Tips

Tips

• The roadblocks of business intelligence growth

  Training and cost are the two biggest business intelligence challenges impeding organizations' BI usage and expansion, according to a survey conducted by ESG.  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

• 6 essential big data best practices for businesses

  These best practices can help businesses put their big data strategy on the right track to meet analytics needs and produce the expected business benefits.  Continue Reading

• Big data vs. machine learning: How they differ and relate

  Big data and machine learning are a powerful analytics pairing. Here's an explanation of the difference between them and how they can be used together.  Continue Reading

• Top 8 business intelligence challenges and how to handle them

  BI teams face various technical and project management challenges on deployments. Here are the top BI challenges, with advice on how to address them.  Continue Reading

• Python code formatting: Tools you need and why it matters

  Computers don't care about the style of your code, so why should you? See what Al Sweigart has to say about code formatting, and get a sneak peek at his new book.  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

• Key elements of a DataOps framework for BI and analytics

  DataOps brings speed and agility to BI processes and helps align data management to business goals. Learn about the key elements of a DataOps framework.  Continue Reading

• Top data visualization techniques and how to best use them

  BI and analytics teams and self-service BI users can choose from various types of data visualizations. Here are examples of 12, with advice on when to use them.  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

• 7 steps to create a modern business intelligence strategy

  Business intelligence can boost performance and create competitive advantages for companies. Here are seven steps to take in implementing an effective BI strategy.  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

• 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

• 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

• 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

• Expert urges data pros to hone data science skills

  IT expert William McKnight shares job tips for data professionals looking to prosper in a changing enterprise. His first piece of advice: continually foster data science skills.  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

• 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

• 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

• Choosing the best visualization tools for big data analytics

  Data-driven enterprises use visualization tools to tell the stories hidden in big data -- stories which help users turn information into profit. Here's how to choose the right tool.  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

• 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

• Building a data science team in today's data-centric climate

  Finding and training data scientists to build a data science team can be challenging. But in a recent webinar, a Gartner analyst offered tips on how to do it.  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

• Beat the challenges of predictive analytics in big data systems

  Big data and predictive analytics may seem synonymous, but understanding the constraints of each discipline is the key to extracting business value from projects that combine them.  Continue Reading

• How predictive analytics techniques and processes work

  Predictive analytics is no longer confined to highly skilled data scientists. But other users need to understand what it involves before they start building models.  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

• Tableau shows software subscription model is not just for cloud

  Subscription-based software pricing associated with the cloud can also be useful in on-premises deployments, as shown by Tableau's full embrace of the subscription approach.  Continue Reading

• Ten steps to start using predictive analytics algorithms effectively

  A successful predictive analytics program involves more than deploying software and running algorithms to analyze data. This set of steps can help you put a solid analytics foundation in place.  Continue Reading

• Apply data science models effectively, not just for their own sake

  Having data scientists build analytical models doesn't do much for an organization if the models don't help generate business benefits. Here are some ways to make sure they do.  Continue Reading

• Right data mining data sets are a must for proper predictive modeling

  Predictive analytics applications can go off track quickly if data scientists and other analysts don't make good choices on the data sets they're looking to mine and analyze.  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

• Real-time streaming analytics systems need help from message brokers

  Real-time analytics applications typically involve multiple streams of data that need to be properly organized and coordinated, a job that calls for new message queuing technologies.  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

• Five ways to get the most out of A/B testing tools

  Many businesses lack a clear A/B testing strategy when implementing changes to their websites, which means they often miss out on potential opportunities to drive improvements in site performance.  Continue Reading

• Compelling data story starts with effective visualization of data

  Data visualizations can transform analytics data into actionable business information. But remember to keep things easy to understand and remember your audience, an expert cautions.  Continue Reading

• Telling data stories calls for common syntax on analytics info

  Data storytelling is just the latest phase in the long history of passing on knowledge to people, expert David A. Teich explains. It's just the tools that have changed.  Continue Reading

• IBM Watson computer strives for analytics stardom

  After achieving Jeopardy! success in 2011, IBM is trying to boost its Watson cognitive computing system back into the spotlight through acquisitions and by building out its Watson-based commercial products.  Continue Reading

• Hike use of business intelligence via the Eisenhower Matrix

  IT managers must work closely with the business to increase the number of people using BI tools in companies -- and a time management framework bearing the name of Dwight Eisenhower can help.  Continue Reading

• Tips on building big data, advanced analytics programs

  TDWI analyst Fern Halper offers up expert advice for organizations looking to go beyond basic intelligence practices.  Continue Reading

• Look to business needs in deciding what big data sets to analyze

  How much is enough? A full set? Part of one? No one answer will fit every organization. BI teams should start by assessing the business problems.  Continue Reading

• Keys to avoiding pitfalls on analytical models: testing, relevancy

  Predictive modeling can lead to some pretty bad insights when done poorly, but overcoming some common issues can help users sidestep problems on predictive analytics projects.  Continue Reading

• Social media analysis has promise -- but also pitfalls to avoid

  Social media analytics can give businesses a better idea of what customers think about them, but there are some common challenges to be aware of before getting started on projects.  Continue Reading

• BI data storytelling pegged as key to driving executive action

  At the 2014 TDWI Executive Summit in Boston, consultant Tony Bodoh provided tips and outlined a step-by-step process for creating effective business intelligence presentations that motivate business executives to take action.  Continue Reading

• Big data implementation checklist for analytics project managers

  Consultant Rick Sherman details a series of steps that he says organizations should take to set their big data analytics programs on the right path.  Continue Reading

• BI dashboards need measured approach on data visualizations

  Getting too flashy with graphics in business intelligence dashboards can confuse users -- and send data visualization programs down the wrong path.  Continue Reading

• Tips on building an analytics infrastructure from scratch -- quickly

  Creating an analytics architecture out of thin air may seem like a daunting task, but it can help make businesses more competitive in a hurry.  Continue Reading

• Data discovery tools open up new opportunities for marketers

  Big data is opening up a world of new opportunities to marketers. Data discovery tools can help them take advantage.  Continue Reading

• Self-service analytics helps marketers overcome shortage of data pros

  All industries are having a hard time finding enough skilled data workers, but marketers see self-service analytics tools as a potential solution.  Continue Reading

• Six misconceptions about deploying BI applications and systems

  Faulty project management assumptions can limit the effectiveness of business intelligence tools. Don't let them lower your return on BI investments.  Continue Reading

• Coca-Cola overcomes challenges to seize BI opportunities

  The Coca-Cola Co. understands that analytics challenges can be overcome and that a team approach helps businesses take advantage of BI opportunities.  Continue Reading

• Collaboration is key to business intelligence tool implementations

  Business units and IT departments aren't always on the same page, but they need to be in order to successfully implement business intelligence tools.  Continue Reading

• Four factors to weigh in planning an analytics big data architecture

  Consultant Lyndsay Wise details four key elements to consider when planning a big data infrastructure to ensure it will support your analytics needs.  Continue Reading

• No more shortcuts: Sound data quality strategy a must for BI success

  In the past, companies might have gotten away with not building data quality efforts into business intelligence programs. That's less likely now.  Continue Reading

• Strategies for de-cluttering business intelligence dashboard designs

  Expert Brian Jordan shows business intelligence managers how to avoid developing dashboards that overwhelm business users with too much information.  Continue Reading

• BI architect has new options to meet growing data, analytics demands

  Designing a business intelligence architecture that can meet the expanding needs of BI users is an exercise in finding the right combination of tools.  Continue Reading

• Improve customer data analytics: Tips for using metrics, technologies

  Get expert tips for leveraging customer data analytics, and read some examples of common mistakes that companies have made, including a analytics case study.  Continue Reading

• Exploiting the value of customer data with analytics

  Find out how customer data analytics helps organizations improve customer experience, service and sales – and learn more about what’s possible with customer data analytics today.  Continue Reading

• Trends and tips for using business intelligence and analytics in retail

  Read about business intelligence trends in the retail industry and get tips for using BI and analytics technology, with real-world examples from retailers.  Continue Reading

• Real-life examples of effective dashboard design

  Browse through these optimized dashboard examples from BITadvisors, Inc., including examples of strategic, tactical and operational dashboards. Find out how to encourage interactivity in dashboards and how to set up your dashboard.  Continue Reading

• Ten key elements for effective dashboard design

  Learn why focusing on business needs, including current data and key performance metrics, is an effective design strategy for encouraging dashboard usability.  Continue Reading

• How to decide whether it is time for BI consolidation

  According to experts, it's important for organizations to consider consolidating BI systems. Get expert advice about when to consider business intelligence consolidation and how to approach consolidation at your organization.  Continue Reading

• Making the business case for a BI consolidation project

  Find tips for getting enterprise buy-in and building a business case for BI consolidation in this chapter of our e-book on managing a business intelligence (BI) consolidation project.  Continue Reading

• Tips for successfully consolidating business intelligence systems

  In a recent study by Boston-based Forrester Research, close to 80% of companies polled said they use three or more business intelligence (BI) products. According to experts, it is important for organizations to consider consolidating BI systems, but...  Continue Reading

• Business intelligence appliances: Disruptive technology or distraction?

  Rick Sherman discusses the emergence of business intelligence appliances and renewed competitive pressures in the data warehousing (DW) marketplace.  Continue Reading