"Black Data is like black box ready to be discovered with the right roadmap put in place. "

Introduction

 

The world is in transition since the advent of discoveries, industrialisation and technological development and advancements contributing to the economic growth and human comforts. The Information technology has brought revolution in every industry and in the life of an individual. When we look back in 1990's the use of computer was limited to laboratories and research and development wings and medical science centres.

 

The use of cellular phones has become indispensible part of life. Today, smart phones are the devices performing multiple functions with a touch of finger tip. The traditional jobs are disappearing and new jobs with added functions are emerging every day. The new titles and job responsibilities are phrased on daily basis.

 

This is not anymore multi-function arena but multiple-discipline faculty. The nano- technology is changing the entire face of technology and use of in every industry. This is a horizontal as well vertical change in the hierarchy of technology. The business models are changing day by day. The recent learning from the Chinese airlines' competitive edge in the world of aviation.

 

The traditional business management is adopting to functional business model, project zed business model and performance based functional model. These models are based on the developing best practices and adopting them in present context and in future business practices for better performance with well established governance to comply with regulations in relation to well placed controls.

 

These changes coin new titles. The popular titles are known Chief Technological Officer, Chief Financial Officer, Business Intelligent Officer, Business Team Leader, Quality Officer, Strategic Solutions Consultant, Product Genius, and Central Business Liaison. Business Analyst and the most significant in the today's world is Manager/Director/ VP Business Analytics. These are the most popular titles according to the recent information.

 

This is an endeavor to define Business Analytics, the functions of a Business Analytics and the role of Business Analytics in the various industries, specifically, in health industry and oil and gas industry.

 

Analytics have been used in business since the time management exercises that were initiated by Frederick Winslow Taylor in the late 19th century. Henry Ford measured pacing of assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications.

 

Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix analytics, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.

 

Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. Since analytics can require extensive computation the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Analytics is a multi-dimensional discipline. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data - data analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology.

 

Business Analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods.

Business Analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions.

 

Business Analytics can answer questions like why is this happening, what if these trends continue, what will happen next- predict, what is the best that can happen- optimize.

In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business intelligence is querying, reporting, OLAP, and "alerts."

 

Banks, such as Capital One, use data analysis (or analytics, as it is also called in the business setting), to differentiate among customers based on credit risk, usage and other characteristics and then to match customer characteristics with appropriate product offerings.

 

Harrah’s, the gaming firm, uses analytics in its customer loyalty programs. E & J Gallo Winery quantitatively analyzes and predicts the appeal of its wines. Between 2002 and 2005, Deere & Company saved more than $1 billion by employing a new analytical tool to better optimize inventory.

 

Types of Analytics

 

Descriptive Analytics: The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. The descriptive analytics is to gain insight from historical data with reporting, scorecards, clustering etc. Descriptive Analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting - such as sales, marketing, operations, and finance - uses this type of post-mortem analysis.

 

Predictive Analytics: The predictive modeling uses statistical and machine learning techniques. Predictive Analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events. Predictive Analytics answers the question what will happen. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. Predictive Analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.

 

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.

 

Prescriptive Analytics: Prescriptive Analytics is the third phase and it has been since about 2003, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.

 

Since Prescriptive Analytics is the third and final phase of business analytics(BA) which includes Descriptive, Predictiveand Prescriptive Analytics. Prescriptive analytics recommend decisions using optimization, simulation etc. The technology behind prescriptive analytics synergistically combines hybrid data, business rules with mathematical models and computational models. Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions.

Prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe.

 

Thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured- numbers, categories and unstructured data- videos, images, sounds, texts, and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.

 

There are analytics that helps Decisive analytics: supports human decisions with visual analytics the user models to reflect reasoning. Contextual data modeling - supports the human reasoning that occurs after viewing "executive dashboards" or any other visual analytics.

The analytics has the wide range of domains. These Basic domains within analytics are: Retail sales analytics, Financial services analytics, Risk & Credit analytics, Talent analytics, Marketing analytics, Behavioral analytics, Cohort Analysis, Collections analytics, Fraud analytics, Pricing analytics, Telecommunications, Supply Chain analytics, and Transportation analytics.

 

Analytics as a Business Tool

Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year. This type of data warehousing required a lot more storage space than it did speed.

 

Now business analytics is becoming a tool that can influence the outcome of customer interactions. When a specific customer type is considering a purchase, an analytics-enabled enterprise can modify the sales pitch to appeal to that consumer. This means the storage space for all that data must react extremely fast to provide the necessary data in real-time.

 

Analytics is a tool that helps businesses identify trends and make decisions. Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting.

Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy. Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization, customer analytics- segmentation.

 

Web Analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture. Web Analytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques.

 

Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc.

 

These tools and techniques support both strategic marketing decisions such as how much overall to spend on marketing and how to allocate budgets across a portfolio of brands and the marketing mix and more tactical campaign support in terms of targeting the best potential customer with the optimal message in the most cost effective medium at the ideal time.

 

Portfolio Analysis is common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status such as wealthy, middle-class, poor, etc. of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.

 

The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk.

 

The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

 

Risk Analytics' Predictive models in banking industry is widely developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behaviour and also scores are widely used to evaluate the credit worthiness of each applicant and rated while processing loan application. Furthermore, risk analyses are carried out in the scientific world and the insurance industry.

 

Digital Analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations.

 

Analytics applications in Oil and Gas Industry

Analytics is the largest industry in the world, $6 trillion in size. The processes and decisions related to oil and natural gas exploration, development and production generate large amounts of data. Many types of captured data are used to create models and images of the Earth’s structure and layers 5,000 - 35,000 feet below the surface and to describe activities around the wells themselves, such as depositional characteristics, machinery performance, oil flow rates, reservoir temperatures and pressures.

 

Prescriptive analytics software can help with both finding and producing oil and gas. It can take in seismic data, well log data, production data, and other related data sets to prescribe where to drill, how to drill to maximize production, and minimize cost and environmental impact.

 

Prescriptive analytics software can accurately predict production issues by modeling numerous internal and external variables simultaneously. Prescriptive analytics software can also provide decision options and show the impact of each decision option so the operations managers can proactively take appropriate actions, on time, to guarantee future exploration and production performance.

In the area of Health, Safety, and Environment, prescriptive analytics can predict and pre-empt incidents that can lead to reputational and financial loss for oil and gas companies.

Pricing is another area of focus. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geopolitics, and weather conditions. Gas producers, pipeline transmission companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk.

 

Prescriptive analytics software can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option.

 

Analytics Applications in Healthcare Industry

Multiple factors are driving healthcare providers to dramatically improve business processes and operations as the United States healthcare industry embarks on the necessary migration from a largely fee-for service, volume-based system to a fee-for-performance, value-based system.

Prescriptive analytics is playing a key role to help improve the performance in a number of areas involving various stakeholders: payers, providers and pharmaceutical companies.

 

Prescriptive analytics can help providers improve effectiveness of their clinical care delivery to the population they manage and in the process achieve better patient satisfaction and retention.

 

Providers can do better population health management by identifying appropriate intervention models for risk stratified population combining data from the in-facility care episodes and home based tale-health.

 

Prescriptive analytics can also benefit healthcare providers in their capacity planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.

 

Prescriptive analytics can help pharmaceutical companies to expedite their drug development by identifying patient cohorts that are most suitable for the clinical trials worldwide - patients who are expected to be compliant and will not drop out of the trial due to complications.

 

Analytics can tell companies how much time and money they can save if they choose one patient cohort in a specific country vs. another.

 

In provider-payer negotiations, providers can improve their negotiating position with health insurers by developing a robust understanding of future service utilization. By accurately predicting utilization, providers can also better allocate personnel.

 

Challenges of Business Analytics

In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data.

 

Once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.

 

The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.

 

Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities.

 

For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies. This is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.

 

These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.

 

One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set.

 

For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data.

 

To combat this, some analytics tools for educators adhere to an over-the-counter data format, embedding labels, supplemental documentation, and a help system, and making key package, display and content decisions to improve educators’ understanding and use of the analytics being displayed.

 

One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models.

 

Business analytics depends on sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available.

 

Technology and software solutions for Business Analytics

 

Business analytics is aptly described as the fusion of technologies, competencies, practices, and applications for the constant review of business performance to achieve insights and facilitate business planning. It utilizes several factors such as data; quantitative and statistical analyses; extrapolative and explanatory modeling; and, management based on facts to propel decision-making processes.

The business analytics is a combination of science, mathematics, management science, technology and computer programming to arrive at the final conclusion to outperform. There are various software applications to use for the successful functions of business Analytics. For example.

 

IBM's next-generation business analytic solutions help organizations of all sizes make sense of information in the context of their business. We can uncover insights more quickly and more easily from all types of data even big data and on multiple platforms and devices. And, with self-service and built-in expertise and intelligence, we have the freedom and confidence to make smarter decisions that better address business imperatives.

 

If cost and deployment are a concern, IBM offers software as a service options for business analytics. We can transform data into competitive advantage with and business analytics capabilities such as Business intelligence, performance management, predictive analytics, risk analytics and regularity compliance.

 

Oracle Business Analytics

Oracle Business solutions help organizations of all sizes thrive by enabling them to discover new ways to strategize, plan, optimize business operations, and capture new market opportunities.

 

As the market leader in Business Analytics software, they deliver the most complete and integrated solutions that let customers find value in big data, gain insight into every aspect of their business, plan ahead, and act with confidence-anytime, anywhere, on any device.

 

Business Intelligence, Enterprise Performance Management, Information Discovery, Advanced Analytics and Oracle Exalytics-Oracle. Exalytics is a powerful package of optimized hardware and software that delivers a high-performance and scalable platform for BI, in-memory analytics, and planning applications.

 

SAP Business Analytics

Analytics solutions from SAP using analytics tools to collect massive amounts of Big Data from the organization and. extracting the meaning of that data and using it to drive real growth is the aim of SAP solutions. 

 

Business analytics from SAP can help us unleash the power of collective insight by delivering enterprise business intelligence, agile visualizations, and advanced predictive analytics to all users – on any device or platform.

 

The products are Applied Analytics, Business intelligence, Data warehousing, EPM and Governance, risk and compliance and predictive analytics. The SAP Business One solution, version for the SAP HANA platform, can help your business increase margins and grow. With this innovative application, you can instantly analyze growing volumes of data and gain the benefits of fast application performance without complicating your IT landscape. And with new embedded analytics and high-performance apps, you can work more efficiently than ever before. 

 

Unstructured data represents 80% of all data today. And the amount of unstructured data is expected to continue growing by 80% annually – from social media, email, customer service calls, even imagery. Make more informed decisions by analyzing both structured and unstructured data. It is important to learn and discover hidden value with text mining analytics. Consolidate structured and unstructured data to gain accurate insights. Develop better understanding of customers and their behaviour anytime and anywhere.

 

The Google Analytics Platform 

Google Analytics Platform measures user interactions with business across various devices and environments, at Google speed and scale. And gain new insights and optimize the performance of the business. Google Mobile App Analytics enables visualize user navigation paths, to measure user interactions with UI elements, to measure in-app payments and revenue and to create your own report dimensions and metrics.

 

As the leader in business analytics software and services, SAS provides solutions and technologies that empower you to solve today's complex problems and capitalize on tomorrow's opportunities. SAS industry-leading advanced analytics deliver timely insights for taking strategic action and driving impact.

 

SAS is leading the way in high-performance analytics. SAS helps customers tackle complex problems using big data, and gain highly precise insights to speed information and outperform competitors. Gain insights in minutes or seconds that once took days or weeks!

 

SAS solutions offer Industry specific solutions, cross functional solutions and data management-facilitating business and IT collaboration in achieving real data asset management, while reducing risk. SAS combines data access, data integration and federation, data quality, data governance and master data management in one unified way. SAS provides a suite of intuitive interfaces and the ability to deploy real-time analytics directly to mobile devices. The important suits include supply chain intelligence and sustainability management.

 

Tableau Software offers solutions to create rich analyses and share your insights with colleagues in seconds. Tableau Desktop, Tableau Server and Tableau Online.

 

Net Suite isCloud business software that provides all-in-one solution that grows with business. It eliminates IT costs and concerns associated with maintaining and upgrading separate applications. Net Suite is a cloud-based solution, giving you and your employees the ability to make better, faster decisions and access info from virtually anywhere. There are many other solution providers in the business analytics area.

 

Conclusion

Although analytics-driven business is a growing trend, most organizations are still in the early stages of using analytics technologies effectively. There are two critical factors in successfully implementing an analytics-driven business. First, a clear business strategy and vision must be set.

The strategy should create a roadmap for how the organization will move forward in a series of measureable successes. Second, organizations must develop the capability to actually execute on a winning strategy.

Business analytics is becoming increasingly strategic to all types of organizations. In the 2012 IBM Global CEO Study, 73% of CEOs indicated that they were making significant investments in their organizations’ ability to draw meaningful customer insights from available data.

 

Purchasing the technology is only part of achieving analytics success. Organizations also need to educate their people, develop processes and create an organizational culture of business analytics in order to be successful. There are 5 key areas that businesses should focus on in order to increase the success of a Business Analytics Program: Strategy, Value, People, Process and Technology.

 

Strategy: A successful Business Analytics Program starts with a well-defined, coordinated business and IT strategy. The strategy requires ongoing focus and adjustments to ensure the organization understands the goals and expected outcomes, sets priorities and connects the analytics strategy to the corporate strategy.

 

Value: It is important to understand and document the value of the overall Business Analytics Program. As a result, building a value portfolio, defining outcomes and targets and measuring success often falls behind other priorities, causing difficulty when teams want to go back for additional resource investment.

 

People: This is a very critical element of a Business Analytics Program. It involves a variety of things, such as understanding the maturity of the organization and its analytics culture; managing executive involvement and getting the right people on board; building the organizational design – the Business Analytics Center of Excellence (ACE); identifying the skills, talent and roles required; and managing relationships communication and evangelism.

 

Process: A Business Analytics Program needs to implement processes, policies and guidelines that will help assist the team’s success. Processes needs to be implemented, monitored and continually evaluated to ensure organizations can grow yet maintain an agile Business Analytics Program.

 

Technology: Technology is the backbone of the entire Business Analytics Program. It is important that technology does not lead the program, but follows it. Organizations need to implement a technology solution that will meet the needs of the business. At the same time, because innovation is rapid today, users may not know the ―art of the possible. A balance between the two must be recognized.

 

An analytics-driven organizational culture creates a competitive advantage and leads to higher business performance. Analytics technologies help executives, managers and employees better monitor their business, plan collaboratively among various stakeholders and integrate diverse types of data that can be transformed into knowledge. The creation of an analytics-driven culture in which analytics is accessible and used by all business groups as a strategic priority of the organizations. Business analytics helps organizations create a competitive advantage and outperform their peers.

 

Thus there is a big opportunity to make better decisions using that data to drive incremental revenue, decrease cost and loss by building better products, improving customer experience, catching fraud before it happens, improving customer engagement through targeting and customization- all with the power of data.

 

According to the recent IBM Institute for Business Value study, organizations that used analytics for competitive advantage were 2.2 times more likely to substantially outperform their industry peers.

 

Sources-

•   "Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data". The Economist. May 26, 2011. Archived from the original on 3 June 2011. Retrieved May 26, 2011.

•   U.S. Department of Education Office of Planning, Evaluation and Policy Development (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. United States Department of Education (ERIC Document Reproduction Service No. ED504191)

•   Davenport, Tom (November 2012). "The three '..ties' of business analytics; predictive, prescriptive and descriptive". CIO Enterprise Forum.

•   Stewart, Thomas. R., and McMillan, Claude, Jr. (1987). "Descriptive and Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering". NATO AS1 Senes, Expert Judgment and Expert Systems, F35: 314–318.

•   http://www-03.ibm.com/software/products/en/categor...

•   https://developers.google.com/analytics/devguides/...

•   http://www.sas.com/en_us/software/enterprise-solut...

•   http://www.oracle.com/us/solutions/business-analyt...

•   http://www.sap.com/pc/analytics/strategy.html

 

QUOTES

 

"Harvested Data makes us richer."

-Prince Amir Al Saud

 

"Business Analytics is to analyse unthinkable for the wonders in the taming economic growth at go local level. (GOLOCAL= GLOBAL+ LOCAL) ."

- Prince Amir Al Saud

       

"The price of light is less than the cost of darkness."

-Arthur C. Nielsen, Market Researcher & Founder of AC Nielsen

 

“Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway."

-Geoffery More

 

"War is ninety percent information. "

-Napoleon Bonaparte, French Military and Political Leader

 

"I never guess. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts."

-Sir Arthur Conan Doyle, Author of Sherlock Holmes stories

 

"Data! Data! Data! I can’t make bricks without clay! "

-Sir Arthur Conan Doyle

"A person who is gifted sees the essential point and leaves the rest as surplus."

-Thomas Carlyle, Scottish Writer

 

"If you do not know how to ask the right question, you discover nothing." 

-W Edward Deming

 

"He would serach for pearls must dive low." "

-John Dryden