Welcome back to Count Me In. I'm your host, Adam Larson. And today, we have a special guest, Jane Sarra Latt, author of Managing Data Integrity for Finance. Join us as we dive into the crucial concept of data integrity and its essential role in the accounting and finance sector. Jane will break down the differences between data integrity and data quality using easy to understand analogies.

She will also share vital tools and techniques to ensure our financial data remains accurate and dependable. We'll discuss the impact of big data, AI, and the latest technological advancements in financial management. Don't forget to check out Jane's book for more detailed guidance, and connect with her on LinkedIn for continuous updates. Stay tuned for more. Well, Jane, I'm really excited to have you on the Count Me In podcast today as we're gonna talk about different things related to the book you wrote called Managing Data Integrity for Finance.

But I think to start off our conversation, it would be really good to talk about what is data integrity and what does it what does it truly encompass?

I'm very excited to be here, Adam, while speaking with Drew about the book I wrote. And so, you know, Maddie, data integrity and if we compare it with data quality, which is another concept, we could probably compare that with a shipping company. So for example, a shipping company, when you send something to that company, it has to ensure that the the package that you're sending gets to the per the customer, to the other person on the other side in a way that it's, not corrupted, it's sealed properly, and gets there on time complete. So if you compare that with data integrity and data quality, data integrity would be the concepts related to making sure that that data is, gets to the receiver or the consumer in such a way that it's not corrupted and it's complete wherein whereas data quality would be making sure that it there arrives timely as well as valid as well. So that could be something that could be used in order to compare what data integrity is and compare it to data quality?

Mhmm. So we're looking at data integrity. And so, obviously, data integrity is hugely important. We're talking about big data. There's so so many different things happening within the especially the accounting and finance industry, and there's nothing but data because we constantly have reports.

We have, you know, different systems we need to connect to. There's the rise of AI, which we'll get to later. But how important it is for you know, why is this so important, especially for the accounting and finance professional and management accounting professionals today?

Definitely. You actually raised a very good point there, Adam, because when I moved from financial accounting to management accounting more than 7 years ago, I realized that as management accountants, we have a lot of access to the data that we've produced for the reports. And at the same time, we do a lot of reconciliation, reporting, and giving insights to the management analysis that we have done. And so that's why being able to access that data and making sure that the the data that we're working on and the data that we're basing our insights on is actually correct or already, dependable. That's why data integrity is so important.

And it's actually, incredible because, something that I realized is that as management accounting professionals, we actually have a lot of opportunity given the changes in technology at the moment. We're seeing a lot of developments in the space of AI, and at the same time, there's also a lot of tools available in the marketplace that we could use. And there are already tools that we've already that are already there, like Microsoft Excel and Google Sheets that we're using already, and that was something that I also covered in the book.

No. That's that's very important. Being able to understand what tools to use and what different things you need to do. And so maybe you can just help our audience understand why what was the genesis of writing this book, and why did you write it, and what why was it so important to write it right now?

Yep. So I wrote the book because I felt like it was the right time to actually show management accountants and professionals that there's a lot of opportunities in what we're seeing right now. In the World Economic Forum Future of Jobs report, it was mentioned that there will be a lot of jobs displaced the next few years because of the developments in technology. But at the same time, there will also be millions of jobs that will be created because of it. And so I believe I really believe that this is a very good time for management accountants to be able to understand that the changing landscape is actually an opportunity for us given that there's a lot of tools available in the marketplace that will help us in our jobs.

For example, management accountants are usually seen as call centers in the company, not revenue generating. And so what's what's happening is that we need to make sure that we use our time on things that are actually valuable, which leads us, for example, to the concept of automation and why is that important. So with these tools that are available in the marketplace, we can actually make our jobs faster in making sure that the data that we're working on is clean, that we can rely on that data, and then we can get the the proper insights from the reports that we're producing. And so I really believe that now is a very good time to be able to understand what data integrity is, how these tools that are available in the marketplace could be used to make our jobs faster and more efficient, and so that we could be more valuable to the companies that we're working for and then provide proper insights to the management.

AI think it's it's more important than ever that management accountants are upskilling and continuously learning new things so that we can keep up with what's happening in the market. Now I know in the book you share a number of tools and different techniques, and maybe you can give us a little preview of some of those tools and techniques that you address to address data integrity.

Yes. Definitely. So I could probably divide those tools and techniques into the 3 major ones that I've covered in the book. So the first one would be, on the the spreadsheets, the the Google Sheets, and the Microsoft Excel. That will be the first one, which we will discuss further.

And the second one would be the business intelligence tools that are available in the the most commonly used ones that I've covered in 3 chapters in the book. And lastly, the more advanced ones, and particularly the Amazon QLDB, for example, which I presented at the Women's Tech Conference, last month. So going back with the Google Sheets and, Microsoft Excel. So these are tools that we most commonly use as a management account as professionals, particularly Excel. And there are already a lot of built in function functionalities in there that we use, on a day to day basis, like, for example, Power Query wherein which could be used for the ETL processes or the extract, transform, and load, or at the same time, with Google Sheets, like the the sharing functionality wherein you could only provide access to people who really need access to that.

So there's already a lot of that in there, and just diving deeper into what these powerful tools is actually something that, we should really work on because these are, like, foundational for management accounting professionals, and being able to really maximize our abilities into using them would be definitely beneficial for us.

Mhmm.

The second one would be, like, business intelligence tools. We've heard about Power BI, Tableau by Salesforce, or even Alteryx. And so these are what I've covered in the 3 chapters in the book, much more deeply because there's a lot of tools and techniques that are available that we can learn in in order to dive deeper into the data. For example, if cover, data profiling, for example, by just loading information into the Power Query and Power BI, which is slightly different with Microsoft Excel, which is, as I understand, just slightly or but not in a major way. But then by loading that data into the Power Query and Power BI, we can actually do a lot of cleaning, a lot of validation already at that level.

And at the same time, there's also data visualization that we can do in Power BI and Tableau as well. And by using those functionalities, we could actually we could actually see the anomalies or the outliers immediately by using that. The third one would be data lineage, for example, which is available in Power BI service and then at the same time in Tableau Cloud, wherein we could monitor how the data flows throughout the data life cycle and then be able to see what's been done with the data. So with regards to Amazon Quantum Ledger database or QLDB, this is one of the databases produced by AWS. And it's a very unique one given that when you make changes to the transactions in the database, the original transaction is actually not changed.

And so the changes are only append only, and you're able to see how that transaction changed over time. And what that does is that it actually helps you with the data integrity because you're able to see how the transaction is without changing the original one. And this is being used by various companies, which need to have information that is immutable and verifiable given the different kinds of regulations that are affecting the the company or industry. And so overall, these are actually the tools that I've mentioned, Google Sheets, Microsoft Excel, the business intelligence tools, and lastly, the Amazon Quantum Ledger database. These are tools that are available right now that one can use so that to improve the integrity of the data.

Mhmm. No. That's that's that's really huge because your data integrity, if you you know, bat in, bat out kind of thing. So if your data is not integral, as you've made very clear, you want to make sure everything is right. But, you know, with the rise of generative AI, you know, you can't have this conversation about data and data integrity without talking, mentioning generative AI, which you cover in the last section of your book.

How can we use generative AI to kind of improve our data integrity? And, you know, where are you looking? And where is it gonna look as we can as it continues to develop into the future?

Definitely. Machine learning has been here for a long time. And what's really incredible with the rise of generative AI in the previous years is that we're able to upload information, for example, into chat GPT and use the data analysis, GPT in there to analyze the data for us, get insights from the data, and at the same time, update the information there by only typing or providing it prompts in our own language. And so that rise of the NLP or the natural language processing actually makes it easier for us to actually just tell, for example, ChatGPT what to do with the information. The challenge there is that there are times when Chat GPT does not provide us the right answers, and that's called, hallucinations, wherein it could be quite convincing when telling us something back, but then it's actually not referring to a correct or valid information.

So we all what we need to do is to be aware and be prudent that not everything that is being told to us is actually dependable or reliable. So we need to be more prudent and really understand that.

Well, Jane, you know, that was a I think this we've covered a lot of great stuff, and I really encourage our listeners to check out the link to the book in the show notes. But do you have any final message or any final things that you wanna cover about this topic of managing data integrity, especially for the finance and accounting professional? Are there any final things you want our listeners to walk away with?

My message for, the your the listeners, Adam, is that this is actually a very good time for management accounting professionals in order to dive deeper into the tools available into the marketplace. Some of them mentioned earlier during the podcast because, I know that there's a lot of changes happening into the technology space. And what I do believe is that management accounting professionals, given the changing landscape, should take a more active stance into upskilling themselves and learning about these changes. Because, these tools that we've mentioned, for example, in earlier in the podcast, these can actually make us more productive and make us more effective in our jobs. And so taking that active stance into learning about them would actually help us be more productive and then provide more insights into the business.

And, of course, of course, if they would like to buy the book on Amazon, that would definitely be a good start. But, also, I would like to point out that the tools that I've mentioned here, they're also available on YouTube, on the Internet, and even on the websites of, like, the companies that I've mentioned here in the book. They actually have a lot of their documentation on their own websites, and they're for free. And so even if if they don't buy the book, they can actually just go on to Internet and then learn about them. So that's my message.

And I, yeah, I really I'm really excited for this time because whenever there's a change, it's also there's also an equivalent amount of opportunity in there.

Mhmm. There really is. And I really appreciate you coming on the podcast and sharing your knowledge, sharing everything you've learned, especially in writing this book. And we really encourage everybody to check out the topics we covered and, check the links in the show notes and connect with Sarah on LinkedIn. And, Sarah, thanks so much for coming on.

Thank you so much, Adam. I really enjoyed being here on your show.

This has been Count Me In, IMA's podcast providing you with the latest perspectives of thought leaders from the accounting and finance profession. If you like what you heard and you'd like to be counted in for more relevant accounting and finance education, visit IMA's website at www.imanet.org.