Coming to grips with our data problem
Digital transformation initiatives have reshaped the tech landscape. Advancements in robotic process automation (RPA), AI, IoT and machine learning (ML) have contributed to the way organisations do business.
However, the advent of new technologies has also hindered businesses’ ability to digitally transform. While companies rush to innovation, they neglect proper execution and planning. As a result, there’s a disarray of data and a lack of streamlined processes because none of these advancements has been truly ‘operationalised’.
This year business leaders are realising that AI, ML and other advanced technologies are becoming ubiquitous within the enterprise.
Furthermore, leaders are realising that the future of business will be less about the volume of data and more about how data and process act in concert.
The enterprise will be leaner, and it will have to reconcile the fact that people, bots and AI all must work together for the next revolution in efficiency and customer care to succeed.
Fortunately, there are steps organisations can take to weed out inefficiencies. Here are three ways enterprises can better manage data.
1. Prioritise the data that matters to your bottom line
Enterprises must realise that their businesses generally run on less than a dozen core data concepts, such as employee, customer, asset, etc. The problem is that the pieces of content that make up these data points are strewn all over the enterprise.
Start small with the most important pieces of data in the most important apps. That way, your development team can feel confident enough to tackle the biggest data challenges.
Try to achieve early successes that have real impacts to show that the impossible can actually be done. While everyone wants to get to pristine data, you don’t have to get there first.
2. Break down the barriers between applications
Enterprises need to create a flexible data strategy that enables them to build applications that rely on decentralised data. We simply can’t wait for someone to figure out how to centralise all data and make it accessible to multiple applications.
And, even if we did, things are so fluid and dynamic that decisions made today could actually hamper progress tomorrow.
Instead, enterprises should stay away from data strategies that require applications to be in possession of the app or require a data structure that is unchangeable. Let data lie where it is in these disparate legacy apps that are spread across the organisation, and challenge your developers to create ways to knock down the walls and build connections between the apps that allow them to share data.
It’s important for enterprises to think about not just data as a virtual construct, but where it physically exists as well. But be clear about the ownership for each. While your data staff can handle the physical, the applications themselves should be thinking about data virtually.
The key is to not have apps that must process the physical data to be successful. Apps should instead be agnostic to where the data itself lives, and be able to access it from anywhere.
3. People, processes and data coexist on an equal level
Enterprises need to take into account the impact their architecture and structural decisions have on their people, processes and data.
Often, enterprises solve a data problem and end up creating a slew of process problems. Or they solve a process problem but make things worse for users.
As we look ahead, keep in mind that decisions should be made with all three elements in mind. People, processes and data must all operate in sync with one another.
Although enterprises hold AI, ML, IoT and other advanced technologies in their toolkit, they will never be able to reap the benefits as long as data management challenges exist.
In order to accelerate progress, organisations must look to operationalise these advanced technologies. This includes prioritising data and apps with the potential for the most impact, building connections between datasets rather than trying to centralise data, and taking a holistic approach that takes people, processes and data into account.
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