The Sliding Doors for Creating Value with Data

In the popular 90’s film “Sliding Doors”, the life of the lead character, played by Gwyneth Paltrow, takes two divergent paths based on whether she catches or misses a train. Spoiler alert if you have not watched it: one path ultimately (and surprisingly) leads to a better outcome than the other.

When it comes to solving your data challenges, it’s unfortunately too common to take a path that leads to less favorable results than if you had chosen a different – in hindsight, better - path. That better path is not necessarily the obvious or most convenient path at the time, and many different circumstances may have led you to not taking it. Yet there are best practices and proven strategies that can help lead you towards - or at least shape -the path that will create the most value for your business from your data.

In a new series for this blog, I will explore these practices and strategies, while also exposing the paths to avoid and the lessons learned. We’ll look at it through the lens of strategic initiatives and use cases that we know are top of mind for many organizations, whether it’s implementing AI projects, harnessing the increasing volume of unstructured data, or meeting new environmental, social and governance (ESG) regulations, for example.

And we will bring in some real-life examples of organizations’ own “sliding doors” moments to help illustrate all this – because every organization has had these moments at one point or another. That includes a prestigious, global intergovernmental organization that took a wrong turn and stayed stuck doing the wrong things for a while, before finally righting the course and not only doing their business a great service, but the planet too. I look forward to telling you more about that one in an upcoming post…

And what about generative AI, will that actor feature in the film? I think you can easily guess the answer to that question. As our latest trends report showed, the rise in generative AI has been the catalyst for a new model that promotes better — and trusted — data. When looking across the 4 Vs of big data, while volume and velocity remain relatively easy to deal with, variety – particularly with the need for more unstructured data and validity – the quality and provenance of data needed for training models, has most certainly not been addressed, and should definitely influence the righting of any path.

Qlik is uniquely positioned to bridge that gap. Our whole purpose is to help our customers create value for their business by solving their greatest data challenges. We can do this across your pipeline – whether it’s in one area, or multiple areas – through AI, data integration and quality, and analytics. We’ll explore this more in this blog series as well.

Your Sliding Doors

Ahead of my next post, I would love to hear about your own “sliding doors” for data and analytics: the paths that you have taken that led to less than desirable outcomes, and the ones that produced great value for your business. And most importantly, the lessons you learned along the way.