3 considerations for AI-ready data

No one wants to miss out on the business benefits AI brings to the table: predictive analytics and forecasts, intelligent scenario planning, natural language chat interfaces, and so much more. PwC found that 73% of US companies have already adopted AI in at least some areas of their business.

But AI solutions are only as good as the data that fuels them. What’s the use of a forecast based on old data, when so much changes in an instant? Yet, traditional data management solutions require hours of manual labor (and often multiple tools) to extract and unify data across sources, including operational sources like ERPs. 

Let’s take a look at how to solve three challenges posed by traditional data management solutions when it comes to fueling AI innovation.

#1: Siloed data

You’d be hard-pressed to find an organization out there with a single source of enterprise data. Most organizations have different operational source systems for each business unit or region they operate in, including those inherited through acquisitions. Beyond your data sources, you may also have multiple data management solutions – from data warehouses to data lakes – that each have their own formats and challenges.  

It’s not the number or variety of data sources that’s the problem – that’s just reality. The issue is bringing all those sources together. If your data isn’t unified, your AI solutions won’t be able to provide a holistic view across your organization, instead delivering answers that are only informed by pieces of the larger whole. And if you aggregate this data to too high of a level, you may miss critical details from the source systems that you need for later analysis.

You need a way to easily bring together data from each of your sources. Better yet, you need a solution that provides both aggregate and transaction-level data, so you and your AI tools can drill down as far as you need to for the most accurate analysis.

#2: Manual effort and time

In respect to aggregation, it takes hundreds of hours of manual labor to move data from one system to another. Your team ends up bogged down extracting data from the various source systems, then shaping, aggregating, and transforming that data so that it can be used by your other business applications. And the more your data is manipulated, the more room there is for human error to creep in. An AI solution using inaccurate data can generate false assumptions that can become costly for your business.

Not to mention that all that effort required means that your AI solutions won’t be getting live data. Instead, data could lag by weeks, sometimes even months, meaning your predictions and forecasts can’t respond in real time as the situations change and new variables enter the picture. Think of a manufacturer who has to quickly respond to an event like a late delivery from a vendor. They need fresh data to decide how best to move forward.

A solution that not only automates data ingestion and integration – freeing up your staff’s time for more value-added work – but also is able to do so in real time ensures your models are running off the latest, most accurate information, without the added burden on your team.

#3: Cost

There’s obviously a cost component to all of those hours of manual labor. But the true cost of integrating all of these data sources using traditional means is even higher. From various data prep tools to pricey consultants, the dollar amount for unifying your data to use in AI solutions can skyrocket.  Every organization has a threshold when resources (dollars and people) can no longer keep up with analytics needs. This often leads to stagnant and piecemeal data supporting your strategic models.

Turning to a complete solution for data ingestion, transformation, and analysis can save you money in the long run, as well as reduce operational overhead. Your internal decision-makers can become empowered to find better answers faster without the need to bring in external consultants or spend unbudgeted time and money, transforming the decision-making process.

Empower AI with live operational data

The scope of these challenges does not have to be overwhelming. AI solutions require a modern approach to working with operational data. Incorta is a complete operational ingestion and integration platform that helps you power AI solutions with live, accurate data while reducing cost and manual effort. Using its proprietary Direct Data Mapping® functionality, Incorta creates a digital twin of your operational data from multiple ERPs and other sources, so your data reflects what is happening in your business in real time. 

By integrating Incorta with Google Cloud solutions like Google BigQuery and Google Cortex Cloud Framework, you can use this live data to make more informed decisions, fuel AI models for more accurate predictions and answers, and save money by reducing manual effort.

Want to learn more about how Incorta and Google Cloud solutions help unify data, while reducing manual effort and saving costs? Read our eBook, “Empower AI with live data,” and check out our integration on the Google Cloud Marketplace