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IBM Launches New Cloud for Data Science and Engineering (via Datanami)

machine learning, data science, data scientists, data management, data, ai, AI, Big Data, deep learning

IBM today unveiled a new cloud offering called Cloud Private Data that’s designed to help organizations utilize data science and machine learning techniques to generate insight from data, and then engineer AI products that put those insights into use. Separately, Big Blue announced the creation of a new consulting…

Source: Datanami


This is more FYI for us all.  I find it pretty cool that IBM is starting to help out with the creation of the application  that helps with data science and engineering.  It’ll make life and work so much easier for everyone.  Its main purpose is for use by data scientists.  It collects data, cleans and categorizes it.  Machine learning algorithms developed here build models and put them into production.

I think it would be a good thing to check out just because it might help companies to grow.  Think of all the information you could gather from using this and how it could put you ahead of the rest.



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CRM vs ERP: What’s the difference and which do you need? (via CIO)

ERP, CRM, data, data science, data management, data scientist

Both CRM and ERP systems handle contacts, companies, quotes, orders and forecasts… and they may handle line-item configuration, bundles, delivery schedules and invoices. Where does one start and the other stop? Behold this guide for the bewildered.

Source: CIO

Review of Article

What are the differences between CRM and ERP?  Both have similarities and differences at the same time.  CRM’s purpose is sales and support type companies.  In other words, they work directly with the customer, but don’t directly deal with fulfilling orders.  ERP users on the other hand focus on the process and logistics of producing items to sell.  They don’t usually call customers unless it’s to reply to complaints.

In larger companies they use both types of IT systems.  ERP handles the distribution centers, supply chains, currencies and manufacturing plants.  CRM benefits support purposes, sales and marketing, both domestic and international.  Smaller companies don’t need the entire package, they only will use fragments of either CRM or ERP,  such as accounting packages and contact management systems.

What is ERP?

ERP deals with financial data, production and optimization.  This type of system manages transactions, accounts payable and receivable, taxes, cash flow management, and quarterly statements.  It also handles production schedules, procurement, inventory, fulfillment centers and supply chain management.  ERP lastly coordinates production across many manufacturing plants, wants to find ways to maximize profitability, and improve performances of supply chains to name a few things that it does.

What is CRM?

CRM deals with sales force automation.  CRM has to support these business processes:

  • Lead Qualification
  • Forecasting and Pipeline Management
  • Creating Quotes and Construct Orders
  • Account Management
  • Renewals / Repeat Orders


Personally if my company were big enough I would consider buying and using bits and pieces of both, not integrating the two systems together, but doing something like before mentioned.  It makes sense to me to just get the parts I need to run my business, as much as I can.  The question becomes, which one is what your company needs?  Do you focus more on the customer directly or strategies and logistics behind the scenes?  Or do you think you’ll need bits and pieces of both?


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De-mystifying the Big Data Business Model Maturity Index – (via InFocus Blog | Dell EMC Services)

Big Data, ai, deep learning, machine learning, analytics, business modeling, data modeling, data science data scientists, W3C,

Bill Schmarzo illustrates each stage of the big data maturity journey, with the new Big Data Business Model Maturity Index (BDBMMI) infographic

Source: InFocus Blog | Dell EMC Services


This is such a helpful article as it goes through all the stages of Big Data maturity in your business.  There are five stages that companies go through to reach maturity.

5 Stages to Maturity

  1. Business Monitoring – Most companies get stuck here.  Implementation of Business Intelligence optimization is a constant and they think that is enough.  In order to move to Big Data there are steps considered, the biggest being the use of data analytics like data mining, machine learning, AI, and blockchain.
  2. Business Insights – Predictive analytics sorts out all the information being gathered through transaction/operation data,  internal unstructured data like emails and customer comments.  Also gathered is publicly gathered data like social media, tax records and home values for data that might be beneficial for your company.
  3. Business Optimization – As for prescriptive analytics it helps to make recommendations for the business and for customers.  This helps to improve business performance and aims the company in the right direction.
  4. Insight Monetization – This is where your company will leverage insights gathered from all the data gathered.
  5. Business Metamorphosis – Your company will change here to adapt to all the new insights gathered.  In turn it makes your company a lot more mobile and flexible to change, therefore giving it much more of a competitive advantage.


Through using data science your company can become much more flexible to change, and can help it grow.  The company has to optimize key business processes, improve customer experiences, and create new revenue opportunities for the sake of taking advantage.  Just make sure that the systems that you have in place can handle so much data, as it can become a problem if it isn’t.

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How Internet of Things Applications Evolve – (via DATAVERSITY)

IoT, Internet of Things, AI, linked data, machine learning, data management, data modeling, deep learning, smart data, W3C

Right now, even with many IoT use cases running successfully, businesses have to deal with IT standards, Data Management limitations, infrastructure limitations, and skills shortages.



The Internet of Things (IoT) is changing how business works.  Data gathered from many sources is then put together to analyze, predict and model the information.  There is much opportunity out there to take advantage of, but the process still needs smoothing out.

Things to Consider

There are IT standards to take into consideration, data management and infrastructure limits, plus a shortage of qualified people to run everything.  IoT can give many different types of benefits for business, but the question becomes what kind of technology will you use to progress?  Will you take advantage of the Cloud, Big Data or machine learning?  There are many opportunities available.  Here are nine benefits to consider.

9 Benefits of IoT

  1. It connects platforms to apps
  2. Smart cities with smart energy, self-driving transports, and intelligent security
  3. Smart homes and businesses connected with smartphones
  4. Wearable technology to keep track of your health
  5. The Industrial Internet
  6. Smart farming
  7. Connect engines to remote diagnostics
  8. Industrial safety
  9. Supply Chain monitoring

It provides real-time analytics that all can take advantage of in some way.  In the future all aspects of our lives will become more informed-decision than data-driven like it is now.  What do you think of that?

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What a Big-Data Business Model Looks Like (via Harvard Business Review)

big data, data, data science, data scientist, business, business intelligence, BI

There are three main ways to profit from the data revolution.

Source: Harvard Business Review


Interesting method of creating a business model by using big data.  This article is about three models that are becoming more popular than others and how they are going about the process.  One is through using results to create differentiating offers than others in your industry.  Another brokers the information gathered.  The third most popular method found is building networks to deliver data where and when needed.

The 3 Methods Broken Down

  1. Information Differentiation – Offer new services, customer satisfaction, give contextual relevance
  2. Information Brokering – Sell raw data, offer benchmarking, provide analysis and insight
  3. Information Delivery Networks – Support marketplaces, deal making, advertising


There are many ways to profit from using data as a business model.  But you have to choose wisely as to which model you would prefer your business to use and follow.  Take your time to decide, as using big data can really help you to get a head start compared to your competition.

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Big Data and Business Intelligence: What’s the Difference? – DATAVERSITY (via DATAVERSITY)

big data, data security, linked data, machine learning, deep learning, business intelligence, BI, AI, data science, data scientists, data models, data quality

Big Data has been in the media constantly recently, but its definition and use still eludes some enterprise decision-makers. Their enterprises have invested heavily in Business Intelligence (BI) processes and applications.



This is a very interesting article going over the differences between big data and business intelligence (BI).  BI uses both software and services to better transform data into what doable intelligence.  It helps make strategic and tactical business decisions.  BI is knowledge of what is going on and what it has to track.  The business even knows what and how to analyze the data and how to report it.

Big Data is similar but has major differences too.  It deals more with the unknown.  The goal is to learn what questions need to be asked by sorting through operations and machine data.  After they become known, use BI if wanted to find more information and in creating reports.  A huge plus of using Big Data is that it integrates analytics to business operations as events take place.  In other words, Big Data impacts business results directly unlike BI.

5 Challenges Big Data Addresses

  1. Capturing and storing large amounts of data efficiently.
  2. Analyzing data so companies can find a better understanding of what it does/what its customers want and how to discuss the needs found.
  3. There are huge amounts of data being collected here. How it can support the processing and analysis directly in secure fashion?
  4. Companies sifting through data and asking important questions. They need to know how to visualize the results too.
  5. How to cut delays and latency.  Then analysis could add to the operations of the company.


If your company can adjust to the changes found here in real-time, it can really give you a huge competitive advantage.  Especially with using Business Intelligence along with Big Data.  Your agency could go a long way compared to the rest of your competition.

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10 signs you’re ready for AI — but might not succeed (via CIO)

AI, ML, machine learning, artificial intelligence

Article Intro

Not every problem can be solved by machine learning, and not every company is poised to apply AI. Here’s how to know whether your IT organization is ready to reap the benefits of artificial intelligence.

Source: CIO

The Review

Now this is pretty interesting.  I would’ve thought that all companies were ready to use AI.  But now I am finding that isn’t true.  Here’s what I have found.  Each of the 10 points below have pluses and minuses for consideration.

10 Points

  1. There’s plenty of data – You have all the information that you need.  The question is, will you use the right model to find the answer to your questions?
  2. There are enough data scientists – Are you hiring people who say they can really do it?
  3. The company can track/acquire factors that matter – Is all the data there or is some missing from the models?
  4. The company has ways to clean and change the data when needed – Do you really have everything that you need to clean the noise and change when needed?
  5. Statistical analysis has already been completed as exploration – Use the right analysis after finding out the information, not before.  It can lead up to overdetermined systems.
  6. You use many different approaches to find the best model to find the data you need – Do not use what you are comfortable using.
  7. The company has the capacity to train deep learning models – It takes more time as the system has to learn more.
  8. The machine learning (ML) models outperform the statistical models – If the statistical model cannot raise the bar for the ML model problems can arise.
  9. The system can deploy predictive models – It has to run on the cloud, server, PC or smart phone.
  10. It can update models from time to time – It has to update as data always changes.  If updating isn’t completed there will be errors until fixed.

Intriguing isn’t it?  Just make sure that you follow the suggestions so that your company is ready for AI implementation.


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What Is Machine Learning? (via Forbes)

machine learning, ai, technology, business

Data is being generated faster than at any other time in history. Is your business properly utilizing the power of machine learning?

Source: Forbes

This is a very interesting topic that I never paid much attention to before.  I’ve seen a lot of conversations about it recently and have decided to do some research on it to help me better understand what everyone is talking about.  I have to say that I think it’s interesting how computers can take information out of big data now and make decisions about what to do with it on their own, using the right algorithms.

I can also see how some people might be afraid of it as it can seem like computers might take over the world like in Terminator.  So I can understand both Elon’s and Marc’s options and how they differ so much.  I don’t know what side I’m on, I’d say somewhere in the middle.  I can see pluses and minuses for machine learning and AI.  It’ll be interesting to see how it all turns out in the future.  What do you think?

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3 Goals of Machine Learning (via

machine learning, ai, algorithms

Definition of Machine Learning

Machine learning, what is it?  Basically it is learning to do better in the future from what has been done in the past.  This is done through algorithms that make the computer learn to complete actions through automatic means.  In other words, the computer learns to do stuff on its own, not through getting help from people.  It’s a part of artificial intelligence, a sub-menu.

Examples of Machine Learning

  • Optical character recognition
  • Face detection
  • Spam filtering
  • Finding topics
  • Understanding spoken languages
  • Medical diagnosis
  • Customer segmentation
  • Detecting fraud
  • Predicting weather

Goals of Machine Learning

  1. Make general purpose algorithms
  2. Learning algorithms
  3. Find prediction rules easy for people to understand

Very interesting article on learning more about machine learning.


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Risky AI business: Navigating regulatory and legal dangers to come (via CIO)

ai, machine learning, business

AI Intro

Artificial intelligence poses a range of hidden and unknown dangers for enterprises deploying the technology. Here’s how to guard against the legal and compliance risks of AI.

Source: CIO

Now who would have expected this?  I sure didn’t.  It’s a wake up call when you have to consider regulations and possible legal ramifications of using AI in certain industries.  “Context, ethics, and data quality are issues that affect the value and reliability of AI, particularly in highly regulated industries,” says Dan Farris, co-chairman of the technology practice at law firm Fox Rothschild, and a former software engineer who focuses his legal practice on technology, privacy, data security, and infrastructure matters. “Deploying AI in any highly regulated industry may create regulatory compliance problems.”  Source: CIO

Best Practices

  • Have a thorough grasp of how the machine makes its decisions
  • Design systems to gather reasonings of decisions made
  • Make sure you’re in regulations
  • Develop industry specific rules
  • Knowing when it is safer to use Artificial Intelligence to human brain power

Very interesting information.