Mike Leone

Mike Leone

ESG Senior Analyst Mike Leone focuses on big data, data management, and data analytics, including emerging segments such as machine learning, artificial intelligence, and IoT. Since joining ESG, Mike has primarily been a part of the validation team, where he provides third party validation of claims made by vendors about their solutions with coverage across all aspects of enterprise IT. Mike has a strong technical background with early roles in software/hardware engineering and gradually moved into marketing roles by helping translate deep technical concepts into understandable business benefits. His enthusiasm for bleeding edge technology is contagious and his combo engineering/marketing background helps IT vendors improve everything from product development and marketing to go-to-market strategies and road-map adjustments.

Recent Posts by Mike Leone:

The Impact of Server Refreshes on the Server and HCI Markets

As of late, the server market has been heating up. Companies like Dell, IBM, Cisco, Super Micro, and Huawei all saw growth in server shipments, while many saw double digit revenue growth year-over-year. So what’s driving the growth? Some argue it’s from the big web-scalers like Google, Microsoft, and Amazon, and to an extent, that is true. When the big guys do something, many follow suit, but I think the growth is also due in part to the mandate within many organizations to digitally transform by leveraging HCI.

Topics: IBM Cisco VMware nutanix Lenovo HPE dell-emc HCI hyperconverged infastructure supermicro

Artificial Intelligence and Machine Learning - More Than Just Buzzwords

The traction over the last few years in the artificial intelligence (AI) and machine learning (ML) space is remarkable, and I’m not just talking about consumer-based products like self-driving cars, or virtual assistants like Google Assistant, Alexa, or Siri. While those products get the headlines, AI/ML is rapidly spreading across the enterprise IT space. I feel like I can’t go a day without a company mentioning AI or ML as part of their product or forward-looking strategy. It’s not just for crazy, sci-fi predictive analytics projects in a bunker somewhere. While that definitely still happens, AI and ML (and deep learning too) are being used across all aspects of IT: big data, cloud, IoT, security, infrastructure, systems management, etc.

Topics: Data Management & Analytics machine learning artificial intelligence big data and analytics

It's Still Early for Converged and Hyperconverged – Predictions for 2018 (Video)

I was waiting on releasing my 2018 predictions for converged and hyperconverged infrastrucuture because I wanted to leverage a key data point from our recent spending intentions research. From an IT infrastructure standpoint, this year’s data had some particularly compelling data points regarding areas of opportunity where senior IT decision makers feel they can significantly streamline costs. More than half of organizations (54%) feel their on-premises storage and/or networking infrastructure is where their costs can be streamlined. My colleague Mark Peters recently wrote a great brief on the subject, but here is my quick take...

Topics: Converged Infrastructure hyperconverged infrastructure 2018 Predictions

Analytics in the Cloud – Usage Is Only Going Up (Video)

So you have a decision to make: behind door number 1 is a huge, costly on-prem infrastructure that requires expertise across a bunch of different tools involved in the data pipeline for support, and ongoing management and maintenance. Behind door number 2, you have a no-upfront-cost cloud infrastructure that requires minimal expertise, and a single point of support and management. Which do you pick? There are a lot of considerations, such as performance, availability, and arguably most importantly, long-term cost. What is my overall data footprint and growth over the next 3-5 years? How much data does my organization process on a monthly basis? Is the cloud a final destination for my data sets? And that’s just the start. Now with that said, for organizations that are looking into becoming more data-driven as quickly as possible without a potentially massive upfront investment, I would like to be the first to say…welcome to the cloud my friend.

Topics: Data Management big data and analytics cloud analytics

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

Targeting the Best of Both Worlds with Next-generation SQL Databases

Conventional relational databases and recently-developed NoSQL databases have led some enterprises to an impasse. They want to scale the systems that are handling their data. However, an RDBMS used to guarantee transaction integrity is difficult to expand. NoSQL systems, although scalable, typically do not offer full transaction integrity via the ACID properties discussed in earlier posts. The latest solution in the SQL/NoSQL saga—next-generation SQL databases—may have the answer.

Topics: Big Data Data Management database rdbms NoSQL SQL ESG Lab

NoSQL - The Great Escape from SQL and Normalization

For the last decade or so, data and data structures have been moving at the speed of the web. They change rapidly to keep pace with end-users and markets that are in constant flux. The data model is volatile and will continue to be as more and more unstructured data (images, videos, social media content, online purchase histories, and more) is generated. The solution of the pre-Internet era, meaning the RDBMS, can’t keep up. Enter the NoSQL database—a solution for handling data with highly variable formats in massive quantities at lightning speeds.

Topics: Big Data Data Management database rdbms NoSQL SQL ESG Lab

Why Won’t the RDBMS Go Away?

The relational database management system was a breakthrough when it first appeared about 40 years ago. A relational database puts power into a user’s hands. Few assumptions are needed about how data is related or how it is to be extracted. Data can then be viewed in a variety of ways, each one illustrating different connections or correlations. This power, history, and a little user inertia have led to the RDBMS being implanted and used in practically every sector of business today. Well-known RDBMS product examples are IBM DB2, Microsoft SQL Server, and Oracle databases.

Topics: Big Data Data Management database rdbms NoSQL SQL ESG Lab

Today’s Database Landscape at a 50,000 Foot Level

We all know the drill—data is exploding in size, but it’s not just the volume of data that is wreaking havoc on organizations. It’s how quickly it’s growing, how many different forms it can take, and how it’s constantly changing. And that’s just scratching the surface. How can the potential of data truly be harnessed? The database technologies for organizing the data that we generate and manipulate continue to morph and multiply. The hugely successful relational database management systems (RDBMSs) continue to soldier on using principles now over 40 years old, while newer database technologies have come along and been widely adopted to address specific needs in the data storage, management, and analytics space. Over the next few weeks, I’ll be going through the evolution of database technology at a 50,000 foot level to highlight the old and the new, how they’re used today, and what vendors to keep an eye on.

Topics: Big Data Data Management database rdbms NoSQL SQL ESG Lab

Converged and Hyper-converged – We’ve Got It Covered

In the last few years, we’ve seen a major boom in the converged infrastructure space. This coincides with ESG research findings, where more than 50% of respondents said they currently or plan to utilize a converged solution in their IT environments. We’re not at all surprised, since they make infrastructure tasks significantly easier for IT pros – easier to plan, easier to configure, and easier to grow. By deploying a converged solution, companies have realized gains across all facets of IT, from faster deployment times and improved service and support, to ease of management and improved TCO, scalability, and agility.

Topics: Private Cloud Infrastructure Mark Bowker integrated computing platform ESG Lab integrated infrastructure Hyper-converged Converged