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Did 2019 Big Data Predictions Surpass the Hype? Featured

"Rocky creek with a waterfall" "Rocky creek with a waterfall"

In 2018 there were numerous Big Data predictions from industry leaders. For instance, there will be a high demand for data scientists. Big Data will become more accessible. Information retrieval and NLP will see a surge in use. Now that we are in Q4 of 2019, did those and other predictions live up to the hype or go beyond industry expectations?

What were the top five 2019 Big Data Predictions?

As we wait for Gartner’s year-end predictions for the coming year, let’s look back on the top five 2019 Big Data predictions. What did the industry giants have to say? What were their forecasts, and did they surpass all the hype?

  1. Building Data Lakes

Data warehouses are becoming a thing of the past. That’s the sentiment one gets listening to Sam Underwood of Futurety. Erecting data lakes and flexible storage environments was a 2018 priority. His projection sees these systems housing the most critical data due to their limitless capabilities. He goes one step further, pointing to a business’s operations having endless possibilities and being entirely data-driven.

August 2019, Adwait Joshi, CEO of DataSeers, brings to light how that Facebook, Google, LinkedIn, and Yahoo had to innovate their data and data usage. The traditional data warehouse was no longer suitable. New technologies emerged. They were designed, giving one’s needs flexibility when managing unpredictable data inputs.

  1. High Demand for Data Scientists

Back in 2018, Harry Dewhirst, President at Blis, stated that data would become the currency that powers our future economies. Along with that, he warns businesses to start planning for the integration of a data scientist.

According to IBM’s Quant Crunch report, back in 2015, 2,350,000 jobs were listed for DSA positions. By 2020 that demand for data scientists will see an increase to 2,714,000 jobs to fill. However, instead of a specialized employee position, the data scientist must have a set of skills to perform needed duties regardless of their functional role.

  1. Offering Database As A Service

CTO and Co-Founder of Instaclustr, Ben Bromhead, had much to say about the relationship between Database-as-a-Service (DBaaS) providers and Big Data. His prediction saw DBaaS providers starting to embrace big data analytics solutions and grow.

According to G2’s current website, they’ve listed 66 current providers and rated the top ten fastest-growing as:

  • Amazon Aurora
  • Amazon Relational Database Service (RDS)
  • IBM Db2
  • Amazon DynamoDB
  • MongoDB Atlas
  • Ninox Database
  • Aiven
  • Oracle Database Cloud Service
  • Azure SQL Database
  • Amazon DocumentDB
  1. Increased Data Cleaning Efforts

In 2016, due to poor data quality, the U.S. alone lost an estimated $3.1 Trillion. “Cluttered and incorrect data is one of the biggest issues facing most businesses,” stated Jomel Alos of Spiralytics Performance Marketing in 2018. Jomel’s prediction cleansing and organizing data will become automated using various tools.

Looking over Synthio’s July 2019 best practices data cleaning list, Jomel is on to something.

  • Develop a Data Quality Plan
  • Standardize Contact Data at the Point of Entry
  • Validate the Accuracy of Your Data
  • Identify Duplicates
  • Append Data
  1. Instant Information Retrieval and NLP Collaboration

The final prediction came from KG Charles-Harris, CEO of Quarrio. “The most fundamental prediction for big data, information retrieval from repositories, will be instantaneous using natural language. People will ask questions in common language, and the system will answer back in ordinary language, with auto-generated charts and graphs when applicable.”

According to the Facebook AI twitter feed from June 2019, they stated: “We’ve developed a tool that applies natural language processing (NLP) and information retrieval techniques directly to source code text, in order to produce a machine learning-based code search system.”

With Big Data seeing huge strides and implementation since the start of 2019, what does 2020 have in store for our industry and Big Data? Care to make a prediction?

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