We thought it would be interesting to start some conversations on the advances, challenges and successes in data that our community might soon see, so we asked IT leaders to tell us what the biggest data news will be in 2018. They distilled their experience in the field and their knowledge of cutting edge practices to predict that this is the year that data integration will move beyond traditional ETL practices to use different, more seamless tools. A common theme among our IT experts was that data will come out of the relational basement, so to speak, and will become easier to see and understand by the business user, will be more trusted across the enterprise, and will drive more practical and economic uses.
In 2018, we will see data integration across silos become more trusted, and that, in turn, will fuel the API economy.
The key components of trusted data are:
These are just three of the key requirements of a robust data governance practice.
Another trend I predict you will see is the increase in the use of analytical data by APIs.
While data enrichment done by analytical applications has always been valuable, historically, it has not been easy to share with customers because of the limitations of business intelligence applications.
We’ll see a shift toward combining the operational data that customers currently see with analytical markers specified by predictive models created by data scientists. This combination is key to the personalized user experience that the API economy demands – and a seamless integration, rendered for API consumption will ensure a delightful user experience.
Mike Fillion, Vice President of Data Services at Tahzoo, specializes in data integration and analytics, leads teams of application architects and data modelers, and is a globally recognized data evangelist who speaks and leads workshops on practical data implementations around the globe.
I have noticed three trends, and one set of common elements.
Beverly Jamison, Ph.D., is a computer scientist and IT architect who specializes in semantics, AI/machine learning and data integration. She has taught grad and undergrad classes in computer science and served as director of IT architecture and publishing solutions at the American Psychological Association. Currently, she is a consultant at Practical Semantics.
Data integration remains one of the most intractable and critical constituent pieces of the new era in information management. Unless information seekers can look across sources—information silos—they will not be able to put together the whole picture of what is happening, what patterns are emerging, what risks are looming. Combining human understanding with the speed and volume that machine learning offers is probably the only way to tackle this barrier.
Sue Feldman, founder of the Cognitive Computing Consortium and CEO of Synthexis, provides business advisory services to vendors and buyers of cognitive computing, search and text analytics technologies. She is the author of the book, The Answer Machine.
Below are two trends I see in data integration in 2018, particularly in the life sciences industry and other regulated industries.
Harsha Rajasimha, Ph.D., Senior Director, Life Sciences at NTT Data, is a scientist and executive leader with more than 15 years of distinguished experience in the fields of life sciences consulting, systems biology, IT systems integration, big data analytics, genomics of rare diseases, and precision medicine.
Enterprises’ digital transformations include adapting to the changing demands of different formats of data – structured, unstructured, semi-structured – and different velocities of data – batch, near-real time, real time. This has put data integration front and center, and while ETL vendor tools are still relevant, we are still seeing organizations use a bouquet of data integration solutions (scripting and commercial ETL engines) for their ingestion, data processing and transformation, curation and consumption/syndication needs.
In 2018, I foresee convergence into more seamless tools so that companies don’t have to buy multiple tools and still resort to engineering and coding custom routines/scripts in Scala, Python etc.
Another trend that I anticipate in 2018 with the emergence of data engineering, is the convergence of data preparation and wrangling within the core data integration framework. For too long we have viewed data prep and wrangling as a pure consumption/analytics enabler. We are now seeing the entire data integration value chain of Ingest > Prepare > Catalog > Secure > Govern > Access as one giant data prep step in a way and the DI tool vendors are also starting to align their capabilities accordingly.
Parthasarathy Rangachari, Senior Director, Analytics and Information Management, Cognizant Technology Solutions, is a leader and practitioner in big data technologies specializing in enterprise data lakes, data management and governance and analytical use cases.
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