Business Intelligence Archives

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How to Become a Business Intelligence Analyst

July 18, 2023 No Comments

As with many buzzwords emerging from the intersection of business and technology, the phrase “business intelligence” (BI) is often misunderstood. In a nutshell, it refers to the skill and practice of extracting insights from data to realize new goals, strategies, trends, and values. A business intelligence analyst, working with a network of other knowledge workers (such as data stewards and data governance specialists), helps an enterprise thrive.

Business Intelligence Explained

Business intelligence refers to the perspectives gained from analysing the business information that companies hold. Since that data may be spread across many locations and departments, business intelligence is an amalgam of analytics and mining that can empower management with the tools needed to make informed decisions that may not otherwise be apparent.

Today’s data-driven businesses are growing at an unprecedented pace, often along unpredictable paths. Because of this, you might think that business intelligence should largely be an automated affair – even the domain of AI. However, algorithms and automation alone cannot harness the creative connections and nuanced insights required within the field. Although IT is obviously a major part of the equation, business intelligence requires human intelligence.

Curious about what it takes to become a business intelligence analyst? Read on for the skills and education you’ll need and the responsibilities you’ll have if you follow this career path.

What Is a Business Intelligence Analyst?

As is common among data-centric professions, a business intelligence analyst (BIA) must wear many hats and have skills that fall across various areas. Still, the core of the job boils down to creating regular reports that summarize a company’s current data holdings in relation to parallel financial reports and current market intelligence.

Typically, these reports cogently present salient trends in an identified market that could impact the goals and actionable items on a company’s agenda, plotted as a function of the various data assets at the organization’s disposal.

Although a business intelligence analyst is much more than a glorified office assistant, the job is best understood as a support role for executive decision-makers. A BIA must provide meticulously supported analytical insights that reflect the current realities of both the enterprise and markets in question. At the end of the day, the key outcomes of the analyst’s work are to bolster the company’s place in the market, streamline the efficiency of the staff, amplify overall productivity, and even upgrade performance at the level of customer experience.

The business intelligence analyst is a relatively new vocation but growing fast: Forbes recently tapped the BIA as one of the most sought-after positions in the greater STEM marketplace.

Since there’s a demand for BI expertise across so many industries – healthcare and medicine, insurance, finance, e-commerce – professionals working in the U.S. can expect to command a salary of roughly $80,000 per year (with even higher figures in especially tech-heavy states).

What Skills Do Business Intelligence Analysts Need?

Just as one would expect from the job title, the lion’s share of a business intelligence analyst’s skill set involves crunching data. They need to have a strong command of data at every level, including organization, storage, mining big data, and analysis – all with a keen and responsive eye for spotting key performance indicators and business-critical priorities in a company’s data troves.

Beyond data, a top-tier BIA will have some proficiency in tools tailored specifically for BI, programming languages, and systems analysis.

Data and tech know-how may anchor the position, but it’s nothing without a raft of communications skills to translate data insights into actionable steps. This entails critical thinking and the ability to make presentations that speak to the needs of stakeholders in easy-to-understand language and data visualizations.

Typical skills required for business intelligence analysts:

Extensive knowledge of software in user interface, database management, enterprise resource management (proficiency in Python, R, C#, Hadoop, and SQL)
Presentation and reporting in a timely and cogent manner (mastery of PowerPoint and business functions of Zoom are obvious assets)
Upper-level background in integrating software and programs into multiple tiers of data services
A knack for problem-solving in both technical and interpersonal contexts; at least five years of engagement in analytical and critical thinking skills in a professional setting
Ability to build rapport with both individuals in management and interdepartmental teams (especially in cases of implementing new software and tech that may result from BI recommendations)

BI Roles and Responsibilities

As much as business intelligence can be about interpersonal action, much of an analyst’s duties are solitary ones, chief among these authoring procedures for data processing and collection. From there on, expect reporting and more reporting, including analytical reports that can be personalized for the needs of stakeholders, highlighting the most departmentally relevant findings.

A business intelligence analyst also needs to maintain an active role in the various life cycles of data as it moves throughout the organization. After all, data reports are built upon regularly monitoring the way data is collected, looking at field reports, product summaries from third parties, and even through public record.

As a function of this, a BIA may want to continually track burgeoning trends in tech or emerging markets that could potentially offer efficiency or value within the industry and their specific enterprise.

Working in concert with specialists in data governance and stewardship, a BIA must oversee the integrity, security, and location of data storage. This should be performed in the organization’s computer database and may be done in conjunction with new operational protocols that make the most of the database as it evolves in tandem with updates and unique program features. Finally, BIAs benefit from taking a step back for meta-analysis, forging new methodologies that improve analysis at every step outlined above.

Required Education and Training

There are several routes you may follow to prepare for a career in business intelligence. Most obviously, you can earn a bachelor’s degree directly in business intelligence, which incorporates a study of analytics with elements of marketing, tech, and management.

Alternatively, a beginner in the field may want to proceed more obliquely, garnering a B.A. in a related field, such as computer science, accounting, finance, management, or business.

A bachelor’s is enough to open the door for most entry-level positions in business intelligence, but a master’s in a more comprehensive discipline such as business analytics can make the difference in landing more competitive, elite jobs.

By Shauna Frenté

Sourced from DATAVERSITY

In News

How Web Data Extraction is used in Business Intelligence

April 30, 2020 No Comments

By fuyili.

Business Intelligence is a process of transforming the data into information and turning information into actionable insights. However, a successful business intelligence strategy is only on the premise that we have enough valuable data in a structured format for us to generate in-depth analysis. But how? As of today, the amount of data scattering across the internet is far beyond our capacity to consume, let alone digging out valuable information. But don’t worry! If there’s a problem, there is a solution.

Web data extraction refers to an automated process to collect data that replaces the traditional way of manual work of copy and pastes. There are many ways to achieve automation, either writing code by yourself or hiring a freelancer to do the job for you. However, the most cost-effective method would be a SaaS to manage the process with a reasonable time.

I list four real-world examples of how web data extraction plays into the system of business intelligence.

Table of Contents
· Social Media Intelligence
· Price Intelligence
· Brand Intelligence
· Product Intelligence

Social Media Intelligence
Social media data comes with many forms. They can be blogs, reviews, posts, images, comments, social engagements and more. Social media data extraction can explore business opportunities, track competitors, monitor consumer sentiment by extracting this information on a regular basis.

Price Intelligence
E-commerce practitioners often need to look out for prices from single or multiple websites. They also need to compare competitors’ with what they offer daily to optimize their marketing efforts accordingly. Web data extraction makes it possible to track prices every few minutes and update the information to your database. This allows you to monitor the price volatility and make a dynamic price strategy.

Brand Intelligence
Business needs to track and improve their presence and visibility across social media. Data extraction can collect positive, negative mentions and the people who mention the product on time. As such, you can react to grievances in time. Even better, build a relationship with those who speak highly about your brand, and turn them to your brand evangelists.

Product Intelligence
If you need to track how your competitors are handling their products, you can leverage web data extraction to collect the product information across multiple websites including Amazon, eBay, Walmart, etc. As a result, you can take a better assortment decision.

These are just a few examples of data extraction applications in business intelligence. But please be aware that the business intelligence environment is way more complex. It involves methodology, applications, and technologies to enable entire information processing. And a sufficient volume of quality data enables us to draw a conclusion from data analysis, discover patterns and forecast future events, eliminate risk. In this case, data extraction has a great impact on business operations.

Choosing the right method to extract data is crucial. Traditionally, people would write code to extract web data. The most common programming languages would be python or R. These coding-approaches can get you a sheer volume of data at a certain time. Yet, as soon as the structure of the webpages changed, they have to rewrite the code or even have to change the entire approach.

Web pages are constantly changing. They are dynamic, and it challenges us to get data from the internet. In this sense, the data extraction tool would be the most cost-effective method. An intelligent web data extraction tool like Octoparse can achieve real-sense automation（BTW, Octoparse 8.1 is coming soon. Please check Octoparse 8.1 Upcoming Features Announcement）. Its advanced features ensure that you can extract data from dynamic websites while also being intuitive and user-friendly without coding.

By fuyili

Sourced from codementor Community

In News

Data Analytics Versus Business Intelligence – And The Race To Replace Decision Making With Software

July 18, 2019 No Comments

By Christian Ofori-Boateng

Business intelligence is only as intelligent as the data that goes into it … a nd only as smart as the people using the data.

As companies big and small take up the arms race that is big data, what’s getting lost in translation is the process — and difference — between gathering business intelligence and using data analytics to make real business decisions that have an impact.

If you are scrambling for a “BI” solution with hopes of solving your biggest problems with software alone, let me save you from wasting thousands of dollars sending your organization down the rabbit hole that is business intelligence.

Before you even think of what data analytics software you need, or what data you want, you need to understand the problem you want to solve.

Data Analytics And Business Intelligence Are Not The Same

If you are wondering what the difference between data analytics and business intelligence is, you are probably not alone.

Both terms are used interchangeably, with business intelligence being the generalized term that encompasses analytics, but there are distinctions.

The major difference between business intelligence and data analytics is that analytics is geared more toward future predictions and trends, while BI helps people make decisions based on past data.

The Uncommon Truth About Business Intelligence

You will never get the right answer without asking the right question.

No one would argue with that.

Unfortunately, with the rise of software tools touting business intelligence and automation, the new norm is thinking that having these tools will make or break your business.

Where most companies go wrong is attempting to adopt new technologies too fast across their entire organization without having a plan in place for how they’ll actually use the tools to solve a clearly defined problem.

Here’s why: We create 2.5 quintillion bytes of data daily, and that rate will only continue to accelerate. Incredibly, we generated 90% of the data in the world just within the past two years alone. The sheer volume of data being generated means that large organizations that are taking the first step toward adopting BI must first understand the problems they want to solve and create hypotheses on how BI will solve these problems.

It is the process — or lack of — that either builds or kills the value found in data analytics and business intelligence tools.

Why Data Analytics Is Not Enough

You cannot make smart decisions without the right information to act on. The right tool will help you illustrate data in a meaningful way, and then distribute this information to the right people at the right time — this is what business intelligence is all about.

In order to get the most out of adopting data analytics and business intelligence software into your organization, the first thing you need is a clearly defined problem that can be solved with BI.

The smartest organizations focus on scaling business intelligence targeting one problem at a time. It’s much more efficient to rally your board, C-suite and IT department around a single problem that has a meaningful impact if solved.

You Need A Road Map

Whether you are starting from nothing, moving from spreadsheets or looking to uplevel the way your organization uses data, you need a plan of action.

Here is how to implement business intelligence into your business:

1. Decide on what problem(s) you want to solve. Start with a clearly defined problem with goals that are smart, measurable, actionable, realistic and timely.

2. Understand what stakeholders will be involved. Proper planning prevents poor performance. What information do they need? How they will use it? What data will provide this information?

3. Figure out what data you need and how you will get it. One hundred percent pure quality data is a bit unrealistic, but you need to have a data management practice in place to make this work. Good data in means good analytics out.

4. Decide how success is measured using KPIs. What gets measured gets managed. You need an objective way to gauge the effectiveness and success of your rollout. Establish key performance indicators with your team that everyone will rally behind.

5. Set up systems and processes that turn data into action. Automate reporting. Create systems and processes that automate the delivery of information to the right people at the right time, and set deadlines for acting on information.

Software comes last. Business intelligence is not solved with a software solution alone. You need organizational buy-in. You need clearly defined problems and a process for solving them. All the intelligence in the world will go nowhere if your organization isn’t structured to take action on the information you uncover.

Solve the people and culture challenge first, and business intelligence opens up a completely new way to streamline business processes, accelerate growth and uncover new opportunities.

Feature Image Credit: Getty

By Christian Ofori-Boateng

Co-Founder & Chief Executive at ChristianSteven Software

Sourced from Forbes

In News

What Is Business Intelligence?

June 17, 2019 No Comments

By Terin Miller

Business Intelligence software collects, stores and analyses data, turning it into useful information to help businesses make better decisions.

As the internet is used by companies to discern and target potential consumer trends, the desire to collect vast amounts of data has grown exponentially. Making sense and use of the data collected requires a system for collecting, storing and analysing it.

If you’re old enough to remember, or have seen the original “Star Trek” series, you may recall that Captain Kirk and others could merely ask a computer a question, and the computer, after some blinking of lights and strange beeps, would deliver an answer either by voice or print-out.

With Business Intelligence software, an employee at a desk with a computer merely needs to type in a query, and within a much shorter time and without the strange beeps, an answer is displayed.

What Is Business Intelligence?

Business Intelligence, or BI, is the term given software applications that change raw data into meaningful and useful information to help businesses make better decisions. The term Business Intelligence actually came into use around the 1950s. It grew out of early computing technology called ‘decision support systems.’

Business Intelligence systems have grown more powerful since then, due to increased data collection and greater storage capacity, and the burgeoning use of smartphones and wearable devices that all help in data collection.

In the 1950s, companies didn’t have access to smartphone metadata, internet usage records, social media activity, or “smart home” assistants like Alexa and Echo.

How Does Business Intelligence Work?

The main purpose of Business Intelligence platforms is to sift through data to find patterns and trends.

There are usually four components to BI software:

Data analysis, the reports from which can influence company direction, product line ups and even hiring decisions;
Data mining, which is the analysis of large sets of data to find patterns and correlations;
Text analytics software, which sifts through unstructured textual data to find patterns and is used to analyse sentiment in social media posts or online customer feedback, and
Business analytics, which has its own three main forms:

Descriptive, which describes data you already have, to look to identify trends and relationships inside of it, like page views, and even sales numbers within a specific department.
Predictive, which searches for a correlation between a single unit or factor, and the features pertaining to it to find some correlation between different sets of data. This allows companies using it to predict future patterns from past trends, and is according to experts the fastest growing form of analytics.
And decision analytics, which helps companies make decisions by analysing not only past data, and extracts trends, but also looks at future conditions such as manufacturing trends, or what the market it going to be like in a few years. Decision analytics even makes predictions on shortages of resources, to help map out the safest course for a company to take over a number of years.

The main way to make use of all the info available is “data visualization,” which also is growing sub-field in Business Intelligence. Data visualization is the graphic display of results of your data mining efforts, or analytics, and can update in real time.

There are three main types of data used in Business Intelligence functions:

Structured, which resides in a fixed form, is labelled, such as with a name and other information collection boxes on websites, or address fields for shipping information, has a header, and can be put into a database program like Excel, and you can query it or search it with a computer, so it can be analyzed.
Semi-Structured, which has elements of both structured and unstructured data.
Unstructured, which has information that can’t be easily read by computers, and is difficult to organized in traditional databases, because it can’t be stored or collated in rows or columns. This is the most common form of data found on the internet.

Data usually resides across different systems, such as CRM programs, marketing automation systems, customer information – such as consumer sentiment – or reviews in social media platforms.

The first step in BI is to take inventory of data your company already produces, and figure out how you can analyse it and how you can cross-reference them.

The more often data is extracted and analysed, the more up-to-date analytics reports from the data will be.

A method of collecting open-source software utilities to facilitate use of a network of many computers to solve problems that involve massive amounts of data, Apache’s Hadoop, is used with Business Intelligence by large customers such as Facebook (FB – Get Report) , which customizes it, and Ebay (EBAY – Get Report) .

Why Is Business Intelligence Important?

Lowe’s (LOW – Get Report) , America’s second-largest home improvement store chain, uses BI to optimize its supply chain efficiency and cut down on the rate of fraudulent returns in its store.
Starbucks (SBUX – Get Report) , through its Loyalty Card Program, has amassed individualized purchase data on millions of customers, and uses BI to predict purchases or offers a customer might be interested in.
Amazon (AMZN – Get Report) uses BI to personalize product recommendations and market products. Ever wonder how Amazon is able to offer items “you might be interested in”? Amazon (AMZN – Get Report) uses its BI software for logistical business decisions, too, enabling its massive supply chain to run smoothly, optimizing shipping routes and allocating inventory.

Business Intelligence Trends

The trends in BI seen most important in a survey of 2,679 users, consultants and vendors by BI-Survey.com were master data and data quality management, data discovery, and self-service BI.

According to the survey, “While master data and data quality management builds a strong foundation for handling data, the significance attached to data discovery and self-service BI shows that the empowerment of business users is a consistently strong trend.”

The same survey also found that agile BI development and advanced analytics and analytics teams are increasing in importance; agile BI development is connected to a cooperative approach between lines of business and IT, while advanced analytics expresses the need for businesses to use data in a more beneficial way. Advanced analytics also includes machine learning, tightly interconnected to the sphere of artificial intelligence, the survey said.

Meanwhile, real-time analytics and mobile BI appear to be decreasing in importance, the survey discovered. Either current tools and systems aren’t able to provide these kinds of applications, or priorities have changed, the survey suggested.

Feature Image Credit: Shutterstock

By Terin Miller

Sourced from TheStreet

In News

Seven essential SEO tips for small business owners

January 18, 2019 No Comments

By John Andrew

For a small business, a solid SEO strategy is essential to driving growth and building interest in a brand – these tips will help to take your small business to the next level

Starting a business is not an easy process and it becomes even harder when you are looking to expand your business. If you have a small business, you need to be present in most digital platforms to advertise your products or the services you are offering. Some of the digital platforms you should join include social media and search engines. However, start-ups and small-to-medium enterprises (SMEs) might find consultants and SEO services quite expensive – especially if they have not done thorough research and they are looking to keep costs low.

Here is an SEO checklist that will help you prioritize your SEO tasks for a small business enterprise:

1. Know your target audience well

This is very important because you will know who you are selling to and your target market. When you know who your target audience is or will be, it becomes easy to market your products or services, create content and search for the keywords to use among other things. Therefore, whether you are hiring an SEO agency or you are doing it yourself, you need to research your target audience to make the right choices.

2. Fix technical issues and optimize your page

If you do not know anything about SEO or you have a piece of limited knowledge about SEO, it is advisable to hire an SEO expert agency or an expert to offer SEO services. Your website should not have any technical issues like duplicate content, broken links and slow page speed. If these issues are not fixed, they will affect your rankings and traffic to your page, making it hard to benefit from SEO strategies.

After fixing the technical issues, you need to optimize the page by creating quality content by conducting thorough research. Some of the things you need to optimize when creating content are:

Titles.
Meta descriptions.
Alt text and images.
Body content.

3. Register and optimize Google My Business

When starting a business, you need to make it very easy for people to find you on Google places and on the internet. Therefore, you will have to register and optimize your Google My Business to enable customers and other people to find you with ease. Optimizing your Google My Business is easy because you only need to claim and verify it on its website. Some of the things you should remember when optimizing your business are:

Fill out as many fields as possible to enhance your listing (e.g., adding videos and photos).

Choose the right category for your business.

4. Manage the local business listings and citations

When optimizing your business, you need to understand that consistency and accuracy is very important to avoid confusing people. As a result, your business’s name, address and phone number (NAP) should be accurate to improve your local presence. If you don’t know your accurate NAP, you can look for a service that will distribute your NAP information so that you can update the info on your page.

5. Add schema markup

Schema markup usually sends search engine signals about the components of a page, including:

Phone number.
Address.
Business number.
Ratings.
Business hours.

6. Register with Google and Bing webmaster tools

If you have a website already, you need to register with search engines such as Google and Bing and submit your sitemap. This is very important because you will find out what the search engines understand and know about your search engine content.

7. Register with Google analytics

Even though the webmaster tools can offer you a wide range of information from about search engines, Google Analytics offers a lot of information about users’ views. Knowing your users’ views is important because you will know their experience on your website and understand how you can offer them a better experience by improving your content or services. Google analytics should help you find out the following:

The most popular landing pages.
The most popular exit pages.
The pages that are commonly visited.
Major traffic sources on your page.