How Can Blue Pearl Help Your Business Grow?
BUSINESS AND TECHNOLOGY MEET
We Craft Solutions That Fit Your Business
We have applied ourselves to creating the right portfolio of Data based services which assist in digital transformation within your business. Our service model is structured by strategy through Technology Implementation and Managed Services. Blue Pearl has established the right technology partnerships and has continually built the right culture required for effective collaboration, all aimed at meeting your companies Data and Informational demands. We are a team of problem solvers led by Data Scientists and Data Engineers with a relentless dedication to solving our clients’ toughest challenges—fast.
We work with you to determine what your needs are, where your challenges lie, and how we can help. At the end of it all, we provide you with a step-by-step roadmap that will give you all the information you need to accomplish your data and analytics goals. We measure client satisfaction through our value proposition — Business Impact, Execution Predictability, and Relationship Experience.
DATA ANALYTICS
Our data and analytics services are design-led and framework-based, which reduce time to delivery and improves accuracy and impact of the outcomes. Unlike solutions that are solely led by technology, Blue Pearl’s services combine both strategic advice and industry-leading technology to align your business’s needs. Our special brand of proven and emerging solutions drive targeted business outcomes by helping Business Leaders looking to gain a competitive advantage for their companies. and to improve their process operational efficiencies innovate by and through their data.
With more information in structured, semi-structured and unstructured data form coming from internal business systems as well as external sources like Social Media and Market Data, many organizations and their leaders struggle to manage their data’s exploding volume, diversity, and complexity.
Blue Pearl provides a range of Analytics Services from basic Data Management to sophisticated Analytics Consulting addressing proactive Risk Management, enhanced Operational Efficiency, ongoing Market Intelligence, and as a result, smarter decisions that drive cost reduction and revenue maximization.
SOFTWARE DEVELOPMENT
We are able to assist with web application, mobile and desktop application development.
Business Intelligence
Business intelligence (BI) is a technology-driven process for analysing data and presenting actionable information to help executives, managers and other corporate end users make informed business decisions.
- Block Chain Advisory and Implementation
- Big Data Services
- Social Media Analytics
- Predicative Analytics
- Artificial Intelligence Implementation
- Machine Learning
“The block chain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.”
Don & Alex Tapscott, authors Blockchain Revolution (2016)
By design, a blockchain is resistant to modification of the data. It is “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way”. For use as a distributed ledger, a blockchain is typically managed by a peer-to-peer network collectively adhering to a protocol for inter-node communication and validating new blocks. Once recorded, the data in any given block cannot be altered retroactively without alteration of all subsequent blocks, which requires a consensus of the network majority. Although blockchain records are not unalterable, block chains may be considered secure by design and exemplify a distributed computing system with high Byzantine fault tolerance. Decentralized consensus has therefore been claimed with a blockchain.
Current usage of the term big data tends to refer to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime, and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics.
Social media analytics is an interdisciplinary area that is used in social science and computer sciences interchangeably. Social media analytics provides a human trace to the social scientist which could be used in wide spectrum of disciplines such as sociology, political sciences, and geology. Social media provides two broad contexts from social scientist perspective; it provides a wide range of data in already well-established social science subjects such as political sciences and sociology, and social media sometimes is seen as a fundamental change in underlying assumptions of the social theory.
Political scientists can follow unfolding political protest online and the exchange of information between communities of different languages. Meanwhile, it is very difficult to connect the social scientific understanding of social to social media data. For example, the concept of conventional friendship hardly applies to the concept of friendship in social media.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, mobility, healthcare, child protection, pharmaceuticals, capacity planning, social networking and other fields.
In the field of computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving”.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon is known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, has become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.
Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Management Services
- IT Management Service
- Project Consulting
- Project Management
- Change Management Services
- Process Optimization Specialist (Lean Six Sigma)
Managing this responsibility within a company entails many of the basic management functions, like budgeting, staffing, change management, and organizing and controlling, along with other aspects that are unique to technology, like software design, network planning, tech support etc.
The temporary nature of projects stands in contrast with business as usual (or operations), which are repetitive, permanent, or semi-permanent functional activities to produce products or services. In practice, the management of such distinct production approaches requires the development of distinct technical skills and management strategies.
It includes methods that redirect or redefine the use of resources, business process, budget allocations, or other modes of operation that significantly change a company or organization. Organizational change management (OCM) considers the full organization and what needs to change, while change management may be used solely to refer to how people and teams are affected by such organizational transition. It deals with many different disciplines, from behavioural and social sciences to information technology and business solutions.
When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all others within their constraints. This can be done by using a process mining tool, discovering the critical activities and bottlenecks, and acting only on them.
CYBER SECURITY AND COMPLIANCE
- Vulnerability Assessments & Penetration Test
- CYBER RISK MANAGEMENT
- IT Governance, Risk & Compliance Assessments
Before deploying a system, it first must go through from a series of vulnerability assessments that will ensure that the build system is secure from all the known security risks. When a new vulnerability is discovered, the system administrator can again perform an assessment, discover which modules are vulnerable, and start the patch process. After the fixes are in place, another assessment can be run to verify that the vulnerabilities were actually resolved. This cycle of assess, patch, and re-assess has become the standard method for many organizations to manage their security issues.
The primary purpose of the assessment is to find the vulnerabilities in the system, but the assessment report conveys to stakeholders that the system is secured from these vulnerabilities. If an intruder gained access to a network consisting of vulnerable Web servers, it is safe to assume that he gained access to those systems as well. Because of assessment report, the security administrator will be able to determine how intrusion occurred, identify compromised assets and take appropriate security measures to prevent critical damage to the system.
Governance, risk management and compliance (GRC) is the umbrella term covering an organization’s approach across these three areas: Governance, risk management, and compliance. The first scholarly research on GRC was published in 2007 where GRC was formally defined as “the integrated collection of capabilities that enable an organization to reliably achieve objectives, address uncertainty and act with integrity.”
The research referred to common “keep the company on track” activities conducted in departments such as internal audit, compliance, risk, legal, finance, IT, HR as well as the lines of business, executive suite and the board itself.
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