As coined by British mathematician Clive Humby, “data is the new oil.” Like oil, data is valuable but it must be refined in order to provide value. Organizations need to collect, organize, and analyze their data across multi-cloud, hybrid cloud, and data lakes. Yet traditional ETL tools support only a limited number of delivery styles and involve a significant amount of hand-coding. In turn, enterprises are increasingly looking for machine-learning-powered integration tools to synchronize data for analytics, improve employee productivity, and prepare data for analytics. To achieve this, we will examine five steps an analyst at a fictitious financial services company, named Raviga, will take to be successful in their data integration project using IBM DataStage on IBM Cloud Pak for Data. 1. Ingest the Data Data must be ingested before it can be digested. First, the Raviga analyst needs access to its data sources before it can procure analytics. They need to access their data from multiple sources…

For the company that popularized CRM -- and indeed uses that abbreviation as its stock symbol -- Salesforce faces a potential identity crisis in 2019 as competitors such as Oracle, SAP, Zendesk and others pivot from CRM to customer experience management. Salesforce CX-enabling tools will likely be one focus of the San Francisco-based cloud software vendor in the coming year, as will Salesforce integrations that make customer data move more fluidly between its clouds. Large companies, such as Nike and Mastercard, that sell both to businesses and consumers -- or B2B2C in marketing parlance -- expect their platforms to support CX improvement initiatives that focus on customer retention and buttressing retail store experiences with online alternatives. Salesforce CX-facing features ripe for upgrading include AI-based personalization tools, as well as those that can measure campaign effectiveness against advanced segments and metrics, said Chris Jacob, director of product marketing for Salesforce Marketing Cloud. These tools must become more sophisticated by design, because Salesforce customers pour more data into their marketing and sales efforts every year.

More vendors are offering new integration tools for meeting growing enterprise demand for faster delivery of application software. Among the approaches is automating key elements of the continuous integration and continuous delivery pipeline using emerging application container services and cloud-based open source development platforms. That's the route taken by SnapLogic, the self-service application and data integration specialist, which released an "integration cloud" this week that automates key software development bottlenecks. The Silicon Valley company also announced an update to its AI platform for automating routine development tasks along with a catalog of data pipeline components. SnapLogic, San Mateo, Calif., said its integration with GitHub Cloud and support for the Mesosphere container platform would "provide the glue needed to streamline the software development lifecycle."

Technology integration is complex and an evergreen business challenge for IT teams. Enterprises setup manual & point to point connections to exchange data from business partners. However, data disruptions emerge when the business IT systems expand and prevents organizations from sharing data with partners. Information exchange with this approach requires more resources and manpower is required. For such scenarios, information solution experts recommend to use the B2B data exchange tool to support the Electronic Data Interchange.

This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle. The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase of 10.5% from the end of 2014 [2016 Gartner Report – Data Integration Tools]. Data acquisition for business intelligence (BI), analytics and data warehousing-- extracting data from operational systems, transforming and merging that data, and delivering it to integrated data structures for analytics purposes. Data migrations/conversions -- although, traditionally addressed most often via the custom coding of conversion programs, data integration tools are increasingly addressing the data movement and transformation challenges.