Today's integration complexities are supersized. Businesses must contend with unprecedented volumes and varieties of data at a time of growing IT resource scarcity and aging integration software. Throw into the mix the high demands—and even higher expectations—placed on analytics as a way of driving business performance, and it's easy to see why many integration environments are overwhelmed and underperforming.
In today’s competitive environment, organizations not only want to analyze the past, they also want to understand the present and predict the future. Likewise, organizations want to gain insights from a wide variety of data, ranging from structured, transactional data and unstructured text to spatially enhanced data or machine data from the Internet of Things. TDWI research indicates that many organizations are at an inflection point with analytics—they are making the move from visualizations and dashboards to more advanced analytics such as predictive analytics, text analytics, geospatial analytics, graph engines,and streaming analytics.
Fern Halper, Ph.D.
More and more, organizations want to base decisions on facts, have complete views of customers, manage operations by the numbers, predict and plan strategically, and compete on analytics. As a foundation for achieving these goals, organizations need a modern infrastructure for data warehousing and business analytics.
Philip Russom, Ph.D.
SAP and Intel
Business intelligence is critical to getting answers from data, but for many users it is also a huge source of frustration. Since its beginning, the mission of BI has been to make it faster and easier to locate the right data, query it, and return meaningful answers for reporting and analysis. Newer data visualization and discovery tools have improved the user experience, and data warehouses and data lakes have added terabytes to the data within reach. Yet, it still can be a slow and difficult process to get to the most relevant data without help from technical experts. Users often have to wait for their answers and unless the technical experts also have a strong understanding of the business, the answers are usually inadequate—and the process starts all over again.
More often, organizations are realizing that analyzing data in motion- i.e., data that arrives continuously as a sequence of instances- can provide substantial business value. This data comes from sensors, social media feeds, traffic feeds, and much more. TDWI has seen growing interest in event stream processing as well as the real-time, continuous analysis of streaming data.
Fern Halper, Ph.D., David Loshin
Data preparation is a hot topic today because modern technologies and practices are finally giving users and IT an alternative to traditionally slow, manual, and tedious steps for getting data ready for business intelligence (BI) and analytics. Data preparation covers a range of processes that begin during the ingestion of raw, structured, and unstructured data. Processes are then needed to improve data quality and completeness, standardize how it is defined for communities of users and applications, and perform transformation steps to make the data suitable for BI and analytics.
Alation, Alteryx, Attivio, Datameer, Looker, Paxata, Pentaho, RedPoint Global, SAP and Intel, SAS, Talend, Trifacta, Trillium Software, Waterline Data
More often, organizations are looking to the cloud for analytics. The cloud can provide flexibility, elasticity, and convenience. Organizations are using the cloud for a range of business use cases from reporting and sandboxes to production and IoT analytics, and much more. Cloud analytic services offerings are evolving too and becoming more popular – especially with business customers. As a Service (aaS) offerings can target specific subject areas such as churn-detection-as-a-service or fraud-detection-as-a-service. These can help to jump start improved business outcomes much faster than in-house efforts.
Fern Halper, Ph.D.