while saving money at the same time. 

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Top 3 Cybersecurity Trends (via iamwire)

cybersecurity, security, cloud, iot, internetofthings, hackers, allowing hackers

Source: iamwire

Introduction  

Keeping up with cybersecurity measures to keep your computer and system safe is becoming more and more difficult.  Hackers are coming up with new and unusual ways to break into systems all the time.  Sometimes it’s for work, sometimes it’s for fun and sometimes it’s for criminal purposes.  The thing is, if you want to have a reputation for being a safe online store where people can visit and shop without fearing that their data will be taken, then you always have to be on top of what’s going on in the cybersecurity world. 

Just some statistics from 2016 to help show how important cybersecurity is today.  It’s a little outdated, but it gets the point across.   

  • 18 million new malware samples were taken in the 3rd quarter alone. 
  • In the year 2016, there were 400 ransomeware attacks every day. 
  • 78% of people still click on unknown links even though they know about the danger of doing this.  

3 Cybersecurity Trends  

  1. More IoT for Business Operations – The Internet of Things is becoming more popular every day in working environments.  Because of this, it’s making corporate networks vulnerable as there are so many windows where attacks can come through now.  These devices being used for IoT are not secure, allowing hackers to make programs and fake apps that people might download to make work easier, not knowing that they are allowing hackers entry into the system.  Vigilance is key to predict and find where an attack is happening. 
  2. Mobile Threats are Rising Smartphones are the PDA of the ’90’s.  Hackers can use these phones and are using them to spy, use extortion and steal data from companies.  As the smartphone becomes more popular as a tool when offsite, working from home, or in the company, it allows for many more types of opportunities for criminal hackers to gain entry. 
  3. Security of the Cloud –  As the cloud is becoming popular as a place to store all sorts of data, make sure to check the security being used by the company you’re working with.  If their security isn’t up to date, it doesn’t make sense to use them as your data will be vulnerable to attacks.  Make sure to look up their security policies and see when they conduct updates so you get a better idea of how well they take security seriously.  If it isn’t to your standards, then keep shopping.  There are lots of third-party vendors out there dealing with cloud environments.  Also make sure to manage the cloud program after implementing it, along with login credentials.  If either is being poorly taken care of, hackers will find a way in.  

Summary 

Invest in an upgrade, making your company safer for your employees and shoppers to work and shop there.   They will appreciate it and so will you in the long run.  Better to spend the money this way instead of having to hire someone to come and fix your system after being infected by an attack. 

 

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Top 10 Strategic Tech Trends for 2018 (via gartner)

tech, trends, AI, ai, internet of things, iot, cybersecurity, security, machine learning, deep learning

Source: www.gartner.com

Introduction  

With all the trends coming out for this year alone, I decided to look at strategic information systems planning (SISP) trends for a change.  It’s very interesting to see how they actually plan on possibly implementing these new trends that are coming out all the time.  Here’s the list… 

 10 Trends for Strategic IS Planning

AI and Machine Learning Immersion

  1. AI Foundations – This is considered to be the next big battlefield between vendors who build these systems until at least 2020.  AI is so popular and in demand that it can be no other way.  Now how to use AI is the next big question that needs answering by the vendors and the customers who buy it.  Artificial intelligence needs to be able to help make strategic decisions, enhance the business model and ecosystem, and drive user/customer experiences through to 2025.   Strategic planning in this aspect involves data preparation, integrations, creating new algorithms to run AI, new ways to train personnel how to use AI, and creating new data models.  Personnel involved here are data scientists, developers and business process owners who will have to work together closely. 
  2. Intelligent Apps and Analytics – Apps are beginning to implement AI in how they work.  They cannot exist without AI and machine learning capabilities.  Because of this, it is, in turn, changing how people and systems work together at the workplace.  It doesn’t replace people. It’s a strategic area where analytics uses machine learning to better automate data prep and finding and sharing insights.  People in the company using this are business users, operational workers, and citizen data scientists.   
  3. Intelligent Objects – These are physical things that use AI to operate.  Included here are self-driving cars, robots, and the Internet of Things.  Self-driving vehicles are already in use in agriculture and mining but will take more time spreading to other vehicles, like cars.  By 2022 more streets will become more user-friendly towards them, but more studies need completing. 

Blending of the Digital and Physical Worlds

  1. Digitial Twins –  It defines a representation of real-world entities and/or systems.  This deals with IoT for the most part.  If the twin is built correctly then it can really support improving business decision making processes.  The twin is linked to their counterpart in being able to better understand the state of the system, better responding to changes, improve operations and add more value to the company as a whole.  In the future, they will fuse together with their counterparts and AI type capabilities.  Consequently, people in the company who will use this are city planners, digital marketers, healthcare professionals, and industrial planners. 
  2. Edge Computing – This is where information processing, gathering and delivering content is put closer to the sources of information.  Companies need to begin using edge computing as it supports IoT, creating better connectivity, bandwidth, and functionality.  Cloud helps support this as it doesn’t need a central location.   
  3. Conversation Platforms – This is going to be the one that drives the next change with how people perform and interact with the digital world.  People can talk to computers and the system will be able to act on it.   If the person using the computer is confusing it, it can ask for clarification.  It’s becoming a primary design goal using with hardware, core OS features, platforms, and apps.  Developers need to work on the communication aspect of the platforms, as it’s still very difficult to make them work.  People need to speak very clearly about what they want, and it causes more frustration than anything else right now.   
  4. The Immersive Experience – Virtual, Augmented and Mixed Reality are all here.  For any benefits for business, they need to look at how it can help employees work better.  The design, training and visualization process is key to making this work.  When dealing with Mixed Reality, it’s creating some great technology that might actually be good for the work environment.  Tools already developed and in use includes Head Mounted Displays (HMD) for VR and AR, smartphones and tablet-based AR and environmental sensors. 

Exploiting Connections

  1. Blockchain – This technology is rapidly spreading everywhere, not just for the cryptocurrency.  The blockchain is a disruptive technology as it changes how business is ran, for both startups and enterprises.  The only problem here is that blockchain technology is more promise right now as it’s immature for the next two or three years. 
  2. Event Driven – This includes anything from recording an electronic purchase at your website, to a recording when a plane lands.  Anything action completing dealing with the company is an event.  Because of this IoT, cloud computing, the blockchain, memory data management, and AI are included here.  With these platforms, its ability to find and analyze events more thoroughly grows.  As a result, people involved here include IT leaders, planners, and architects. 
  3. Continuous Adaptive Risk and Trust –  This deals, for the most part, cybersecurity.  There have to be real-time, risk and trust-based decisions and adaptive responses.  Basically, the company needs to embrace opportunities and manage the risk involved with having digital business actions completed.   

Summary 

Strategic information systems planning is very complex.  There are so many different areas needing planning in the next five years in order to keep up with all the new technology.  Most are in its infancy, but it’s growing fast.  Better start planning now! 

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Information Technology Trends 2018 (via comptia)

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

Source: www.comptia.org

Introduction

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.   

Summary 

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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Big Data Trends for 2018

AI, artificial intelligence, machine learning, internet of things, IoT, iot, business intelligence

The expansion of the Internet of Things (IoT) has added innumerable new sources of Big Data into the Data Management landscape and will be one of the major Big Data Trends in 2018 and beyond.

Source: DATAVERSITY

There are so many different types of devices that are adding to big data every day.  Because there has to be a secure and safe way to handle, transfer and store all the data, new ideas and trends are popping up all the time.  Here are just a few trends for 2018 on big data. 

Business Intelligence Trends  

  1. Business Intelligence   
  • Using the cloud for business intelligence will increase 
  •  Improving data visualization models/self-service software through analytics 
  • Expanding into new markets will be done using big data to help with decision-making 

Cloud Trends  

  1. Create a Niche – Will help build better research options and more competition 
  2. Hybrid Cloud – Not everyone wants to turn all their data over to the cloud, so hybrid formats will become more popular.  Some will be kept on-premise and some in the cloud 
  3. More Departments will Access the Cloud – Interfacing with Cloud technology is much easier than before, so IT departments won’t be as needed for this  

Data Analytics Trends 

  1. Include Visualization Models – Data discovery has begun to include presentation along with finding and analyzing the data.  Because of this visualization models are becoming more popular as a way to show what the data is telling you.  Improving these models has become very important as people can absorb visuals better than just words and numbers. 
  2. Predictive Analytics –   Most companies in the past have just used historical big data to find answers to solutions.  Now they’re switching over to predictive analytics because it’s more helpful and gives real-time results through knowing your customers. 

Internet of Things Trends (IoT) 

  1. Improving Retail – IoT gains information, therefore allowing you to market better to your customers and prospective customers.  Some companies are already beginning to invest in sensor-based analytics so they can see which part of the stores are being visited the most. 
  2. Healthcare – Big data now drives healthcare solutions.  It could also change how people pay their medical bills and access healthcare sites.  Wearable technology can help improve treatment, networked devices can tell patients when to take their medicine… 
  3. Changing Security Challenges – In 2016 hackers really damaged the Internet when they attacked through using the IoT.  These weaknesses will continue to grow the more IoT is used. AI and machine learning can help better secure the connections.  More workers will need to become experts in these fields to be able to handle issues that come up. 

Machine Learning (ML) Trends 

  1. Business Development – Digital business needs to move towards automation.  Machine learning opens the door to being able to use machines with limitless applications to help your business grow. 
  2. Education – It helps to improve teaching.  The algorithms are creating lists of resources to help teachers find sources to use for their classes.   
  3. Healthcare – Finding and treating diseases correctly is a big part of why healthcare is starting to use ML.  Scientists are also creating algorithms to find tumors.  What ML is being used for now by the healthcare industry is: 
  • Behavior change 
  • Predicting outbreaks 
  •  Discovering drugs 
  • Radiology 
  • Making records all electronic 
  • Diagnosis and disease id 

Artificial Intelligence Trends 

  1. Research – Presently scientists are looking to make algorithms that will make it easier for people and technology to communicate with each other easier through natural speech.  Right now, AI and ML help people through automating jobs like:
  • Being able to read the handwritten material 
  •  Identifying faces 
  • Helping people to learn better 
  • Take care of cognitive skills like planning and reasoning with partial information 

AI and Cyber Security 

  1. AI to combat AI hacking – Make sure to look into this one as it can really help protect against future attacks.  These kinds of defense systems can look into the past to learn what happened, then make decisions on how to protect against it.  Baselines can be created so the programming knows when abnormal activities are taking place.  AI can change the playing field as it can create defense strategies based on what it finds out. 

Summary 

There is so much going on now and it’s so exciting to learn about it.  So many choices to consider if you want to try.  Which would you like to learn more about?