Systems | Development | Analytics | API | Testing

The digital revolution has truly transformed modern organizations, embedding data and analytics in every business process and customer interaction. Advances in technology enable smart supply chains with predictive analytics, automated logistics for same-day delivery, and AI advisors that reduce medical errors. As this continues, workers in all roles will need new a new skill—data literacy—to collaborate with these systems and each other.

Businesses and organizations of all types have embraced cloud integration to transform data into business intelligence. The reason for this is simple: more and more business operations are happening in hybrid cloud — or even fully cloud-to-cloud – environments, and without proper tools to manage data in the cloud, data can become siloed, overlooked, or lost altogether.

The Covid-19 pandemic has resulted in an unprecedented global economic landscape that is dominated by loose monetary policies, low borrowing costs and influx of capital in the equity markets. Against that backdrop, Mergers and Acquisitions (M&A) activity has surged since 2021 as companies are trying to take advantage of the current environment and adapt to the new business realities shaped by the global pandemic.

The first step in most analytical workloads is to ingest the data that you need for your analysis into your data warehouse. For geospatial analysis involving point, line, or polygon data, ingesting data can be complex because geospatial data comes in myriad data formats. Two of the most popular geospatial formats are GeoJSON and GeoJSON-NL (newline-delimited geoJSON).

In 2019, global venture capital investment in Fintech totaled at least $33.9 Billion. For years, the incumbent players had been warning the sector that when the next recession hit, or when fintech faced a “real” crisis, that the sector would, at worst, collapse, and, at best, see a whole bevy of fintech players disappear

It’s the ultimate Catch-22. As businesses grow, executives need to keep a close watch on operations — but that growth itself can blur visibility. Multiple teams, data sources, and silos of information can create challenges in determining who is doing what, where inefficiencies exist, and what improvements should be made.

Data Science tools, algorithms, and practices are rapidly evolving to solve business problems on an unprecedented scale. This makes data science one of the most exciting fields to be in. As exciting as it is, practitioners face their fair share of challenges. There are well-known barriers that slow down predictive modeling or application development. Finding the right data and getting access to it are two of the top pain points we hear from our customers.

I’ve been blogging for about a year about the power of misinformation and our obligations as data professionals to combat it. In a March 2021 blog post, titled “The Power of Misinformation,” I outlined some of our biological instincts that make us susceptible to misinformation and how tricksters exploit them.

I don’t think anyone could argue that the last two years have changed the workplace forever. Entire nations have been confined to their homes during months of lockdowns, and the way we conduct business and interact with each other has passed a point of no return.