This second session of a 5 part series explored the topic of replatforming your legacy tooling to be ready for modern data science and AI. To succeed in AI, in addition to implementing new capabilities, you need to retire technical debt and bring all contributors in a unified environment. Dr. Michael Gonzales, Chief Data Scientist with Prolifics, Julianna DeLua, SME Data Science and AI with IBM and John Radi, Global Sales Leader with Prolifics discuss the following:
Key considerations when you decide to replatform
Architecting for the era of AI talent, technology, process and business
Diversity of tools for diverse talent – visual data science, programmatic data science and other tooling
How to get started with Watson Studio Premium for IBM Cloud Pak for Data
Often new data governance programs take on too much, then stall or fail. In this video you’ll learn how to find and govern your most sensitive data first – creating an early win and setting yourself up for success. This talk is led by data governance experts John Radi, Global Sales Leader, Data and Analytics with Prolifics, Brian Kordelski, CRO and Data Sentinel, and Kevin Downey, Chief Technology Officer at Data Sentinel.
This discussion originally aired on Dataversity’s Data Governance Demo Day in February 2021.
If you’ve ever stepped on a bathroom scale, you’re familiar with – at the most basic level – the concept of collecting and using data to affect behavior. Based on our experiences and personal knowledge, we can make the connection between eating too much pizza and what the scale will tell us.
But what happens when enterprises want to understand their customers’ behavior? How do we derive the connections and find the answers to affect the positive outcomes we desire when the behaviors become so complex, with so many more variables and other considerations? That’s what our solution – Predictive Behavior-Based Value Modeling – is for. It uses artificial intelligence (AI) and sophisticated data analytics to look at behaviors and data, make connections among naturally occurring “digital footprints,” and generate valuable, actionable information better and faster than a human ever could.
Our Client
Wichita State University (WSU), a public university, is the only metropolitan university in the Kansas Board of Regents’ system. The University’s 14,000 students study business, education, engineering, fine arts, health professions, and liberal arts and sciences at the undergraduate and graduate levels. The University’s vision statement includes being known for providing impactful student experiences and driving prosperity for the people and communities the schools serves.
Challenge
WSU (and the educational system in general) has wrestled with the continuing impacts of COVID-19. Once thought to be a short-term crisis to be endured until normalcy returned, the pandemic has evolved into a longer-term threat to overall student success. Time away from campus and the newness of all online classes increased the risk of students falling back or dropping out.
The University recognized this issue and, in line with their vision, sought a more proactive way to help ensure their students succeed both at school and beyond. The school wanted a way to identify “at risk” students – students who were likely to either drop out or not graduate on time. But the normal methods to identify these students (such as grades and attendance) are too reactive in nature. It takes time for grades to get into the system, so by the time an “at risk” student is identified under these methods, precious recovery time has been lost.
Instead, WSU wanted to predict which students are likely to become at risk. This would enable them to provide earlier intervention and keep those students on track before it became harder to help them.
Action
The Prolifics Predictive Behavior-Based Value Modeling solution is composed of the following specific steps. The solution leverages third party data and AI offerings, as well as Prolifics accelerators for data and AI, and other Prolifics IP assets and services.
We applied these steps with at WSU as follows:
Prolifics Predictive Models Workshop
During this workshop, we walk through specific predictive use cases to determine the scope and data readiness to achieve the desired models. There was significant work necessary to prepare the client data. The University suffered from data silos and a myriad of different running tools that hampered its ability to effectively manage its data.
WSU also needed to include its community college network as part of this initiative. University students often take classes across this network, so access to that information was an important piece of the puzzle. In addition, the school wanted to incorporate student activity from the community. The University placed a heavy emphasis on community involvement as part of the overall educational experience, so they needed real time access to data from their community partners.
Deploy Data Platform
Although represented as a “detour” on the above chart, at least 90 percent of our clients must execute this step and the one that follows. Often the initial hurdle is that client data is trapped in older, legacy and siloed systems. This was the case with our client. To centralize their data access, Prolifics deploys a centralized data platform to serve as the hub for all data feeding the predictive models.
Migrate and Cleanse Data
To resolve the University’s internal data silos into the new centralized data catalog, Prolifics migrated their current data and implemented a managed approach to the data governance, business terms and their data glossary.
Build Predictive Models
Prolifics built models around predicting student success and student risk. These models leveraged specific real-time indicators to predict how well students would perform; it also indicated where instructors or counselors will need to give extra attention to students who may be struggling.
Build Visualization and Alerting
We created and deployed a customized dashboard with alerting capability. This gives WSU the easy, immediate access to the information needed to make real-time decisions for intervention needed for their students, to ensure they stay on track.
Results
This solution is scheduled to go live January 2021, so production metrics are not yet available. The University will use the following metrics for improving student success:
• Reduced dropout rate (how many students drop out before graduating) • Increased on time graduation rate (how many students graduate within 4 years) • Improved student satisfaction scores
In addition to caring about students and their success, the University’s achievement of the above metrics are expected to generate tangible business outcomes. Specifically, WSU will improve its ability to market itself to new students, promoting a continuous cycle of improvement. This will ultimately raise WSU’s brand and position it for a bright future in a competitive market. Follow-on metrics include:
• Increased number of applications received for incoming freshman and transfer students • Improved quality of the applicant pool (ability to attract students with higher GPA and SAT/ACT test scores) • Improved quality of incoming freshman classes (better students will typically yield better student outcomes)
Technology
Specifically, for this client, we utilized the following technologies for data:
BM Cloud Pak for Data 3.5 running on Red Hat OpenShift 4.0 – to provide the underlying data layers with cloud platform flexibility.
IBM Cloud Pak for Data DataStage Cartridge for ETL
IBM API Connect – for data sharing as needed.
Prolifics Migration Factory Accelerator – This enables us to rapidly migrate ETL jobs from competitive platforms to IBM Cloud Pak for Data. This accelerator covers the gamut from analyzing the old systems and developing the new environment to testing, deployment and support. Prolifics can cost effectively provide this migration, cleansing and organization of data through our offshore component.
Prolifics Cloud Pak for Data OpenShift Accelerator – This speeds the interoperability between Cloud Pak for Data and Red Hat OpenShift. In collaboration with IBM Offering Management, we created this accelerator and documented the nuances to ensure a successful implementation of Cloud Pak for Data on OpenShift.
Specifically, for this client, we utilized the following technologies for AI:
Watson Assistant, Watson Discovery, and Watson Explorer to uncover, search and explore structured and unstructured client text and data across all channels.
Watson Studio and Watson Machine Learning to build and deploy automated AI workflows, and Auto AI to manage and simplify the AI lifecycle.
Prolifics Predictive Model Templates for Watson. This asset speeds the creation of custom predictive models by using a set of standard models as a starting point. Prolifics has created these standard models specifically for IBM Watson products.
NEXT STEPS
We’re fielding interest in this solution for a number of use cases across the whole spectrum of industries:
Retail – What indicators would gauge customer interest in a new product?
Healthcare – What would indicate a potential relapse and readmission of a recently discharged hospital patient?
Finance – What are the early warning signs that a customer may default on a loan?
About Prolifics
Prolifics is a global digital transformation leader with expertise in cloud, data and analytics, DevOps, digital business, and quality assurance across multiple industries. We provide consulting, engineering and managed services for all our practice areas at any point our clients need them – giving them fast, complete solution delivery experiences that they find nowhere else. Whether it’s initial advising and strategy; design and implementation; or ongoing analysis and guidance, Prolifics helps companies take charge of their digital future. Vision to Value. Faster. It’s not just the Prolifics’ tagline, it’s what drives us. Email solutions@prolifics.comor visit us at prolifics.com.
Prolifics helps Health Coalition with Short-Term Modernization and Long-Term Possibilities
Our client, a Health Coalition, is a non-profit organization that connects, collects and integrates health information in an exchange in partnership with its state’s department of health. Participating healthcare professionals (with patient consent) can access electronic health information and securely exchange data with other state participants. This improves patient outcomes, reduces tests and procedures, and lowers costs.
Challenge
Our client (“Health Coalition”) gathers health data from participants all over its state – providers, payers, agencies and related organizations. Already busy, the COVID-19 pandemic created a massive increase to the non-profit’s workload. Health Coalition collected all the health data from the participants into its systems through application programming interfaces (APIs) behind a secure API gateway.
This API gateway was coming to its end-of-life and would no longer be supported by the vendor. Therefore, Health Coalition needed alternatives, with these factors to consider:
The organization was subject to budgetary constraints, generally in the form of government grants that applied to particular projects. In this case, the funding available was specifically designated to replace an unsupported product with a supported product, with no leeway for extras.
100 percent of Health Coalition’s collected data is subject to HIPAA, so compliance and security were top concerns.
The gateway had to support a substantially larger amount and inflow of data than before, because of COVID-19 information.
From a resource perspective (in large part due to the COVID-19 workload), Health Coalition’s ability to learn, implement and support different, updated technologies was extremely limited.
Therefore, the immediate goal was to give them exactly what they had and knew how to use, but on a supported product; without boxing them into a solution that had no flexibility or room to grow.
Action
We discussed various avenues with Health Coalition, one being going to the Cloud. However, the client knew that their on-prem, physical data center was secure and HIPAA compliant. So, for purposes of the project at hand, they did not want to go through the security and compliance exercise, however perfunctory, with a new Cloud provider.
The answer, though, was still under a Cloud umbrella. We recommended IBM Cloud Pak for Integration (made up of lightweight, enterprise-grade, modular cloud solutions). When licensing IBM Cloud Pak for Integration, the licensee has access to several different other solutions – all of which are considered part of that Cloud Pak. One of those solutions is IBM DataPower Gateway.
IBM DataPower Gateway is a single multi-channel, secure API gateway appliance that our client could easily manage. It in effect gave them the solution they had but was obviously more robust (as needed for COVID-19 data) and supported than the end-of-life gateway they needed to replace.
In addition to solution recommendation, Health Coalition entrusted us with the entire migration from the old system to the new.
Result
IBM DataPower Gateway achieved the specific goal of getting our client’s current situation from unsupported to supported. Our migration project of old system to new system came in on time and on budget, with high customer satisfaction. Health Coalition is in production today with the DataPower system.
This Prolifics’ client is an example that application modernization does not mean you have to throw out everything old and put in everything new. You don’t have to do it all at once – you can do it in stages and steps to meet your needs in the context of limited resources.
Health Coalition was in a dead-end technology, and Prolifics helped them refresh that. And, with the licensing flexibility under IBM Cloud Pak for Integration, the table is set for Health Coalition to step up their digital transformation whenever they want to. They can experiment with other products under Cloud Pak to take their technology forward, such as IBM API Connect – a scalable API platform to create, securely expose and manage APIs.
Working together, Prolifics now has Health Coalition in a technology that has a roadmap to the Cloud – and the future.
Technology
IBM Cloud Pak for Integration helps support the speed, flexibility, security and scale required for all your digital transformation initiatives. It comes pre-integrated with functionality including API lifecycle, application and data integration, messaging and events, high-speed transfer and integration security. Pair IBM Cloud Pak for Integration with an agile integration strategy to meet escalating demand, reduce costs and increase operational agility.
IBM DataPower Gateway helps organizations meet the security and integration needs of a digital business in a single multi-channel gateway. It provides security, control, integration and optimized access to a full range of mobile, web, application programming interface (API), service-oriented architecture (SOA), B2B and cloud workloads.
What’s next
Short term, we’re talking to our client about embedding a Prolifics consultant for a few months to ensure a smooth transition to the newly modernized solution. We’re also proposing a managed services arrangement that would essentially provide them with a 24/7 help desk and on-call people for the software and related solution we implemented. For now, though, we have software architects from our practice area checking in with the client periodically and offering advice on how to move forward.
AI is “artificial intelligence” not “accidental intelligence”. Every organization must embrace AI as a fundamental component in each aspect of their business but it must be done correctly from the start. Before even thinking about tools you must start with a modern and adaptive governance practice. AI only works if you trust the data feeding the engine and allows for effective decision intelligence.
You must then have a strategy in place to embed AI into your applications and processes. Making AI part of the design of new applications or modernization of existing ones allows for the analysis of data much quicker and seamless for the end user. Some key areas to focus on are:
Enhanced Customer Experience
Process/Task Automation
IT Operations
Self Integrating Apps
Predictive Maintenance
Behavioral Analytics
Security & Risk Management
Virtual Assistants
Fraud Detection/Prevention
Prolifics has a core set of capabilities and offerings that will help at every step of your data and analytics journey. We start with a set of workshops designed to frame out the business needs and to chart your journey to success. Additionally, we have frameworks which set a critical foundation in place which is necessary to achieve successful business results along the way.
Matt Garst leads sales for Digital Automation and Cloud Solutions for Prolifics. Matt has over 20 years of experience in sales in the information technology industry. Most recently, he was responsible for the IBM software business within the US Army and Missile Defense Agency. Prior to his role at IBM, he was the Vice President of Worldwide Sales for Enterprise Information Management, where he led a sales team focused on software and professional services. Matt began his career as a soldier in the U.S. Army where he served six years in the Infantry.
Do you have consumer data anywhere in your organization? Of course you do. Then this installment of Prolifics’ Geek Out series is crucial for you. Yep – it’s all about data privacy.
Data privacy is not slowing down or going away. More governments at all levels are proposing and adopting privacy regulations. You’re going to have to deal with it – the only question is when.
And dealing with it means technology – this is not a manual process you can throw a few employees at. You’ll need to find, itemize, tag, delete, update and otherwise manage consumer information wherever it exists in your company – quickly and efficiently. Fines and penalties await those who don’t.
The data experts from Prolifics and Data Sentinel are here to “geek out” on the new technologies behind data privacy and data governance. This tech will not only help you toward privacy compliance, but will lead to better decision-making, improved efficiencies, and increased revenue and growth.
After you watch the video, read more about Data Sentinel’s Sensitive Data Audit … and connect with us at solutions@prolifics.com to discover the data privacy solution that’s best for your business.
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
Basically, 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. For example, one of Dr. G’s clients had a mid-tier, BusinessObjects server farm and Teradata for the back-end. They pushed data requests back and forth using Structured Query Language (SQL). The BusinessObjects developers were writing whatever they needed to suck out as much data as they could, then manipulated it in the BusinessObjects server farm. That’s what they were comfortable with. They didn’t really know what Teradata could do – how it could manipulate data beforehand. On the back-end, the Teradata people didn’t know anything about the manipulation and reporting done upstream, their role was monitoring SQL requests for data.
“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?
As 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.”
Analytic Platform Optimization will take you right to the program, right to where that word usage is being applied. You’ll know which platform is not being leveraged properly, meaning an imbalance showing both platforms are not performing correctly.
Then what?
At Prolifics, we not only point out where the issues are, but can also come up with an agile plan (a sprint plan) to correct them, along with an army of our own code developers (experts in different platforms) to help you with the optimization. We recommend doing the worst culprits first, in terms of language usage, then going from worst to the least offensive. Prolifics can drive and close the entire process. So 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 and analytics, DevOps, digital business, and quality assurance across multiple industries. We provide consulting, engineering and managed services for all our practice areas at any point you need them – giving you fast, complete solution delivery experiences that you will find nowhere else. Whether it’s initial advising and strategy; design and implementation; or ongoing analysis and guidance, Prolifics will help you take charge of your digital future. Email solutions@prolifics.com or visit 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?
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:
all formal antecedent documentation
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.
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
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.
