Think Economics -- Not Features -- When Evaluating Big Data Value

Traditional enterprise data warehouse solutions helped to open the eyes of many organizations to the value of their data. Although these are significant systems, organizations quickly learned to monetize the actionable insight extracted from these systems, which led the rampant growth of the industry. Big data did not get big just from data growth. It got big because of its potential value, opportunities, and savings.

The more cost-efficiently you can capture a lot of data, plus the number of ways you can analyze it, equals the more worthwhile all that data could become. Value is results divided by costs. These (pseudo-)equations of big data value now extend not only to the disruptive power of transformative technologies like Hadoop, but also to increasingly popular cloud services for databases and data warehouses.

Topics: Big Data Data Management google data warehouse economic value validation BigQuery

Data Makes the World Go Round...

...Or at least data can be used to model the Earth's rotational vectors and predict trajectory locations over time. A few things have got me thinking about the world of data and the data of the world. First, I watched "Hidden Figures" with my girls last night. Amazon's machine learning models accurately predicted that we would like this film and positioned it strategically on our suggested titles list. There was a lot to like, including worthy themes around:

  • Math, science, engineering, analytics, and computers.
  • Women earning respect for their ability to excel in these areas.
  • Minorities demonstrating that diversity in teams improves outcomes.
  • Healthy patriotism in the context of advancing human potential.
  • Strategic government funding of research and innovation.
  • and Kirsten Dunst (assuming that her inclusion here doesn't undermine all my previous points).
Topics: Big Data Data Management machine learning data set

Database Forecast: Cloudy with Increasing Chances

ESG has recently published an overview on IT market adoption of cloud-based databases. Shall we just call them cloudbases? Perhaps not. A major trend is emerging. While relatively few are choosing cloud as their primary mode of deployment, majorities are currently running at least some of their production workload in the public cloud. Attitudes and adoption vary considerably by age of company (and age of respondent!), reflecting how deeply entrenched traditional on-premises offerings and processes may be for different businesses. How many, how many, and how much, you ask? ESG research subscribers can read the full report here.

Topics: Cloud Computing Big Data Data Management

Big Data, Database, ML and AI Spending Trends: What You Need to Know

Psst...hey buddy, are you an IT professional? Maybe interested in big data, databases, data warehouses, BI, machine learning, and AI? Planning to invest this year? Have I got a sweet deal for you! I can tell show you what your peers are planning. Top trends. Surprise insights. Hot stuff, but in the sense of interesting, not stolen. Check out this little number:

61%

Topics: Big Data Data Management database artificial intelligence

The Big Data Bubble Rises

As the father of two girls, I have deep appreciation for a fascination with bubbles. They are easy to make, they're shiny, and they float carelessly with the breeze. That said, a big bubble is much harder to create and more fragile than dozens of little ones. The loss is felt more acutely.

Topics: Big Data Data Management

Delta-V Awards - 2016 Edition - The Top Five

Ladies and gentlemen, data scientists of all ages, here is the big finale to 2016. The ne plus ultra companies that have accelerated and improved their positions in the big data and analytics marketplace. It's been no easy task to select, much less rank, all the top 20. I'd like to also note once more that there are many, many other worthy companies ticking right along and executing on their vision, earning new customers, and generally succeeding. And yes, it's all a bit subjective, but I truly believe these are ones that have made the most progress. Here are my final five choices:

Topics: Analytics Big Data Data Management Teradata MapR Amazon Web Services delta-v awards google cloud platform Confluent

Delta-V Awards - 2016 Edition - 10 to 6

New to this year's Delta-V citations? Check out our previous winners ranked 20 to 16 here and 15 to 11 here. As we move into the top 10 count down, it's probably worth a reminder that these 20 companies were chosen for their impact on the big data and analytics market. They aren't necessarily ESG clients, either, in fact fewer than half have done any official project work with me in the past year. It's all about recognizing innovation, pure and simple. That said, let's roll right into the next quintet!

Topics: Analytics Big Data Data Management Oracle bluedata delta-v awards Cask Qubole ClearDB

Delta-V Awards - 2016 Edition - 15 to 11

If you're just joining us now, please see the previous post for winner numbers 20 to 16. We'll continue to recognize the top 20 companies that accelerated the big data and analytics marketplace this year with a countdown from 15 to 11 here:

Topics: Big Data Data Management Data Analytics

Delta-V Awards - 2016 Edition - 20 to 16

It's that time again. Time to reflect on the year that was. And what a year it was for big data and analytics, with many massive advances in the technologies that power our understanding of the world around us. I don't know about you, but I certainly need a better understanding of the world. Thankfully, the industry continues to deliver innovations and gives me plenty of choices for recognizing those companies whose efforts are changing the velocity of insights. An introduction to the Delta-V awards and past winners can be found here. 

Topics: Big Data Data Management Data Analytics

Why Machine Learning is the Future of Big Data

Just as big data has emerged to heavily disrupt traditional databases and data warehouses, machine learning will be the next big wave of advancement in data management. Why, you ask? There is a simple, one word answer for you, "economics." They say any innovation has to be 10x better, faster, or cheaper to overcome the inertia of a traditional approach in IT. Apache Hadoop made it at least 10x less expensive to house data by distributing it across commodity hardware using open source software. Of course, there were (and still are) some rough edges and hidden costs, but this was compelling enough to get significant market traction versus legacy hardware and software. Weirdly, the core utility of Hadoop distributions has morphed towards being utilized mostly as a storage layer, with an ecosystem of other tools building analytics value above it.

Topics: Big Data Data Management Data Analytics machine learning