Category Archives: BI/Analytics

10 Essential Skills for Business Analysts in 2024

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10 Essential Skills for Business Analysts in 2024

10 Essential Skills for Business Analysts in 2024

In modern business terrain, business analysts play a crucial role in driving business success. Companies strive for data-driven insights, and streamlined processes and only skillful business analysts can cater to these needs. In this article, we will discuss about 10 essential skills that are essential for business analysts to thrive in todayโ€™s complex business environment.

Business Intelligence Tools:

BI tools, such as SAP BusinessObjects, IBM Cognos, and Microsoft Power BI serve as a bridge between raw data and actionable insights. SAP BusinessObjects, IBM Cognos, and Microsoft Power BI are all prominent BI platforms that facilitate the transformation of complex datasets into easily digestible and visually engaging formats. Business analysts leverage these tools to present key performance indicators, trends, and patterns in a manner that is accessible to stakeholders.

Data Analysis:

Proficiency in data analysis tools and techniques is essential for business analysts to extract valuable insights from large datasets. Expertise in SQL allows analysts to interact with databases, executing queries to retrieve and manipulate data. Microsoft Excel serves as a versatile tool for data organization and preliminary analysis. It also provides a familiar interface for tasks such as data cleaning and basic computations. Furthermore, the utilization of data visualization tools like Tableau and Power BI enhances the communicative aspect of data analysis.

Requirement Management Software:

Requirements management tools play a crucial role in the systematic handling of project requirements from inception to completion. JIRA is a widely used tool for project management and issue tracking. Confluence, on the other hand, is a collaborative platform that allows teams to create, share, and collaborate on documents. IBM Rational DOORS offers capabilities for capturing, tracking, and analyzing requirements in a structured manner. Proficiency in these tools enables business analysts to document project requirements, which ensures that all stakeholders have a clear understanding of what needs to be achieved.

SQL:

Database knowledge and SQL proficiency is crucial when dealing with structured data. Business analysts often engage with large volumes of structured data, and a solid understanding of both relational databases, such as Microsoft SQL Server, MySQL, and Oracle DB, as well as NoSQL databases, is deemed essential. Relational databases are fundamental to data storage and retrieval in many organizations, and business analysts are expected to be adept at working with them. The ability to write and execute SQL commands is crucial, as it enables analysts to access, retrieve, manipulate, and analyze data effectively.

Process Modeling:

Business analysts are expected to possess knowledge of process modeling techniques, especially BPMN (Business Process Model and Notation). Additionally, proficiency in process analysis tools such as ARIS or Visio is crucial for effectively mapping, analyzing, and improving business processes.

 

Read more atย 10 Business Analyst Skills Ensuring Business Success in 2024

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How retailers can leverage next-generation business intelligence and augmented analytics in 2023

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In 2023, retailers will be able to leverage next-generation business intelligence and augmented analytics in a number of ways to improve their operations and drive growth. The current generation of BI tools and platforms has already had a major impact on the way businesses operate. However, the next generation of BI is expected to be even more powerful and transformative.

Here are eight key areas where retailers should consider deploying next-generation BI to drive major business impact and further insulate in times of uncertainty:

1. PERSONALIZED MARKETING AND CUSTOMER EXPERIENCE

Augmented analytics can help retailers analyze customer data and create personalized marketing campaigns and shopping experiences. For example, a retailer could use augmented analytics to identify customers who are likely to respond to a particular type of promotion, and then target those customers with personalized marketing messages.

2. INVENTORY MANAGEMENT AND SUPPLY CHAIN OPTIMIZATION

Next-generation business intelligence can help retailers optimize their inventory management and supply chain operations. For example, a retailer could use BI to analyze sales data and forecast demand for particular products, helping them avoid overstocking or running out of popular items.

3. FRAUD DETECTION AND PREVENTION

BI and augmented analytics can also be used to identify and prevent fraudulent activity. For example, a retailer could use these technologies to analyze customer behavior and identify patterns that may indicate fraudulent activity, such as unusually large purchases or suspicious payment methods.

4. PRICE OPTIMIZATION

BI and augmented analytics can also help retailers optimize their pricing strategies. For example, a retailer could use these technologies to analyze sales data and identify the optimal price for a particular product, taking into account factors such as competition, demand, and margin.

5. INTEGRATION WITH OTHER TECHNOLOGIES

Next-generation BI is also expected to be more closely integrated with other technologies, such as the Internet of Things and blockchain. This will allow retailers to take a more holistic approach to data analysis, gaining insights from a wide range of sources.

6. REAL-TIME ANALYSIS

Another key trend in next-generation BI is the ability to analyze data in real time. This will allow organizations to make timely, informed decisions based on the most up-to-date information available.

7. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

One of the major trends in next-generation BI is the use of artificial intelligence and machine learning to analyze and interpret data. With these technologies, BI systems will be able to automatically detect patterns and trends in large datasets, providing insights that would be impossible for humans to uncover on their own.

8. ENHANCED COLLABORATION AND ACCESSIBILITY

Finally, next-generation BI is expected to be more collaborative and accessible than ever before. With advanced visualization and collaboration tools, teams will be able to work together more seamlessly, sharing insights and making decisions in real time.

 

Read more atย How retailers can leverage next-generation business intelligence and augmented analytics in 2023

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Healthcare Business Intelligence Market : A Breakdown of the Industry by Technology, Application, and Geography

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The global healthcare business intelligence market is anticipated to reach value of USD 15.14 Billion by 2027, according to a current analysis by Emergen Research. The global healthcare business intelligence(BI) market is expected to expand significantly during the forecast period, owing to increasing demand for improved claim management solutions in the healthcare industry. Rising demand for better cost management solutions is likely to drive the global healthcare business intelligence market further in the near future. Moreover, rising adoption of data-driven decision-making solutions in the healthcare industry is also expected to drive the global healthcare BI market during the forecast period.

๐“๐จ ๐ ๐ž๐ญ ๐š ๐ฌ๐š๐ฆ๐ฉ๐ฅ๐ž ๐œ๐จ๐ฉ๐ฒ ๐จ๐Ÿ ๐ญ๐ก๐ž ๐†๐ฅ๐จ๐›๐š๐ฅ Healthcare Business Intelligence Market ๐ซ๐ž๐ฉ๐จ๐ซ๐ญ, ๐ฏ๐ข๐ฌ๐ข๐ญ @ https://www.emergenresearch.com/request-sample/460

The study outlines the rapidly evolving and growing market segments along with valuable insights into each element of the industry. The industry has witnessed the entry of several new players, and the report aims to deliver insightful information about their transition and growth in the market. Mergers, acquisitions, partnerships, agreements, product launches, and joint ventures are all outlined in the report.

๐‹๐ข๐ฌ๐ญ ๐จ๐Ÿ ๐“๐จ๐ฉ ๐Š๐ž๐ฒ ๐‚๐จ๐ฆ๐ฉ๐š๐ง๐ข๐ž๐ฌ ๐๐ซ๐จ๐Ÿ๐ข๐ฅ๐ž๐ ๐ข๐ง ๐ญ๐ก๐ž Healthcare Business Intelligence Market Domo Inc., Tableau Software, Sisense Inc., Microsoft Corporation, Qlik Technologies Inc., Infor Inc., SAP SE, Salesforce.com, Inc., Oracle Corporation, and MicroStrategy Incorporated

๐Œ๐š๐ซ๐ค๐ž๐ญ ๐ƒ๐ฒ๐ง๐š๐ฆ๐ข๐œ๐ฌ:

The report offers insightful information about the market dynamics of the Healthcare Business Intelligence Market . It offers SWOT analysis, PESTEL analysis, and Porterโ€™s Five Forces analysis to present a better understanding of the Healthcare Business Intelligence Market , competitive landscape, factors affecting it, and to predict the growth of the industry. It also offers the impact of various market factors along with the effects of the regulatory framework on the growth of the Healthcare Business Intelligence Market

Increasing demand for improved claim management solutions in the healthcare industry and rising demand for better cost management solutions are driving the healthcare business intelligence market.โ€” Emergen Research

๐…๐จ๐ซ ๐ฌ๐ญ๐š๐ญ๐ข๐ฌ๐ญ๐ข๐œ๐š๐ฅ ๐š๐ง๐š๐ฅ๐ฒ๐ฌ๐ข๐ฌ ๐ฌ๐ญ๐ฎ๐๐ฒ ๐จ๐ง Healthcare Business Intelligence Market ๐ซ๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก ๐ซ๐ž๐ฉ๐จ๐ซ๐ญ, Request for Free Sample Report

๐’๐จ๐ฆ๐ž ๐Š๐ž๐ฒ ๐‡๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ ๐ข๐ง ๐ญ๐ก๐ž ๐‘๐ž๐ฉ๐จ๐ซ๐ญ :

In August 2020, Knarr Analytics LLC was acquired by Qlik Technologies Inc. The deal would improve Qlik’s Cloud Platform Active Intelligence capability, which offers comprehensive insights to enable data-driven activities.

During the forecast period, the software segment is expected to retain the largest market share, expanding at a CAGR of 14.7%. Business intelligence software helps healthcare organizations collect, interpret, and process data into appropriate business information, which is projected to fuel the segment during the forecast period.

Read more atย Healthcare Business Intelligence Market : A Breakdown of the Industry by Technology, Application, and Geography

5 Ways Analytics Are Disrupting Supply Chain Management

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5 Ways Analytics Are Disrupting Supply Chain Management

5 Ways Analytics Are Disrupting Supply Chain Management

Harmonizing supply chain management analytics will put organizations on a path to automating their operations.

The evolution of infotech increased customer expectations, economic behavior, and other competitive priorities have caused firms to modify themselves in the current business landscape. Supply chains globally are becoming more complex, thanks to globalization and the consistently changing dynamics of demand and supply. As per a forecast by Gartner, the global supply chain management market was valued at USD 15.85 billion in 2020 and is expected to reach almost USD 31 billion by 2026.

Businesses are channeling the power of big data analytics to disrupt transformations at all levels of supply chain management. Data started as a fundamental component of digital transformation and is now a revolutionary concept. It is the key to achieving breakthroughs in supply chain management systems, and more organizations are integrating data analytics to mine data for proactive insights and accelerate intelligent decision-making.

Big data implementation in supply chain management addresses several issues, from strategic to operational to tactical. It includes everything from building efficient communication between suppliers and manufacturers to boosting delivery times. Decision-makers can utilize analytics reports to increase operational efficiency and boost productivity by closely monitoring the systemโ€™s performance at each level.

What is Big Supply Chain Analytics, and How Does it Work?

Integrating big data analytics with the supply chain makes big supply chain analytics enable business executives to compute better growth decisions for all possible maneuvers by combining data and quantitative methodologies. Notably, it adds two features.

First, it broadens the dataset for analysis beyond internal data stored in existing SCM and ERP systems. Second, it uses advanced statistical techniques to analyze the new and existing data. This generates new insights that help make better decisions for improving front-line operations and strategic decisions like implementing the best supply chain models.

Here are five ways big data and analytics are disrupting supply chain management:

1) Improved demand forecasting

Demand forecasting is one of the crucial steps in building a successful supply chain strategy. With data science and analytics in play, businesses experience automated demand forecasting. This assists them in quickly responding to fluctuations in the market and streamlining the optimal stock levels every time.

2) Enhanced production efficiency

Data science and analytics play a significant role in gauging organizational performance. Accurate application of big data analytics can help organizations track, analyze, and share employee performance metrics in real time. You can identify excellent employees who are struggling to maintain a consistent performance. This could be quickly done with IoT-enabled work badges, which exchange information with sensors installed in production line units.

3) Better sourcing and supplier management

Supply chain management systems have empowered organizations to collate data on multiple suppliers. Using data science solutions, you can leverage this data to gain insights into the historical record of any supplier. With this, you can gauge based on crucial metrics such as compliance, location, reviews, feedback, services, etc.

4) Better warehouse management

Warehouses are acquiring modern technology and have started installing sensors to collect data on the inventory flow. This helps you build an extensive database containing information based on the weight and dimensions of the packages. With sensors installed in your warehouse, you can identify bottlenecks that obstruct the flow and can be easily resolved at the earliest with the big data-fueled systems.

5) Improved distribution and logistics

Order fulfillment and traceability are essential for business productivity and customer satisfaction. Logistics have traditionally been cost-focused and effectively look for ways that provide them competitive advantages. Data science solutions enable logistic providers to leverage data analytics to improve their operations. For instance, they use fuel consumption analytics to improve driving efficiency. With GPS technology, they can track real-time routing of deliveries and reduce long waiting times by allocating nearby warehouses.

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8 Best Data Visualization Tools that Every Data Scientist Should Know

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Data scientists have to analyze, interpret, and visualize large datasets on a daily basis. This is why it is important for them to have the right data visualization tools at their disposal.
It can be difficult for people who donโ€™t work closely with data every day (such as managers or executives) to grasp what they are trying to say if all they give them is words on paper or an Excel spreadsheet full of numbers without any context.
Data visualization tools allow data scientists to communicate their findings more effectively, which is important because it allows them to share their insights with other people who may not be familiar with data science concepts.

Best Data Visualization Tools for Every Data Scientist

In this article, we shall take a look at some of the best data visualization tools for data scientists and how they can effectively increase the efficiency of data scientists. Here are eight important data visualization tools to help data scientists make better-informed decisions.

1. Tableau

Tableau is a data visualization tool that can be used to create interactive graphs, charts, and maps. It allows you to connect to different data sources and create visualizations in minutes.

2. QlikView

QlikView is not just another data visualization tool, It is a data discovery platform that empowers the users to make faster, more informed decisions by accelerating analytics, revealing new business insights, and increasing the accuracy of results.

3. Microsoft Power BI

The Microsoft Power BI is the data visualization tool that is used for business intelligence type of data. It is and can be used for reporting, self-service analytics, and predictive analytics.

4. Datawrapper

Datawrapper is an online data visualization tool that can be used in various contexts. It is very easy to use, and it has a clean and intuitive user interface.

5. Plotly

Plotly is a data visualization tool that is used to create interactive graphs, charts, and maps. You can also use Plotly to create a visualization of a dataset, then share the link of that visualization with your readers on social media or on your blog.

6. Sisense

Sisense is a data visualization tool that allows you to easily create interactive visualizations from your data. With Sisense, you can quickly and easily create extensive, informative dashboards that will help you understand your data better.

7. Excel

Microsoft Excel is a data visualization tool that has an easy interface, so it doesnโ€™t have to be difficult to work with.

8. Zoho analytics

Zoho Analytics is a data visualization and reporting tool that can help you to easily create custom reports and dashboards.

Conclusion

In the modern world, data is everywhere and itโ€™s important for brands to be able to decode and communicate their message in an effective manner.
And for data scientists, learning and keeping up with all the latest data visualization tools is paramount, and only after they master this art, they can keep up with the pace of big data, and the fast-moving realms of AI and ML.

Top 2,500 Data Science, Big Data and Analytics Websites

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The following comprehensive listings were produced by analyzing our large member database, extracting websites that our members mentioned or liked, and for each web site, identifying

  1. When it is first mentioned by one of our members
  2. The number of times it was mentioned
  3. Keywords found when visiting the front page with a web crawler, using a pre-selected list of seed keywords

The design of the member database (non-mandatory sign-up questions and choices offered to new members on sign-up) was done by our home data scientist (me) long ago, precisely with the purpose in mind of performing analyses like this one, down the road. Other analyses produced in the past include: 6,000 companies hiring data scientists, best cities for data scientists, demographics of data scientists, and 400 job titles for data scientists: see related links at the bottom of this article.

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10 Web Scraping Tools

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Web Scraping tools are specifically developed for extracting information from websites. They are also known as web harvesting tools or web data extraction tools. These tools are useful for anyone trying to collect some form of datafrom the Internet. Web Scraping is the new data entry technique that donโ€™t require repetitive typing or copy-pasting.

These software look for new data manually or automatically, fetching the new or updated data and storing them for your easy access. For example, one may collect info about products and their prices from Amazon using a scraping tool. In this post, weโ€™re listing the use cases of web scraping tools and the top 10 web scraping tools to collect information, with zero coding.

Use Cases of Web Scraping Tools:

  1. Collect Data for Market Research
  2. Extract Contact Info
  3. Look for Jobs or Candidates
  4. Track Prices from Multiple Markets

Tools:

  1. Import.io
  2. Webhose.io
  3. CloudScrape
  4. Scrapinghub
  5. ParseHub
  6. VisualScraper
  7. Spinn3r
  8. 80legs
  9. Scraper
  10. OutWit Hub

Read more atย 10 Web Scraping Tools

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A Tale of Two Disciplines: Data Scientist and Business Analyst

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data scientist and BA

The ability to use data to achieve enterprise goals requires advanced skills that many organizations donโ€™t yet have. But they are looking to add them โ€“ and fast. The question is, what type of big data expert is needed? Does an organization need a data scientist or does it need a business analyst? Maybe it even needs both. These two titles are often used interchangeably, and confusion abounds.

Business analysts typically have educational backgrounds in business and humanities. They find and extract valuable information from a variety of sources to evaluate past, present, and future business performance โ€“ and then determine which analytical models and approaches will help explain solutions to the end users who need them.

With educational backgrounds in computer science, mathematics, and technology, data scientists are digital builders. They use statistical programming to actually construct the framework for gathering and using the data by creating and implementing algorithms to do it. Such algorithms help businesses with decision making, data management, and the creation of data visualizations to help explain the data that they gather.

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