The promises of Big Data are many, including the ability to react faster to opportunity and risk, to achieve a panoramic view of customers, and to use client data to create hyper-personalized experiences.

The demand to become a data-driven business with a competitive edge in the digital economy is greater now than ever. As we embrace the idea that the analytics economy will power the digital economy by compounding the value of data and analytics assets, executives must know where to focus their leadership efforts. This session takes a close look at the five key factors necessary to capitalize on the growing opportunities of the analytics economy and generate substantial business value, internal and external.

How can you reap the benefits of the massive data volumes now available to your organization? To truly harness the power of your data, you need a solid strategy that incorporates everything—from security to data governance to choosing the right technologies. Learn about the five key elements of a successful data strategy; what is driving the need for a new type of data platform (including analytics, data science, and machine learning); how a modern data platform can deliver self-service analytics, empowering more people with data; new use cases that are impacting how companies make technology choices; and what you can do now to take your data strategy to the next level.

Companies are collecting treasure troves of information about their customers, but they need the right tools and technologies to connect that data to create the comprehensive 360° view that is critical to leveraging the data’s full value.

A 360° view of customers makes generating more of the right customers—who become successful, pay their bills on time, renew, and grow—possible. However, according to Gartner, less than 10% of companies aggregate data for a 360° customer view that can enable business growth. Pines provides a practical approach to achieving the 360° view of customers, along with the questions you should be asking of your business to ensure it’s on a growth path. You also learn about emerging tools and techniques that can be used to connect marketing, sales, support, and finance data to power your analytics with data that paints a holistic picture of your customers.

Data intelligence about people, places and things, along with the connection between physical and digital addresses has a serious impact on understanding your customer. Knowing your customer all starts with knowing where they are located, detailed intelligence about what is around them, and how to reach them at the right time with the right message.

Innovative organizations in a range of fields are putting data to work to predict what customers want, give them products they need, and engage with them more effectively.

How can you use analytics to succeed? It may well be in different ways than you are accustomed to thinking about analytics. Analytics tools that provide actionable insights are the gold standard in today's Big Data (and even small data) world. Data intelligence enables advanced and agile analytics, digital enterprises, and robotics. Come learn from a team passionate about data.

Joel Sehr shares how to analyze more data, faster, at lower cost, whie reducing MPP overload. Leverage more comprehensive analytics for targeted product and service offerings.

Faster time to insight is driving the use of big data technologies, but far too much time is still spent preparing data for analysis. New approaches are available to help take down the barriers to analytics.

Data preparation, or "data wrangling" as it's often referred to, is widely considered the biggest bottleneck in any analytics process—taking up more than 80% of the time and resources in any data project. Davis reviews the inefficiencies of traditional data preparation techniques and why there is a need for a new set of self-service tools to create new levels of analytics productivity. New data wrangling solutions combine the latest techniques in data visualization, human-computer interaction, and machine learning to enable a wider set of data workers to prep data themselves, as well as improve the speed and accuracy of these processes.

Collecting data is one thing; using it to make better decisions is another. Understanding which analytics approaches will yield the best results is critical.

Lindy Ryan reviews recent research she's done on the future of analytics and concludes that data visualization will be a major job skill needed by businesses of all types. Visualization helps people understand data and communicate the meaning of data to others. Although many data visualization tools exist, the one highest in popularity is Tableau.

A forthcoming text published by Taylor & Frances considers the intersection of business analytics and knowledge management. Most of the "data analytics" in use today has not changed in its essential methods—only in terms of the tools used. The application of stochastic/basic statistical methods to text and language is known to introduce risks for business decision makers, while the use of analytical methods with linguistic engines reduces the risks. It is only when knowledge elicitation and representation methods are combined with the use of linguistic/semantic tools that we significantly reduce risks. Bedford and McBreen consider eight recent applications based on stochastic methods that produced unreliable results, and then, in contrast, describe success stories—one from the private sector and one from the public sector— that have resulted from the combined method now referred to as knowledge analytics.