The growth of Data Science in today’s modern data-driven world had to happen when it did. If one really takes a careful look at the growth of data analysis over the years, without Data Science, traditional (descriptive) Business Intelligence (BI) would have remained primarily a static performance reporter within current business operations. With the rising volume and complexity of data, and growth of data input technologies, Data Science came at an essential time to provide some methods to the expansive data volumes overcoming many modern enterprises. The question of this evolution and the similarities and differences between Data Science vs. Business Intelligence is an important subject for many dealing with these technologies.
In 2022 and going forward into the future, the shift is towards “Data Science and BI,” not one or the other. As businesses need to analyze past and current events as much as they need to forecast future events, both Data Science and BI have permanent roles in the world of business.
Defining the Terms: Data Science vs. Business Intelligence (BI)
It is important to begin with some basic definitions of the two terms, taking a deeper look at the two distinct (though closely allied) fields within data analytics. Data Science, as used in business, is intrinsically data-driven, where many interdisciplinary sciences are applied together to extract meaning and insights from available business data, which is typically large and complex. On the other hand, Business Intelligence or BI helps monitor the current state of business data to understand the historical performance of a business.
So, in nutshell, while BI helps interpret past data, Data Science can analyze the past data (trends or patterns) to make future predictions. BI is mainly used for reporting or descriptive analytics; whereas Data Science is more used for predictive analytics or prescriptive analytics.
The Major Similarities Between Data Science and Business Intelligence
Both Data Science and BI enable business users to make smart decisions with data. Both Data Science and BI focus on “data,” with an aim to provide favorable outcomes, which in case of business may be profit margins, customer retention, new market capture, and so on. Both these field have the capability for “interpreting data,” and usually engage technical experts who translate or transform data-enriched results into friendly insights or competitive intelligence.
In a typical business environment, neither senior executives nor managers often have the time or inclination to learn the technicalities hidden behind data analytics or BI, but they need quick and accurate decision-support systems to make critical decisions in hours of need.
BI often deals with the types of queries that can be answered with available data like why sales in one quarter was poor compared to other three quarters, which product is currently bring in maximum ROI, or who are my premier customers? In BI, the final goal is detecting trends and patterns for developing actionable insights. In Data Science, the queries usually deal with future (unknown) events like how likely is an employee to quit his job, or which product will have the highest sale next quarter, or how much revenue increase will happen next year? The predictive and prescriptive nature of analysis here lends itself to testing a hypothesis through experiments (statistical models).
Both BI and Data Science offer reliable decision-support systems to busy executives, managers or even front-line operators who are experts in their respective fields of work and expect reliable help and support from data experts for making data-driven decisions. The major point of difference between Data Science vs. Business Intelligence is that while BI is designed to handle static and highly structured data, Data Science can handle high-speed, high-volume, and complex, multi-structured data from a wide variety of data sources. Whereas BI can only understand data “preformatted” in certain formats, advanced Data Science technologies like big data, IoT, and cloud can together collect, clean, prepare, analyze, and report many types of free-form data gathered from widely distributed operational touch points.
The article titled How Is Business Intelligence Different From Data Science claims that years ago, the business personnel who worked with data were known as data analysts. Then businesses, to remain competitive, started moving away from just reporting past performance to “predicting” future trends and offering “prescriptions” for success. That is where Data Science came in.
Data Science, armed with a formidable arsenal of technologies and tools began studying past data to discover trends, find patterns, and predict future business behavior. All of a sudden, businesses were equipped with very powerful insights and intelligence that had the potential to change their future!
The Major Differences Between Data Science vs. Business Intelligence
The Forbes post Data Science: Getting Real demonstrates that ever since big data has stormed into the mainstream business landscape, businesses have had no choice but to rely on the wisdom and expertise of data scientists to extract meaning from an avalanche of data flowing in from endless input sources.
As businesses get increasingly data dependent, the importance of Data Science as the ultimate decision-making technology will only soar. With the promise of Data Science automation, everyday business users will have access to centralized Data Repositories and automated tools to extract insights and intelligence when and where they need it.
In the past BI, though important for business decision making, still remained an activity of those in IT; Data Science breaks that barrier and promises to bring core analytics and BI activities to the mainstream business corridor.
The data scientists of the future will be those “few” experts who will be brought in to operationalize data, and once that is done, and then provide support only when it is needed. Also review the post How Is Business Intelligence Different from Data Science to better understand how data scientists and BI experts can work together to provide the best data solutions to enterprises. As businesses get more competitive, BI experts will need to work with data scientists to help build those fantastic “models” for instantaneous insights. Remember the Algorithm Economy of the near future?
In 10 Differences Between Data Science and BI, the author observes that while data has become bigger and more complex, the traditional BI platforms have become inadequate to handle such data. While BI-equipped businesses with “retrospective” wisdom, Data Science, for the first time, offered more advanced reactive and even proactive insights.
Data Science has been viewed as a “game changer” in the current decade, as it has advanced by leaps and bounds by providing technologies for handling complex data, Data Governance and cleansing, drilled-down data analysis, and custom reporting. As the article titled Impact of Big Data in Enterprise Information Management discusses, today’s businesses cannot just survive on static reporting alone; they must have much more, especially in terms of quick decision making.
How Data Science Contrasts with Business Intelligence
A major difference between advanced BI and advanced Data Science is the range and scale of built-in machine learning (ML) libraries, which enable automated or semi-automated data analytics possible by ordinary business users. Thus, Data Science, in a way, is moving toward a more democratized “business analytics” world, where one day, any data users will be able to conduct advanced analytics and BI on their desktops with a few mouse clicks.
Data Science or AI-enabled Data Science promises to relieve the ordinary business users of heavy-duty technology, so that they can concentrate more on the goals and outcomes of their Analytics tasks rather than on the analytics process itself.
In traditional BI, ordinary business users are forced to rely on the expertise of the resident analytics team to extract meaningful insights from their data, but ML-powered Data Science has now launched self-service BI platforms, where ordinary users can easily view, analyze, and extract insights from the enterprise data without any help from technical teams.
In the DATAVERSITY® article Self-Service Business Intelligence is Big, but is it for Everyone?, readers are asked to assess the validity of the claim made by a published report by Research and Markets, which states that the self-service BI market is well positioned to grow up to a $7.3 billion market by 2021. The field experts are now pondering whether the citizen data scientists at a corporate environment will really be able to utilize the self-service features without any support from a technical expert.
Data Science has often been defined by an evolution of BI by experts. While BI teams provided solutions for the present, by supporting core decision making, data scientists aim to provide future solutions by continuously refining their algorithms. In principle, both BI and Data Science are working to enable smooth, accurate, and fast decision making, but the approaches are different. Read the article titled Data Science? Business Intelligence? What’s the Difference? to understand this concept well.
In the arena of deliverables, BI outcome is always expressed through visual reports – dashboards or advanced visualization platforms. The BI reports frequently include “narratives,” to persuade the audience to accept the insights and business recommendations. On the other hand, Data Science deliverables are usually statistical models (regression model or decision tree) or predictions, which have been precisely optimized to answer a set of queries about data.
While BI relies heavily on a core set of Analytics tools, Data Science takes a more holistic approach to Data Management, by providing the total framework for Data Governance, data analytics, BI, and advanced data visualization. Small or medium-sized businesses with a finite number of analytics needs may benefit from an average BI solution available in the marketplace, while larger businesses with a need for highly automated processes will benefit from a ML-powered BI system, which again will require the presence and involvement of qualified data scientists.
The article Data Scientists vs. BI Analysts: What’s the Difference? argues that both fields aim to help derive business insights from the available data.
How Data Science Reinforces Business Intelligence
Data Science projects usually require cooperation between different experts like business experts, data engineers, statisticians, and software developers. Data scientists may have deep understanding of statistics but they do not understand the software-development or business side of issues. That is where BI experts can step in and with their historical wisdom (analyzing past data), they help the Data Scientists predict the future. As BI experts deal with simple assumptions and fixed data points – they can easily arrive at “metrics” that can be shared with other data team members for discovering the future of business.
Both data scientists and BI experts share a love for data analysis. Both use algorithms to varying degrees, and now both use advanced visualization tools to capture the nuggets of wisdom, which can very well make or break a business.
Data Science certainly differs from traditional BI in three main areas though: the variety and volume of data, the predictive capabilities, and the visualization platforms. The article Business Intelligence & Data Science: Same, but Different offers an interesting contrast between the two analytics methodologies. In advanced BI systems, users have come across “data discovery tools,” but these tools are often limited by the quality and quantity of data they process. Data Science breaks the glass ceiling of “data,” and allows any kind of structured, unstructured, or semi-structured data to be collected, cleansed, and prepared for analysis.
While BI teams have always provided decision support to executives or managers, Data Science has enabled those managers and executives to become self-empowered, analytics experts. In an ideal business environment, the BI team should manage the Operational Analytics, while the data scientists, if any, should spend more time refining the existing Analytics and BI footprint and automate the system as much as possible, so that everyday business users can get their work done expediently and accurately.
In fact, if BI experts and data scientists work together, then BI analysts can prepare the data for data scientists to feed into their algorithmic models. BI experts can offer their current understanding and knowledge of analytics requirements of a business and help the data scientists build powerful models to forecast future trends and patterns.
Both the BI expert and the data scientist have their places in an Enterprise Analytics team – BI collects data to understand events in the past; while DS generates data to model events that have not yet occurred. Together, the BI expert and the data scientist can gradually build a powerful, in-house analytics platform that ordinary business users can learn and use without any technical help.
Data Science & BI Together: What’s in Store for the Future?
Consider the global retail business to understand how traditional BI has progresses toward Data Science to convert just-in-time insights into profitable business outcomes. In the article titled Retail Minded: From Business Intelligence to Data Science, the author observes that though most businesses are reaping the rewards of this transition, some businesses with limited knowledge or talent are still struggling with Data Science. For those left behind, this article offers some suggestions for implementing successful Data Science frameworks for profitable results.
In the blog post How McKinsey’s 2016 Analytics Study Defines the Future of Machine Learning, the author shows McKinsey arrived at its conclusions about the impact of machine learning in at least 12 industry sectors. According to the author, McKinsey has convincing data to prove that Data Science, with its rich data (big data) and advanced analytics capabilities (machine learning), certainly reigns supreme over traditional BI, where static or historic data did not provide users sufficient justifications for “predicting” or “prescribing” future business events. Data Science and machine learning have hugely benefited the enterprise IT Team, and provided tools to make fast and accurate predictions from existing data patterns.
According to McKinsey, for an enterprise analytics platform to work, an effective support structure for enterprise Analytics activities, good architectures, and senior management involvement – all three are required. McKinsey also observes that businesses that have effectively invested in Analytics and BI infrastructures have seen up to 19 percent increase in their margins over a period of five years.
Data Science vs. Business Intelligence: Final Thoughts
One of the biggest stumbling blocks that face technologically able enterprises is the rapid growth of allied technologies, which used together, can make business transformation for winning in the marketplace happen. Today, enterprises are frequently at a loss about how to keep pace with the speed of technological change, and how to integrate newer and better capabilities with the existing ones. For example, advanced technologies such as big data, IoT, machine learning, and serverless computing can together transform the business landscape, but how many businesses actually know how to integrate these solutions to build a powerful analytics platform?
If there are only two things you take away from this post, they are:
- Data Science and BI are two equally important players on the same team. Their individual roles are distinct but together they serve the broader business analytics world.
- While there are differences in the way Data Science and BI handle objectives, tools, data, and deliverables, the end goal is same – winning with data.
Technologies, tools, processes, and talented manpower – these need to work together to gain the maximum advantage of data and analytics. In this McKinsey Report, strong claims have been made in favor of newer data types and integrated Data Management systems. The author of the report believes that fully integrated analytics & BI platforms will help break the barriers between isolated silos, and enable unified views of business data and insights for faster decision making well into the future.
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