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Article: Scientists Invented AI Made From DNA

Scientists Invented AI Made From DNA

Now, this is just so awesome! Don’t you think so? Scary to in a way. But I saw another article on something similar to this, and that we’re running out of space to store data due to no one deleting anything, especially businesses. Everything is considered critical, so they won’t get rid of any of their data. To combat this scientists have found a way to store Big Data in DNA. It’s a revolutionary concept, isn’t it? But DNA can store so much more and offers new opportunities. Decisions, decisions…


Last Wednesday, researchers at Caltech announced that they created an artificial neural network from synthetic DNA that is able to recognize numbers coded in molecules. It’s a novel implementation of a classic machine learning test that demonstrates how the very building blocks of life can be harnessed as a computer.
This is pretty mind-blowing, but what does it all mean? For starters, “artificial intelligence” here doesn’t refer to the superhuman AI that is so beloved by Hollywood. Instead, it refers to machine learning, a narrow form of artificial intelligence that is best summarized as the art and science of pattern recognition.

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Article: Surveillance That Can See Through Walls

Surveillance That Can See Through Walls: MIT Develops ‘RF-Pose,’ Which Can Identify And Track People


This is an amazing article about new technology being developed at MIT. How would you like to see what people are doing on the other side of a wall? This is being completed by using AI. It’s not meant to be threatening technology, it’s meant to help certain industries right now, like law enforcement and healthcare. It’s an interesting idea to consider. What do you think?

Here’s an excerpt of the article.


Researchers at MIT have developed a new technology, which they have dubbed “RF-Pose.” With RF-Pose, you can, in a sense, “see through” walls — the technology can identify the person, and their actions. This includes whether they are walking, how fast they are moving, waving, sitting, or standing. It integrates artificial intelligence, and offers somewhere around an 83 percent accuracy in identifying people from “a known group.”

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5 Myths of Big Data to Ignore

big data, data, data modeling, data science, data scientist



Big data is so popular and so talked about that people forget that it’s still a new field.  Digital data has been used and has been gathered for a little more than a decade; it’s useful for more than just big companies like Google or Amazon.  Every company can benefit from big data in some way as it aggregates the data.  Data exhaust and metadata are also created here and all three are integral for business analytics.  With all the information that’s out there, it still isn’t well known with more myths than facts being shared.  Here are five myths about Big Data, along with the debunking of them. 

5 Myths of Big Data  

Myth #1. Big Data is Only About Huge Amounts of Data – The three elements of Big Data are Variety, Velocity, and Volume.  How much data is gathered is the least important of the three.  Volume is the starting point, but it’s fluid and changes all the time.  The other two are better indicators to go from.  Variety deals with the different data types, be it files, video, social media posts, etc.  Velocity is the rate of change and how quickly it has to be used before it changes again.   

Myth #2. You Have to Use Hadoop for Big Data – Hadoop is open-source software from Apache to use with Big Data.   Consequently, Big Data is too big and too varied to be found, sorted and defined.  You really can’t just use one program, it takes many programs.  Hadoop is one of three different classes to work with Big Data.  The other two are NoSQL and Massively Parallel Processing (MPP).  On top of that, not all Hadoop components are built to work with Big Data and can be replaced with something that’ll work better.   

Myth #3. It Means Unstructured Data – A better term for it is multi-structured as there are so many forms and types of data that can be gathered.  Data models are built when the data is going to be used.   

Myth #4. It’s for Social Media Feeds and Sentiment Analysis Only – It does do this, but it isn’t the only thing that Big Data analyzes for you.  Any type of data is possible for analysis through using Big Data.  Don’t restrict yourself from its full potential. 

Myth #5. NoSQL means not only SQL – These types of data stores offer different ways to find and sort the data.  Technologies here includes key-value stores, document-oriented databases, graph databases, and big table structures to name a few.  SQL access can use many tools to complete its work.    


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Blockchain Technology – What Does it Entail?

blockchain, technology, data, data management, data security, iot, internet of things



I’m writing this post because I want to know more about blockchain technology, and I know you guys might want to know more about it also.  It’s so new I’m still a little confused about what it completely entails and what exactly the big deal about it is.   So, in writing about it, maybe we’ll all learn something new about it or get clarification on what it does.  Let’s dig in! 

What is Blockchain? 

As we all know, blockchain was first created to help support Bitcoin.  But many more benefits are being found to support other technologies as it’s so secure.  It’s very difficult to change anything that enters blockchain, so it makes it one of the most secure methods to be able to store any type of business transaction.  Only people allowed to use the system can get in to do any kind of modifying of information stored there, creating a workspace for trusted staff.  The blockchain is creating an environment that is for the most part safe from malicious intent.   

Blockchain Example 

Look at how Google Docs / Sheets works.  Instead of using the traditional method to change documents through sending an email and then having to wait for the edits to be done, with Google Docs and Sheets many people can work on documents at the same time, with the original visible to them through the entire process.  It makes such an easier work environment for everyone.    

Durability and Robustness 

The robustness is built into blockchain.  Because it stores blocks of information across the network, it can’t be controlled by single entities and doesn’t have a single point of failure.  For example, when Bitcoin was invented in 2008, it operates without much disruption besides of hacking attacks and mismanagement.  In other words, the only problems blockchain is having is from human actions alone, not from programming.   

Transparency and Incorruptible 

It automatically checks itself every 10 minutes, completing self-auditing actions at that time.  Those transactions are the blocks of information that was just mentioned in the last section.  This creates transparency data that are embedded in the network, therefore making it all public.  Because of this, it makes an environment that’s next to impossible to corrupt.  Changing any block of information kept in the network requires a huge amount of power.  It needs the power to override the entire network. 

What’s the Network? 

The network is comprised of a bunch of nodes, or computers attached to the network using a client.  The client validates and relays transactions, then transfers a copy to the blockchain, downloading it automatically when it reaches the network.  This creates a second-level network, changing how the Internet works.  Each node joins voluntarily, creating a decentralized community.   What decentralized means is that the network involved works peer-to-peer, creating an environment of collaboration. 

People Who Will Use it 

Financial gurus due to all the money transfers occurring online.   It’s being investigated for other industries, but this is the only one that uses it now. 

Will it Create a New Web 3.0? 

This provides Internet users another way to create value and verifies digital information.  Possible new business applications that can come from this are: 

  • Smart Contracts  
  • Sharing Economy  
  • Crowdfunding  
  • Governance 
  • Supply Chain Auditing 
  • File Storage 
  • The Predicting Market 
  • Protecting Intellectual Property 
  • Internet of Things 
  • Neighborhood Microgrids 
  • Managing Identities 
  • Data Management 
  • Anti-money Laundering and Know Your Customers 
  • Land Title Registration 
  • Trading Stocks 


There are so many opportunities for using blockchain for business.  It sounds like it can be pricey to carry out though, based on what Bitcoin uses to run its platform.  The good news is that research is being completed on how to better blockchain so more agencies can take advantage of all the advantages that it provides.  Hopefully, something is found soon, plus improving energy consumption so more can take advantage of it.    

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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


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


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


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.


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


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 


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. 


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


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. 


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


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. 


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


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. 


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.