In my experience, 15, 20 years in management consulting, data is typically, one, messy, all over the place, unclean, in pieces, unstructured or it's missing. It's actually, you wish it were there and it's not, so it takes an enormous amount of skill and energy, diligence and basically just heart, just effort and enthusiasm to make some sense out of it. The two icons here, you can see on left-hand side, that's the best one that I could find for messy, and we'll talk about what that means, and then also missing. Sometimes you need to go and dig for data because it's not sitting where you can plainly see it. The irony, of course, is that messy and missing problems with data creates a consulting project. Because I would say, frankly, if the data was perfect and readily available, the client would have done this work themselves. It's pretty rare to find a project with data that's beautiful, ready to use, and super useful, so expect to get your hands dirty. You are going to be digging in the dirt with your hands. For those of you who've worked in a corporate setting, have pulled data from an IT system, or who have looked through filing cabinets at paper records, we all know that data tends to be more fragmented and more siloed than the client wishes. I mean, think about your own office or think about your kitchen drawer. There may be 30 percent of you watching this video who were like John, I don't know what you're talking about. My files are in perfect shape. My computer folders are perfectly organized, and that may be true. But there are a lot of us, including myself, who that's not true. Sometimes I have difficulty finding stuff, and if you take that example of one person who might be a little unorganized, in their kitchen or in their office, or on their computer folder structure. If you take that one person's example and multiply that times a whole company of 300,000 employees that have merged. They were different companies, but they merge together, and you take that example and you can see why you might have different IT systems that don't talk to each other. You might have some paper based processes were frankly, you ask the client and they're like, sure, I will get that PDF to you. There's also some industries where there's privacy concerns, whether it's health care or financial services or other things where you have to be pretty careful using the customer's information, and as a result of that, I mean, for good reasons we want privacy to be respected, but because of that reason, this system and this system don't talk to itself. The biggest, perhaps in my opinion, root cause of messy data, of the fact that sometimes data doesn't work to our advantage is this point here. Unless somebody is actually responsible for making sure that the stuff that gets entered into the system is quality, no surprise, junk in, junk out. If people can enter information in the system whenever they want to, there's no governance. If it's not clear what the definition is of what you're entering, it's completely subjective. The sloppier the input is, clearly the quality of the data coming out's also going to be very sloppy. This is something that I didn't realize until I had been a consultant for five or six years. Clients, interestingly, are often skeptical of their own data. You might ask, wait a minute, how is that possible? It's your data. How could you not like your own data? We talked about it a little bit before on the previous page. If the information coming in is junky, then the data itself and the output in the reports and analysis is also going to be junky. Junk in, junk out. What you find is that there's lots of skepticism on the data quality, junk in, junk out. Also the definitions. What did you mean by that? The frequencies, sometimes you measure it weekly, sometimes you measure it monthly. Here's the kicker. Something I didn't even think about. There's a little bit of fear when consultants are hired. When management consultants are hired, let's say, to improve profitability. It could mean cost reduction. Cost reduction sometimes means layoffs, job cuts. Well, you can see how people would be afraid of that situation. You can see how clients are afraid that you're going to use the data against me. I've also heard this many times. Well, yeah, that's your data, not my data. The key takeaway here is data can lie. For you, as a consultant who is basing a lot of their credibility, basing a lot of your recommendations on data, you need to do, one, do a good job of it; number 2, validate it; and then number 3, be a little bit emotionally intelligent about it. Know who the audience is, know what some of the flaws, F-L-A-W, the problems with the data. Definitely understand the definitions if you're ever asked. It's just a fancy way of saying that data is very important and data can lie. Therefore we need to be smart in what we do with it and how we use it. Let's take a look at a hypothetical eight-week project that I put here. Since data can be clunky, messy, it does require a fair amount of energy. It really requires that energy at the beginning of a project. Once you start getting into Week 6, 7, 8 of an eight-week project, you better not be fighting with the data. If you're fighting with the data in week 7, there's no pretty way to say this, you're in trouble because there's no way that you're going to finish the project on time, on budget and have enough time to work with the clients to put together a recommendation that, one, they find acceptable; number 2, makes logical sense; number 3, you can socialize and pre-wire with other people. Just like the red star shown here on the left-hand side, a lot of the lift and the effort and the digging and the cleansing and all this, frankly, fairly unglamorous work happens in the beginning. I made a list of a lot of different activities that we're going to talk about in more detail later. But, yeah. I mean, you're going to know what you're looking for. You're going to find the right person to ask. You're going to ask it in the right way. Then you'll notice here on the left, I only highlighted one bullet point in bright blue font because it's the one that you're going to focus a lot of your time on. You need to follow up again and again and again for a couple of reasons. One, you ask that person for the data. They have a day job. In other words, they don't work for you. You are the consultant and you're asking somebody in the client organization to do extra work. No surprise, people don't like to do extra work. You are a hassle to them. No matter how nicely you ask, no matter if they're boss tells them to work with you frankly, it's not on their priority list. You need to learn to nudge, and I'll just write it down. You need to nudge nicely. You need to get very good at being polite and nagging them a little bit. N-A-G. Learn those skills, use your emotional intelligence, be friendly, be respectful, be useful, but frankly, do what you need to do to get the data as soon as possible because just getting it once, isn't enough. Once you get it, you actually need to validate it. Is this the right data? Is it useful? Is it in the right format? Did you give me enough of it? This is all in the process of helping the client to win. But if you don't get good data very early in the process, it's like you trying to cook a Thanksgiving dinner without going shopping for groceries. You can't do anything. A couple of key takeaways, and you might find that I'm getting a little bit excited and emotional about data , but there's a reason. If you're a new college graduate, represented by the green graduation cap here, one thing I would urge you to do, is look at previous client work. You have the previous client deliverable here. It can be a project that's already finished. It's long done. Take a look at the analysis that they did and the recommendations they had. It's already finished, so it's already complete. It's like you're looking at a finished cake that's been already baked. Take a look at the cake, open the cake up and try to see what are the ingredients that I would need in order to bake that cake. I'm asking you to reverse engineer the deliverable and think, what are the elements? What is the data that I would need to make that graph? If you are working inside a corporation shown by the blue ladder, you're an internal consultant. I think one thing you might want to think about is, what is my wish list? There's no way, it's just like Christmas time, you make a long wish list at Christmas time when you're 10 years old, there's 50 things you want. Trust me, you're not going to get 50 gifts. But what's your wish list of data prioritized? What's supercritical? Then what's nice to have? Then what's not needed? Put together that list and start thinking ahead of time. For the solo consultants, the entrepreneurs, the folks that I showed here with the red airplane. For you, a lot of your life frankly, is around sales. Finding potential customers who will love the work that you do, who are a good fit, who their personality and what they're trying to do, it's easy for you to do a great job. A lot of your focus is new business. What I would ask you is, do you have external datasets? Maybe work that you've done in the past, data that you've analyzed before, and have you scrubbed them? Have you cleaned them? Have you made them clean? Have you taken out any client proprietary information? Do you have those datasets that you can use to show to future prospects and show them one, I've done the work. Number 2, if you're willing to give me your data, I can compare it to what I have and tell you where you're better than average and below average. Basically, have you done data analysis in the past that can help you sell future work?
