Business Intelligence Job Family

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Business Intelligence Job Family – Business intelligence (BI) refers to the procedural and technical infrastructure that collects, stores and analyzes data produced by a company’s activities.

BI is a broad term that includes data mining, process analysis, performance benchmarking and descriptive analytics. BI analyzes all data generated by a business and presents easily digestible reports, performance measures and trends that inform management decisions.

Business Intelligence Job Family

The need for BI stems from the concept that managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information. Financial modelers know this as “garbage in, garbage out.”

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BI attempts to solve this problem by analyzing actual data that is ideally presented in a quick metrics dashboard designed to support better decisions.

Most companies can benefit from incorporating BI solutions; managers with inaccurate or incomplete information will tend, on average, to make worse decisions than if they had better information.

These requirements mean finding more ways to capture information that is not already being recorded, checking the information for errors, and structuring the information in a way that allows for extensive analysis.

However, in practice, companies have data that is unstructured or in different formats that do not make collection and analysis easy. Thus, software firms provide business intelligence solutions to optimize the information gathered from the data. These are enterprise-grade software applications designed to unify a company’s data and analytics.

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Although software solutions continue to evolve and become increasingly sophisticated, data scientists still must manage the trade-offs between speed and depth of reporting.

Some of the insights that emerge from big data leave companies trying to capture everything, but data analysts can usually filter the sources to find a selection of data points that can represent the health of a process or business area. in general. This can reduce the need to capture and reformat everything for analysis, saving analytical time and increasing reporting speed.

BI tools and software come in a variety of forms. Let’s take a quick look at some common types of BI solutions.

There are many reasons why companies adopt BI. Many use it to support various functions such as hiring, compliance, manufacturing and marketing. BI is a core business value; it’s hard to find an area of ​​business that doesn’t benefit from the best information to work with.

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Some of the many benefits that companies can experience after adopting BI in their business models include faster, more accurate reporting and analysis, improved data quality, better employee satisfaction, reduced costs and increased revenue and the ability to make better business decisions.

BI was created to help businesses avoid the “garbage in and out” problem that results from inaccurate or insufficient data analysis.

If, for example, you are responsible for production schedules for several breweries and sales are showing strong month-on-month growth in a certain region, you can approve additional shifts in near real-time to ensure that your factories can fulfill the request.

Similarly, you can quickly shut down the same crop if a cooler-than-normal summer starts to affect sales. This production manipulation is a limited example of how BI can increase profits and decrease costs when used properly.

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Lowe’s Corp, which operates the nation’s second-largest home improvement retail chain, is one of the earliest adopters of BI tools. Specifically, it has relied on BI tools to optimize its supply chain, analyze products to identify potential fraud, and resolve issues with collective distribution fees from its stores.

Coca-Cola Bottling had a problem with its daily manual reporting processes: they limited access to real-time sales and operations data.

But by replacing the manual process with an automated BI system, the company completely streamlined the process and saved 260 hours a year (or more than six 40-hour work weeks). Now, the company’s team can quickly analyze metrics such as delivery operations, budget and profitability with just a few clicks.

Power BI is a business analytics product offered by software giant Microsoft. According to the company, it allows both individuals and businesses to connect, model and visualize data using a scalable platform.

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Self-service BI is an approach to analytics that allows individuals without a technical background to access and explore data. In other words, it gives people across the organization, not just those in the IT department, control over data.

Disadvantages to self-service BI include a false sense of security to end users, high licensing costs, lack of data granularity, and sometimes too much accessibility.

One of IBM’s flagship BI products is its Cognos Analytics tool, which the company promotes as a comprehensive AI-powered BI solution.

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The growth of A.I., Machine Learning and Data Science in recent times has been unprecedented. Every industry is gearing up for the race to embrace A.I. as a necessary tool to pursue new paths in strengthening or diversifying their existing offerings. Therefore, this new adventure has led to the massive employment of humans with relevant A.I. skills. However, given the huge demand and supply gap combined with the nebulous requirements from different organizations, the expectations of a data scientist and similar roles are largely misunderstood.

Recently, with the hype around Data Science, the market has floated some fancy roles that further fueled confusion among new entrants into the field. For aspiring enthusiasts, figuring out where to start and which job family to pursue is truly overwhelming. This blog post aims to define the differences between the various job families within data science and help you understand how each role is different and what skills are required to pursue each of the respective job titles.

I assume you understand the basic meaning of the terms A.I., Machine Learning and Deep Learning. In case you are still a bit confused, please feel free to refer to my previous post which sheds light on the aforementioned complicated conditions.

Artificial Intelligence professional and published author of books on Machine Learning, Deep Learning and IoT. Research Scientist @ Amazon Ads Career maps help organizations understand that not every job role transition requires a retraining framework. The idea is to build personalized learning modules by identifying adjacent job roles/skills and predicting potential career paths. Transitioning into work roles within the family matches certain skills and requires minimal training to achieve a new career path.

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A Business Intelligence Analyst can handle Data Quality and Visualization workloads as they have related skills and are capable of performing such tasks. HR managers identify needed skills with the help of talent intelligence software. This helps them create a powerful skill development framework for their inner talent.

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