Articles on Big Data

Do you need machine learning? Maybe. Maybe not. Everyone's talking about machine learning but its not the answer to every problem.

In a post titled Data Mining - A Cautionary Tale, I share the idea that data mining can be dangerous by sharing the story of Cornell's Brian Wansink, who

Want to be a data science rockstar? of course you do! Sorry for the clickbait headline, but I wanted to reach as many people as I can with this important

When analyzing data, one of the best (and worst) things you can do is data mining. When done right, its great but when done wrong, beware the data mining.

The shift from information to intelligence requires an analytics foundation that can unleash the power of data

Are your digital transformation initiatives stuck? It could be the data you need to drive transformation remains in silos, or maybe it’s just bad data.

What would you do if you had so much data about your customers that you know could know (almost) everything about your customer when they contacted you?

Proper data integration and management will help you spend less time sifting through data and more time divining insights from it.

The role of Chief Data Officer plays a critical role in data-driven transformation. But only if they’re set up to succeed.

Accuracy and Trust in Machine Learning - are they mutually exclusive or can you have both when building machine learning models?

Do you know what the big four machine learning risks are? Do you know how to mitigate these risks? If not, check out this article to learn more.

How much is bad data costing you? It could be very little or a great deal. An example of what the cost of bad data really looks like.

Just because your machine learning or AI models look good on paper does not guarantee they will work in the real world.

I'm regularly asked about how to get started with big data. My response is always the same: I give them my big data roadmap for success.

Guidance for non-technical people on how to ask the right questions to evaluate whether a machine learning model is good enough for the job.

When should deep learning be used? the answer isn't a simple one. The answer depends on the problem, data size and number of other factors.

Stop thinking about big data technologies.  Think of ways to 'analyze, contextualize, internalize' your data instead.

Data and culture must go hand in hand in modern organizations.

When working with data, don't just find the answers to your questions. Keep digging and find new questions to ask.

Many organizations are jumping on the big data bandwagon (rightfully so). A good portion of them aren't thinking about data quality before data analytics.

Doing data science without 'science' is nothing more than throwing darts at a dart board and thinking the results are meaningful.

You (and your company) probably don't need machine learning. Most companies just need good data management with good regression modeling.

Your data project is going to fail. You can plan everything perfectly, use the best systems, hire the best people yet your project will fail.

Big data isn't the answer. Big data is a just one more tool that can be used in the toolbox that an organization can use to improve.

Spend the necessary time in the data modeling phase of your next data project and you may be surprised at the quality of the output of your data analytics.

Data preparation is extremely important to your data analytics / big data projects. Good data preparation can lead to good data analytics outcomes.

Data Analytics means different things to different people. I discuss prescriptive vs descriptive analytics and try to explain why you should care.

So, what digital projects should you be chasing? Chase the ones that are focused on improving the customer experience

If you teams ask questions and dig into the data, you can be sure that you've do everything possible to minimize the possibilities of being "theranosed"

Good data science isn't about finding answers to questions. Good data science is about setting up your data and systems to allow you to find more questions.

I spend a lot of time talking to companies about big data and data science. Many conversations are with people at the CxO level (CEO's, COO's, CFO's, etc

I'm not a basketball fan so I wouldn't normally have caught a great quote from the Golden State Warriors head coach Steve Kerr after a recent loss. Thanks

The three roadblocks to big data success are: Starting too fast, Going too big and Corporate Culture

Based on the predicted rates of growth in big data, it looks like the future of big data is still a positive one.

Are companies failing at big data? Sure...but I think that's just because most companies are still very early in the learning cycle for big data.

Focus on what the data tells you, not the data itself. If you cannot turn data into information and knowledge, your data initiatives will fail.

Big data doesn't have to require big projects, big budgets or big teams, especially when starting out.

if you want a great data analytics culture, build a great communications culture. You can't have one without the other.

Open data allows organizations to take the fullest advantage of the world of big data.

We are just the keepers of the data and need to think about systems and processes that allow the organization to use that data.

Today's data challenge is the same one that has plagued organizations for years. Data and analysis are useless unless you actually act on them.

Collecting and analyzing data means nothing if you do not use it for more than taking up storage space. That is wasted money and time.

In order to use data within your business, you must first collect that data. Seems simple enough right?  You capture some data, store it somewhere and the

In “Big Data, Big Analytics and You”, I wrote: “Big data is obviously important to most organizations. There’s plenty of data out there and even more data

Is bit data worth it? Absolutely...but only if you put the time and effort into the analysis of that data. Big data isn't easy, but it is worth it.

Big data has been a popular topic over the last few years.  Many organizations have been studying big data systems and processes as well as the science

In what might be the best titled article I've read in a while, Vince Kellen writes about the dangers of confirmation bias (or 'finding what we want to

There's no 'easy' button or 'secret' to success in big data. It takes hard work and listening to the data to let it tell its own story.

Big data and analytics is no longer about the size or type of data but about how fast data can be converted into useful business information.

Being data-driven means nothing if data does not lead to better decisions. Find the data systems that work for your business and use them well.

Implementing big data is more than buying software. It requires building a data culture across the organization.

Today, we are drowning in data and starved for information. What can we do about it?

When I talk to people about big data and data analytics I try to tailor the message to their experience level. For example, if I’m talking to data

You don't need Hadoop for big data, but it is a great fit. Hadoop provides the underlying platform for analyzing very large data sets at scale.

What are the important skills for a data scientist?

Using modern day interactive analytics systems allows organizations to analyze and visualize their data in a very dynamic manner

Insurance companies are using big data and social media analytics to make more informed pricing decisions. Here is what that means for the industry.

Make sure you're OK with doing 'janitor work'. Make sure you're OK spending most of your time working on the unseen pieces of the iceberg.

Big data is complex and difficult, but with proper planning and strategic thinking, you can prepare for many of the challenges you will face.

To succeed with big data, treat it differently than the data warehouse. Open up your data and allow easy, interactive access across departments.

Data is not everything, but with proper planning and management it can deliver real, long-term value. Drive your data, do not let it drive you.

Big data is about more than just finding new revenue streams or finding ways to bring in more revenue from existing customers.

The world of big data has been growing over the last few years. Everywhere you look today you see people talking about big data and discussing how their

If you ask 100 people to define the value that data brings an organization, you’ll most likely get 150 different answers. Yes...that’s right...150

I'm often asked the following question: What is the difference between Business Intelligence and Big Data? Before getting into my approach to answering

According to the  2014 IDG Enterprise Big Data survey, most large organizations today (almost 49% of survey respondents) claim to be well along the

Last week I shared the real-world story of the Point Defiance Zoo and Aquarium's use of data and analytics to change the way they managed and operated

I’m always on the look-out for stories of companies using data and analytics to manage their business. In a recent case study, IBM presented the story of

Midwest IT Survival » Evolution of Corporate IT and the Future Impact of Cloud Quote: All in all, corporate IT is in constant evolution. The rate of

Over on the Obsessive-Compulsive Data Quality blog, Jim Harris recently wrote:  While organizations of all sizes are rightfully excited about the business

Last week I noticed this little gem over on ReadWriteWeb.com: Gartner highlights that only 8% of enterprises have actually deployed big data projects

We often read anecdotal evidence of how companies and consultants are using big data to solve ‘big’ problems but it is rare that we see real world

In “Incorporating Big Data into your Business”, I wrote: Big data is worth investigating and incorporating into your businesses. You may not be able to

I read a lot of articles and blogs about big data. There are a lot of people singing the praises of big data and a lot of folks trying to sell services

I’ve written many times about big data and the benefits and challenges that come along with attempting to ‘do’ big data. The one thing I haven’t said much

Big data is everywhere these days. Everyone’s talking about using data to make better decisions. According to most, big data is the answer to everything

Over the past few months, I’ve heard and/or read the following statements from consultants: Big data is complex. Big data is complicated. Big data is the

I just finished reading Big Data for Small and Medium Businesses on IBM’s Forward View. In the article, IBM VP Paul Zikopoulos talks about big data and

Dark Data can arise in many ways. One way is via Shadow IT and the Data Disconnect

Big data is everywhere. Everyone wants to be doing big data. There’s one big data induced problem that many aren’t talking about or acknowledging.  That

About a year ago, I heard the following two lines from well-respected IT professionals. The first comment: 1.) Big data is the future And the second:: 2.)

Finding the right people has always been a problem for IT organizations for many reasons. There’s always been a “build or buy” decision for organizations

Cross Posted at TradeTheSentiment.com While working up my data analysis chapter of my dissertation, I came across some interesting tidbits of information

I just finished reading "Can big data technology be used to replace creative marketing?" and felt the need to vent a bit. This first part of this vent:

According to Wikipedia, predictive analytics is described as: Predictive analytics encompasses a variety of techniques from statistics, modeling, machine

Before we get started - a definition of “construct” is needed. Taking a page from my years in quantitative research, I submit this definition (based on

I'm a fan of data. I love using data to solve problems and find answers. I love combining context and data to help organizations find identify issues and

I just finished reading Big Data and Marketing: A Confused Relationship? over on Marketing Pilgrim. There's some good stuff in the post - but what really

A CIO I spoke with last week mentioned that she and her team are struggling with their BI solution today. The solution works perfectly for analyzing their

IBM just released a few announcements related to Big Data. Namely, the use of big data and analytics to better understand and target customers. The

In a previous article titled Context and Data, I wrote about the need to understand the 'context' surrounding data. Without context, data is data. You

Last month I wrote a post titled "Is Big Data to Big for Small Business?" where I asked the questions: Is there a place for small organizations in the

A few weeks ago I wrote about Big Data and Small Business. From that post, I wrote: As its defined, big data might be too big for small business, but the

Big Data.  Two small words with huge meaning. Do a search for "big data" on Google and you'll find over 23 million results. Do a book search on amazon for

Do small businesses have the tools and skills to take advantage of big data, or will it drive innovation for large companies and leave them behind?