Here is How Data Scientists are Changing The Business Intelligence Industry?
The planet produces approximately 2.5 quintillion bytes of data regularly. The biggest problem faced by organizations is handling enormous amounts of data. We are therefore in constant pursuit of data professionals with overall expertise in gathering, storing, sorting, analyzing, creating business intelligence and delivering comprehensive, evidence-based, actionable solutions. The phrases “market intelligence researcher,” “market analysts,” and “data scientists” have been used interchangeably in the past. In recent times, however, each position has become distinct from the other.
Although there are many parallels between the Business Intelligence Analyst (BIA) and Business Analyst (BA), the only minor distinction between the two is that; B.I. uses past and current data to analyze and offer opportunities for contemporary performance. In comparison, B.A. analyses historical and current data to train organizations for future decisions. This blog will dig deeper and reveal the main differences as well as help you understand the roles gap. You will learn later on how a BIA and B.A. vary from a Data Scientist.
Human beings are walking data centers and, no matter how small, our experiences and activities create a near-infinite pool of accumulated data throughout our lives.
Through the study of our usage, experiences, and attitudes, these data can be used to enhance the quality of our lives; in business today, data has become a competitive advantage and essential component of product growth.
Business Intelligence vs Data Intelligence
In the modern age, business intelligence and data analytics are two of the most common concepts. Although both require data use, they are entirely different from each other. Data Science is the larger pool with more knowledge, B.I. can be considered a part of the larger picture. You’ll get a good view of their differences by the end of this Business Intelligence vs Data Science post.
Data is universal. Today it is used in every sector of the country. But data science is like a massive ocean of numerous computer operations. The data is continuously changing and has many uses in various industries. One such application, known as Business Intelligence, is in the business sector which uses data to make careful business decisions. Research Business Intelligence vs Data Science to compare them to each other to get a better understanding of these subjects.
Business Intelligence is a method where the data are gathered, processed, analyzed and displayed. Executives and executives can get a deeper understanding of decision-making through Business Intelligence. Via electronic services and resources, this process is carried out.
Organizations can use Market Intelligence to make both strategic and tactical business decisions. In addition to this, B.I. Methods are used to evaluate and generate reports. These are often used to create maps, dashboards, summaries, and charts to help business managers make better choices.
Business Intelligence uses data that is stored as company warehouses. It also supports real-time data generated from the services. Business Intelligence is also used for strategic decision-making.
Besides, business intelligence is used to automate business processes, improve operating efficiency and gain customer insights, providing a lead over competitors. Businesses can track market dynamics using B.I. software, and answer business concerns as well as customer inquiries.
Monitoring efficiency and quantifying progress in achieving the business target are two of Business Intelligence’s essential applications.
The quantitative analysis is conducted by statistical analysis and modeling.
Visualization and storage of data in data warehouses and their further processing in OLAP.
What is Data Science?
Data Science is nowadays the world’s most popular buzzword. Harvard Business Review referred to it as “the 21st century’s sexiest sector.” Very few people, however, know the real context behind the word data science. It is a paragliding concept used to describe all of the data operations that underlie it. Data intelligence is like a collection of several methods used to shape data. A Data Scientist is usually concerned with identifying trends inside data. It’s a multidisciplinary field, which means data science is a combination of multiple disciplines. Three main areas are–math, statistics, and programming are the core of data intelligence.
In general, a Data Scientist is about discovering trends inside data. It’s a multidisciplinary field, which means data science is a combination of multiple disciplines. Three main areas are–math, statistics, and programming are the core of data science. Apart from that, data scientists need domain expertise to identify trends in the data.
Data intelligence is a method that collects, manipulates, visualizes, preserves data and creates predictions. A data scientist should know various computer operations as well as algorithms related to machine learning. Industries can draw insights and forecast their success using Data Science.
Business Intelligence vs Data Science — Definition
Data Science and Business Intelligence are all about results. Although Data Science is the larger pool of more knowledge, however, Business Intelligence can be seen as part of the larger picture. Besides, Business Intelligence is confined to the business context. B.I. is about designing dashboards, providing insights into the market, organizing data and gathering knowledge that will help companies expand.
On the other hand, however, Data Science is gaining a much larger picture. Data Science utilizes a wide variety of complex mathematical algorithms and predictive models. Compared with Business Intelligence, data science is much more complicated. In market intelligence, historical data are analyzed to grasp the company’s present patterns. We do, however, use data in Data Science to make future predictions and forecast business growth.
Business intelligence resources are also limited to evaluating details about the management and curating company strategies. A data scientist’s resources, however, include complex algorithmic models, data analysis, and even big-data resources. Although B.I. focuses on report generation based on the internal structured data, Data Science focuses on extracting insights from the data. Such ideas are generated by sophisticated predictive analytics, and the presented output is not a report but a data model. This data model is a predictive platform that uses Machine Learning to gain insights into the future and to capture data trends.
Difference Between Data Science and Business Intelligence
Although both Business Intelligence and Data Science are often based on “information” to provide successful business strategies such as; improve profit margins, attract customers, expand into new markets and so on; there is a significant difference between these two concepts.
B.I. can handle static and organized data while a data scientist can manage high-speed, dynamic, high-volume data from a variety of sources using advanced technologies such as Big Data, IoT, and cloud.
Also, in a conventional B.I. environment, companies are forced to use the expertise of the resident Analytics team to extract insightful knowledge from the data. However, Machine Learning and Artificial Intelligence (A.I.) driven Data Science has introduced self-service systems that allow users to easily access, analyze and extract results from the database without the need for assistance.
Considered one of the sexiest areas to enter (InformationWeek’s terms, not mine), the data scientist is now helping businesses of all shapes and sizes make sense of these large data sets to better understand the business performance.
Checkout The best data mining techniques.
In recent years, the sector has become much more accessible, and predictions are just as bubbling about its growth.
The General Assembly told TNW that from 2013 to 2014, the number of data science students enrolling at G.A. would more than double, and the number of completed applications for the program has already tripled in 2014 vs 2013.
In a 2011 survey, the McKinsey Global Institute predicted 4 million Big Data related jobs in the U.S. would exist by 2018.
And businesses are not only aggressively recruiting, and their pocketbooks are opening up. The median salary for data scientists in the United States is $115,000, according to Glassdoor reports.
And why, and now why, computer scientists?
I talked to several data professionals across a range of sectors and noticed some of my patterns in why their career is so challenging.
Accessibility To Data Is Greater Than Ever Before
Companies are looking at the various experiences within a site between different people, from how they came in, to the activity taken on the web, to how they performed a specific task. With the data, teams can look at the optimal functionality of users that have achieved their goals.
We have developed in our digital lives, apart from mere transactional data, so that we publish more meaningful information online than ever before.
The Data Science sector is in a transitional phase in terms of how to use the new data developments for a competitive advantage in solving market problems. Data Scientists will be doing their job very differently shortly. As big data, economics, IoT, and cloud analytics continue to become commonplace across global industries, companies must need to adapt the new strategic approaches to remain ahead of the curve. The two most striking aspects of this transformation are improved data process automation and the provision of immediate analytics solutions.
The Forbes study McKinsey’s 2016 Analytics Report Defines The Future Of Machine Learning provides an overview of the ability of Machine Learning (ML) to improve predictive analytics ‘ current state of play. Forbes also notes that the McKinsey study had identified 120 ML used cases across 12 different industry sectors and also surveyed more than 600 industry experts on the future effect of Business Analytics machine learning. Possibly Business Analytics is the number one technology field where in the future data science as well as future data scientists can play an important role.
Most Analytics solution vendors ‘ future aim is to provide enterprise users with fast, automated tools so they can get their Enterprise Analytics done with the least amount of fuss. A report from Cloud Computing News, Why Automation Won’t Replace Data Scientists Yet, discusses why it is possible to automate unique Analytics tasks while others can not. Because the simplicity of use will play a key role in separating the central Analytics systems from the other solutions, the vendors are now concentrating on ease of use in automating analytic tasks. Data Preparation, data creation, and data processing tend to be the top automation priorities among the significant providers of solutions.
The Reinvented Data Scientist of the Future
The Datanami article, “The Future of Data Science” notes that the data scientists will now be able to innovate and address market issues more holistically with time-consuming and complicated processes such as Data Processing being automated. Data scientists have historically spent 80% of their time and energy in gathering and preparing multi-sourced data for practical analytics, leaving them with little ability to undertake advanced analytics tasks.
Now, with Machine Learning software to manage all route operations, the data scientists will be able to concentrate on the real problem — the step of data analytics.
Unlike significant tech companies, businesses usually only dip their toes into Data Science and A.I. It doesn’t go simple as usual with early adoption — most ventures don’t go past the process of Proof of Concept, which is perceived to be a disappointment from a business perspective. There is no reported figure as to how many Data Science projects have failed. However, I am sure it’s even higher for big data projects as compared to the disappointing 85% figure.
Why is the rate of failure so high? Mainly because it is new to data science. It brings new forms of collaboration, needs, and culture into existing business environments. Complications caused by such novelties are always at first hard to see. But at some point, they still get too complicated when they start unraveling. However, these complexities are usually not mentioned in all kinds of marketing presentations that sell A.I. and Data Science to businesses.
Business Intelligence is a paragliding term identifying principles and strategies for enhancing business decision-making through the use of fact-based support systems. Modern Business Intelligence is not just a reporting activity. It is a mature framework offering collaborative dashboards, planning what-if, web analytics, etc. It also requires broad back-end components for retaining reporting power and governance.
Because B.I. is a paragliding concept, it can vary from one company to another. With others, it may only be a simple reporting Key Performance Indicator (KPI) with all supporting equipment, and some organizations may use sophisticated forecasting approaches based on mathematical models and specialized tools. But regardless of the methods or instruments used — they provide market stakeholders with information for decision making, according to their requirements.
From a Business Process point of view, there is not much difference between Data Science and Business Intelligence — both help evidence-based business decision making. It is possibly why companies are beginning their first Data Science or A.I. ventures frequently believe that Data Science is the same old Business Intelligence that works much cleverer. It follows from this premise that a Data Science project can be carried out on top of existing B.I. infrastructure and processes.
It turns out that a data scientist needs data from the network as a CSV file, but corporate security policy won’t allow that; data scientists create their models using external libraries and tools that infrastructure teams don’t and won’t help in production, and so on.
Any first Data Science project in any organization must start from the start creating new demanding criteria for existing teams. What are you? Why does this happen when Data Science is doing the same thing as B.I.? Why those odd requirements?
It is very different in Data Science: companies come with their actual data and some question that was never answered before. It is now up to a Data Scientist to test several solutions and choose the right one, balancing a production platform’s precision, flexibility, usability, and capabilities. When the model is chosen and accepted with the company, it becomes an established method to address the query, and it becomes a Data Analytics topic rather than a Data Science topic.
All of these variations stem from the distinction, which at first seemed insignificant. Indeed, a Data Scientist would use a trial and error method when the solution is unclear from the outset. In these circumstances, it would be prudent to use resources and techniques that allow fast turnover of ideas, so that each new trial does not take too much time to prepare: new data should be readily accessible if required, new software and libraries implementing the next approach to try should be readily available for installation and download, infrastructure should be ready to support further software or frameworks.
That means most major non-tech corporations ‘ I.T. systems are minimal and slow to introduce changes. Any data science project in these organizations will face several frustrating hurdles like:
A long cycle of analyzing data from business processes, inability to operationalize the solution because the existing I.T. infrastructure cannot accommodate containers and microservices, etc.
Those who manage organizational structures can have very different goals and different mindsets. They won’t necessarily be excited about making improvements to their systems or adding new ones, and they may worry about compliance with security when signing off access to company data, and so on. Business end users may not be very excited to incorporate A.I. or Data Science into their positions as well. Instead of a false impression of the A.I. danger they received from the media, they may be worried about the lack of information in the new field or the protection of their jobs.
All these people are nevertheless crucial for every Data Science project.
Business Intelligence, on the other hand, is already a well-established feature of a traditional corporate environment and, by design, B.I. is mostly free from those issues. Typically, B.I. functionality is supported by a single or very few platforms already integrated into the I.T. architecture and processes. Business users are increasingly familiar with it, so they are comfortable. B.I. projects must deal with known unknowns, which ensures that there is a way to identify such unknowns and the project can thus be well prepared in advance. B.I. doesn’t contain much trial and error. Besides, a company should typically have strong experience and a track record of successful B.I. projects and a strong project
Both Data Science and Business Intelligence play the same role in the Business Process from a company perspective — both offer fact-based data to help business decisions. Yet from another viewpoint, they are radically different, making it different: beliefs, processes, resources used, etc.
The difference is in the type of questions they address: B.I. deals with known unknowns by using an established formula to measure a known KPI’s new value, while Data Science deals with unknown unknowns to answer data questions that no one has addressed before.
This low, definitional difference means a lot. Data Scientists use a trial and error approach without a formulation or a specified process. In these conditions, Data Science is usually unable to guarantee progress until a project starts, it cannot determine how many steps it will take to find a solution, and what it would look like.
Data Science uses speed-optimized tools and methods to find solutions as quickly as possible: programming languages, libraries, Docker containers, architecture for microservices, etc. It is very different from a traditional corporate setting where the control and reliability of The systems are set up. The gap alone causes many difficulties for an existing company’s first Data Science projects.
Yet that’s not anything! Another problem waiting around the corner is the use of machine learning. Introducing ML into a business setting can be a huge cultural shock to market analysts, who design and enforce business rules in their life. Such practices will no longer be needed for solutions that use Machine Learning! Why would you want this kind of change? I will touch upon the tectonic shift that Machine Learning brings into an existing corporate culture in the next short section.
