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• 6 data preparation best practices for analytics applications

  Analytics applications need clean data to produce actionable insights. Six data preparation best practices can turn your messy data into high-quality fuel for analytics operations. Continue Reading

• Business intelligence reporting: What it is, how it works

  BI reporting presents data in various formats so employees can interpret and act on that information in a timely manner without relying on a data analyst. But challenges exist. Continue Reading

• 7 examples of augmented analytics in the enterprise

  Augmented analytics uses machine learning, AI and natural language processing to help business users interpret complex data sets. Explore use cases across industries. Continue Reading

• Use LLMs for data analysis to improve business operations

  Data professionals can use LLMs for data and predictive analytics work. Still, the analysis of large amounts of textual and visual data requires human oversight to succeed. Continue Reading

• 9 types of bias in data analysis and how to avoid them

  Analytics can exhibit biases that affect the bottom line or incite social outrage through discrimination. It's important to address those biases before problems arise. Continue Reading

• How generative AI can make data visualizations accessible

  Data teams can use generative AI to make data visualization creation approachable for business users of all technical skill levels -- if organizations can manage the challenges.Continue Reading

• 6 key features of embedded analytics software

  Data-driven organizations need employees of all technical skill levels to be able to access and use data. Embedded analytics software has six features that make data more usable.Continue Reading

• How to improve analytics maturity

  Improve analytics maturity with advanced analytics capabilities and the proactive use of data. Navigate cultural challenges to progress to higher maturity levels.Continue Reading

• Generative AI can improve -- not replace -- predictive analytics

  Generative AI improves predictive analytics through synthetic data generation. Managing data bias and ethical AI risks can enable GenAI to widen the scope of simulated outcomes.Continue Reading

• Geospatial analytics bolsters predictive capabilities

  Geospatial analytics provides insights that help organizations analyze current situations and use historical data to predict future outcomes.Continue Reading

• 8 ways to drive business value with advanced analytics

  It can be difficult to get buy-in for analytical operations. These eight bottom-line benefits of data analytics -- with real-world examples -- can win over execs.Continue Reading

• Real-time analytics presents challenges to unlock benefits

  Real-time analytics enables organizations with faster decision-making. However, bad data, or a culture that isn't ready for real-time analytics, can undermine the benefits.Continue Reading

• Data analytics pipeline best practices: Data governance

  Data analytics pipelines bring a plethora of benefits, but ensuring successful data initiatives also means following best practices for data governance in analytics pipelines.Continue Reading

• How IT departments enable analytics operations

  IT departments enable analytics in organizations by ensuring that the data architecture is in place, including tools, processes and procedures.Continue Reading

• Collaborative analytics model boosts decision-making

  Organizations are adopting a collaborative analytics model to tap the full potential of their workforces and increase data sharing and decision-making through collaboration.Continue Reading

• Keys to building a successful business intelligence team

  Organizations looking to maximize BI use may consider constructing a business intelligence team consisting of four key roles -- the expert, designer, analyst and steward.Continue Reading

• Develop a data literacy program to fit your company needs

  Organizations can cultivate a data-literate and data-driven culture by designing a data literacy program around its employees, so they engage with data to meet business objectives.Continue Reading

• How to identify and implement embedded analytics opportunities

  Business users need to consider data science workflows and software development to identify opportunities for implementing embedded analytics for business value.Continue Reading

• 6 challenges of building predictive analytics models

  The use of predictive analytics in marketing can bring benefits companywide. But building a good predictive analytics model is not trivial. Here are six challenges.Continue Reading

• 7 top predictive analytics use cases: Enterprise examples

  Across industries, companies are using predictive analytics to forecast future trends and actions. Learn about the most popular use cases for predictive analytics in 2022.Continue Reading

• Harness the power of data literacy through democratizing data

  Author Jordan Morrow weighs in on enterprise strategies to improve democratizing data through expanded use of BI and augmented analytics.Continue Reading

• Data literacy aids democratization of data

  In this excerpt from 'Be Data Literate,' author Jordan Morrow discusses how 'hyped' area of analytics such as BI and embedded analytics contribute to the democratization of data.Continue Reading

• Data warehouses and holistic business intelligence

  Data warehouses help companies gather analytics on individual systems and data for a holistic view of company performance, spot correlations and make informed decisions.Continue Reading

• Data and analytics key to ensuring water quality

  In an interview, Meena Sankaran, founder and CEO of water intelligence vendor Ketos, discusses water safety and how data can help monitor water quality and prevent problems.Continue Reading

• How augmented analytics in healthcare improves patient outcomes

  High quality data has become essential to quality healthcare. Explore how augmented analytics is aiding healthcare organizations' efforts to provide better care for patients.Continue Reading

• Why do enterprises outsource analytics?

  As business data complexity increases, enterprises are turning to third parties for their analytics needs. Here are 10 reasons why companies are choosing to outsource analytics.Continue Reading

• 10 BI dashboard design principles and best practices

  BI dashboards are a key tool for delivering analytics data to business users. Here's how to design effective dashboards that can help drive informed decision-making.Continue Reading

• Why using graph analytics for big data is on the rise

  Graph analytics is being used across industries for different reasons. Read on to see how they can improve organizational decision-making, network analysis, production and more.Continue Reading

• The data science process: 6 key steps on analytics applications

  The data science process includes a set of steps that data scientists take to gather, prepare and analyze data and present the analytics results to business users.Continue Reading

• Data literacy framework must-haves for enterprises

  As data becomes increasingly important to enterprises, employees beyond the data team need to be data literate. And data literacy programs need certain components to succeed.Continue Reading

• How predictive and prescriptive analytics impact the bottom line

  With all the data organizations collect now, they need a good way to analyze it, and that's where predictive and prescriptive analytics can be useful.Continue Reading

• 8 self-service BI best practices for larger organizations

  Self-service BI programs can streamline the analytics process, but scaling one out to thousands of business users requires proper planning and project management.Continue Reading

• Student engagement data helps educators with remote learning

  Distance learning is necessary for most institutions during the COVID-19 pandemic. See how institutions are using analytics to measure their success in the shift.Continue Reading

• The COVID-19 impact on analytics professionals

  The global pandemic has forced many businesses to make quick changes. The COVID-19 impact on analytics professionals makes them imperative to changing business goals.Continue Reading

• Key differences of a data scientist vs. data engineer

  Data scientists and data engineers often work together, and sometimes the positions are treated as the same. Read on to find out what makes the roles different from each other.Continue Reading

• Trends and top use cases for streaming data analytics

  As more enterprises adopt real-time analytics, new infrastructure and best practices are appearing. Here are some trending practices for streaming data analytics platforms.Continue Reading

• Varied experience builds strong data science background

  Data scientists need many skills and learn those skills from different backgrounds. Experts say relevant experience can come from nearly any quantitative field.Continue Reading

• Embedded BI software creates common ground for diverse analytics

  Learn how embedding separate business intelligence capabilities into one application empowers users to drill down, access and analyze data without opening a separate tool.Continue Reading

• How to enhance your data science storytelling

  How do you create substantive, compelling stories for business executives from cold, hard numerical data? Experts share their tips on how to improve your data storytelling skills.Continue Reading

• NLP makes augmented data discovery a reality in analytics

  BI vendors are increasingly using NLP technology to make their products work more like a web search, with simplified user interfaces and improved ease of use for customers.Continue Reading

• Tableau vs. Qlik Sense: Pros and cons of the BI tools

  Products from the two top data visualization vendors are starting to resemble each other as the need for strong visualizations and scalability in self-service BI has crystalized.Continue Reading

• Ethical data mining and analytics elude privacy, usage snafus

  This handbook examines the ethics of data mining and offers advice on missteps to avoid when mining and analyzing customer data to help drive marketing campaigns.Continue Reading

• Governance, compliance, ethics in data mining: Separate but equal

  In the ethical mining and analysis of data, governance, compliance and ethics are mistakenly taken as one in the same. Data managers need to be aware of the critical differences.Continue Reading

• How to navigate today's business analytics governance challenges

  Don't let a traditional analytics mindset lure you into complacency when it comes to advanced analytics governance. Here are the biggest governance roadblocks and how to avoid them.Continue Reading

• Today's top data pipeline management challenges

  IT executives say pricing models, agility and auditability are some of the biggest challenges they have faced in managing today's increasingly complex data pipelines.Continue Reading

• Social media analytics applications live and die by the data

  This handbook provides insight and advice on how to use analytics to get information on customer sentiment and marketing opportunities from sets of social media data.Continue Reading

• Social media analytics best practices tempered by privacy laws

  Social media provides a fertile landscape of information and insight into consumer behavior, but collecting and analyzing that data carries all sorts of privacy pitfalls.Continue Reading

• 6 tips for effective customer data mining

  Digging into customer data can improve sales opportunities -- but how do you balance that against data privacy concerns? Get insights from data professionals.Continue Reading

• McDonald's orders up customer service analytics, shakes up fast food

  The fast-food giant is acquiring Dynamic Yield, a big data analytics platform, in pursuit of a more personalized customer experience on drive-thru and digital orders.Continue Reading

• Beyond customer sentiment: How to put NLP technology to work

  Natural language processing tools and apps have finally arrived -- but how are organizations putting NLP to work? Here are some possibilities that might not be obvious.Continue Reading

• A future data scientist needs business, deep learning skills

  As automation grows, data scientists will focus more on business needs, strategic oversight and deep learning and less on model creation and other routine tasks.Continue Reading

• How to integrate Power BI and SharePoint via embedded reports

  Expert Brien Posey explains two methods for including Power BI reports on pages in SharePoint Online's cloud service: publishing a link to a report, or embedding one.Continue Reading

• Analytics an uneasy balance between data collection and privacy

  In the age of GDPR and privacy regulations, attention must be paid to user privacy. Data management tools that employ AI as part of analytics can help achieve that.Continue Reading

• 3 best practices for harnessing social media analytics data

  How can organizations leverage social media analytics data? Analytics professionals offer tips, starting with picking metrics that track to your goals.Continue Reading

• What to consider when choosing big data file formats

  While JSON may be the go-to data format for developers, Parquet, ORC or other options may be better for analytics apps. Learn more about big data file formats.Continue Reading

• Information Builders CEO on cloud-first approach, machine learning

  Information Builders' new CEO, Frank Vella, said the company's focus on cloud and machine learning will help it solidify a firm position in today's BI and analytics market.Continue Reading

• 5 tips for migrating to BI in the cloud without overpaying

  Moving BI and analytics to the cloud requires a strategy to avoid excessive costs. Get tips from experts and IT pros on what to watch out for and what to address.Continue Reading

• Better sentiment analysis can bolster customer data analytics

  Customer data analytics are easy to gather in the social media era -- but they can be misleading if based on sentiment analysis culled from automated social media monitoring.Continue Reading

• How to make a self-service BI tools deployment less painful

  Self-service BI can be a big change for everyone in an organization. Expert Rick Sherman offers three principles to keep in mind that could make things easier.Continue Reading

• Bolster citizen data scientists with support, training

  As more citizen data scientists take on work traditionally tasked to business analysts, organizations must consider how to support them. Start with a centralized data team.Continue Reading

• 4 ways natural language querying in BI tools can benefit users

  Natural language queries help ease access to BI data and improve analytics insights. See how organizations are putting natural language querying techniques to work.Continue Reading

• 5 tips for enabling citizen data scientists

  Self-service analytics tools are enabling citizen data scientists to dig deeper into BI than ever before. Experts offer insights into how to empower data democratization.Continue Reading

• How the rise of augmented analytics tools affects BI vendors

  BI vendors are responding to interest in augmented analytics capabilities with simplified interfaces and features that allow for access to deeper insights.Continue Reading

• Why data literacy skills still matter with augmented analytics

  Democratizing data analytics gives everyone access to tools and information, but data literacy is still required for analyzing data and delivering successful outcomes.Continue Reading

• The benefits and challenges of augmented data discovery tools

  Augmented data discovery tools enable users to gain faster insights into data, via automated data prep and pattern discovery, but they aren't without their challenges.Continue Reading

• Tips for creating curated data sets for self-service BI users

  Data curation initiatives can help streamline BI processes by reducing the amount of time users spend locating and preparing data. Get four tips for preparing data sets.Continue Reading

• How Morgan Stanley scaled up Tableau self-service analytics

  Morgan Stanley wanted to empower users to make data-driven decisions. A Tableau analytics platform was the answer. The problem: introducing it to 30,000 users.Continue Reading

• Data silos can live or die by a self-service BI strategy

  Self-service BI is a driving force behind the reshaping or possible demise of data silos. But sound data governance and corporate attitude adjustments are needed first.Continue Reading

• Rules change for self-service BI subscription pricing models

  As self-service BI tools become commonplace, look for subscription pricing models to change according to the cloud, group data usage pricing and how data is shared.Continue Reading

• What data management challenges do analytics programs face?

  Expert Anne Marie Smith shares five reasons why organizations' analytics programs might fail and how a data management framework and other programs can help.Continue Reading

• 10 dos and don'ts for deploying self-service BI tools

  Self-service BI doesn't just happen. Organizations must ensure data quality and watch how analysts work. Experts offer 10 tips for enabling a self-service culture.Continue Reading

• 10 features to look for in visualization tools for big data

  Big data is meaningless if it isn't understandable. Experts explain why users need data visualization tools that offer embeddability, actionability and more.Continue Reading

• 6 big data visualization project ideas and tools

  These data visualization project examples and tools illustrate how enterprises are expanding the use of "data viz" tools to get a better look at big data.Continue Reading

• 10 tips for implementing visualization for big data projects

  Organizations need to keep users and design at the forefront when launching data visualization efforts, according to experts. Find out why colors and sizing matter.Continue Reading

• 3 ways to make machine learning in business more effective

  Dun & Bradstreet analytics exec Nipa Basu offers three tips on how to integrate machine learning tools into business processes to help drive better decision-making.Continue Reading

• What-if business planning simulation at its predictive best

  Simulating timely and accurate business scenarios can be an essential competitive weapon for predicting the performance, pitfalls and benefits of strategic initiatives.Continue Reading

• Data science teams use business ties to boost data knowledge

  To ensure that advanced analytics applications are relevant to business operations, data scientists are collaborating with workers who are experts on business data.Continue Reading

• Options arise for expanding data scientists' skills

  Data science programs aren't just for universities anymore. Now, data scientists can turn to massive open online courses, online texts and other resources to boost their skill sets.Continue Reading

• Streamlining predictive analytics in retail marketing

  Online flash-sale retailer Zulily uses BigQuery and Tableau to help power its predictive analytics, which, in turn, boosts its marketing efforts and ability to manage incoming data.Continue Reading

• Ten KPI templates for your dashboards

  KPIs help companies gauge success, but how do you choose the right metrics to create useful reports? Here you'll find 10 KPI examples to inspire your executive dashboards.Continue Reading

• Data visualization process demands smart design, accurate data

  Well-designed data visualizations can enable executives to make more-informed business decisions, increasing the potential ROI of BI and analytics applications.Continue Reading

• Seven good data visualization practices for visual integrity

  Data visualizations need visual integrity to ensure that the data they present can be interpreted correctly. Follow these design steps to help make visualizations trustworthy.Continue Reading

• Rethinking analytics processes spurs enterprise innovation

  By taking a fresh look at the makeup of their analytics organizations, enterprises can innovate their business models and take advantage of digital disruption.Continue Reading

• Diversified data sets for analytics deliver top results

  Analytics teams should focus on data diversity to ensure that their projects deliver the most meaningful insights -- but they must be wary of some stumbling blocks.Continue Reading

• How to boost the value of BI in today's analytics landscape

  Basic BI reporting still gives businesses valuable information. But its value can be increased by incorporating it into a broader analytics and data visualization platform.Continue Reading

• New Qlik CEO Mike Capone outlines his business strategy

  Mike Capone, Qlik's new CEO, discusses recent layoffs and says the company needs to be more aggressive about touting its successes to avoid falling behind in the self-service BI market.Continue Reading

• Newest AI technology set to disrupt tech sector, job markets

  Emerging AI technology will lead to the creation of influential new software companies and disrupt job markets, says a Goldman Sachs researcher -- predictions that are discussed here.Continue Reading

• Qlik Sense vs. QlikView: How the two Qlik tools compare

  The differences between the two main Qlik software offerings may be small, but there are still some key considerations for potential customers to weigh.Continue Reading

• Guide to using advanced analytics and AI in business applications

  AI hype is becoming reality as the technology gains ground in businesses. Get tips on machine learning, cognitive computing and other advanced analytics initiatives in this guide.Continue Reading

• Talking Data: Is there a difference between AI and analytics?

  Any AI definition should include things like predictive analytics, according to some users, even though those practices are straightforward and have been around for decades.Continue Reading

• Infographic: The evolution of the chief data officer role

  The CDO role, which has never been rigidly defined, is undergoing a face-lift as emerging technologies present new opportunities to enterprises.Continue Reading

• Predictive analytics projects can bolster business decisions

  Blind faith in predictive models can result in flawed business decisions. Analytics teams need to manage predictive processes carefully to keep things on the right track.Continue Reading

• Hiring vs. training data scientists: The case for each approach

  Hiring data scientists is easier said than done -- so should you try to train current employees in data science skills? That depends on your company's needs, writes one analytics expert.Continue Reading

• How SAS Enterprise Miner simplifies the data mining process

  The SAS Enterprise Miner data mining tool helps users develop descriptive and predictive models, including components for predictive modeling and in-database scoring.Continue Reading

• Location-based analytics helps Wendy's find its way to new sites

  Most people remember geography class as a liberal arts concentration. But John Crouse of Wendy's is showing that geographic knowledge can make for a valuable pairing with statistical analysis as part of location intelligence efforts.Continue Reading

• Design a business intelligence system properly -- for business users

  Rick Sherman explains how to avoid common mistakes when planning and designing BI systems, saying that the varying needs of different business users must be front and center.Continue Reading

• The justification for running multiple mobile analytics tools

  Finding a mobile analytics tool that will properly serve all end users is nearly impossible, eBay's BI manager says.Continue Reading

• Anxieties about big data technology trends are valid

  Criticism of big data technology trends may go too far, but proponents shouldn't dismiss the critiques.Continue Reading

• Author: Data mining projects depend on excavating meaningful data

  In a Q&A, book author David Nettleton offers advice on avoiding data mining pitfalls and pinpointing valuable business data to mine and analyze.Continue Reading

• Changes on the horizon for big data analytics tools

  The market for big data analytics tools remains fragmented, but users are expecting consolidation in the near future.Continue Reading