How Machine Learning and AI will transform business intelligence and analytics!

Machine learning and AI advances in five areas will ease data prep, discovery, analysis, prediction, and data-driven decision making.

For 10 years the prevailing trend in business intelligence (BI) and analytics has been the move toward self-service. That’s about to change. In 2018 and beyond, we’ll see a growing list of what many call “smart” capabilities powered by machine learning (ML) and artificial intelligence (AI). These features are sure to help us move beyond the limits of the self-service era.
Expect a steady drumbeat of announcements throughout 2018 and beyond about ML applied to tasks including cleaning and combining data, discovering new data, and suggesting new combinations of data that could, in turn, uncover important insights. Non-technical business users will appreciate ML-powered suggestions on best-fit data visualizations. Automated modelling features, meanwhile, will help non-technical business users tap into the power of predictive analytics.
Some of these capabilities are already starting to appear. For example, natural language (NL) querying based on keywords available in column headers has been with us for years. Some vendors are now using more advanced NL capabilities that can discern nuances and intent in complete sentences (whether typed or translated from voice with speech-to-text capabilities). On the cutting edge, systems are starting to retain the context of queries; instead of asking one isolated question at a time, you’ll have a responsive dialogue with the data, drilling down and exploring from an initial query.
Savvycom-Is modern AI intelligent enough?
Of course, many business users are more interested in action and outcomes than interpreting reports, dashboards, and data visualization. These are the users more likely to take advantage of the growing list of smart, ML- and AI-powered prescriptive applications emerging. Here’s where the context of decisions is built into business applications for sales, marketing, HR, supply chain, logistics, and more. In these cases, the data analysis can be tuned to deliver recommended next steps or even to automate actions sure to lead to desired outcomes.
These emerging capabilities will make BI, analytics, and data-driven decision-making that much more accessible, understandable, and actionable for non-technical business users, but embracing the new won’t be as easy as waving a magic wand. I’ve spoken to practitioners who were surprised and dismayed to see employees responding to ML- and AI-powered recommendations in unexpected ways. Salespeople, for example, sometimes stubbornly pursue leads deemed as less than promising by predictive scores. Here’s where change management will be crucial. As I explain in my report, delivering transparent and explainable AI that instils trust will be crucial to making smart systems succeed.
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Savvycom Launches New Artificial Intelligence (AI) Lab

The next innovation AI Lab in Vietnam, with advanced tech as its main driving force

Clients rotate to the new, they demand both innovative solutions for their toughest challenges and the technical know-how to effectively deliver those solutions. That’s the idea behind our AI Lab – where our experts and engineers obtained PhD degree in AI and data analysts from the top university in the US, Europe… in Vietnam, can research and apply AI technologies as well as focus on prototyping AI applications for client problems across various industries.
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Savvycom AI Lab is part of a complete range of software product development that helps quality engineering professionals be a catalyst for speed, agility and business performance while achieving radical productivity. Savvycom serves over 100 international clients across the US, Australia, Singapore and other European countries. It is recognized as the Top 30 Global App Developers by Clutch.
At AI Lab, Savvycom technology professionals also gain unique opportunities to learn, hone and master the fast-evolving skills they need to serve clients’ future digital needs.

“Our expertise includes Intelligence Automation, Blockchain, Machine Learning and Data Analysis”

Shoppers are facing Discovery problems, which are lots of options for them to choose on sellers’ websites. To deal with these issues, Savvycom introduces a new search tool which can quickly process images and identify specific objects within the image, then generate visually similar results. We call it “Visual Search” – one of AI Lab applications.

“The future of visual search engines is most likely to be a shopper’s paradise in
the right retailer’s hands”

Research showed that Visual-oriented Search engines were interested in nearly 70% of young clients. Visual search is very useful in the E-commerce industry that helps shoppers decrease the number of choices and find the products they want to purchase efficiently.
Enter the world of AI Lab and learn what drives our passionate team of researchers!

For further enquiries, please do not hesitate to contact Savvycom at:

15 Data Analytics Trends That Will Dominate 2017

2016 was a landmark year for data analytics with more organizations storing, processing, and extracting value from data of all forms and sizes. In 2017, systems that support large volumes of both structured and unstructured data will continue to rise.

Analytics without application to an actionable strategy is meaningless”- Mike Grigsby

There’s always something new on the horizon, and we can’t help but wait and wonder what technological marvels are coming next. Savvycom team has outlined the predictions for what 2017 will bring data and analytics.



1, The emergence of the data engineer
2, Artificial intelligence (AI) is back in vogue.
3, Big data for governance or competitive advantage.
4, Companies focus on business driven applications to avoid data lakes from becoming swamps
5, Data agility separates winners and losers.
6, Blockchain transforms select financial services applications
7, Machine learning maximizes microservices impact.
8, Intelligent networks lead to the rise of data clouds.
9, Real-time machine learning and analytics at the edge.
10, More pre-emptive analytics: from post-event to real-time, pre-event analysis and action.
11, Ubiquity of connected modern data applications.
12, Data will be everyone’s product.
13, The emergence of the data engineer.
14, Security: Growth of IoT leads to blurrred lines.
15, Hybrid wins, thanks to certain enterprise-ready cloud applications.

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