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• 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

• 8 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 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

• Top 5 predictive analytics use cases in enterprises

  From marketing to healthcare, looking at past data can be beneficial to forecasting future trends. Read on to understand the most popular use cases for predictive analytics today.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

• Four challenges to successful predictive analytics models

  Analyzing customer interactions to create a predictive analytics model isn't foolproof. Expert David Loshin shares four factors that could stymie your efforts.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

• Five steps to build better predictive analytics applications

  Predictive analytics initiatives must be planned and managed effectively to ensure that they don't miss the mark on meeting business needs. Here's an approach to help keep them on 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

• What sports can teach about data-driven decision-making process

  MIT lecturer Ben Shields says other businesses can learn a lot about analytical decision-making from the progress that sports teams have made on effective use of data in recent years.Continue Reading

• Better Tableau implementation gives BI dashboards a boost

  Building good Tableau dashboards is about more than just pretty visualizations. Users say the process should incorporate strong data management practices and a sharp business strategy.Continue Reading

• Becoming a data-driven enterprise requires art, science

  Business analytics strategy often seeks to squeeze as much subjectivity from the process as possible. But when data is limited, human-algorithm pairings can perform well.Continue Reading

• Talking Data: How to make data science and analytics pay

  Businesses are still looking for ways to unleash the full benefits of data science and analytics. This podcast looks at ways some companies are finding success.Continue Reading

• Barbecue chain eyes using Alexa to augment BI dashboards in kitchens

  Dickey's Barbecue Restaurants plans to use Amazon Alexa so franchisees can get operational data while cooking, with dashboards still available for querying outside the barbecue pit.Continue Reading

• Tips on how to get the most out of data dashboard software

  When used right, data dashboards can lead to major business value. But planning and the overall user experience are both key to getting the most out of them, consultant Mico Yuk says.Continue Reading

• HR metrics and analytics make workforces more valuable

  Employing metrics and analytics to improve logistics, operations and forecasting is a good first step, but not enough. Your workforce is the most important resource to optimize.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

• Deep learning algorithms demand nearly limitless supplies of data

  More data almost always makes deep learning projects more effective. One data science manager says there's essentially no limit to the amount of data he wants to have access to for his team's projects.Continue Reading

• Deep learning models hampered by black box functionality

  A lack of transparency into how deep learning models work is keeping some businesses from embracing them fully, but there are ways around the interpretability problem.Continue Reading

• Citizen data scientist trend compensates for lack of skills

  Self-service analytics tools are fueling the citizen data scientist trend, helping companies compensate for a lack of analytics skills in the market and making analytics more accessible.Continue Reading

• Uber has high hopes for its open source data visualization software

  A few months ago, Uber released its self-developed data visualization tool into the open source world. Now it hopes innovation will flow back into the company.Continue Reading

• Data monetization strategies add new business opportunities, IT needs

  Most companies have data monetization opportunities they could exploit, experts say. But a clear strategy and long-term plan are crucial to getting the desired benefits.Continue Reading

• Data science and analytics methods must balance innovation, value

  In this episode of the Talking Data podcast, find out how data science teams at Cisco and LinkedIn navigate the process of doing advanced analytics work in an accessible way.Continue Reading

• Deloitte report reveals the power of unstructured data analytics

  The analysis of unstructured data and other so-called dark data types can deliver significant business value, according to a new report from consulting group Deloitte.Continue Reading

• Three predictive modeling flaws that cripple data science projects

  Data science can be incredibly valuable if done right, but just as damaging if done wrong. Here, a data science expert discusses three common predictive modeling pitfalls.Continue Reading

• BI self-service needs new thinking to truly serve business users

  To be truly useful to a broad set of business users, self-service BI tools need to become easier to use. An increased focus on metadata and artificial intelligence can help with that.Continue Reading

• Easy-to-use data science tools power new startup

  At DonorBureau, a startup in the nonprofit space, easy-to-use tools for data science are a must. Lucky for the company, that's where the analytics software industry is going.Continue Reading

• How PetSmart continues to see value in mobile BI software

  Mobile BI apps may have lost some of their luster from earlier years, but PetSmart, for one, continues to find real business value in apps for their managers and executives.Continue Reading

• Don't learn lessons on predictive modeling techniques the hard way

  Deploying and using predictive modeling tools without heeding best practices doesn't guarantee analytics success, a fact that many analytics managers and election forecasters know all too well.Continue Reading

• Predictive analytics tools hinge on good data, business expertise

  Predictive analytics helps businesses maintain a competitive edge, but only with the right data. And even then, the most advanced analytics tools can't replace expertise.Continue Reading

• Missions for monetizing data need lift from upfront groundwork

  Organizations launching data monetization strategies should factor some key initial steps into their plans to develop revenue-generating data products and analytics services.Continue Reading

• Researchers work on AI algorithms to detect fake news

  A new challenge to identify fake news will test the boundaries of AI technology and offer a proving ground for innovative new approaches to the technology.Continue Reading

• Don't let a data-driven approach ax judgment from analytics equation

  Data analytics can help improve decision-making in organizations. But human intuition and judgment need to be part of the picture to keep predictive models and analytical algorithms from going awry.Continue Reading

• IT teams take steps to simplify big data analytics process

  Companies such as Yahoo, Merck, Macy's and eBay have moved to clear obstacles that were blocking the path to success with big data analytics applications.Continue Reading

• Users tap mix of tools to mine big data analytics architecture

  Predictive modeling, machine learning and other advanced analytics applications help dig the business value out of big data systems -- but for many users, it takes a lot of tools and effort.Continue Reading