Business Intelligence and Analytics

  • Researcher: Mike Kimbe Go
  • Date Published: 31 August, 2020
  • Source: Google

What is Business Intelligence and Analytics?


Business intelligence and analytics are data management solutions implemented in companies and enterprises to collect historical and present data, while using statistics and software to analyze raw information, and deliver insights for making better future decisions.

Let’s face it: both terms provide insights into the business operation and future decisions, but it comes down to the differences into how they do it and what information exactly do they provide.

It seems clear that there isn’t one standard “correct” definition of the differences between the two terms. The varying opinions given by the experts is evidence of that. So, instead of trying to find the “right” answer, let’s find a useful distinction between the two that can be used simply and clearly to help you in your work. The most straightforward and useful difference between business intelligence and data analytics boils down to two factors:

  • What direction in time are we facing; the past or the future?
  • Are we concerned with what happened, how it happened, or why it happened

Keeping in mind that this is all a matter of opinion, here are our simplified definitions of business intelligence vs business analytics.

Business intelligence – Deals with what happened in the past and how it happened leading up to the present moment. It identifies big trends and patterns without digging too much into the why’s or predicting the future.

Business intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current, and predictive views of business operations. Common functions of business intelligence technologies include reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability.

Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, BI is most effective when it combines data derived from the market in which a company operates (external data) with data from company sources internal to the business such as financial and operations data (internal data), when combined, external and internal data can provide a complete picture which, in effect, creates an "intelligence" that cannot be derived from any singular set of data. Among myriad uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts.

BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW" or as "BIDW". A data warehouse contains a copy of analytical data that facilitate decision support.

Business analytics – Deals with the why’s of what happened in the past. It breaks down contributing factors and causality. It also uses these why’s to make predictions of what will happen in the future.

Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.[1] Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. 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 analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling,[2] 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 intelligence is querying, reporting, online analytical processing (OLAP), and "alerts."

In other words, querying, reporting, OLAP, it is alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (predict), and what is the best outcome that can happen (optimize).

Examples of BI Tools


Power BI

is a business analytics service by Microsoft. It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Power BI provides cloud-based BI (business intelligence) services, known as "Power BI Services", along with a desktop based interface, called "Power BI Desktop". It offers data warehouse capabilities including data preparation, data discovery and interactive dashboards. In March 2016, Microsoft released an additional service called Power BI Embedded on its Azure cloud platform. One main differentiator of the product is the ability to load custom visualizations.

Tableau

was founded in 2003 as a result of a computer science project at Stanford that aimed to improve the flow of analysis and make data more accessible to people through visualization. Co-founders Chris Stolte, Pat Hanrahan, and Christian Chabot developed and patented Tableau's foundational technology, VizQL—which visually expresses data by translating drag-and-drop actions into data queries through an intuitive interface.

Since our foundation, we’ve continuously invested in research and development at an unrivaled pace, developing solutions to help anyone working with data to get to answers faster and uncover unanticipated insights. This includes making machine learning, statistics, natural language, and smart data prep more useful to augment human creativity in analysis. And we not only offer a complete, integrated analytics platform, but also proven enablement resources to help customers deploy and scale a data-driven culture that drives resilience and value through powerful outcomes.

Tableau was acquired by Salesforce in 2019, and our mission remains the same: to help people see and understand their data. Today, organizations everywhere—from non-profits to global enterprises, and across all industries and departments—are empowering their people with Tableau to drive change with data.

SAP Business Intelligence

offers several advanced analytics solutions including real-time BI predictive analytics, machine learning, and planning & analysis. The Business Intelligence platform in particular, offers reporting & analysis, data visualisation & analytics applications, office integration and mobile analytics. SAP is a robust software intended for all roles (IT, end uses and management) and offers tons of functionalities in one platform.

MicroStrategy

is a business intelligence tool that offers powerful (and high speed) dashboarding and data analytics which help monitor trend, recognise new opportunities, improve productivity and more. Users can connect to one or various sources, whether the incoming data is from a spreadsheet, cloud-based or enterprise data software. It can be accessed

Datapine

is an all-in-one BI platform that facilitates the complex process of data analytics even for non-technical users. Thanks to a comprehensive self-service analytics approach, datapine’s solution enables data analysts and business users alike to easily integrate different data sources, perform advanced data analysis, build interactive business dashboards and generate actionable business insights.

SAS Business Intelligence

SAS’ most popular offering is its advanced predictive analytics, it also provides a great business intelligence platform. It is self-service tool that allows to leverage data and metrics to make informed decisions about their business. Using their set of APIs, you are provided with lots of customisation options, and SAS ensures high-level data integration and advanced analytics & reporting.

QlikSense

is a product of Qlik, a company also known for another business intelligence tool called QlikView. You can use QlikSense from any device at any time. The user interface of QlikSense is optimized for touchscreen, which makes it a very popular bi tool. A big difference with QlikView is the feature Storytelling. Users add their experience to the data and by using snapshots and highlights making the right analysis and decisions has become a lot easier

Oracle BI

is an enterprise portfolio of technology and applications for business intelligence. This technology gives users pretty much all BI capabilities, such as dashboards, proactive intelligence, alerts, ad hoc, and more. Oracle is also great for companies who need to analyse large data volumes (from Oracle and non-Oracle sources) as it is a very robust solution.

IBM Cognos Analytics

is an AI-fueled business intelligence platform that supports the entire analytics cycle. From discovery to operationalization.You can visualize, analyze and share actionable insights about your data with your colleagues. A great benefit of AI is that you are able to discover hidden patterns, because the data is being interpreted and presented to you in a visualized report.

Designed by BootstrapMade