....| AI/ML & Cloud Data Management | Data Science | Bigdata Analytics | Data Management | Analytics | Insight & Data | BAO | IM | BI | Dw | EIS | MIS
Way back in the late 80s and early 90s, Software professionals coded COBOL programs to generate reports and analysis.These programs were collectively known as MIS (Management Information System); a few years later these were referred to as EIS (Executive Information System).
Then came the big Data warehousing wave (around Y2K) which morphed into the BI (Business Intelligence) tsunami. Evolution and refinements to the data disciplines continued and we had definitions like
- Information Management
- Business Analytics and Optimization
- Insight and Data
Though the underlying technology and platform changed but all along the base tenets have remained the same - Users and Businesses need consumable and actionable data (information) and services providers use a combination of technologies, architectures and processes to deliver it. In 2021 we are calling it AI/ML and Cloud Data management, I am sure in another year or so, we will refer to the data & information discipline by a different name.
A lot of seasoned data and information professionals (me inclusive) find themselves swamped and insecure when the concepts, jargon and technology of the next wave hit them. Yes, there is definitely advancement, refinement and maturity (most times) in every generational data management shift; and Yes, there is a lot to learn.
But if we start from the Why & Who
- Why are we building this application/report/visualization/model/database/table...cloud?
- Who is going to use this application/report/visualization/model/database/table...cloud?
And address the How later we just might put ourselves in a more comfortable position to handle the generational shifts in data management.
Are your Digitalization strategies qualified?
3 年Interesting read and putting things in perspective, or should I say Old wine in a new bottle with a freshly designed label. Way back in 1992, when I did my BE Final Year project, we created a sequential logic program (we called it program back then and not app or application) for a Doctor to diagnose common cold on Turbo Prolog, which was the prevalent AI Language and very unpopular in the early 90s. I wish I could have stuck to AI since then and by this time, might have coined newer jargons. Anyways, what I wanted to say, is that, industry would always brand, re-brand and invent fancy words and we have to keep with those because CIOs and CEOs read glossy magazines while flying business-class and encounter these words. If our presentation doesn't contain those words, then they might think, we are not Tech-enough. Responses to questions about Why, Who and How can be best answered by the Googles and the Facebooks of the world, as they are the ones most benefitting. AI can win or lose an election for a party -- Remember this.
Enterprise Data Architect | Cloud Data Engineer | Real Time Data Analytics | Hadoop Professional | Passionate Educator | Aggressive Learner |Manager at Accenture
3 年I completely agree with you , but now data format is changed ---shapeless -- now anything and everything need to be analyzed --- AND the days are coming when the business will be concentrating on ALL REAL TIME ANALYSIS --> REAL TIME B I --> more old the data less value to it --> So now a days .. two terminologies are ---HOT data & COLD data ---> more focus on HOT data for injecting the business ---> COLD data , even i think historical time duration also will be less --may be 1 month old data can be considered as historical data not for years like before ---so now the time to focus on DATA DEV & MANAGEMENT part --not on infrastructure management or administration -- so the CLOUD is ready to give us infrastructure management or administration flexibility .