Jamming with Jason E134: Data Analytics is NOT About Exception Reporting with Nathan Pickard

What are some of the biggest challenges when #internalaudit starts incorporating #dataanalytics? How can thinking of data analytics as a process instead of a part of a project make a HUGE difference in your success?

These questions and more are answered in my discussion with Nathan Pickard, CEO of 9b. This #jammingwithjason episode will give you a whole new way to look at how to “do” data analytics in your organization that are repeatable and aid in continuous risk assessment, agile projects, and reporting … making your job much, much easier.

Learn more about Nathan and 9b at: https://www.9bcorp.com/

Transcript

1
00:00:01.439 –> 00:00:19.740
Jason Mefford: Hey everybody, I am back in talking with Nathan Picard today from nine b. And we’ve got some I met him a few months ago and we just kind of, you know, touch base back and forth has a really cool product that they’re working on that they’re that they’re rolling out here.

2
00:00:21.090 –> 00:00:31.230
Jason Mefford: That’s very relevant to what most of you are doing. And I think some of the pain points. Many of you are feeling as you’re trying to transform

3
00:00:31.680 –> 00:00:40.530
Jason Mefford: Your internal audit department. So I’m not going to tell you too much more now. But we’re going to get into it because you gotta listen to the whole episode so Nathan, welcome.

4
00:00:40.890 –> 00:00:41.760
Jason Mefford: How you doing today.

5
00:00:42.420 –> 00:00:43.080
Nathan Pickard: Doing well.

6
00:00:43.770 –> 00:00:46.170
Jason Mefford: It’s, uh, yeah. Like I said, it’s, it’s

7
00:00:46.590 –> 00:00:51.720
Jason Mefford: It’s exciting when we talked a little, a little while ago about what you guys are doing. It’s like

8
00:00:53.010 –> 00:00:55.680
Jason Mefford: Kind of like a little Mind Blow. For me, because it’s like

9
00:00:56.280 –> 00:01:11.520
Jason Mefford: Oh man, I can see how this can really, really help lots of people. Right. So, so maybe just jump into the first just kind of explain a little bit about you know what you do what your company does, and then we’ll, we’ll dig in. Because I think like is like I said at the beginning.

10
00:01:12.570 –> 00:01:27.210
Jason Mefford: There’s certain pain points, people are feeling and they’re like, ah, how am I going to get through this. Well guess what, listen to this episode because you’re going to find out. You’re going to get some answers today as actually some easy ways for you to get past some of this stuff.

11
00:01:28.830 –> 00:01:47.610
Nathan Pickard: Yeah, I’m happy to share. So nine we were all about data analytics and was specifically with a focus on internal audit analytics and I have about 15 years experience doing data analytics started out with the City of Tulsa and

12
00:01:48.810 –> 00:01:53.580
Nathan Pickard: Have a certified internal auditor certified information systems out of their designations, but

13
00:01:54.540 –> 00:02:01.230
Nathan Pickard: Really just over the years, you know, went to all the classes all the training on Continuous Auditing

14
00:02:01.800 –> 00:02:12.630
Nathan Pickard: Always got super excited and never really knew how to put to pull it off with all the other duties and responsibilities of being an employee and not a department and

15
00:02:13.470 –> 00:02:28.110
Nathan Pickard: Then I went over to Williams, which is an energy company here in Tulsa and they wanted me to help start a data analytics function in their audit department. And we had a lot more resources there to do amazing things.

16
00:02:29.370 –> 00:02:42.480
Nathan Pickard: Learned a lot had a had a team of data analysts and still we got stuck in this phase of like helping our auditors with their audits and building out great.

17
00:02:43.620 –> 00:02:49.920
Nathan Pickard: Great data analytics being excited about the coding, we did about the data discovery.

18
00:02:50.400 –> 00:03:07.200
Nathan Pickard: But then once that audit was done, we never saw our data, all that work that we did that we put into the coding, we never really got to see it come around again. So just still had that dream of the idea of continuous audit continuous risk assessments.

19
00:03:08.790 –> 00:03:17.190
Nathan Pickard: And it wasn’t until I left Williams and I had some requests from different companies to do consulting that I really had this chance

20
00:03:17.790 –> 00:03:34.200
Nathan Pickard: To say, hey, what if we approach this as building software. And so I got the head of agile here in Tulsa to be my scrum master and we just like brainstorm with thousands of post it notes. As you can see behind me.

21
00:03:36.150 –> 00:03:39.420
Nathan Pickard: How to actually do do

22
00:03:40.440 –> 00:03:54.660
Nathan Pickard: Continuous Auditing from a software perspective, though, so we do when we take this agile approach where we spend for every module we build out we spend two weeks doing data discovery and planning.

23
00:03:55.800 –> 00:04:04.200
Nathan Pickard: Where we just go in. We learn all of the tables that are important for for a process like AP and

24
00:04:04.980 –> 00:04:13.110
Nathan Pickard: How they relate to each other. We build out an entity relationship diagram that shows how everything is working from like a data flow perspective.

25
00:04:14.010 –> 00:04:27.150
Nathan Pickard: And then we all at the same time me as the internal auditor. So I’ve got, I’ve got a guy that’s just great at programming that’s that’s digging into the database and then is as more of an internal auditor background.

26
00:04:28.920 –> 00:04:47.520
Nathan Pickard: Using the prospectors and terminal analytics are important and working with the developer who’s, who’s in the data. And I’m saying, hey, can you find this. Can you find this. So I’d like to do this kind of analytic and so we build out a backlog.

27
00:04:48.540 –> 00:04:57.720
Nathan Pickard: Kind of in this agile Scrum backlog of analytics during the planning phase and then we spend a two week sprint where we just build out those analytics.

28
00:04:58.110 –> 00:05:11.490
Nathan Pickard: Sometimes we have 100 analytics that we want to build out in two weeks. And so we have to prioritize and we create user stories based on what the user of this analytic is going to need. And all of that is very

29
00:05:12.450 –> 00:05:31.440
Nathan Pickard: According to the Scrum method. And so a lot of times we’ll end up with 30 to 50 analytics that we built out over two weeks because we got all the data just perfect for us to build out analytics and then we do one more sprint where we visualize it and so that we use tablo for the joy.

30
00:05:32.610 –> 00:05:50.190
Nathan Pickard: And every analytic it’s built in this software coding methodology to where it’ll work together with every other analytic. So what we end up with over over building the software is this beautiful like one page report.

31
00:05:51.240 –> 00:05:59.700
Nathan Pickard: And then one page report shows you over the quarter or the month, whatever, whatever kind of timeframe one

32
00:06:00.300 –> 00:06:20.490
Nathan Pickard: What has happened across your organization. So across AP across your purchasing card. Your GL we’ve done. I think we’ve done around eight to 10 modules now and and it really just shows you just like a real time risk assessment of what’s going up what’s going down.

33
00:06:21.780 –> 00:06:32.910
Nathan Pickard: And then one when you choose one that’s gone up, let’s say in tablo. It will then tell you where to do your next agile audits and

34
00:06:33.450 –> 00:06:43.470
Nathan Pickard: And it gets specific it says like this process step is where maybe 60% of the increased risk has occurred. And so that’s where you should do your agile.

35
00:06:43.890 –> 00:06:53.130
Nathan Pickard: And and then one of the things that we weren’t planning on was just how effective this is for the management side as well so

36
00:06:53.640 –> 00:07:01.230
Nathan Pickard: Another whole piece is how you do training of the process performers and so that’s for management to work on.

37
00:07:01.920 –> 00:07:14.010
Nathan Pickard: And so the tool also says like these four people cause 80% of the risk. And so, and here’s, here’s the areas where they need to be trained the exact problems they’re having

38
00:07:14.370 –> 00:07:20.850
Nathan Pickard: And so you can go in and train them and then the most awesome part is you get to see all the results.

39
00:07:21.240 –> 00:07:35.820
Nathan Pickard: Of your work. So you did your agile audit on this process step and next month. You can see how the risks performed that they actually go down and you can really start using your retrospective time and your agile.

40
00:07:36.210 –> 00:07:47.820
Nathan Pickard: Process to say, okay, what happened with it with these risks that they go down. How can we change our process of agile to do better in that thing. I don’t know, those, those are big. No, sorry.

41
00:07:49.710 –> 00:07:53.610
Jason Mefford: I missed. It’s so what I’ll try to do too, because I know you know you’re

42
00:07:54.780 –> 00:08:03.060
Jason Mefford: You’re much on the technical side too. And I’ve been doing, you know, software development coding for a long time. So even though you’ve got the audit background, you’re still kind of a coder.

43
00:08:03.330 –> 00:08:05.850
Jason Mefford: So if I need to have to. I’ll try to

44
00:08:06.960 –> 00:08:08.100
Jason Mefford: Translate, a little bit.

45
00:08:09.120 –> 00:08:16.170
Jason Mefford: Harsh for people that aren’t. But, you know, a couple things that you said in there where I’d like to dig in a little bit deeper because

46
00:08:16.470 –> 00:08:25.170
Jason Mefford: You know, we’ve been talking about data analytics for a long, long time. I mean, I remember going back into the 90s.

47
00:08:25.470 –> 00:08:46.740
Jason Mefford: Yeah, we were doing stuff and and but it’s just never really got the traction. And one of the things that you said that I think is, is one of the reasons. One of the mistakes that most people are making is they’re treating data analytics as part of a project.

48
00:08:47.910 –> 00:09:02.220
Jason Mefford: Instead of building it into the overall process, right, so back again when you were at Tulsa Williams and you’re like, Hey, you know, we did this great work. And then the projects over and we never use it again.

49
00:09:02.850 –> 00:09:10.200
Jason Mefford: Well, you’re not really leveraging all of that work right. You’re thinking of it as a discrete project.

50
00:09:10.710 –> 00:09:20.100
Jason Mefford: Here and then never actually using it again. So you’re never getting the efficiencies out of it. You’re always trying to relearn redo

51
00:09:20.610 –> 00:09:26.610
Jason Mefford: All of these things where the beauty, you know, if you kind of stepping back and now helping companies with this is

52
00:09:26.970 –> 00:09:42.390
Jason Mefford: Look, don’t just do it once you know you build it, you build it into the process and now it actually helps you in doing all of these other things. Right. So don’t just build it, put it on the shelf and then never go back to it.

53
00:09:42.690 –> 00:09:59.820
Nathan Pickard: Yeah, that hit me so hard recently when I was talking to Williams. They have a new chief audit executive, I was talking to her, and she didn’t even know what we had done in the past. And she said, we’re starting at ground zero with data analytics and it just killed me because I was like

54
00:10:00.240 –> 00:10:02.160
Jason Mefford: I did all that work and you’re not using

55
00:10:03.120 –> 00:10:17.730
Nathan Pickard: It, you said way over a million dollars on three data analysts that it’s so much work and it’s all nobody even knows where it is now. You know, because we all left and and yeah no continuous part about it at all.

56
00:10:19.740 –> 00:10:33.120
Jason Mefford: Well, and the, and the other thing, like, you know, you were talking about how you can use the different analytics to work with each other as well. And I think, you know, the example that you gave is

57
00:10:34.170 –> 00:10:41.280
Jason Mefford: I think what a lot of people have been what we should be doing, but they’re not realizing that the information is there.

58
00:10:41.760 –> 00:10:52.650
Jason Mefford: Right, like so. You said, hey, the risk went up in this particular area. Well, why did it go up. Well, there’s these four individuals and what they’re doing, you know,

59
00:10:53.160 –> 00:11:03.180
Jason Mefford: Prayed up principle folks we’ve been talking about this forever to right focus on the 20% that causes the 80% impact. Well, now you can actually visualize and see that

60
00:11:03.660 –> 00:11:07.860
Jason Mefford: With some of the analytics. And so we can be much more efficient.

61
00:11:08.400 –> 00:11:26.670
Jason Mefford: In what we’re doing right and that it lines into, you know, as we were talking before we even got started here. You know, the three things. A lot of people are working on continuous risk assessment agile and data analytics. Well, they can all and should all be going together right

62
00:11:27.570 –> 00:11:33.390
Jason Mefford: And and and using them as a process instead of thinking about everything, just as an

63
00:11:33.390 –> 00:11:44.340
Jason Mefford: Individual project. Yeah. Yeah. And I think it seems like that’s a big mindset that audit still hasn’t gotten past

64
00:11:44.760 –> 00:11:50.790
Jason Mefford: And again, like you said, it doesn’t matter whether it’s a small company, whether it’s a big company. I mean, Williams spent a million bucks.

65
00:11:51.240 –> 00:12:00.930
Jason Mefford: On a couple of of analytics and then they’re not using them right i mean you should be continuing to use these things, right. So, so how are you kind of helping

66
00:12:01.620 –> 00:12:09.510
Jason Mefford: companies do this now because I got to imagine there’s a lot of people that are listening that are like, yep, been there. We’ve done this, where, you know, we

67
00:12:09.870 –> 00:12:21.390
Jason Mefford: We haven’t used it. We don’t know where it’s at. People have laughed. We don’t know what they did, right, because there’s a lot of intellectual capital that walks out of the door each year.

68
00:12:22.380 –> 00:12:28.560
Jason Mefford: So how have you kind of helped companies overcome some of these challenges that most every internal audit department has

69
00:12:29.760 –> 00:12:40.620
Nathan Pickard: Yeah, I think that’s where we were trying to come up with a solution when we were in the company’s of how to pull this off and it’s always like

70
00:12:41.070 –> 00:12:56.280
Nathan Pickard: Can you just give us like 50% of our time to work on a continuous on a continuous audit piece and and it just never worked because we did good work and we always had so much demand from the auditors.

71
00:12:56.970 –> 00:13:00.690
Nathan Pickard: For the projects they were doing back then and

72
00:13:01.590 –> 00:13:10.530
Nathan Pickard: And so we never got some really focus. And I think that’s where, that’s where starting a company where where we get contracts to do this and

73
00:13:10.800 –> 00:13:29.430
Nathan Pickard: And we can actually spend the time to develop it out as a software has been has been the answer that I’ve been asking like, you know, just going to all these conferences and they tell you the theory of continuous things like I want to see it actually in real life work, you know, and

74
00:13:30.480 –> 00:13:38.850
Nathan Pickard: So, so I don’t know I think another piece is when you when you put an analyst in a group of internal auditors.

75
00:13:39.990 –> 00:13:47.100
Nathan Pickard: They are very different type of person and and so for for me and the other analysts that I’ve worked with

76
00:13:48.120 –> 00:13:52.770
Nathan Pickard: We’ve always felt very stifled by the environment where whereas we want to be.

77
00:13:54.180 –> 00:14:05.850
Nathan Pickard: I think we’re a lot more creative. I was an art major in college, before I moved into accounting and just a lot a lot more creative and really wanting that like back and forth with other creative individuals.

78
00:14:07.170 –> 00:14:20.880
Nathan Pickard: I think is probably why you see analysts kind of not staying very long in it with a, with an audit group or or just ending up losing a lot of steam from when they start and and

79
00:14:22.230 –> 00:14:28.260
Nathan Pickard: So I think for us it’s been creating a company that’s all about the creative side of

80
00:14:32.160 –> 00:14:38.820
Nathan Pickard: This idea of data analytics in an amazing way. I don’t know. I think that’s the part of the answer that we found.

81
00:14:39.120 –> 00:14:41.910
Jason Mefford: Well, it’s interesting, you know, as you were talking about.

82
00:14:41.970 –> 00:14:45.210
Jason Mefford: Kind of, from your perspective, right, because you have the audit background.

83
00:14:45.960 –> 00:14:55.950
Jason Mefford: Your artistic and creative in nature. And I think sometimes we we kind of forget that because you know coders analysts, they’re really kind of creating art.

84
00:14:56.700 –> 00:15:14.760
Jason Mefford: But it’s it’s numerical heart. And in fact, you know, for those of you that know a lot about art. It’s all about mass at the end of the day, right, to proportions Fibonacci numbers, all kinds of stuff. Okay, but we we don’t have time. On this episode to get into this, but it’s it’s it’s amazing.

85
00:15:14.760 –> 00:15:18.330
Jason Mefford: And I think one of the things that you brought up is probably again.

86
00:15:18.810 –> 00:15:26.760
Jason Mefford: One reason why so many people are struggling with this is they’re bringing in people that need to have a certain skill set.

87
00:15:27.570 –> 00:15:45.000
Jason Mefford: To do the work to do the work, the right way, but they don’t really mesh with the culture that we built an internal audit and so it’s almost like you know you’re the redheaded stepchild or the bastard child. And it’s kind of like people just tell you, well,

88
00:15:45.270 –> 00:15:48.210
Jason Mefford: I need an analytic to do this on this project.

89
00:15:48.270 –> 00:16:05.010
Jason Mefford: Go build it for me. Right. And so you go off, you do a discrete project and you’re over there on the side going yeah but but but but but we could do all these other things right and and it kind of and this is one of the reasons why I love what you guys are doing with your software is

90
00:16:06.210 –> 00:16:11.130
Jason Mefford: Once something has been built. You don’t need to go reinvent the wheel.

91
00:16:11.430 –> 00:16:17.040
Jason Mefford: Yeah, but everybody is trying to reinvent the wheel.

92
00:16:17.610 –> 00:16:20.280
Jason Mefford: And it’s just it’s a huge sock.

93
00:16:20.760 –> 00:16:26.850
Jason Mefford: Of time and energy. Right. It’s like, you know, one of the clients that I have. They’ve got a great software product.

94
00:16:27.390 –> 00:16:40.920
Jason Mefford: And it’s the same thing. It’s like, you know, for 10,000 a year, you can like get all of the knowledge that this company has amassed for 20 years and save yourself hundreds or thousands of hours worth of time.

95
00:16:41.040 –> 00:16:41.340
Nathan Pickard: Right.

96
00:16:42.000 –> 00:16:48.420
Jason Mefford: But there’s still a lot of people that are like no damn it. I’ve got to do it myself. I’ve got a map everything myself and it’s like

97
00:16:49.290 –> 00:16:57.300
Jason Mefford: Why, if somebody already kind of invented it or figured it out. Why don’t you just latch on to it.

98
00:16:57.780 –> 00:17:11.190
Jason Mefford: And I think, you know, again, as we’ve talked a little bit. We haven’t shared much here, but what you guys are doing with your software is effectively. Hey, we’ve already developed a lot of these analytics already. All you gotta do is plug it into your data.

99
00:17:11.790 –> 00:17:15.930
Jason Mefford: Right because somebody already built it in. It’s kind of like a plug and play.

100
00:17:17.160 –> 00:17:18.570
Jason Mefford: Kind of thing that you can do.

101
00:17:19.230 –> 00:17:27.690
Nathan Pickard: Yeah, that’s what that’s what we love is it’s the way we know that it’s very object oriented, so like you’ve got your data layer.

102
00:17:28.140 –> 00:17:37.020
Nathan Pickard: That that you have to do your data discovery, figure out what tables are interacting what falls and get you to those final data points and we

103
00:17:37.500 –> 00:17:51.780
Nathan Pickard: We call that our prep table. And so when our prep tables done, then the next layer is all these analytics that the party built and they can just use that PrEP. And so, yeah, it is. It’s very much a plug and play.

104
00:17:52.800 –> 00:18:01.530
Nathan Pickard: And within. I mean, we get excited about all the ways to make it even more customized where where we have a survey that goes out and says, What are your

105
00:18:01.920 –> 00:18:12.750
Nathan Pickard: P card limit and when they answer that number, then that goes and plugs into our scripts that looks for when they do a split transaction that goes over there. P. Prior to that,

106
00:18:14.370 –> 00:18:18.000
Jason Mefford: Okay, so you’ve actually built it in so that even though the scripts are kind of

107
00:18:19.650 –> 00:18:20.730
Jason Mefford: Prepared pre built

108
00:18:20.820 –> 00:18:24.810
Jason Mefford: Like you said, they, they put in their limit it customize is

109
00:18:25.680 –> 00:18:30.750
Jason Mefford: You know 95% of it is already built. You just need to know what the limits are.

110
00:18:31.110 –> 00:18:35.820
Jason Mefford: So that then the analytic can run based off of the set parameter that you have

111
00:18:35.910 –> 00:18:43.740
Jason Mefford: Right. Beautiful. Instead of having to rewrite the whole script. All you gotta do is tell them hey it’s $5,000. Okay. Sweet $5,000

112
00:18:43.770 –> 00:18:48.600
Jason Mefford: Now we know where all your data is so we know where to pull the information is because, again, there’s

113
00:18:49.710 –> 00:19:06.360
Jason Mefford: It’s, it’s not just as simple as saying here, click, click, click. Right. You still have to select the data source that you’re coming from, but once that work is done then a lot of these analytics that you’ve already built can start functioning operationally for companies as

114
00:19:06.360 –> 00:19:06.600
Jason Mefford: Well, I

115
00:19:06.870 –> 00:19:09.840
Jason Mefford: Guess, and that’s that would save people hundreds of hours.

116
00:19:11.160 –> 00:19:28.320
Nathan Pickard: And the other thing that we’re excited about is really working with all the new cloud earpiece systems because they have a lot less customization on and then the server based ones. And so we get excited when someone comes to us and says we want it for this cloud system because we know

117
00:19:29.460 –> 00:19:31.140
Nathan Pickard: Once we’ve been

118
00:19:32.250 –> 00:19:39.420
Nathan Pickard: Do that discovery. It’s kind of a one and done thing and we can apply it to any other customer of that cloud era pieces.

119
00:19:40.440 –> 00:19:41.610
Nathan Pickard: So we get excited about that.

120
00:19:43.500 –> 00:19:44.760
Jason Mefford: That’s interesting because I never even

121
00:19:44.760 –> 00:19:49.080
Jason Mefford: Thought about that too. But yeah, there’s a software companies have moved more to their cloud.

122
00:19:49.560 –> 00:19:59.520
Jason Mefford: Instances there’s less customization by the by the end user. And so because of that it actually makes the data analytic side of it easier

123
00:20:00.180 –> 00:20:03.180
Jason Mefford: Because you’re not trying to work around a bunch of customization.

124
00:20:04.410 –> 00:20:06.750
Jason Mefford: Wow, I never thought about it that way.

125
00:20:09.420 –> 00:20:21.720
Nathan Pickard: when when when we get a request for a new cloud one or like this. This we can take and and all these other people will get the benefit of the time spend on this data discovery and

126
00:20:22.140 –> 00:20:29.700
Nathan Pickard: And that’s, that’s a big part of our process is is we want every sprint to have really good products on there.

127
00:20:30.480 –> 00:20:49.950
Nathan Pickard: And even the planning sprint, you get like a flowchart of your process that’s that’s helpful and you get in here d of all the tables that are used and how they interact in and you can use that for for one off analytics, if you want. Now you know how those tables work.

128
00:20:51.030 –> 00:20:56.910
Nathan Pickard: On one cloud system we had, we have a data dictionary of 9000 pages and so

129
00:20:58.620 –> 00:21:01.380
Nathan Pickard: And it’s and it’s a PDF. So it’s like a good

130
00:21:02.640 –> 00:21:21.900
Nathan Pickard: Thing word by word, but but that’s that’s the time that you don’t want someone else to have to spend pay for, like, once we do once our system, then the 6000 customers that use that that file system that we’re doing it for can really reap the benefits of that so

131
00:21:23.190 –> 00:21:35.400
Jason Mefford: Well, and it leads me to another challenge that I think a lot of people have is, is, you know, everybody knows they should be doing more with this. So they want to hire people

132
00:21:36.000 –> 00:21:53.310
Jason Mefford: But they can’t find people to hire right and so I might get the numbers a little bit wrong, but the numbers that I was kind of told right is in the industry, there’s only about 10% of the auditors that also have data analytic skills.

133
00:21:53.580 –> 00:21:56.370
Jason Mefford: Right so 10% not very many auditors right

134
00:21:56.790 –> 00:22:11.550
Jason Mefford: So here’s a little career hint for everybody, you know, if you’re interested. There’s only 10% so learn some more about data analytics right if you don’t know where to go. Then there’s a whole bunch of courses on C risk Academy, by the way, it’s pretty easy to just go

135
00:22:11.880 –> 00:22:16.500
Jason Mefford: Click and start learning right at least getting some of the basics down

136
00:22:17.190 –> 00:22:23.880
Jason Mefford: But so, so there’s a huge you know there’s there’s only about 10% of the people that have these skills already

137
00:22:24.570 –> 00:22:30.720
Jason Mefford: But I’ve heard that the job postings that are requiring both skills is about 30%

138
00:22:31.650 –> 00:22:41.430
Jason Mefford: Which means there’s a 20% gap so 20% of those jobs are never going to be filled. Because there’s nobody with those skill sets.

139
00:22:42.090 –> 00:22:51.300
Jason Mefford: Right, so either. We’ve got to get individuals to scale up learn how to do it. So then they can be available for those jobs.

140
00:22:51.750 –> 00:22:58.860
Jason Mefford: Or the other option is you bring somebody in right which is what a lot of people do, they’ll hire a consulting company that comes in.

141
00:22:59.610 –> 00:23:13.950
Jason Mefford: They do analytics on a project basis and then the person leaves. Well, next time you want to do them. What do you have to do. You gotta hire that person again right with your solution. You don’t have that I think right

142
00:23:14.610 –> 00:23:30.600
Nathan Pickard: Yeah, that’s a big goal we see it as more of a SAS tool, a software as a service where where you pay an annual fee and then we make sure that they run every week, every how often you want

143
00:23:32.070 –> 00:23:40.170
Nathan Pickard: These analytics run every week, free to software. So we do bug fixes. If there’s bugs and everyone gets the benefit those, but we also do

144
00:23:40.530 –> 00:23:49.680
Nathan Pickard: A you know every time someone has a nother analytic idea or we have another idea that can be applied to everybody that is that it helps. And so

145
00:23:49.980 –> 00:24:05.700
Nathan Pickard: Yeah, we see it, we see it as a different model that I think really speaks to that problem of, you know, there’s just not enough analysts to go around. So if we can help 100 companies with five inches. I mean that’s that’s a big

146
00:24:07.050 –> 00:24:17.490
Nathan Pickard: That really helps go that gap, I think. And, and we hope that it is way more effective than than trying to hire your own person or train someone

147
00:24:18.840 –> 00:24:22.230
Nathan Pickard: When, when it says start from nothing.

148
00:24:24.240 –> 00:24:28.740
Jason Mefford: Well in because, you know, again, if you just, I’m going to throw out some basic

149
00:24:28.740 –> 00:24:31.110
Jason Mefford: Numbers right and and

150
00:24:33.210 –> 00:24:41.550
Jason Mefford: You know, if you were to hire somebody bring somebody in as a data analyst, I’m guessing you’re probably going to spend at least 100 grand a year for that person. At least here in the US.

151
00:24:41.640 –> 00:24:43.080
Jason Mefford: If not more probably right.

152
00:24:44.370 –> 00:24:53.580
Jason Mefford: But you’re probably looking at a minimum hundred grand for a salary to bring somebody and then you have the one person you’ve still got the problem of if the person

153
00:24:54.030 –> 00:25:04.350
Jason Mefford: You know, because of the culture or you know whatever after six months decides to leave. Well now you know you lose all that knowledge plus. Now you got to go hire somebody else new again. Right.

154
00:25:05.160 –> 00:25:17.220
Jason Mefford: You can you can bring in, you know, a lot of them are the big firms, you can bring in somebody to do a consulting project. And again, I’m guessing minimums probably 50 ish

155
00:25:18.120 –> 00:25:28.980
Jason Mefford: To, you know, like you said, with Williams. I mean, they probably spent a million bucks on on some of these analytics. Right. And then again, if the firm leaves.

156
00:25:29.910 –> 00:25:46.920
Jason Mefford: If the people at the firm leave who did it again. You’ve got these beautiful analytics that you’ve spent hundreds of thousands of dollars on that are not working for you even still right versus actually having them built into a software.

157
00:25:48.180 –> 00:26:01.470
Jason Mefford: In, and this is why again. I mean, I, I’ve said this a couple of times, but I love the model, you guys are using to because it’s it’s it goes along with how businesses are run now.

158
00:26:02.910 –> 00:26:06.900
Jason Mefford: The whole idea that whatever your competitive advantages.

159
00:26:08.220 –> 00:26:12.780
Jason Mefford: You know the unique thing that the company does, that’s what you have in house.

160
00:26:13.380 –> 00:26:13.890
Nathan Pickard: Mm hmm.

161
00:26:13.950 –> 00:26:24.840
Jason Mefford: Everything else, it’s possible you outsource to somebody who can do it better and quicker and faster and cheaper than you do. Right. And so a lot of these

162
00:26:25.440 –> 00:26:33.990
Jason Mefford: back office administrative functions, even in very large companies are not actually in house right

163
00:26:34.620 –> 00:26:43.680
Jason Mefford: It is one of those things. It’s outsourced to other companies accounting is actually even outsourced in some huge companies, right.

164
00:26:44.280 –> 00:26:52.980
Jason Mefford: Because there’s a lot of that stuff in the back and people probably like glossed over what you just said about

165
00:26:53.430 –> 00:27:05.940
Jason Mefford: bug fixing patches, you know, making sure that the analytics are running all the time that you guys are actually doing in the background that the internal audit department doesn’t have to be doing.

166
00:27:06.480 –> 00:27:24.420
Jason Mefford: Right, and it’s not in their core, you know, skill set, either to do that. And so instead of having to build a whole department with maybe multiple people in it, you can outsource most of the heavy lifting on the back end, and then just focus on

167
00:27:24.420 –> 00:27:26.730
Jason Mefford: Hey, what do I want the analytics to do

168
00:27:27.540 –> 00:27:30.960
Jason Mefford: You know, based on a particular project or the process that you’re actually

169
00:27:31.980 –> 00:27:34.260
Jason Mefford: Working on so you know

170
00:27:35.370 –> 00:27:40.470
Jason Mefford: It’s just a great model. And like I said, it goes along with what how businesses are actually working now.

171
00:27:40.920 –> 00:27:42.240
Jason Mefford: Yeah, we

172
00:27:42.510 –> 00:27:43.890
Jason Mefford: We need to start thinking that way too.

173
00:27:45.630 –> 00:27:55.380
Nathan Pickard: Yeah, I’ve become a big believer in that just from starting a company seeing like all of these SAS products that we use now as a small company.

174
00:27:55.920 –> 00:28:09.600
Nathan Pickard: Because those are the skill sets we have and we want someone that does have that skill set to be running that piece for us and we love. We love it and even hiring this Scrum master to teach us how to do the software.

175
00:28:10.410 –> 00:28:19.740
Nathan Pickard: Development piece was just one of those things where it’s like, wow, I mean, we could have tried to, you know, I read all these agile auditing.

176
00:28:20.250 –> 00:28:31.680
Nathan Pickard: Things and went to the seminars and things that I still couldn’t figure out how to do it and then hiring someone who really lives and breathes agile Scrum.

177
00:28:32.250 –> 00:28:43.230
Nathan Pickard: And bringing him in. It was just mind blowing. The difference in. So anyway, yeah. I mean, you’d believe it or not, bring in the experts expert away your expert.

178
00:28:44.220 –> 00:28:49.800
Jason Mefford: Well, and don’t don’t waste your time trying to figure it out. When somebody else has already figured it out.

179
00:28:50.550 –> 00:28:53.970
Jason Mefford: Right. I mean, again, so just like you were saying there with Agile right

180
00:28:54.570 –> 00:29:01.530
Jason Mefford: And you can take any, any of these different topics. Right. You read the books you went to seminars or two different trainings and stuff.

181
00:29:02.010 –> 00:29:15.180
Jason Mefford: But you still couldn’t figure out how to click it all together and make it work for internal audit. Right. Well, as an example, one of the guys we just actually came up with a new course on see risk about this took the guy three years.

182
00:29:16.320 –> 00:29:17.190
Jason Mefford: To figure it out.

183
00:29:17.820 –> 00:29:23.190
Jason Mefford: To actually make it work, so that it actually practically works and people can actually do it right.

184
00:29:23.670 –> 00:29:39.600
Jason Mefford: So, you know, just like with what you guys are doing. You know, it’s like, do you really need to spend three years trying to figure it out, or do you just leverage what somebody else has already figured out. Right. And again, I know some people like to make things hard

185
00:29:41.520 –> 00:29:55.320
Jason Mefford: I prefer to have things easy and bring in the people that I need to be able to help with that. Because like you said, I do the same thing in my businesses we outsource a lot of things that yes, we could do internally.

186
00:29:55.800 –> 00:30:13.020
Jason Mefford: Right, okay. We could do it internally. Could we do it cheaper, maybe in in hard monthly cash costs, we might be able to do it easier but long term and risk reduction standpoint way cheaper to outsource it

187
00:30:14.280 –> 00:30:16.860
Jason Mefford: When you start to consider some of the other things that could go on.

188
00:30:17.490 –> 00:30:20.580
Jason Mefford: Yeah, so you know as as internal audit, we need to

189
00:30:21.030 –> 00:30:30.120
Jason Mefford: I think start thinking that way. I talked a lot about us getting out of the hundred years ago. This is one of those areas to. You don’t have to completely build

190
00:30:30.600 –> 00:30:41.670
Jason Mefford: A whole data analytics department to actually start doing data analytics, you can leverage. And actually, you know, utilize what other people have already kind of started to do

191
00:30:42.210 –> 00:30:52.110
Jason Mefford: And bringing people to help you know because yeah and like i said i mean i think this, this is kind of hopeful. Hopefully everybody that’s listening today.

192
00:30:52.770 –> 00:31:01.410
Jason Mefford: You’re starting to see how some of these things can work together, right, and can help you. Overall, and we talked about some of the problems people are having

193
00:31:01.860 –> 00:31:09.480
Jason Mefford: You know, you try to find somebody, you can’t find anybody to hire you hire them they’re here for a while, then they leave.

194
00:31:10.170 –> 00:31:27.300
Jason Mefford: You do data analytics for a particular discrete project you spend a bunch of time and money doing them and then you never use them again. Right. These are all mistakes that people are making, it’s like people stop making these mistakes. There’s, there’s a better way to do it.

195
00:31:27.900 –> 00:31:35.070
Nathan Pickard: Yeah, I think, I think on that topic. Just of bringing in those those companies to do a data analytic

196
00:31:36.750 –> 00:31:49.440
Nathan Pickard: Project, you end up what we’ve seen multiple times. It’s it as a pitfall is people go after analytics thinking of it from kind of the exception report basis.

197
00:31:49.710 –> 00:31:57.510
Nathan Pickard: With where they want to say like, Okay, we’re going to do the sound like it’ll show us all the exceptions and then we’ll go investigate those exceptions.

198
00:31:57.870 –> 00:32:10.530
Nathan Pickard: And AND I’VE NEVER SEEN I’VE NEVER SEEN THAT methodology last because they quickly realize just how many false positives, you end up having it takes so much time.

199
00:32:10.980 –> 00:32:28.170
Nathan Pickard: To deal with all of those and and so that’s that’s something that just over the 15 years, I would like to be a better way to write where you don’t get stuck in this exception like pool that you can’t get out of

200
00:32:29.370 –> 00:32:34.140
Nathan Pickard: And so that’s that’s been the exciting part of building it as a software product is where

201
00:32:34.530 –> 00:32:48.960
Nathan Pickard: Every analytic we just see as a little building block and we always say, don’t look at this one just on its own, like maybe it’s weekend transactions. Well, maybe there’s 100 reasons why they would have a weekend transaction, but

202
00:32:49.170 –> 00:32:52.980
Jason Mefford: So we run our operations 24 seven. I mean, come on.

203
00:32:55.380 –> 00:32:59.760
Nathan Pickard: So that’s where we put together and we were just looking at this before the call.

204
00:33:00.810 –> 00:33:17.700
Nathan Pickard: For purchasing card transaction. When you look at it in our visualization, it actually brings up to the top, the transaction that had the most risks and so this one transaction had 17 risk flats and so it had a duplicate it had

205
00:33:18.870 –> 00:33:28.890
Nathan Pickard: Like the approved or a pre get in less than 30 seconds after proving the one prior so they they didn’t adequately look at the receipts, you know, pull them up and look at and

206
00:33:29.220 –> 00:33:34.410
Nathan Pickard: And that it was 17 of those types of things. And we can transactions with one of them is on the weekend and

207
00:33:34.860 –> 00:33:45.150
Nathan Pickard: And so it’s not that the weekend is one exception that you have to go look at every single exception. But when you see that along with 16 other flags. It’s like

208
00:33:45.510 –> 00:33:49.770
Nathan Pickard: Oh, shoot. Yeah, this transaction is the one that I should be spending my time with

209
00:33:50.250 –> 00:33:57.060
Nathan Pickard: And I think that’s where we have a lot of fun. The software part and just where all the analytics can really work together.

210
00:33:57.330 –> 00:34:12.660
Nathan Pickard: And you can show the highest possibility of fraud or or bring it all up and look at it and say this, this process that is having the highest problem area is the highest problem period. And that’s where we should focus and

211
00:34:13.740 –> 00:34:22.080
Nathan Pickard: So I think that’s where we get excited is really getting away from that, from the exception, like investigation that ball.

212
00:34:22.950 –> 00:34:29.610
Jason Mefford: Well, and I’m glad I’m glad that you brought that up because I think, again, that is probably a mindset issue.

213
00:34:30.420 –> 00:34:35.460
Jason Mefford: That we have that I think a lot of people in audit think oh data analytics.

214
00:34:35.490 –> 00:34:37.470
Jason Mefford: Are a way for us.

215
00:34:37.890 –> 00:34:45.330
Jason Mefford: To catch exceptions right and but but data analytics is more than just catching exceptions. Right.

216
00:34:45.720 –> 00:34:49.830
Jason Mefford: It’s actually looking at where the processor is working right as well.

217
00:34:50.190 –> 00:34:53.970
Jason Mefford: And kind of monitoring that kind of from the Continuous Auditing standpoint.

218
00:34:55.320 –> 00:34:58.440
Jason Mefford: So yeah, that’s probably another mindset thing that we need to kind of shake our

219
00:34:58.440 –> 00:35:01.440
Jason Mefford: Heads in realize. Look, it’s not just about

220
00:35:01.890 –> 00:35:03.450
Jason Mefford: Finding exceptions.

221
00:35:03.870 –> 00:35:08.370
Jason Mefford: You know, it can be so much more than that. And especially when you layer on

222
00:35:08.670 –> 00:35:14.460
Jason Mefford: That was a great example because so many people, they start doing a data analytic and then they throw their hands up.

223
00:35:14.940 –> 00:35:22.860
Jason Mefford: And they’re like, I got 1000 false positives. How the hell am I going to do this right, and so they just stop there, like, oh, it doesn’t work well it doesn’t work.

224
00:35:23.340 –> 00:35:38.790
Jason Mefford: Because you didn’t set it up right to begin with. And when you when you layer in some of these other red flag kind of issues you understand how it works. And you’re using all of the analytics together as a process instead of just a project.

225
00:35:39.300 –> 00:35:44.220
Jason Mefford: Then it makes things much, much easier. And I’m all about easy

226
00:35:46.140 –> 00:35:46.410
Nathan Pickard: Well,

227
00:35:46.740 –> 00:35:56.640
Jason Mefford: Thank thank you for for taking the time today. Like I said, I, I learned stuff today to actually talking with you and kind of got some clarity about a

228
00:35:57.270 –> 00:36:09.120
Jason Mefford: Few things as to maybe why some people still just have such a hard time with data analytics, you know, and so, you know, if you’re trying to do it, rewind.

229
00:36:10.170 –> 00:36:15.030
Jason Mefford: Listen to this again because you’re going to find some things of, like, oh,

230
00:36:15.090 –> 00:36:21.120
Jason Mefford: That’s probably one of the mistakes that I’m making. And maybe why it’s not working quite the way that I

231
00:36:21.480 –> 00:36:37.320
Jason Mefford: That I want it to, you know, and quit trying to recreate the wheel just, you know, somebody’s already figured it out then leverage what they’re doing. So, Nathan. Thanks for sharing with everybody. Today, appreciate you taking taking the time.

232
00:36:37.860 –> 00:36:38.880
Nathan Pickard: Yeah, thanks for having

Facebook Comments