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AI: Ready to Ride the Wave?

Updated: Sep 22, 2023

IT Strategy Development

It’s taken the world by storm and no industry has been spared. Every company and everybody is now asking themselves how they can leverage AI to improve business outcomes and to improve their lives. I think that the question preceding that one should be; “What’s the problem we’re trying to solve?” - but that will be the subject of another blog post.

So how can we leverage AI to improve business outcomes? My simple answer is you can’t until you have a solid understanding and control over your data. If you think you do, you can stop reading here but if you have a doubt, read on!

Generative AI or any time of AI needs data. It needs as much data as you possibly can give it (really?) and it needs “clean” data.

The datavore’s dilemma

As stated by Maor Shlomo in his Forbes article, businesses have a similar problem as us individuals when we go shopping - too much choice. It’s important to feed AI with as much data as we can but it is just as important that we think about which data to feed it with.

According to Maor Shlomo, companies that utilise inappropriate data can encounter a phenomenon known as model drift. This situation arises when a previously reliable model, with a certain prediction accuracy begins to decline unexpectedly. This is an indication that there's a need to "consume" a different set of data, metaphorically speaking. The pandemic is a good example. Overnight, some companies had to adjust their business model to sell their products or services to another market, requiring a different set of data.

In other words, companies need to regularly look at the data they consume and make sure it is still relevant. Unfortunately, this is not something you’ll resolve “once and for all”. It will require you to put in place a process to continuously review, enhance and fine-tune the data you ingest.

Data Governance

In order to help you identify and fix current data problems as well as seize future opportunities, putting in place a governance around data is a must. Data governance is like a set of rules and plans that guide how a company's data should be handled, who should handle it, and how it should be used, to ensure it's accurate, secure, and helps the company achieve its goals. Ideally, it should involve the IT team but also executives and representatives from various business operations within the organisation. Data governance is crucial for several reasons:

  1. Data Consistency and Accuracy: Without effective data governance, inconsistencies may occur in different systems across an organization. For instance, customer names may be listed differently in sales, logistics, and customer service systems. This could complicate data integration efforts and create data integrity issues that affect the accuracy of business intelligence and analytics applications.

  2. Regulatory Compliance: Poor data governance can also hamper regulatory compliance initiatives. Companies need to comply with an increasing number of data privacy and protection laws. An enterprise data governance program typically includes the development of common data definitions and standard data formats that are applied in all business systems, boosting data consistency for both business and compliance uses.

  3. Breaking Down Data Silos: A key goal of data governance is to break down data silos in an organization. Data governance aims to harmonize the data in those systems through a collaborative process, with stakeholders from the various business units participating.

  4. Improved Business Decision-Making: Data governance can help improve business decision-making by giving executives better information. Ideally, that will lead to competitive advantages and increased revenue and profits.

  5. Data Security and Privacy: Another data governance goal is to ensure that data is used properly, both to avoid introducing data errors into systems and to block potential misuse of personal data about customers and other sensitive information (Page 6).

Data Completeness

We have already touched on data consistency and accuracy but one aspect that is not often not mentioned is data completeness and even if it may sound obvious let me give you an example. Often companies will create an item into their ERP and won’t input the dimensions, weight or material the product is made out of. The same would apply when creating a customer or client into your CRM or ERP. Obviously, if the information is not in the system, AI won’t be able to do much! So before creating entries in your systems, make sure that you have reflected on what information should be captured and in what field should this information be inputted.

Harnessing the full potential of AI

Data is integral in boosting the precision of generative AI models. Access to expansive and superior-quality datasets empowers these models to assimilate complex details, leading to more accurate and lifelike results. Additionally, regular interaction with current and pertinent data permits the models to evolve and augment their capabilities over time, honing their potential to produce content that matches human anticipations. If you have a data governance in place, you have a set of rules and standards for inputting data into your various systems and you carefully select the relevant data sources, you’re ready to harness the full potential of AI.

References & Interesting Read:

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