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.

DID YOU KNOW?

Data discovery has increased its impact in the last year. The already mentioned survey conducted by the Business Application Research Center listed data discovery in the top 3 business intelligence trends by the importance hierarchy. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

DID YOU KNOW?

A survey conducted by the Business Application Research Center stated the Data quality management as the most important trend in 2019. It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence.

DID YOU KNOW?

Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies (Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs.

DID YOU KNOW?

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

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.

“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.

Big data refers to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.

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 the process of gathering data from stakeholder conversations on digital media and processing into structured insights leading to more information-driven business decisions and increased customer centrality for brands and businesses.

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.

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyses current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

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 (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task.

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 is the discipline whereby all of the information technology resources of a firm are managed in accordance with its needs and priorities. These resources may include tangible investments like computer hardware, software, data, networks and data centre facilities, as well as the staff who are hired to maintain them.

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.

Consultants provide their advice to their clients in a variety of forms. Reports and presentations are often used. However, in some specialized fields, the consultant may develop customized software or other products for the client. Depending on the nature of the consulting services and the wishes of the client, the advice from the consultant may be made public, by placing the report or presentation online, or the advice may be kept confidential, and only given to the senior executives of the organization paying for the consulting services.
Project management is the practice of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria at the specified time. A project is a temporary endeavor designed to produce a unique product, service or result with a defined beginning and end (usually time-constrained, and often constrained by funding or staffing) undertaken to meet unique goals and objectives, typically to bring about beneficial change or added value.

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.

Change management (sometimes abbreviated as CM) is a collective term for all approaches to prepare and support individuals, teams, and organizations in making organizational change. The most common change drivers include: technological evolution, process reviews, crisis, and consumer habit changes; pressure from new business entrants, acquisitions, mergers, and organizational restructuring.

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.

Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without violating some constraint. The most common goals are minimizing cost and maximizing throughput and/or efficiency. This is one of the major quantitative tools in industrial decision making.

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.

DID YOU KNOW?

Data Quality Management is not only uprising in the BI trends 2019, but also growing to a crucial practice to adopt by companies for the sake of their initial investments. Meeting strict data quality levels also meets the standards of recent compliance regulations and demands.

By implementing company-wide data quality processes, organizations improve their ability to leverage business intelligence and gain thus a competitive advantage that allows them to maximize their returns on BI investment.

DID YOU KNOW?

There are more and more organizations moving their data and all of their applications to the cloud. Gartner states that by 2019, the cloud will be the common strategy for 70% of the companies – while it was less than 10% in 2016. When evaluating the hosting environment, you take risk, speed, costs, and complexity into account, which makes it even harder to pick one solution fitting all your needs.

DID YOU KNOW?

Data discovery has increased its impact in the last year. The already mentioned survey conducted by the Business Application Research Center listed data discovery in the top 3 business intelligence trends by the importance hierarchy. BI practitioners steadily show that the empowerment of business users is a strong and consistent trend.

DID YOU KNOW?

A survey conducted by the Business Application Research Center stated the Data quality management as the most important trend in 2019. It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence.

DID YOU KNOW?

Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe-friend of the human race in movies (Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs.

DID YOU KNOW?

Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness. There are plenty of big data examples used in real life, shaping our world, be it in the buying experience or managing customers’ data.

CYBER SECURITY AND COMPLIANCE

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.

IT risk management can be considered a component of a wider enterprise risk management system. The establishment, maintenance and continuous update of an Information security management system (ISMS) provide a strong indication that a company is using a systematic approach for the identification, assessment and management of information security risks.

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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