Oracle Archives - Vertice

Vertice Awarded Exclusive Oracle Cloud Managed Service Provider expertise

colmmccarthy — Fri, 03 Nov 2023 15:06:06 +0000

Vertice is proud to share the exciting news of a significant achievement and milestone by obtaining accreditation from Oracle as an exclusive Oracle Cloud Managed Service Provider (CMSP) in key specialisations. Marking Vertice’s dedication to delivering innovative solutions and services in Data Analytics and AI. Furthered by our Database and Infrastructure competence.

Vertice has been awarded accreditation in the following Oracle Cloud Platform specialisations:

Oracle Cloud Platform – Business Analytics
Oracle Cloud Platform – Data Management
Oracle Cloud Platform – Database to Oracle Cloud

This achievement not only further highlights Vertice’s proficiency in managing Oracle and non-Oracle workloads within the Oracle Cloud; but also reinforces our commitment to providing high quality solutions to meet the evolving demands of the industry.

As a proud member of the Oracle Partner Network, Vertice offers its customers access to a comprehensive Oracle CMSP journey. Allowing them to take full advantage of the full spectrum of Oracle’s cloud capabilities. This accreditation enables Vertice to provide its customers with a well-rounded, top-tier service that empowers data-driven decision-making for improved customer success and faster time-to-value delivery.

Oracle Partner

Vertice’s CEO, Tony Cassidy, shared his enthusiasm about this significant achievement:

“We are honored to have earned these prestigious accreditations from Oracle. This recognition reflects our dedication to staying at the forefront of Data Analytics & AI and our commitment to empowering our clients with the best-in-class solutions. We look forward to continuing our successful collaboration with Oracle and further enhancing our clients’ cloud capabilities.”

This accomplishment not only represents a crucial step forward in Vertice’s journey to provide the highest quality services and solutions in Data Analytics & AI. It cements our position as a trusted partner for businesses seeking to harness the power of data. Enabling data-driven decisions in an ever-changing digital landscape.

For more information or enquiries please contact: connect@verticecloud.com.

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Health Analytics White Paper

colmmccarthy — Wed, 24 Feb 2021 13:22:33 +0000

Health Analytics – White Paper

Industry Viewpoint Healthcare Achieving data value without the “data pain” Data-driven Health & Social Care

Summary

It is well understood that linking data sets together within services and more widely across healthcare can significantly improve outcomes for patients and staff. Joining up the different data silos opens the possibility to understand the end to end patient journey and generate actionable insights into population health and earlier identification of disease risks. These insights can empower people to take action to prevent illness and support more effective resource management across services.

Currently, data does not flow easily within and across health and care settings. Although there have been pockets of innovation across the system, challenges exist in scaling these initiatives to deliver impact. Data quality is often a core challenge. There are often technical challenges in linking data sources together. Moreover, programmes are often constrained by the bandwidth of the IT department and there are often challenges to connect the value of these initiatives to deliver beneficial impact to the ‘frontline’.

To support Health Care organisations in overcoming some of these challenges, Oracle is actively collaborating with services to ensure that the full potential of data is realised. The Oracle co-innovation programme (entitled ‘Data+’) has been tried and tested in the NHS and more broadly across other industries over several years. Employing a collaborative ‘multi-disciplinary team’ approach to solving the highest value data challenges, the programme delivers a proof of concept and an associated business case that demonstrates the value of innovative ideas – leveraging leading-edge technologies such as Machine Learning (ML) and Artificial Intelligence (AI).

Healthcare & Life Sciences

Health and care services generate billions of data items every single day; data which is collected by multiple agencies across primary, secondary, community, social care and other public services (e.g. housing services, policing etc) all of which is hard to correlate and exploit. With the addition of new digital and model channels, the inclusion of social media and wearable devices now opens a new world of insight previously unavailable. Yet, most of this data is under-utilised, it sits in disparate in unlinked silos, it varies in quality, it often is not standardised and there are complex issues over its ownership.

One of the key enablers of addressing these challenges is leveraging value from a new generation of technologies. Imagine if the NHS could really put this data to work to generate actionable insight: Imagine predicting with an extraordinary degree of accuracy the likelihood of disease across populations. We could then run more effective screening programmes to prevent illness or intervene early with those at highest risk.

Imagine we could have insight into care variations from pathway to pathway together with the associated effect on patient outcomes. We could then really understand what treatments work and drive efficiency in spend. Imagine if we could obtain a holistic view of local activity to predict staffing requirements more accurately across health and care. We could then foresee spikes in activity and know in advance which services require extra resourcing. This is all possible if healthcare organisations can leverage digital technologies and take advantage of data effectively, and become data-driven.

“It’s not just about gearing the current systems to work better – our ambition should be for the use of the best technology available for the NHS and social care sector. The potential of cutting-edge technologies to support preventative, predictive and personalised care is huge,”

Department of Health & Social Care, October 2018. The future of healthcare: our vision for digital, data and technology in health and care

Data opportunities within Healthcare

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. ML is a specific subset of AI that focuses on learning, reasoning, and decision-making. The technologies use statistical models to make predictions (or decisions) without being explicitly programmed to perform the task. The computer ‘learns’ as it increases its data reference points – this is also referred to as predictive analytics.

This fast-developing area of technology is hugely relevant for health and care in view of the wealth of data it captures. The analytics capability – which enables rapid analysis of huge datasets, delivering proactive identification of unwarranted variation in, for example, patient pathways, prescriptions, and cost anomalies – is an area the health and social care sector must progress.

This goes beyond the structured data, opening the opportunity to combine structured and unstructured data, such as Natural Language Processing (NLP), image analysis and Sentiment Analysis.

Scope of impact

Although the potential for data innovation is significant, gearing real-life value from these initiatives has its challenges. Some of the main reasons why data innovation programmes fail to achieve value are:

Data quality is often a core challenge. Complicated by the fragmentation of data collection approaches and data ‘stovepiping’.
There are often technical challenges, such as linking data sources together sitting in disparate systems.
There are also capability challenges as the volume of data increases, point solutions have limited capability versus AI/ML enabled platforms.
Programmes are often perceived to be constrained by available expertise in IT
Data analysts not directed and focused on specific business challenges to solve and are being left to their own devices to hunt down “golden nuggets” of value.
Data is often siloed within departments which are protective about their data and don’t see the value in sharing their data with others.
Data innovation projects tend to be led by specific team and miss out on engaging a ‘multi-disciplinary team’ to co-design the approach and evaluate the outcome.
There are often challenges to operationalise learning models and connect the value back to the ‘business’ or to developers who can create business applications.

The Converged Database

Oracle’s cloud-based digital platforms can integrate any data sources into a comprehensive data platform that supports AI and ML to deliver business value. Data which had previously been locked in silos can now be released and given a voice to help drive better outcomes across the NHS.

As part of this, Oracle converged database has native support for all modern data types and the latest developments all built within the one product. The database supports Spatial data – location awareness, JSON for document stores, IoT for device integration, in-memory technologies for real-time analytics, and traditional relational data. By providing support for all these data types, a Converged Database can run workloads from IoT, Blockchain and analytics with Machine Learning built-in.

By integrating various data types, workloads, and algorithms within the database, this enables mixed workloads in a simple and scalable manner. With all the data within one database, there is less need to manage and maintain multiple systems enabling unified security across the data.

Oracle: Co-innovation at speed

Oracle has developed a co-innovation methodology (entitled Data⁺) to support healthcare organisations in addressing these challenges and to ensure that the full potential of data is realised within their organisation. Together, we explore, frame, design, prototype, and prove technical feasibility over the course of 8-weeks to present evidence to key stakeholders.

The objective of the methodology is to frame a business challenge with significant business value so that we build a prototype and show evidence that we can solve that challenge. The idea is to improve everyone’s confidence by doing rather than talking.

The prototype will typically focus on two things:

Solving an immediate business challenge with data innovation by collecting the appropriate data sets and applying machine learning techniques.
Using the data sets above and exploring other business challenges that we may be able to solve by adding one or more additional data sets.

Data+ brings many benefits to healthcare organisations:

Demonstrates how to solve real-life challenges by using data in new and innovative ways
Provides a repeatable process that tests the technical feasibility of any data-driven idea
Creates a blended team of data analysts, developers, managers, and clinical leads
Develops a validated business case to show the value of data in terms of process optimisation and new opportunities
Creates a solid foundation on which to test the desirability, viability, and feasibility of a data-driven idea.

The philosophy of Data+ is for Oracle and healthcare to work together to agree on the business challenge. With that challenge in mind and a clear idea of what needs to be proved so that all stakeholders have an increased sense of confidence in the desirability, viability, and feasibility of solving the business challenge through data innovation.

Having a collaborative process and a blended team is no good unless they can work on a platform that can execute the ideas. Oracle combines any data source and any data structure into a single data platform that enables the inbuilt AI and ML tools to release valuable insight from your data. Data which had previously been locked in silos can now be released and given a voice to help and new business value across the organisation.

Moving beyond innovation

Since its inception nearly 40 years ago, Oracle has invested deeply across nearly every element of information management – from software to hardware and to the innovative integration of both on-premises and cloud-based solutions. Oracle’s unique value is its long history of engineering the broadest stack of enterprise-class information technology to work together—to simplify complex IT environments, reduce total cost of ownership, and to minimise the risk when new areas emerge.

Vertice is an Oracle Platinum partner specialising in data integration, data management and analytics. With a proven, award-winning partnership, Vertice look to move from innovation through to production. Ensuring solutions are secure and scalable to enable actionable insight to be brought to the front line.

Conclusion

Health and care services are facing unprecedented pressure. Joining up data sets within services and across the system can generate actionable insight that enables new and more efficient services to relieve these underlying pressures. However, the challenge for many services is the lack of organisational bandwidth to explore, identify, deploy, and get value from these potential data innovations.

This is where Oracle and their partner Vertice can help. Through our Data+ programme and enable deployment, we are actively collaborating with organisations to ensure that the full potential of data is realised. Solving immediate business challenges with data innovation by collecting the appropriate data sets and applying Machine Learning techniques to ensure value is delivered early.

From discovering data sources, ingesting, transforming, and returning them and getting the analysis and insight to empower decision making. Oracle has more experience of platform transformation projects than any other provider; with over 20,000+ engagements globally.

Oracle is unique in its ability to deliver powerful solutions to all critical areas of challenge in the healthcare industry. Oracle’s family of data management solutions continue to solve the toughest technological and business problems delivering the highest performance on the most reliable, available, and scalable data platforms.

Use Case

Example: Identifying £1.2 billion in potential savings

The Department of Health asked the NHS BSA to take a proactive role in identifying opportunities to reduce costs and eliminate waste. One way to do this was to and better ways to use the vast volumes of data already collected and held within the organization to help reduce fraud and error throughout the health service.

The NHS BSA needed a new, centralized solution that would enable it to gain better value from its data which is spread across a disparate set of IT systems, data, storage, and analytical capabilities. To achieve this, it chose an end-to-end Oracle solution platform to establish its Data Analytics Learning Laboratory (DALL), investing in both technology and expertise to create insight from its data. Within the first three years of operation, the organisation identified circa £1.2 billion in potential savings.

“We chose Oracle because the solution could cope with very large data volumes running into billions of rows and could scale as volumes increase. In addition, the Oracle solution required no IT team support to run the queries, which enables our team of data analysts to be self-sufficient,”

Nina Monckton, Chief Insight Officer, NHS Business Services Authority

How Oracle supported the execution

Oracle ran a proof of concept (POC) to show the speed and capability of the proposed end-to-end solution. The POC used publicly available data sets for NHS prescription data. It covered 50 million prescribed items, 300 million records, and six months of data. The team concentrated on ending anomalies in the data and carrying out further analysis to understand them before presenting the endings in a clear and straightforward way.

Following the POC, Oracle worked with NHS BSA and its data centre partner, Capita, to complete the implementation. During implementation, Oracle provided the NHS BSA with access to a virtual environment. This enabled the team to get some experience with the tools before completing the implementation. As such, NHS BSA was familiar and confident with using the new analytics tools from day one, saving considerable time and gaining immediate value.

“Working with Oracle has been a very positive experience. The team has been incredibly responsive and provided a number of experts to help us get up and running as quickly as possible. With one vendor providing the whole solution, it’s very easy for us. If we need help, we know where to go.”

Nina Monckton, Chief Insight Officer, NHS Business Services Authority

About the Author

Dr. Abi Giles-Haigh is the Chief Data Science Officer at Vertice.  She has over 10 years’ experience working with data from database management, report writing and advanced analytics. Previously she was part of the Data Analytics Team at the NHSBSA identifying savings and improving patient care. She is an Oracle ACE Director, UKOUG Technical Speaker of the Year 2016, as well as a Nominated Digital Leader 100. Abi is a technical evangelist in the field of predictive/prescriptive analytics and data mining. She holds a PhD in Computational Modelling and a Bachelor of Science in Computing Science, from Newcastle University.

If you have any questions, don’t hesitate to get in touch

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MLOps: What it is and Why it Matters

colmmccarthy — Thu, 18 Feb 2021 13:23:42 +0000

MLOps, a combination of Machine Learning and Operations, is a practice which aims in making the machine learning production seamless and efficient. The data science community agrees that the MLOps is an umbrella term for best guiding and practices around machine learning- not a single technical solution. It is a practice for communication between data scientist and operation professionals which helps in managing the ML production lifecycle while focussing on business and regulatory requirements.

There are three components of MLOps :

Machine learning
DevOps (IT)
Data engineering

These three components are the key elements which works towards the machine learning lifecycle loop within an organization.

Why MLOps is important?

Mlops is deeply collaborative in nature, automate as much as possible to enable richer and consistent insights with machine learning. In any organisation, MLOps allows the data scientist to execute their best operations which can take the business decisions off their plates where they can deploy models, helping to reveal deeper insights more quickly and efficiently.

Advantages of MLOps:

The major advantages of MLOps for any company are:

Through robust ML lifecycle management, it helps in rapid innovations.
Helps in creating reproducible workflow and models.
Helps in deploying the high precision models easily in any location.
Easy evaluation of the key features with minimal bias.
Helps in improving the model performance by using advance data bias analysis.

Major disadvantages to MLOps:

One of the main obstacles for Machine Learning being deployed in production is the level of disruption that may occur with the inclusion of ML into forms/ front end applications. This may come from preconceived views on ML or poor deployments in the past. The second challenge for the business is about the evaluation consideration of model risks when actualizing a machine learning model. One of the major technical challenge is regarding the lack of coordination and improper handoffs between the data scientists and operation teams which can lead to delays and errors.

Where does it overlap with data or model governance?

Under the model governance, MLops by applying to monitor the attributes on a massive scale can provide rich model performance. For analysing critical moments MLops can provide the ability to take the snapshots of the pipeline. Not only this, the logging facilities and audit trails can be used for continuity of compliance and reporting. Parallelly by applying the MLOps practices, an organization will ensure that it should be compliant and conducting governed practices.

How can it be achieved with Oracle tech?

Oracle has extended its collaboration platform for data scientists to manage and build machine learning models with the help of managed service available from the Oracle public cloud. This offering is Oracle cloud infrastructure, which is a data science platform which works with the variety of data sources. It not only works with different data sources but also supports use of open-source (Python) libraries and frameworks. In addition, the Oracle cloud infrastructure enables Git/Github to be used enabling code control throughout the development cycle. For performance and scalability, algorithms can be passed down to the database to be ran through Oracle Machine Learning (OML) or with Enterprise R.

Additionally, Oracle also utilises the graph data store (spatial and graph for Oracle databases) for various risk management, use cases like detecting and analysing cyber threats or understanding who in the business has access to which part of a data warehouse. These graph stores and ML models feed on each other enabling a greater depth of understanding and insight as well as enriching MLOps.

Is it easy to achieve?

According to the top researchers, MLOps is difficult to achieve for some companies. The root cause of this is the fundamental difference between the ML and traditional software. As ML is not just the code, it is code plus data. Since the model’s behaviour depends on the input data (Training data) which it will receive at the prediction time, which you cannot determine in advance. This means for auditing purposes; we need to know the model settings but also what the model was trained on. This all needs to be considered during MLOps and letting the business and potentially your customers know how this is achieved and used.

“ML is pretty mature, but Business impact and deployment is not”

How does Vertice achieve MLOps?

An oracle platinum partner, Vertice has a dedicated Oracle-certified data science practices in Europe. In Vertice we use the Oracle Infrastructure Cloud (DS platform), Oracle Analytics cloud and Autonomous data warehouse to perform the MLOps, enabling your business to get the most from your data.

To manage the ML scripts and models we use Machine Learning catalogue where we create the data flows in Oracle Analytics Cloud (OAC) to train the ML models to predict and visualize the results. Depending on the requirements or restrictions and by combining existing open-source libraries, Vertice can implement their own in-house MLOps processes.

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If you have any questions, don’t hesitate to get in touch

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Security By Design

colmmccarthy — Tue, 16 Feb 2021 11:54:39 +0000

What is security by design?

Security by design is a software engineering approach in which the security tactics and patterns are first thought before designing a product. The best security approach is implemented in the architecture design and it acts as a guiding principle for developers. This approach has also been adopted in the area of the Internet of things (IoT) and Big Data Analytics. Like traditional IT systems, IoT deployments are also susceptible to a host of cyberattacks such as phishing, malware attacks, etc. This approach prevents the cybersecurity breach in an organization, and it teaches best practices to implement important software features using design as the primary driver for software. The main security principles which are important for all applications are confidentiality, integrity, and availability.

In the field of big data analytics, the extensive collection and processing of personal information have given rise to serious privacy concerns related to profiling and disclosure of private data. Security by design approach helps to integrate appropriate data protection safeguards in the core of the analytics value chain which will allow the benefits of analytics without invading an individual’s private sphere. By using this approach, the privacy requirements are identified early at the big data value chain and the necessary organisational and technical measures are implemented.

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Image Reference: (Spivey, 2017)

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Why is it important?

This method is important because it prevents system errors from an early stage. The security tactics and patterns provide solutions for enforcing the necessary privacy, data integrity, and safety even when the system is under attack. The security of the software system is especially important so to achieve it, we should build a robust security architecture and should preserve the architecture during software evolution.

What advantages does it have vs other design techniques?

The security by design is built in from the beginning instead of testing the security of a system when it is done, so solving the security issues at the beginning is much cheaper and it reduces potential disruptions. Usually, in the Software Development Lifecycle (SDLC), the pressure is extremely high at the end of the development process, so it is not the best time to implement any security methods. To address the big data risks in the analytics, field the security by design approach is very critical.

The security strategies are analysed in each phase of big data analytics along with the technical implementation measures. Security by design is a very resilient process where security is built-in rather than added as a fix at the end.

Where does it overlap with data governance?

As we know that data and information are typically the heart of any organization. Data security, protection, and privacy are must when private individual information is concerned. Effective data governance helps to avoid such issues. The security and privacy principles are included in the GDPR (General Data Protection Regulation). But it is very much essential for an organization to understand that they not only have to meet their compliance requirements, but they also must diminish the cybersecurity risks. This means that organizations must protect the individual’s rights by integrating safeguards to comply with the GDPR’s requirements and they must implement the data protection principles by implementing appropriate organizational and technical measures.

The most common security approaches which have been followed by an organization are role-based and policy-based security approaches. In the role-based approach, access to certain resources is allowed based on a person’s role within an organization. This approach has become one of the main methods for advanced access control. The policy-based approach is a framework that evaluates a user’s access dynamically. This policy follows the zero-trust approach and implements strict access control within an organization.

Let me highlight a few biggest data breaches of the 21st century.

1) Yahoo data breach 2013-2014, 3 billion user accounts were impacted. This was one of the biggest data breaches in history. In 2014 the attackers compromised the usernames, email addresses, date of birth, and telephone numbers of 500 million users. In 2016, a different attacker compromised the names, email addresses, date of birth, passwords, and security questions and answers of 1 billion users. In 2017, Yahoo revised the estimate of users who were impacted, and it was 3 billion accounts (Swinhoe, 2021).

2) Facebook data breach 2019, 419 million users were impacted. The phone numbers of these 419 million Facebook accounts were leaked (Winder, 2019).

3) Microsoft data breach 2020, 250 million customer service and support records were exposed to the web. A database containing 250 million customer records were found unsecured online (Winder, 2020).

As most organizations are moving to a risk-based approach to security, data governance is the key that makes security, privacy, and risk more effective and easier. Within an organization, data governance protects high-quality data throughout the lifecycle of the data which includes security, data integrity, and consistency. On continuous basis security by design maintain, manage, and monitor the security risk governance and management

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How can it be achieved in Data science?

In this digital world, cybersecurity is undergoing a massive shift in technology and its operations, this change is driven by data science. In recent years, due to increasing dependency on digitalization and the Internet-of-Thing (IoT) many security incidents like a data breach, unauthorized access, malware attack, phishing, etc have grown.

The key to making a security system automated and intelligent is extracting security incident insights or patterns from cybersecurity data and building the data-driven model. To understand and analyse the actual phenomena with data various machine learning techniques and processes are used.

How can it be achieved with Oracle Technology?

Many business leaders, organizations are moving their critical business-critical workloads to the cloud. One of the very efficient and reliable options for them is the Oracle Cloud Infrastructure platform. Oracle is empowering businesses of all sizes on the journey of digital transformation. The security practices in Oracle are multidimensional.

Oracle Cloud Infrastructure was architected with security as a primary design principle including isolated network virtualizations. The security approach for Oracle Cloud Infrastructure is based on seven core pillars which are Data Encryption, Customer Isolation, Visibility, Security Controls, High Availability, Secure Hybrid Cloud, and Verifiably Secure Infrastructure. Each of these pillars has multiple solutions designed to maximize the security and compliance of the platform.

Is it easy to achieve?

Good security wins customers and the “security by design” approach is not easy to apply if the workforce does not understand the security protocols properly. The necessary security by design training can be included in the company’s culture to enable this approach to be successful.

Even in the big data analytics field, it is not an easy task to implement this approach since it requires a lot of research. The right balance by highlighting security as a core value of analytics and examining how technology can be used on its side can make this approach successful.

Most proactive firms try to integrate this approach into their systems. If security by design is done effectively then it reduces the cybersecurity risk from external and internal threats. As I said before this approach is better for return on investment, and it is also increasing customer satisfaction.

How does Vertice achieve security by design?

Vertice is an Oracle platinum partner we are a specialist in Oracle service offerings. Our workforce fully understands the security protocols and follows them very strictly. We also provide the necessary training to our employees to achieve this, as well as training customers.

We use the Oracle Cloud Infrastructure, Autonomous data warehouse, and Oracle Analytics Cloud (OAC) platforms for data warehousing, data analysis, data modelling, and data visualizations. We make sure that the security is implemented in the design lifecycle, authentication is appropriate, and the data flows are secure. We also use multi-factor authentication which is one of the biggest friction points for the users. We also monitor how users are interacting with data daily to know what data is pulled, and how it is investigated, enriched, and analysed.

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Shalini Mahajan is a Senior Data Analytics Consultant at Vertice. She has over 6 years of experience working with database management and data analytics. She began her career as an Application Software Development Consultant at NTT Data after she finished a Bachelor of Engineering degree in ECE from Visvesvaraya Technological University. She is very passionate about analytics and business service innovation. She holds a Master’s Degree in Business Analytics from UCD Michael Smurfit Graduate Business School.

If you have any questions, don’t hesitate to get in touch

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Self-Service Analytics for Property Management with Oracle Analytics

colmmccarthy — Wed, 18 Nov 2020 10:40:59 +0000

Oracle users can perform Self-Service Analytics with Oracle Analytics. In this Bitesize Analytics demonstration, we highlight Data Preparation, Data Visualisation, Storytelling, Sharing & Collaboration for a sample Property Management Project.

If you have any questions, don’t hesitate to get in touch

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Governed Analytics through OAS

colmmccarthy — Thu, 12 Nov 2020 16:02:45 +0000

Oracle Analytics Server (OAS) offers many benefits for businesses looking to get the most out of their data. The following key features highlight how accurate and actionable insight is achieved through OAS.

If you have any questions, don’t hesitate to get in touch

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People-Centric Analytics for HR

colmmccarthy — Fri, 30 Oct 2020 12:00:08 +0000

Oracle users can perform People-Centric HR Analytics within both Oracle Analytics Cloud and Oracle Analytics Server for deeper insights.

Sign up for our hands-on Bitesize Analytics demo session focusing on People-Centric Analytics to learn more on how your business can utilise these techniques:

If you have any questions, don’t hesitate to get in touch

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Oracle Analytics Rise in HR

colmmccarthy — Tue, 27 Oct 2020 13:16:12 +0000

Oracle users can perform People-Centric HR Analytics within both Oracle Analytics Cloud and Oracle Analytics Server for deeper insights. Click here for more information, or to get in touch.

Sign up for our Bitesize Analytics webinar focusing on People-Centric Analytics to learn more on how your business can utilise these techniques: Reserve your place here.

If you have any questions, don’t hesitate to get in touch

The post Oracle Analytics Rise in HR appeared first on Vertice.

ODTUG Oracle Analytics Storytelling Challenge

colmmccarthy — Tue, 20 Oct 2020 15:28:56 +0000

The Vertice team took the second “ODTUG Oracle Analytics Storytelling Challenge” in August 2020. The challenge was taken on by Paritosh Gupta, Mayank Jain, and Shalini Mahajan. In this storytelling challenge, we showed how we analysed the data and took this challenge while working remotely during the current pandemic.

Methodology We Followed

As an analytics team we used the CRISP data mining technique which is a robust and well-proven technique, we also followed the Scrum methodology which is an Agile framework.

The Architecture and Technical Aspects

Once we got the data on Monday 24th August at around 4 pm Irish time, we started with the initial data exploration. After the initial analysis we found out that the data consisted of Customer Data & Product sales information, so we decided to do Customer Segmentation based on three business segments which are; Demographics, Geographical, and Behavioral.

Since the dataset was very large, we chose to use the k-means clustering algorithm because it can efficiently handle large data sets and iterates quickly to finding good solutions. We were provided with 9 tables by the ODTUG team. Since the data volume was very high, we joined these tables using PL SQL and did Clustering in the Oracle Autonomous Data Warehouse (ADW). To answer one of the business questions we used the machine learning technique Sentiment Analysis in Oracle Analytics Cloud (OAC).

The Workflow

The main business objectives which we set for this challenge were to find the answers to the below business questions. These questions were decided in our first virtual sprint planning meeting which took place on 25th August.

How to optimize marketing strategies by demographic segmentation?
How are different countries and products linked together by sales?
Which country has only negative or positive comments, and what can be the reason behind it?

After data understanding, accordingly, we prepared and modelled the data. We scheduled daily virtual stand-ups every morning at 11 am for 15 mins to catch up on what we did yesterday, what we need to do today, and what are the current impediments. We evaluated our model according to our business objectives. We had our virtual sprint review meeting for feedback on Thursday 27th August. In the end, we created the following canvases in OAC to showcase our work and tell the data story.

Demographic Analysis: This canvas answers our first business question.

Which country has only negative or positive comments, and what can be the reason behind it?

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Geographical Analysis: This canvas answers our second business question.

How are different countries and products linked together by sales?

Behavioral Analysis: The below two canvases answers our third which is our last business question.

Which country has only negative or positive comments, and what can be the reason behind it?

Emotions by Country: In this canvas, we have shown the total number of positive, negative, and neutral comments filtered according to the country.

For this challenge, we were able to easily utilize the Sentiment Analysis ML model in OAC data flow. In ADW, it was easy to join the provided nine tables using PLSQL and we also used Clustering methodology which conveniently dealt with the large dataset. Clustering uses machine learning to identify the pattern of log records, and then to group the logs that have a similar pattern and it also helps significantly to reduce the total number of log entries that you have to explore and easily point out the outliers. For this task, we used the advance graphs for the data visualisations in OAC to demonstrate the best data story on the provided data.

So, this is how the Vertice team pulled off the ODTUG Oracle Analytics Storytelling challenge during this difficult time while working remotely from home and following the below famous quote said by Albert Einstein; “Strive not to be a success, but rather to be of value.”

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The post ODTUG Oracle Analytics Storytelling Challenge appeared first on Vertice.

Vertice Illuminates Opportunity with Oracle Analytics

colmmccarthy — Tue, 28 Jul 2020 11:00:40 +0000

“We’ve all got both light and dark inside us. What matters is the part we choose to act on.” This is one of our favourite quotes from Harry Potter and the Prisoner of Azkaban, and also, a fitting adage for the work that our team at Vertice has always done and continues to do in partnership with Oracle Analytics.

As Chief Data Science Officer and Analytics Success Manager, respectively, at Vertice, our job is to shine a light in the dark places. In our jobs, we are in some ways tour guides waving a flashlight, helping companies find the path to start their analytics journey, and then leading them as far as they need to go until we can safely hand the source of light directly to them so they can continue on their way.

Analytics is a Journey

Data analytics isn’t only a technical journey. It’s also a people journey. The tech is a system that supports the people in the company and enables their success. So, when enterprises make the decision to adopt Oracle Analytics for their analytics solution, we offer the advice, know-how and support to ensure the people are successful.

When we work with a client, we remind them at the outset that analytics is a journey. We start at the very beginning, setting our core process and foundation. When a company comes to us and says they want to invest in an analytics journey, we make sure they understand that you have to walk before you can run, which means you need to start the journey with a clear understanding of goals and objectives, as those set direction through an augmented analytics project.

With Oracle Analytics, we can be a lot more agile and responsive to companies who want to see the art of the possible. The technology is a tool we wield to create an environment where we can very rapidly have their data secured and show them the rich potential impact data science can provide. Rather than us giving them a blank piece of paper, we can actually offer them a concrete vision of what analytics can mean for their business.

Beginning with a Question

The roll-out plan is a critical piece of our work that can set the tone for an entire journey. To build this roll-out plan efficiently, we start with a critical question: “What is the business problem you are trying to solve?” This forms the core of all of our early conversations with clients. Then we help them navigate via a number of packaged offerings.

A lot of data science projects either fail or stall because they lose sight of the business problem, they set out to solve. So, we always make sure we define this question at the outset so we can show a solid return on investment for our clients and clearly demonstrate the benefit of analytics.

Creating a Solid Data Strategy

Once we have identified the business goals and know which of our packaged offerings we’ll use to reach them, we get data governance in place. We start with standard processes and make sure there’s a data strategy in place.

Once the plan is solidified, we can move toward the next layer, which is data management. Generally, we put in place Oracle’s Autonomous Data Warehouse to map where data is coming from and bring data integration through it.

What sets our work apart at Vertice and makes it so rewarding is the deep collaboration and close relationships we build with our clients. We don’t just head off into a dark room and crunch numbers. Rather, we work together to identify a problem, set deliverables and then solve that problem. Our services are bespoke for each client and the only way to provide such a curated and personalized solution is to work closely together, effectively co-designing them.

Eaglemoss: Aligning Data and Processes

Last year we worked with Eaglemoss, to help them sift through the darkness of their data and bring it to light through analytics. In the process, they became like family.

Eaglemoss is the world’s leading collectables company and partwork publisher. They operate in 13 languages, on five continents. They make items that people love, which makes them a company easy to love as well. They are a one-stop-shop for superhero figurines, for Harry Potter and Marvel collectables, and for books, accessories and other fun items.

When Vertice started working with Eaglemoss, they had identified a significant business problem: their data was stored across multiple regions and in various places, and timely access was a problem. They were working with not one but several data partners, each using a different format to report and provide information. They needed consistency and a single version of the truth.

The need for a cloud analytics and data management solution was strong from the outset. And Oracle Analytics Cloud, alongside the Autonomous Data Warehouse, provided a clear solution. OAC offered Natural Language Generation and Natural Language Processing. It offered citizen data science access, data flows, and data preparation and enhancement. And the Autonomous Data Warehouse was scalable, with a fast load of data and simple monitoring.

So we utilized Oracle Data Integrator on Oracle Cloud Infrastructure, loaded Eaglemoss’s data to the Autonomous Data Warehouse, and then utilized and extracted the load and transform approach to enable additional data quality and standardization. We used three data sources with key customer information, integrating into Oracle Autonomous Data Warehouse. We provided consistent and accurate governed analytics through dashboards in Oracle Analytics Cloud. We also provided training on self-service analytics, which enabled Eaglemoss to add forecasting to their analytics and increase user adoption.

Samuel Sonnenfeld, digital and IT director at Eaglemoss, said it well: “With OAC, we went from dark data to enlightened data in an easy manner. Now, we can better understand our customers, bring a better quality of service, and increase our sales.”

There’s so much data today that it can feel like a huge storm darkening the horizon.

But whenever the darkness rises, analytics can be the light that rises to meet it. With the right tools and the right support team, you too can be an Analytics wizard.

To learn more about our analytics best practices, tune into our session at the Oracle Analytics Summit here.

This article is co-authored by Dr Abi Giles-Haigh, Vertice Chief Data Science Officer & Oracle ACE Director), and David Heraty, Analytics Success Manager at Vertice. If you have any questions relating to your business, please don’t hesitate to get in touch by clicking below, or by connect@verticecloud.com.

If you have any questions, don’t hesitate to get in touch

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