How emerging powerful technology influences new data strategies

Corporate data strategies are constantly evolving. Technology was always a significant driver, and today’s data leaders are already adjusting to (and often thriving in) the rapidly growing technology landscape like never before. Modern tech has changed how companies can capture, access and analyze data.

Technology and data platforms increasingly become a commodity. Rapid experimentation and ability to connect the dots (of your data landscape) can really mean the difference between winning and losing in business.

In the past, relational database management systems (RDBMSs) were the go-to technology for handling structured data. Now there’s a slew of cloud data stores that can handle both structured and unstructured data, and they make it accessible for analytics and machine learning use cases in increasingly convenient ways.

Cloud data warehousing is now practically a given. It’s a very important foundation for new data strategies, as companies are increasingly realizing they need to open up their data and make it accessible for analytics. It enables companies to capitalize on the explosion of data.

This data warehousing approach transforms how they look at their entire data landscape, and how it needs to be organized. It’s a significant mindset shift that might be hard to deal with, but that is also very liberating when you go through the change. The blueprint of an organization’s data strategy can fundamentally change with a native cloud data warehouse coming into the picture.

Cloud-based analytics and data science platforms are also becoming more prevalent and directly cost effective. Businesses can achieve significant operational efficiency gains by automating large portions of analytics processes, making it easier for non-data professionals to get value out of their data.

Cloud data science and analytics platforms can centralize access to all the data sources and analytics tools needed for day-to-day analytical needs.

Add to that the emerging low-code and solutions and cloud data pipelines, and suddenly you find yourself in a dream where you can rapidly build out a powerful cloud tech landscape at a fraction of time and cost that would apply only a few years ago.

Another significant element are the Auto ML tools and features. They allow you to get your data and models to production at low time/cost, and they are a very practical way to keep up with the ever-increasing pace of change in tech.

Unfortunately, there is also a lot of hype and downright charlatanism. So, here’s some advice:

  • Always take all sales pitches from vendors with a grain of salt – their technology might have limitations they are not super keen to disclose.
  • Experiment, experiment, experiment – make sure the technology fits the purpose. If experimentation is long/costly/not accessible – walk away.
  • Do your research – don’t jump on the first thing you found.
  • Talk to some data pros in the community, involve data strategists – they might save you a ton of time and money.
  • Under no circumstances buy a solution that would lock your data up – you need to be able to access and export all your data (and, preferably, metadata).

It’s a brave new world out there. Business leaders need to not just accept this new data landscape, but actively embrace it. Those who do would move at a much faster pace than their competition.

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