Big Data Roadmap – A roadmap for success with big data

Eric D. Brown

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. Most organizations want to jump in a do something ‘cool’ with big data. Build a data lake (and utilize it).

Data Quality – The most important data dimension?

Eric D. Brown

Take a Look at Your Data , I discuss the importance of data quality and data management in an organization’s digital transformation efforts. What is data quality? … To be of high quality, data must be consistent and unambiguous.

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Data, analytics, and AI solutions showcased at the Strata Data Conference

Social, Agile and Transformation

Two years ago when I reported from Strata NYC I shared how new data prep technologies were bringing data integration and data quality capabilities to business users. Instead of waiting for IT to ETL experimental data into the data warehouse or data lake, business users apply tools like Tableau Prep, Trifacta Wrangler, or Talend Data Preparation, to perform profiling, cleansing, and. agile data AI big data data management data scientist innovation

Is The Data Ops Workbench A Thing?

Forrester IT

Data ops, data engineering, data development — oh my! From new roles and teams to new skills and processes, the hot topic on everyone’s mind is data ops. data management

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Strata Data Keynotes Day 2 on Dimming the Noise, Faster Data, and Becoming Boring

Social, Agile and Transformation

Here's my synopsis on a great start to day 2 of Strata Data day two. It began with a series of thought provoking topics; eliminating noise; driving faster data; successful data science by becoming boring; and Eliminating Noise and AI for Compliance Amber Case of the MIT Media Lab talked about the noise, and the abundance of alerts that surround all of us. agile data AI big data cio data management data scientist digital transformation

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Data Maturity before Digital Maturity

Eric D. Brown

In this post, I want to talk about one of the most important aspects of digital maturity: Data Maturity. Before you can even hope to be digitally mature, you must reach data maturity. What is Data Maturity? You’ve got to understand the following: where the data came from?

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Big Data Project Management: Data Must Flow!

CTOvision

I'm currently researching big data project management in order to better understand what makes big data projects different from other tech related projects. Many if not most organizations still have a lot to learn when it comes to making use of emerging big data analysis techniques.

Data Analytics – The importance of Data Preparation

Eric D. Brown

While not as life-and-death as the above questions, data preparation is just as important to proper data analytics as learning the basics of driving before getting behind a wheel. Data preparation is important. The Importance of Data Preparation.

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Data Analytics – Data Modeling, a Necessary first step

Eric D. Brown

What do you think of when you hear the term ‘data modeling’? Just typing ‘data modeling’ almost made me go to sleep. Data modeling has many different definitions and connotations. You don’t have a data strategy?

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What is the cost of bad data?

Eric D. Brown

How much is bad data costing you? In this article I give an example of what the cost of bad data really is. The cost of bad data. What do they know about data management? The cost of bad data is high regardless of what the number actually shows.

The Data Way

Eric D. Brown

The world has become a world of data. According to Domo , the majority of the data (roughly 90% of it) that exists today has been created within the last two years. That’s a lot of data. Actually…that’s a LOT of data. That’s the data way.

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Paxata: Adaptive Data Preparation

CTOvision

Paxata developed the first Adaptive Data Preparation™ platform built for the business analyst. With seamless connections to BI tools like Tableau, QlikView, and Excel, users can combine data on their own or work with peers in a shared, transparent environment as they shape data for analytics.

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Five Best Practices To Scale Data Science Across The Enterprise

Forrester IT

Nearly all firms want to do more with data science. However, executives and data scientists alike are frustrated by the difficulty of turning new initiatives into business impact at scale.

Information Risk: Balancing the Good and Bad of Data Analytics

CEB IT

Managers like to talk about the power of data and analysis, and the opportunity it gives them to launch new products and reach new customers. The former has opened up companies to new security risks while the latter has left some customers feeling uneasy about the privacy of their data.

Hortonworks’ Big Data Scorecard

CTOvision

Often times, the journey to data driven business transformation can not only be challenging, but also confusing. The first step towards overcoming this challenge is mapping out the journey to fully utilize the value of your company's Big Data. Mapping Big Data (oreilly.com).

How Should CIOs Handle Data From Employee’s Wearables?

The Accidental Successful CIO

More data is always better, but what if it’s personal data? As the person with the CIO job, you are the one who is responsible for managing all of the data that the company collects. How should a CIO deal with these new very personal data streams?

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Who should own data analytics in your company and why

Datacponomy

Data management strategies have undergone a significant change over the last decade. A decade ago the responsibility of data management laid with the IT department, while data analytics were performed in other departments individually based on the needs.

Data Analytics – Prescriptive vs Descriptive

Eric D. Brown

You’ve collected tons of data. You’ve got terabytes and terabytes of data. You are happy because you’ve got data. But, what are you going to do with that data?You’ll How does data analytics come into play?

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Data Ownership within an Organization

Eric D. Brown

Recently, I was having a conversation with an IT VP about data management, data access, reporting and analytical approaches to the data stored within the organization. Many view data as their ‘property’ because they are the ones tasked with storing and protecting it.

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Your Data Is Worth Nothing, Unless You Use It

Forrester IT

I’m often asked the question, “What’s my data worth?” As the data economy heats up, the fear of being left behind leaves some company execs in a cold sweat. Forward-looking board members ask about data commercialization strategies and expect answers. data insights

Why It’s Important For Your Organization to Know The Difference Between a Data Scientist and Data Engineer

CTOvision

In particular, there has been a significant increase in demand for data scientists. Companies are searching and competing for increasingly scarce data scientists as the […]. Artificial Intelligence Big Data and Analytics Cloud Computing CTO artificial intelligence big data data data engineer data scientist Enterprise

Is the DataOps Workbench a Thing?

Forrester IT

Data ops, data engineering, data development – oh my! From new roles, teams, skills and processes, the hot topic on everyone’s mind is data ops. I started to notice the data ops emergence back in 2015 as companies began to look at agile development to spin up new data capabilities rapidly. Later, as data preparation […].

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The Agile Data Center

Eric D. Brown

I participated in the #DataCtrChat Twitter chat last week to join in on the conversation about the Agile Data Center. The #DataCtrChat is a great one to be a part of, especially if you’re interested in the data center. Every company has a different view about their data center.

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How Artificial Intelligence is Making Big Data Better Than Ever

CTOvision

The concept of Big Data is a relatively new one. It denotes the availability of vast volumes and sources of data, which were not available before. By itself, Big Data is powerful, and when combined with Artificial Intelligence and machine learning, the opportunities presented by this combination are just endless. As big data moves to the […]. Artificial Intelligence Big Data

Building the Agile Data Center

Eric D. Brown

T he modern data center is a complex environment with many different systems and many different objectives. Over the years, many data centers have become the dumping ground for all things technology. Hardware will simply be the delivery system for the software defined data center.

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Why “Data Ownership” Matters

CTOvision

In Understanding data ownership in the data lake Elizabeth Koumpan, Executive Architect at IBM, writes: Depending on the organizational point of view, different ownership rules may apply in different situations to data. It is a tricky part when we deal with Data ownership while using external sources, especially if we use social data which is an essential element, as we build our cases for front office digitization, customer sensitive analysis and so on.

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Dynamic Data Centers

CTOvision

Big Data and high performance computing (HPC) are on a collision course – from machine learning to business intelligence, the combined power of clustered servers, advanced networking and massive datasets are merging, and a new Big Data reality is on the rise. Big Data News

Six reasons to think twice about your data lake strategy

Datacponomy

Since data has been called the “oil” of the new economy, it’s easy to assume that more is better. You can never have too much oil, so the same goes for data too, right? Hence there has been a lot of hype about data lakes over the past few years.

Recognizing Relevant Big Data and How to Use It

CTOvision

Big Data is everywhere you look, and we have seen how useful it can be. Among billions of terabytes of data gathered, there is a treasure of marketing data that businesses need to understand in order to know what is relevant and how to use it to get better business results. Artificial Intelligence Big Data CTO Cyber Security big data marketing

Right-Sizing Data Center Resources

CTOvision

One of the perennial problems of data centers is monitoring server utilization to ensure right-sizing of resources. There is even a tool to help you understand where to place new hardware in the data center based on available space, power, and cooling, to help avoid hot spots and power issues.

Use Text Analytics Technologies To Handle Mountains Of Unstructured Data

Forrester IT

Enterprises are sitting on mountains of unstructured data – 61% have more than 100 Tb and 12% have more than 5 Pb! Luckily there are mature technologies out there that can help. First, enterprise information architects should consider general purpose text analytics platforms. These are capable of handling most if not all text analytics use […].

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Data and Culture go hand in hand

Eric D. Brown

I wanted to see how well data and culture mixed at this company. He started asking me about big data, how big data can help companies and what big data would mean to their organization. Data is used to support a decision rather than informing the decisions.

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Adatao: Data Intelligence For All

CTOvision

Adatao was founded by a team of highly regarded big data engineers and machine learning masters to build a unified solution for data analysis. Adatao supports both business users and the famous dream unicorn data scientist, all on one unified solution.

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INFOGRAPHIC: Data Scientist vs. Data Engineer by Cognilytica

CTOvision

As AI increasingly gains popularity among enterprises, companies are actively seeking data scientists who possess data science skills. Many enterprises confuse the roles of data scientists and data engineers. Artificial Intelligence Big Data and Analytics CTOEven though some traits, skills, programming languages and tools are shared by both roles, the overall roles and core skill sets are different and are not [.].

The Data Center of Tomorrow

Eric D. Brown

DataThe data center of tomorrow will look much differently than the data center of today. That particular sentence should not be that surprising to anyone who’s been in an organization that has internal data centers. The data center of tomorrow will be an agile data center.