Back to basics: revised data priorities

In the last 30 years of the technological revolution, there is no dispute that data has become the new currency. Initially, companies were drowning in it. But gradually, as programming languages evolved and data storage techniques became more sophisticated, companies started harnessing these gargantuan volumes of data for the greater good – most of the time.

We witnessed the dawn of the era of Big Data and data lakes. Data governance became more of a thing in the latter part of the 2010s, as we saw some eyewatering data breaches hit the headlines. And then the inception of data fabric, the role of artificial intelligence (AI) in data and master data implementation management. The data landscape has evolved swiftly – data innovation is on a very steep trajectory.

Going back to basics

But in this era of economic uncertainty, there are multiple headwinds facing businesses – global inflation, rocketing energy prices, squeezed supply chains. The money that organisations would ordinarily plough into data R&D is dwindling as they struggle to protect their margins and future proof their businesses. And as a result, we are seeing the need to go back to basics.

So against this shaky economic backdrop, what are the key data considerations that organisations need to prioritise?

  • Data analysis: if you do nothing else, data analysis is imperative to have a clear picture of your business and ensure it becomes more effective by eliminating unnecessary costs. Operational excellence can be achieved by analysing and getting a thorough understanding of your data.
  • Data cloud migration: this is becoming more and more popular as lots of companies have too much ‘on premise’ data and want to speed up their data management and improve their access to it. Cloud data options are cost effective and more flexible than onsite solutions.
  • Data governance: data governance is about the processes – assigning responsibility to different data areas and ensuring there is accountability, which is vital if there is a data breach, for example.
  • Master data management: this is the practice of the business working with data to ensure consistency, accuracy and accountability of an organisation’s master data assets, like products and customers. It ensures everyone is on the same page with regards to data and the same data is used across the organisation.
  • Modern data platforms: quick access to data from systems where data is generated and a platform to support that is increasingly in demand. Essentially, rather than migrating all relevant data onto one platform, data can remain in systems inside and outside the organisation and new modelling and platform techniques and technologies can be deployed to integrate all enterprise data siloed across different systems for analysis and the delivery of custom-fit information solutions to business users.
  • Data enabled automation: processing and interpretation of large sets of data (texts, images, videos, code, etc) has become invaluable for cost reduction. Automating repetitive tasks and augmenting decision making through the use of AI models has a direct impact on the company’s profitability because of improved lead times, better decision making and a workforce that can focus on truly value added activities.

As data practices evolve, businesses have to be realistic about what they can do on increasingly constrained budgets. They can’t do everything. It’s easy to disappear down rabbit holes with regards to different data practices – but going back to basics and doing the fundamentals well combined with quick and easy to implement cost reduction through data will stand you in good stead in the short term and enable you to build a better data future for your organisation.

Want to learn more? If you want to learn more about how Valcon can assist your organisation with your data challenges, please email [email protected] and we’ll be in touch right away.