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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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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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10 Key Big Data Trends That Drove 2017 (via Datanami)

hadoop, big data trends, big data, analytics, data modeling, data science, machine learning, ai, deep learing

2017 has come and (almost) gone. It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it’s worth taking some time to ponder about what happened in 2017 and put…

Source: Datanami

10 Big Data Trends

  1. The re-emergence of AI, deep learning and machine learning
  2. Hadoop becomes less popular
  3. Graph databases grow in use
  4. Apache Spark is keeping up with the competition
  5. The Cloud is super popular for storing Big Data
  6. Big Data fabric bypasses integration problems
  7. Big Data swamps are becoming a problem with too much data saved
  8. Big Data company IPOs are becoming popular
  9. Data Science platforms and vendor choices are growing
  10. Look out for GDPR (General Data Protection Regulation) that goes into effect on May 25, 2018
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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

Review

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.

Summary

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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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

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

Summary

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.

Source: DATAVERSITY

Review

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.

Summary

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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4 Preventions Smart Cities Provide

smart cities, prevention, transportation, environment, security, people

Smart cities are becoming more and more popular now.  By 2050 they will be so popular that more people will live in them than out of them.  I find them extremely interesting about how they can help society.

Think of how they help the environment, populated areas, and traffic.  They will be instrumental in the future to creating new technologies for solar power, finding new water sources, and pollution control.  Exciting times are coming.  I can’t wait to see what it brings us.

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3 V’s of Big Data

Big Data, business, upgrade, business intelligence

Big Data. What is It?

Businesses get almost an overflow of big data every day. It’s information gathered through structured and unstructured means. Companies can use the results to make better decisions and plan strategic moves.

3 V’s of Big Data

  1. Volume – Information comes from many sources. Everything including business transactions to social media to machine-to-machine data.
  2. Velocity – Data comes in a super fast speed. So your business has to handle the speed in near-real time to act quickly.
  3. Variety – It comes in various formats. Anything from structured, numeric results to unstructured documents like email, video, or audio files to name a few.

The Potential

Big Data is growing every day worldwide. It offers huge amounts of help to businesses that use it, but even with all the evidence, most companies are still not using it to its full potential. There’s nothing to do with how much data you have, but everything to do with what you want to do with it. Big Data can help with cost and time reductions, new product development, and smart decision-making. Combining big data with analytics can help you with tasks like finding root causes of failures in near-real time, make coupons based on customer’s buying habits, recalculate risk portfolios and sensing out fraudulent behavior before it can affect you and your company.

Who Uses Big Data

Industries carry out Big Data findings when they decide to use them. Banks use it to better understand their customers and lower risk and fraud. Education uses it to find at-risk students, make sure that students are making progress and create better systems evaluating teachers and principals. Retail uses it to market better to customers, better handle transactions, and revive struggling businesses.

Consider This

Make sure to research companies that can handle your data for you. Look for cheap and huge amounts of storage, quick processors and affordable open source, distributed platforms. Look for parallel processing, large grid environments, high connectivity and virtualization. Also look for cloud computing capabilities for resource allocation arrangements.

Do your research. Don’t rush into choosing a company as you want to stay with them for years, not months. Big Data is a huge help for your company. Just make sure that you can get access to it quickly and easily, while having good security options in place to make sure others cannot.