Faster decision-making is becoming a key requirement for businesses to remain competitive in their respective industries. It is vital that businesses gain as much insight as they can from the data that they produce and collect it in a timely manner. This ensures that businesses can identify and capitalize on competitive advantages. What's more, faster decision-making improves operative effectiveness by saving time and money.
Artificial Intelligence (AI) technologies are poised to change the future of how businesses use their data, but the more pressing question that businesses need to answer is how they can start using their data intelligently right now. Too often this issue is entirely overlooked, and CIOs resort to employing multiple outsourced resources to manually do what AI is supposed to do. With extreme consistency, CIOs are shifting AI to the forefront of executives’ decision-making and strategic growth planning in hopes that it will be the solution to all their current data challenges.
The majority of AI applications today are all about decision-based tasks and processes built off data, therefore it would make sense for businesses to pay more attention to what they are doing with that data, how they are using that data for reporting, gathering business insight, and making intelligent decisions. Data Integration Hubs (DIHs) are partly here to solve that gap because they can get data coordinated and centralized.
There is growing optimism amongst information technology leaders and CIOs that the emergence of AI will drastically improve operational processes. But what they see as AI is all too often an algorithm calculating some data out. Which isn't true AI. Take a moment to think about all the different systems a business’s data resides in. Now think about how much time and manual effort would be spent bouncing between these systems; pulling information from each one and bringing it all together so that businesses can use the data to make simple business decisions, this before it for AI applications to ingest.
Businesses first need to collate and centralize data sets if they wish to gain real insights from them. This becomes especially challenging as data grows, because businesses have to pull the relevant information from even more systems.
True AI technology will only realize its promised potential if built upon the foundation of a comprehensive data strategy program for aggregating, integrating, and managing data. That data strategy must be supported by data literacy, data governance, and data management platforms to effectively pull data together, push it out to the right system, and operationalize. By doing this, businesses can begin making intelligent decisions with their data. Without competent data aggregation and integration processes, the journey to making AI a reality could quickly become chaotic, unbalanced, or at worst, fail entirely
It is easy to be seduced by the idea of AI becoming the new “easy” button, but before any business can consider diving headfirst into any AI initiative, businesses need to begin using their data intelligently. This involves preparing data so that it can be operationalized. It is important to understand that there are three main phases of data preparation. Businesses must go through each of these phases in order to unlock the potential of their data.
Businesses are often made up of multiple moving parts and being able to consolidate and aggregate disparate, siloed data is the first step to using data intelligently. When data exists in separate silos and never fully comes together, it holds businesses back considerably, particularly when it comes to an organization’s AI initiatives. Bringing disparate data together so that workflows and unique algorithms can be established builds a solid foundation for AI.
Once disparate data has been consolidated, businesses must integrate data between different applications making the data easier to push out for the right systems to use. By integrating and consolidating data businesses can gain access to full data sets for predictive modeling, reporting, and forecasting.
Businesses that have managed to successfully Integrate and coordinate data between disparate sources are finally ready to begin their AI journey. By using data that has been meticulously prepared, Artificial Intelligence can give organizations a way to extract the most value out of the troves of data they collect, delivering business insights, automating tasks, and advancing system capabilities.
The AI phase is the last step within the data journey and once you reach this stage you begin an exciting new journey. Before businesses can think about venturing into AI they need to move past the first two stages of data preparation.
As businesses continue to produce and leverage exploding volumes of data, the ability to prepare data becomes even more daunting. Data no longer resides solely inside of an organization - it is living in the cloud and across cloud platforms. New data types and formats are adding complexities to the diverse data sets many businesses have to coordinate and make sense of today.
At present, data integration tools seem to be the silver bullet in terms of moving and transporting data from one place to another. But moving and transporting data is the relatively easy part - integrating data is where the real challenge lies. Most business’s technology leaders are under the impression that AI tools will magically integrate data and are left disappointed when AI falls short of meeting expectations. It is for this reason that data integration is becoming a big necessity for the successful implementation of AI. Simply put, the evolution of AI will have to embrace data integration processes to assist with the coordination of data
Data preparation and integration require planning, strategy and patience. For many businesses, the complex nature of any AI-enablement initiative fills them with the FOGS – the Fear of Getting Started. Is this task insurmountable? What’s out there? What are the risks? What plans are in place to deal with the unexpected? It’s not surprising that many businesses fear getting started with their AI projects because the entire process can be daunting.
But it doesn’t have to be.
AI-enablement doesn’t have to be a big bang investment that requires months of work, downtime, and complexity. It can be an iterative approach that takes intelligent steps towards a specific goal. Instead of leaping into a convoluted clockwork maze of moving parts where one broken cog will cause endless backlogs and delays, the best strategy is to adopt an iterative step-wise approach. Businesses trying to accomplish perfection in terms of their AI goals need to begin by doing intelligent things with their data.
The power of true AI is yet to be fully unlocked and there are steps that need to be taken to get there. While businesses wait for this to happen, they can begin their AI journey by taking proactive steps to get the most out of their data and using it intelligently.
Whatever reason you have for considering AI, you can tap into some of its intended benefits by simply putting the data you already have to effective use. All it takes to start is an open-minded attitude and a willingness to embrace iterative change. To learn more about how Synatic can help you navigate your AI journey and unleash the potential of your data, contact Synatic today.
