Jacek Chmiel, Global Chief of Consulting & Technology at the IT Services Competence Platform:
2019 is bringing more and better automation and less manual work by data scientists; improved pipelines and tooling; and even more AI support to optimize the lengthy pipelines for designing and running data processing. There are more data regulations on the horizon, which impacts how data is stored, processed and analyzed. The EU’s GDPR was just one step toward more strict control of data. The exponential growth in data is not going to stop, as Edge networks and more connected devices are coming. Data visualization techniques are improving, and proper data analyst UX is a key success factor. Distributed ledgers for enterprise adoption are going to be assisted by cloud-based services and improved open-source frameworks and engines.
Lyubomyr Senyuk, CTO & Head of R&D at CoreValue:
Data privacy and personal data control start-ups and initiatives will definitely rock this year. Everybody is experiencing a certain pressure from aggressive marketing, and is wondering how far the internet giants can go with interfering in your personal life when collecting information for personalized ads. So the idea of keeping everything that belongs to you in your own pocket is only natural. Imagine being a part of a global network, secured by the blockchain, where each and every opt-in decision is requested by ad platforms and stored with your digital signature. This approach can be used not only for data protection, but for creating systems for community life, political participation and other ‘crowd-‘ initiatives as well.
If you’re selling data solutions in 2019, you’d better use buzzwords like ‘Real-time/ Streaming/Live Dashboard’ instead of the traditional ‘Data Warehouse/ETL/ Reports.’ Real-time data processing is becoming vital for businesses, and you need to come up with an actionable solution for working around the 1 hour latency for e.g. Redshift. Additionally, real-time ML is trending, and customers now want their IoT data livestreams to be not only processed, but used as an on-the-fly re-training data for ML.
Roland Guelle, CTO at Sevenval:
The tools as well as the solutions for automation are improving, hence less manual work will be needed from data scientists in the near future. But in addition to existing frameworks, one key to success when working with big data is ‘knowing the question’ you want to answer before you actually start. Big data doesn’t answer the questions you don’t ask.
2019 will definitely be a year where data can answer your questions. If you are a beginner in this discipline, then start with simple steps: Use data (e.g. log files or application feedback channels) for monitoring, to improve your product. Particularly if it runs on microservice, serverless, cloud native etc. infrastructure without the application of data, you have no clue application run out of your control (app or webapp, 3rd party APIs, container/SDKs from suppliers).
The next installment of the Technology Trends blog series will touch upon trends in the ever hot area of DevOps and cybersecurity trends. Follow our LinkedIn feed to stay tuned.
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