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• What makes up a strong data science team structure?

  Enterprises rely on a strong data science team to get the most from their data. Read on to find out what talents you'll need to employ to support your organization's data. Continue Reading

• Data scientist vs. data analyst: Comparing the 2 data roles

  The differences can be subtle, but in general, a data scientist has more responsibilities and a more advanced background than analyst counterparts. Continue Reading

• What are the key BI team roles and responsibilities?

  A business intelligence team helps an organization deploy, manage and use BI tools. Here are the primary roles on a BI team and an overview of their responsibilities and duties. Continue Reading

• Important resources in a data scientist education

  There are plenty of resources for data science learning for people entering the field all the way up to managers. Read on for key resources and an excerpt from a new book on data science skills. 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

• Definitions to Get Started

  • What is days sales outstanding (DSO)?
  • What is big data analytics?
  • What is continuous intelligence?
  • What is embedded analytics?

  • What is composable analytics?
  • What is a metrics store?
  • What is decision intelligence?
  • What is a business intelligence dashboard (BI dashboard)?

  View All Definitions

• 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

• Data science interview preparation: How to answer top questions

  A successful interview for a data scientist position relies on the ability to effectively communicate and demonstrate your combined experiences and skills.Continue Reading

• Top embedded analytics examples in enterprise applications

  Embedded analytics has been trending for ease of use and accessibility for users. Here are the top use cases for these tools in enterprise applications.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

• Data scientist vs. data analyst: What's the difference?

  Data scientists and data analysts have a lot of crossover in their roles, but they're certainly not the same. Here's a look at some key differences in the positions.Continue Reading

• NLP uses in BI and analytics speak softly but carry a big stick

  Self-service analytics vendors are adding NLP features to their tools to make them even easier to use. Learn about notable NLP applications as well as some caveats.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

• 5 augmented analytics examples in the enterprise

  Here are the top examples of augmented analytics uses that BI vendors support and enable, including data preparation, NLP-based querying and automated insights.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

• Data-rich organizations turn focus to ethical data mining

  As data analytics has increasingly become a core component of organizations' strategies, concerns have arisen around how data is mined. Experts offer tips.Continue Reading

• Qlik Research head talks Associative Engine, NLP and Data Swarm

  Elif Tutuk, research head at Qlik, discusses projects her team is working on -- including a smarter Associative Engine, multi-attribute visualizations and NLP.Continue Reading

• Avoid turbulence when shifting to data analytics in the cloud

  When migrating BI and data analytics to the cloud, factor existing analytics processes, software evaluation, data protection and cost controls into a thoughtful plan of action.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

• 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

• 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

• 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

• 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

• How do augmented analytics and BI tools differ?

  See how augmented analytics compares to traditional BI and self-service analytics tools and what this new generation of AI-powered data analysis platforms can deliver.Continue Reading

• Rising demand for business analytics education programs

  Colleges and universities are increasingly offering business analytics degrees. The graduates can help build IT and business capabilities of small and medium-sized organizations.Continue Reading

• GPU implementation is about more than deep learning

  Simulmedia is using GPU technology to power reporting tools, while eyeing future deep learning applications, helping to justify the cost of the hardware while building experience.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

• 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

• 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

• Location-tracking system improves business efficiency

  For organizations in industries such as healthcare and trucking, analyzing data on the location and movements of employees is helping to streamline operations.Continue Reading

• Big data vendors should stop dissing data warehouse systems

  Wayne Eckerson examines the analytics roles of data warehouses and big data systems and says he's tired of data warehouse bashing by big data vendors.Continue Reading

• Laws leave gray area between big data and privacy

  Laws on data collection and use remain foggy, leaving businesses to feel their way through big data and privacy laws.Continue Reading

• MapR

  MapR Technologies is a distributed data platform for AI and analytics provider that enables enterprises to apply data modeling to their business processes with the goal of increasing revenue, reducing costs and mitigating risks.Continue Reading

• deep analytics

  Deep analytics is the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets comprised of both unstructured and semi-structured data.Continue Reading

• in-database analytics

  In-database analytics is a scheme for processing data within the database, avoiding the data movement that slows response time.  Continue Reading

• OLAP dashboard

  The “OLAP” designation indicates that one or more of the graphs or reports (sometimes referred to as “panes,” in the dashboard) are based on an OLAP (Online Analytical Processing) data source. Continue Reading

• Examples of decision support systems (DSS) aiding business decision-making

  Learn how decision support systems can help the business decision-making process. Find out why decision support is needed and what IT skills business managers need for DSS.Continue Reading

• Creating key performance indicator (KPI) reports and dashboard design

  Learn about key performance indicator (KPI) reports and the benefits of KPI reporting. Read about common executive dashboard design mishaps and see examples of KPI scorecards.Continue Reading