Data & AI

Before You Sign Another Server License, Read This

Prolifics’ Analytic Platform Optimization solution is unique to the industry and will show you how to better balance, and even maximize, the productivity of your existing servers. And we can guarantee it’s a lot less expensive than adding more servers.

The Budget Alarm Bells Go Off – Loud

It’s the first sign – you’re overbudget on your spending for servers. It could be the front-end or the back-end platforms, at this point it doesn’t seem to matter. You turn to your staff and ask:

“Why in the heck are we spending so much money on licenses? And now it looks like we’re going to have to get more servers and our licenses are going to go up even more? Why is this happening?”

The result is generally blank stares or mumbling about the need based on all the work being done. There may be a project assigned to look at the over-budget platform. But that won’t necessarily solve anything because it’s usually not a one-platform problem.

It’s a Platform Ecosystem Problem

Michael Gonzales, PhD (“Dr. G”) is Prolifics’ Chief Data Scientist and the force behind Analytic Platform Optimization.

“Clients ask us, ‘Why are we spending so much?’” says Dr. G.
“And when we get in discussions with them, we make the case for the multiple platforms. Because you’re not going to solve your problem just looking at one of the platforms by itself. If you look at it in isolation, you’re not looking at the total picture. And at Prolifics, we look at the total picture. The problem is that the scale of the total picture is huge. So that’s why almost nobody does it. But we use data science so that we can simplify the whole process and come up with legitimate, actionable results that we can help you implement.”

The Total Picture is All About Balance

The real issue is that the front-end and back-end platforms are essentially out of balance – one is over-used and one is under-used.

Example:

• A client had a mid-tier BusinessObjects server farm and Teradata for the back-end.
• BusinessObjects developers pushed data requests back and forth using SQL, manipulating data on the front-end without knowing what Teradata could handle.
• Teradata staff only monitored SQL requests and didn’t know about upstream manipulations.

“What happens or what’s totally missed, in most of these organizations, is a clear view of all the code that’s been written to move the data,” says Dr. G.
“We take a global view, so we’re not interested in the network speeds or necessarily concerned about specific queries. What we’re concerned with is the global view – the balance of all the code being written. Where is the ‘heavy lifting’ being done, and where should it be done?”  

How Does Prolifics’ Analytic Platform Optimization Work?

Dr. G explains:

“You can’t read all the lines of code to see where the ‘heavy lifting’ is – it’s impossible because of sheer scale and knowledge needed for different platforms. Instead, our process works because it uses data science and text mining to consume thousands of programs and analyze the word usage in the language (SQL or any language). I’ve developed a dictionary for very specific words that I know characteristically are ‘resource pigs.’ These words demonstrate a lack of proper planning – they just consume a ton of resources on one platform versus the optimal use of both platforms.”

Key Benefits:

• Identifies exact programs causing imbalance
• Pinpoints under-utilized platforms
• Optimizes server performance across the ecosystem

Then What?

Prolifics doesn’t just identify issues – we provide a complete optimization plan:

• Agile sprint plans to fix the worst offenders first
• Dedicated expert developers for implementation
• Continuous monitoring and process closure

Put that pen down – don’t sign another server license until you talk to us.

About Prolifics

Prolifics is a global digital transformation leader with expertise in:

• Cloud, Data & Analytics
• DevOps and Digital Business
• Quality Assurance across industries

We provide consulting, engineering, and managed services at any point you need them – giving you fast, complete solution delivery experiences that you will find nowhere else.

Whether it’s advising and strategy, design and implementation, or ongoing analysis and guidance, Prolifics will help you take charge of your digital future.

Contact: Email solutions@prolifics.com

Digital + data + data science = vision to value, faster. That’s the message shared in this presentation hosted by DCO Canada and presented by Chief Data Scientist Michael Gonzales. The conversation starts with this quote from an article published through the Harvard Business Journal* – The worst mistake a company can make is to hire data scientists, give them access to data, and turn them loose expecting them to come up with something brilliant. – In fact, the role of a data science team is to bring business value, business impact. That can’t happen without business involvement. Dr. Gonzales shares valuable insight into marrying your digital and data to receive maximum results.

*Harvard Business Journal Publication – Are You Setting Up Your Data Scientists to Fail?

By Michael Gonzales, PhD

The focus of the Analytic Ecosystem Inventory (AEI) is to collect, document, and quantify the current level of analytics being executed and consumed in the organization. While other assessment instruments collect opinions from a broad range of analytic consumers, the AEI is designed to gather quantitative metrics related to employed analytic applications, the technology and data on which they are implemented, and the communities they serve.

Specifically, the inventory includes:

• Antecedents – Any antecedent related to analytics being conducted, their standards, and corporate objectives
• Applications – Analytic applications, the size of user communities they serve, and their life stage
• Technologies – Analytic technologies on which these applications are based
• Data – Profiles of the data sourced by these applications, including size, number of sources, and data type

An instrument is best to ensure consistency in the collection of information across the ecosystem, even if multiple team resources are used to conduct the inventory. A spreadsheet can readily be crafted to include columns of information outlined in Table 1, including the drop down menus that include the options available for the investigator.

Antecedents

In general, antecedents are any relevant, formal documentation for assessing dimensions of maturity. For example, a business strategy document with content incorporating analytics provides evidence of analytics being leveraged for competitive advantage. Which represents a critical sign that analytics is being embraced by the enterprise. Although not exhaustive, the following are specific artifacts important for the assessment team to consider when evaluating the overall maturity of an organization:

• Business strategy where analytics is referenced (redacted if necessary)
• Analytic strategy document
• Organization chart for analytics team(s)
• Organization chart for data governance
• Analytic development process and implementation standards
• Example of requirements document for the analytic environment
• Example of a test plan for an analytic application implemented
• Example of a Service Level Agreement offered to user communities by the analytics team(s)
• Education curriculum for analytics
• Example of a course evaluation that participants would complete after taking a course offered by/through the analytics team(s)

Few resources are needed to gather antecedents. The assessment team simply requests any relevant documents from the client. This can be done during the kick-off session, coupled with feedback/suggestions from assessment team members, and followed-up with an email providing examples of documents, if not actual document titles (if given by team members). 

Technical, Data, and Application Examination

An Excel spreadsheet can be constructed in order to provide guidance in the gathering of fundamental information regarding the technology licenses, the data used, user communities supported, and applications based on that technology.

For brevity, outlined in Table 1 are the specific columns of the inventory spreadsheet.

Table 1 – Architecture Inventory

Techniques for Analyzing the AEI

There are 2 areas of the inventory that must be assessed:

1. all formal antecedent documentation
2. inventory of analytic applications, supporting technology, and data

This section provides recommendations on how to evaluate each. However, it is important that the assessment team does not merely examine antecedents and the AEI individually, but rather in the context of the entire information gathered during the overall assessment effort. Figure 1 illustrates the importance of overlapping relationship between surveys/interviews, antecedents, and the inventory of analytic applications.

 Figure 1 – Overlapping Information Channels

It is this author’s recommendation that information gleaned from one channel of information be compared and contrasted against information from other areas of the assessment. For example, if SME surveys suggest technology standards used for analytics, there should be evidence of these standards in the technologies (and their versions) supporting analytic applications. Or if SMEs suggest that there does not exist any formal analytic education provided to the users and yet there is a clear and robust analytic curriculum offered, then the assessment team needs to investigate and understand why there exists such a discrepancy.

Analyzing Antecedents

A reasonable approach for examining antecedent documents is for the assessment team to review and comment on each document. However, this process is definitely a “black box” that is difficult to repeat. There are, however, elements of the antecedent evaluation that can be evaluated in a methodical, repeatable manner. Assessment members can leverage a Likert Scale and answer specific questions relating to each document such as those outlined in Table 2.

Table 2 – Assessing Antecedents

Analyzing the Inventory

This inventory provides plenty of opportunities for the assessment team to identify patterns based on the quantitative metrics gathered. For example, questions that can be answered from the information gathered include, but are not limited to, the following:

• Support for Standards – How diverse are the technologies used to support analytic applications? And how many different versions of the same technologies are used?
• Application Maturity – What is the mode of maturity in analytic applications? Are most applications “New” or “Expanding”? Or are they “Mature” or “Legacy”?
• User Communities – Do the applications support large user communities or are they relevant only to a selected group of analysts?
• Identification – Are the majority of analytic applications concentrated in 1 or 2 departments or are they spread across the enterprise?
• Data – Is the latency of the data used for most analytic applications historical and consumed in periodic batches or is the data real-time or available on-demand?

This article is taken from a section in the CAMA Guide: How to Conduct an Analytic Maturity Assessment.

dss42, LLC    Copyrights© 2019-2020    All Rights Reserved

About the Author

Michael L. Gonzales, Ph.D., is an active practitioner in the IT space with over 30 years of industry experience serving in roles of chief architect and senior solutions strategist. He specializes in the formulation of business analytics for competitive advantage in global organizations. Recent engagements include companies in the top global 100.

Dr. Gonzales holds a Ph.D. from the University of Texas with a concentration in Information and Decision Science. He has presented and published his research at leading IT international conferences, including: Decision Sciences Institute, Americans Conference on Information Systems, and Hawaii International Conference on Systems Science. His research streams include analytics against extremely large data and success factors for IT-enabled competitive advantage.

Dr. Gonzales is a successful author, industry speaker and is currently the Managing Partner of dss42, LLC, and Senior Data Scientist at Prolifics

• Download the Comprehensive Analytics Maturity Assessment (CAMA)
• Learn about the instruments that add value to your CAMA

Companies that know how to leverage their analytic and IT resources gain a business analytic-enabled competitive advantage (Porter, 1980; Sambamurthy, 2000), which is the basis of our research. For the purpose of this guide, the term analytics represents a comprehensive view that encompasses the 5 analytic areas listed below and related topics.

The challenge, when creating an analytic-enabled business strategy, is to identify which activities to focus on. To that end, our research identifies factors of analytic-centric initiatives that significantly contribute to the overall maturity and success of a program (Gonzales, 2012). Building on this research, coupled with extensive practical application of maturity assessments for leading companies our Comprehensive Analytic Maturity Assessment (CAMA) creates an index that measures the analytic-enabled competitive maturity of an organization.

The Value of a Repeatable Analytic Maturity Assessment

While it’s important that companies invest in an unbiased measurement of their analytic maturity, it is only a fraction of the value. One key success factor is the ability to periodically conduct the same assessment to measure and monitor the progress of your analytic program(s). If you can demonstrate significant maturity increases, the results will support your argument for additional budget and resources.

Conducting the same assessment periodically means that you must retain the instruments used and methodology applied. Some assessment services will simply not comply.

Dr. Michael Gonzales, Chief Data Scientist with Prolifics, doesn’t recommend that you invest in any assessment that contains black-box processes. “Frankly, if you do not have an assessment that provides visibility to all aspects of how the maturity level is derived, then it’s not worth the price,” Gonzales explains. “Real value from these initiatives is derived when you can internalize the assessment instruments and processes to enable your organization to periodically conduct the assessment.”

The following video describes 4 of the instruments this author recommends.

Five Analytic Areas for CAMA

• Data Science (DS) – an inter-disciplinary field to unify statistics, machine learning, deep learning, big data, and data analysis.
• Machine Learning (ML) – the application of computer algorithms that improve automatically through experience. A sub-set of Artificial Intelligence.
• Business Intelligence (BI) – techniques and technologies used for data analysis of business information including the provision of historical and current views of operations.
• Big Data – a field focused on the analysis of data sets too large or complex to be dealt with by traditional data processing.
• Spatial Analysis – the application of statistical analysis and related techniques to data with a geographical dimension.

More questions about your company can gain and maintain a competitive advantage using advanced analytics? Our experts are here to help.

Embrace and leverage data science for innovative solutions. This presentation is designed to bring together business and data science members to achieve maximum outcomes.

https://youtu.be/Th-BXcfNO88

Data and analytics are drivers for success in every industry – that’s a fact. And the amount of data consumers create and businesses consume is exploding at rates unfathomable just a decade ago. So how do you keep up? Better yet, how do you harness the power of your data to get ahead and stay ahead of your competition. Three legends in the data and analytics space share innovative ways analytics is shaping the next set of industry disruptors. Don’t miss the conversation in the Innovation Sandbox.

Meet the experts!

By Michael L. Gonzales, PhD
Director of Research for Advanced Analytics, Prolifics
Written in collaboration with and for Eckerson Group.

Many believe that we’ve entered a new era of technical progress that forces companies on a path of continuous innovations. Cognitive computing is considered not only part of this new era, but potentially the core technical driver due to its ability to improve human decision-making through automation and augmentation. This paper will define cognitive computing, describe why cognitive computing is an IT-enabling competitive advantage for companies, and outline the necessary steps that IT must take if they hope to leverage the technology.

A Disruptive Climate

In the early 80’s Microsoft announced to the world that computing power was moving from mainframes to desktops and companies that want to be industry leaders must make that move.¹ Apple seems to be willing to continue a long running success of introducing disruptive innovations even at the expense of their own products. The iPhone cannibalized iPod sales and the iPad is eating away at iMac.² Google has been buying up companies in seemingly diverse industries: from smart thermostat to robotics to artificial intelligent companies. The objective is to corner the market on skilled resources in order to create the next generation of human-computer interaction.³

These are examples of disruptive strategies that companies execute with the goal to reshape or otherwise transform entire industries and the market places in which they compete. As companies redefine the market on their terms, they essentially throw the entire space into a period of disruption. Many analysts concede that technical progress is the catalyst for more innovations, which perpetuates disruption and a fierce competitive business climate.

So what do disruptive strategies have to do with Cognitive Computing? Futurists⁴, vendors, and leading companies believe that cognitive technology is a disruptive force in an information-intense age. Put simply: Cognitive Computing enables companies to identify and implement innovative, potentially game- changing products and services.

About the Author

Michael L. Gonzales, Ph.D., is an active practitioner in the IT space with over 30 years of industry experience serving in roles of chief architect and senior solutions strategist. He specializes in the formulation of business analytics for competitive advantage in global organizations. Recent engagements include companies in the top global 100.

Dr. Gonzales holds a Ph.D. from the University of Texas with a concentration in Information and Decision Science. He has presented and published his research at leading IT international conferences, including: Decision Sciences Institute, Americans Conference on Information Systems, and Hawaii International Conference on Systems Science. His research streams include analytics against extremely large data and success factors for IT-enabled competitive advantage.

Dr. Gonzales is a successful author, industry speaker and is currently the Director of Research for Advanced Analytics for a leading IT consulting firm, Prolifics, Inc.

About Eckerson Group

Eckerson Group is a research and consulting firm that helps business and analytics leaders use data and technology to drive better insights and actions. The firm helps companies develop strategies and roadmaps that maximize their investment in data and analytics. Its consultants and researchers each have more than 20 years of experience in the field and are uniquely qualified to help business and technical leaders succeed with business intelligence and analytics, big data management, data governance, performance management, and the internet of things.

¹ Hagel, John et al, Shaping Strategy in a World of Constant Disruption, HBR, October, 2008.
² Fox, Justin, Apple Versus the Strategy Professors, HBR, January, 2013.
³ Rowinski, Dan, Google’s Game of Moneyball In the Age of Artificial Intelligence, January, 2014.
⁴ Mayes, Randall, The Future, The Futurist, November-December, 2014.

This conversation is about IT, OT and IoT! Did you know there are billions of physical objects around the world connected to the internet – collecting data, communicating with each other? That opens a world of opportunities for businesses. Combine that with advanced analytics and the possibilities feel endless. But is IoT practical for you and your business? Our group of leaders in IoT and innovation from Wachter, ARROW and Prolifics explore IoT across industries and platforms. Don’t miss this engaging conversation – It’s all things IoT, including a connected mop!

Get to know our experts.

Data Science is true Geek Out material – what’s not to like about pulling meaningful, actionable insights out of your data that can benefit your business in a big way. That is, when it’s done correctly! With a severe shortage of qualified data science professionals in today’s market, knowing what you can about this hot topic is good for you and your organization. So, if you’re a data scientist, want to be one, or want to hire one, this Geek Out is for you. Prolifics Chief Data Scientist Michael Gonzales, PhD and Prolifics Account Executive Jennifer Johnson team up to talk about data science techniques and walk you through a real data science example – looking for that hidden treasure buried in data.

Take advantage of a unique opportunity to participate in a one-hour whiteboard session with Dr. Gonzalez – Free. Email us now at solutions @prolifics.com.