1 00:00:00,000 --> 00:00:03,880 Hello everyone and welcome to another episode of Microsoft Knowledge Nuggets. 2 00:00:03,880 --> 00:00:07,600 Today's topic is Dataverse and it sounds like a made up buzzword at first. 3 00:00:07,600 --> 00:00:10,440 Most people hear it and think it's just a fancy name for a database, 4 00:00:10,440 --> 00:00:15,200 but it's actually the hidden engine behind most business apps built on Microsoft's power platform. 5 00:00:15,200 --> 00:00:19,160 And here's the thing, it's not just a database, it's something much more practical. 6 00:00:19,160 --> 00:00:23,200 By the end of this episode, you'll understand what Dataverse actually is, 7 00:00:23,200 --> 00:00:28,640 how it organizes data, and why it's the backbone of every power platform solution worth building. 8 00:00:28,640 --> 00:00:31,200 There's one thing Dataverse does that most people don't expect, 9 00:00:31,200 --> 00:00:33,840 and it changes how you think about building apps entirely. 10 00:00:33,840 --> 00:00:35,840 Grab your coffee and let's dive in. 11 00:00:35,840 --> 00:00:37,280 Why Dataverse exists? 12 00:00:37,280 --> 00:00:39,280 Imagine you start a new company tomorrow. 13 00:00:39,280 --> 00:00:41,640 20 years ago, you'd buy separate products for everything, 14 00:00:41,640 --> 00:00:43,960 one server for email, another for file storage, 15 00:00:43,960 --> 00:00:45,840 a different system for customer data. 16 00:00:45,840 --> 00:00:48,960 Each one had its own login, its own rules, its own way of doing things, 17 00:00:48,960 --> 00:00:51,440 and getting them to talk to each other was a full-time job. 18 00:00:51,440 --> 00:00:54,600 Fast forward to today, and the problem hasn't really gone away. 19 00:00:54,600 --> 00:00:56,880 Businesses still use disconnected spreadsheets, 20 00:00:56,880 --> 00:00:59,920 sharepoint lists, and random databases that don't talk to each other. 21 00:00:59,920 --> 00:01:01,680 You've got customer data in one place, 22 00:01:01,680 --> 00:01:04,080 order data in another, and inventory data in a third, 23 00:01:04,080 --> 00:01:06,240 and someone's probably still tracking something important 24 00:01:06,240 --> 00:01:09,440 in a dusty Excel file that only one person knows how to find. 25 00:01:09,440 --> 00:01:11,600 Now, Excel works fine for small teams. 26 00:01:11,600 --> 00:01:14,480 You can track a few dozen assets, a handful of customers, 27 00:01:14,480 --> 00:01:17,120 maybe a simple project list, but it breaks its scale. 28 00:01:17,120 --> 00:01:20,240 There's no real security, so anyone with the file can see everything. 29 00:01:20,240 --> 00:01:22,000 There's no relationships between data, 30 00:01:22,000 --> 00:01:25,920 so you're copying and pasting the same customer name across multiple sheets. 31 00:01:25,920 --> 00:01:28,160 And there's no audit trail, so if someone changes a number, 32 00:01:28,160 --> 00:01:30,240 you'll never know who did it or when. 33 00:01:30,240 --> 00:01:31,760 Sharepoint lists are a step up. 34 00:01:31,760 --> 00:01:33,920 They live in the cloud, support multiple users, 35 00:01:33,920 --> 00:01:36,160 and are included with Microsoft 365. 36 00:01:36,160 --> 00:01:37,280 But they hit a hard wall. 37 00:01:37,280 --> 00:01:39,520 There's a 5,000 item view threshold, 38 00:01:39,520 --> 00:01:42,320 so try to show more than 5,000 items in a list view, 39 00:01:42,320 --> 00:01:43,760 and SharePoint just says no. 40 00:01:43,760 --> 00:01:45,280 And there's no real relational data. 41 00:01:45,280 --> 00:01:47,680 You can create look-up columns, but they're not enforced, 42 00:01:47,680 --> 00:01:49,760 so you can link to a record that doesn't exist 43 00:01:49,760 --> 00:01:52,880 or delete a manufacturer and leave often products floating around. 44 00:01:52,880 --> 00:01:53,520 It's a mess. 45 00:01:54,400 --> 00:01:57,040 So Microsoft looked at all this fragmentation and thought, 46 00:01:57,040 --> 00:01:59,360 "What if there was one unified data platform 47 00:01:59,360 --> 00:02:02,160 where all your power platform apps talked to the same data? 48 00:02:02,160 --> 00:02:04,160 What if you didn't need to worry about security, 49 00:02:04,160 --> 00:02:06,720 relationships, and logic in every single app? 50 00:02:06,720 --> 00:02:08,640 What if the platform handled that for you?" 51 00:02:08,640 --> 00:02:10,480 That's Dataverse, and here's the key point. 52 00:02:10,480 --> 00:02:11,600 It's not just storage. 53 00:02:11,600 --> 00:02:13,840 It's a managed service that handles security, 54 00:02:13,840 --> 00:02:16,400 logic, and relationships, so you don't have to. 55 00:02:16,400 --> 00:02:18,160 You define your data model once, 56 00:02:18,160 --> 00:02:21,040 and every app, every flow, every report, 57 00:02:21,040 --> 00:02:22,640 uses the same foundation. 58 00:02:22,640 --> 00:02:26,080 No more copying data between systems, no more inconsistent records, 59 00:02:26,080 --> 00:02:28,560 no more building the same security logic five times. 60 00:02:28,560 --> 00:02:30,720 Tables, the foundation. 61 00:02:30,720 --> 00:02:32,480 So what exactly is a Dataverse table 62 00:02:32,480 --> 00:02:35,120 and how is it different from an Excel sheet or a SharePoint list? 63 00:02:35,120 --> 00:02:36,720 Let's start with the simplest definition. 64 00:02:36,720 --> 00:02:39,280 A table is the most basic building block in Dataverse. 65 00:02:39,280 --> 00:02:41,520 Think of it like a spreadsheet tab, but smarter. 66 00:02:41,520 --> 00:02:43,920 Each table has columns with specific data types, 67 00:02:43,920 --> 00:02:46,320 text, numbers, dates, choices, currencies. 68 00:02:46,320 --> 00:02:48,400 You define what kind of data goes in each column, 69 00:02:48,400 --> 00:02:50,800 and Dataverse makes sure only valid data gets stored. 70 00:02:51,760 --> 00:02:55,040 No more typing NA in a date field because someone didn't know what to put there. 71 00:02:55,040 --> 00:02:57,280 But here's where Dataverse pulls ahead of Excel. 72 00:02:57,280 --> 00:02:59,840 You get tools that spreadsheets simply don't have. 73 00:02:59,840 --> 00:03:00,960 Auto numbering. 74 00:03:00,960 --> 00:03:03,920 Every new record gets a unique ID automatically. 75 00:03:03,920 --> 00:03:07,360 Calculated columns, a field that computes its value based on other fields 76 00:03:07,360 --> 00:03:10,400 like total price, it was quantity times unit price, 77 00:03:10,400 --> 00:03:12,800 business rules, logic that runs on the server, 78 00:03:12,800 --> 00:03:15,360 not in a macro that someone might accidentally disable. 79 00:03:15,360 --> 00:03:16,720 And you don't start from scratch. 80 00:03:16,720 --> 00:03:19,680 Dataverse gives you standard tables out of the box. 81 00:03:19,680 --> 00:03:23,200 Contacts, accounts, activities, these are ready to use, 82 00:03:23,200 --> 00:03:25,600 already designed with the columns and relationships 83 00:03:25,600 --> 00:03:27,680 that make sense for common business scenarios. 84 00:03:27,680 --> 00:03:30,080 You can create custom tables for your own needs, 85 00:03:30,080 --> 00:03:33,360 products, assets, orders, whatever your business requires. 86 00:03:33,360 --> 00:03:35,120 Here's something most people don't realize. 87 00:03:35,120 --> 00:03:37,040 When you create a table in Dataverse, 88 00:03:37,040 --> 00:03:40,000 a lot happens behind the scenes without you lifting a finger. 89 00:03:40,000 --> 00:03:42,400 Dataverse automatically adds a primary key, 90 00:03:42,400 --> 00:03:44,640 a unique identifier for every record. 91 00:03:44,640 --> 00:03:46,480 It adds created and modified dates. 92 00:03:46,480 --> 00:03:48,640 It adds ownership fields who created this record, 93 00:03:48,640 --> 00:03:49,600 who owns it now. 94 00:03:49,600 --> 00:03:52,080 It adds a status field, active or inactive, 95 00:03:52,080 --> 00:03:54,800 so you can soft delete records instead of permanently removing them. 96 00:03:54,800 --> 00:03:57,040 All of that is there from the moment you create the table. 97 00:03:57,040 --> 00:03:58,880 Let me give you a real world example. 98 00:03:58,880 --> 00:04:00,800 Say you're building an asset tracking system. 99 00:04:00,800 --> 00:04:02,720 You create a table called assets. 100 00:04:02,720 --> 00:04:04,800 You add columns for the asset name, serial number, 101 00:04:04,800 --> 00:04:06,400 purchase date and current value. 102 00:04:06,400 --> 00:04:07,920 That's four columns you define. 103 00:04:07,920 --> 00:04:10,640 But behind the scenes, Dataverse adds another dozen, 104 00:04:10,640 --> 00:04:13,280 the unique ID, the created date, the modified date, 105 00:04:13,280 --> 00:04:15,040 the owner, the status and so on. 106 00:04:15,040 --> 00:04:16,480 You get all of that for free. 107 00:04:16,480 --> 00:04:19,120 And when you start building apps on top of this table, 108 00:04:19,120 --> 00:04:21,040 those extra fields are already there, 109 00:04:21,040 --> 00:04:24,080 ready to use for security, auditing and reporting. 110 00:04:24,080 --> 00:04:26,400 One to many relationships. 111 00:04:26,400 --> 00:04:27,520 Once you have a few tables, 112 00:04:27,520 --> 00:04:29,840 the real power comes from connecting them together. 113 00:04:29,840 --> 00:04:32,640 And the most common way to do that is with a one to many relationship. 114 00:04:32,640 --> 00:04:33,680 Here's how it works. 115 00:04:33,680 --> 00:04:37,200 One record in one table links to many records in another table. 116 00:04:37,200 --> 00:04:38,800 Let me give you a concrete example. 117 00:04:38,800 --> 00:04:41,120 Say you have a table for vehicle manufacturers. 118 00:04:41,120 --> 00:04:42,880 Honda Ford Toyota. 119 00:04:42,880 --> 00:04:44,640 And you have another table for vehicles. 120 00:04:44,640 --> 00:04:46,560 Civic F-150 Camry. 121 00:04:46,560 --> 00:04:48,240 One manufacturer makes many vehicles. 122 00:04:48,240 --> 00:04:49,680 That's a one to many relationship. 123 00:04:49,680 --> 00:04:53,280 The way you build this in practice is with something called a lookup column. 124 00:04:53,280 --> 00:04:56,080 On your vehicle table, you add a column called manufacturer. 125 00:04:56,080 --> 00:04:59,040 And you set it to lookup values from your manufacturer table. 126 00:04:59,040 --> 00:05:00,480 When a user fills in that field, 127 00:05:00,480 --> 00:05:02,800 they don't type the manufacturer name from scratch. 128 00:05:02,800 --> 00:05:03,760 They search for it. 129 00:05:03,760 --> 00:05:05,280 Dataverse shows a search box, 130 00:05:05,280 --> 00:05:07,520 not a drop down of thousands of options. 131 00:05:07,520 --> 00:05:09,520 They pick Honda and the relationship is created. 132 00:05:09,520 --> 00:05:10,560 Now, why does this matter? 133 00:05:10,560 --> 00:05:12,560 Because instead of repeating the manufacturer name 134 00:05:12,560 --> 00:05:13,680 in every single vehicle row, 135 00:05:13,680 --> 00:05:15,360 you store it once and reference it. 136 00:05:15,360 --> 00:05:17,040 The Civic points to the Honda record. 137 00:05:17,040 --> 00:05:18,880 The accord points to the same Honda record. 138 00:05:18,880 --> 00:05:21,040 The CRV points to the same Honda record. 139 00:05:21,040 --> 00:05:22,560 You're not typing Honda three times. 140 00:05:22,560 --> 00:05:23,760 You're storing it once. 141 00:05:23,760 --> 00:05:25,680 And every vehicle just links back to it. 142 00:05:25,680 --> 00:05:28,000 This prevents data entry errors. 143 00:05:28,000 --> 00:05:29,120 No more typos. 144 00:05:29,120 --> 00:05:32,400 Honda in one row, Honda motor in another, Honda cumped in a third. 145 00:05:32,400 --> 00:05:33,440 It's all the same record. 146 00:05:33,440 --> 00:05:34,960 And it makes updates incredibly easy. 147 00:05:34,960 --> 00:05:37,840 Say Honda decides to rebrand and change their company name. 148 00:05:37,840 --> 00:05:41,120 In a spreadsheet, you'd have to find every single row that says Honda 149 00:05:41,120 --> 00:05:42,080 and update it. 150 00:05:42,080 --> 00:05:44,480 Miss one and now you've got inconsistent data. 151 00:05:44,480 --> 00:05:46,960 In Dataverse, you change the manufacturer name once 152 00:05:46,960 --> 00:05:49,200 and every vehicle that links to that manufacturer 153 00:05:49,200 --> 00:05:50,960 automatically reflects the update. 154 00:05:50,960 --> 00:05:52,640 One change, everything updates. 155 00:05:52,640 --> 00:05:55,040 Dataverse also enforces something 156 00:05:55,040 --> 00:05:56,800 called referential integrity. 157 00:05:56,800 --> 00:05:58,640 You can't link to a manufacturer record 158 00:05:58,640 --> 00:05:59,840 that doesn't exist. 159 00:05:59,840 --> 00:06:01,680 If someone tries to delete a manufacturer 160 00:06:01,680 --> 00:06:03,360 that still has vehicles linked to it, 161 00:06:03,360 --> 00:06:04,640 Dataverse will stop them. 162 00:06:04,640 --> 00:06:07,040 No often records, no broken references. 163 00:06:07,040 --> 00:06:08,400 The data stays clean. 164 00:06:08,400 --> 00:06:09,680 And here's where it gets visual. 165 00:06:09,680 --> 00:06:10,880 On the manufacturer form, 166 00:06:10,880 --> 00:06:12,800 you can add something called a subgrid. 167 00:06:12,800 --> 00:06:14,800 This is a small table embedded in the form 168 00:06:14,800 --> 00:06:17,200 that shows all the vehicles belonging to that manufacturer. 169 00:06:17,200 --> 00:06:18,880 So when you open the Honda record, 170 00:06:18,880 --> 00:06:21,280 you see a list, Civic, Accord, CRV, 171 00:06:21,280 --> 00:06:22,640 right there on the same screen. 172 00:06:22,640 --> 00:06:24,480 You can see the relationship from both sides. 173 00:06:24,480 --> 00:06:26,880 The vehicle knows which manufacturer it belongs to. 174 00:06:26,880 --> 00:06:29,680 And the manufacturer knows which vehicles are linked to it. 175 00:06:29,680 --> 00:06:31,840 That's the power of a one-to-many relationship. 176 00:06:31,840 --> 00:06:34,720 Many-to-many relationships. 177 00:06:34,720 --> 00:06:37,120 Now, what about when you need a more flexible connection? 178 00:06:37,120 --> 00:06:39,040 Where many records linked to many other records, 179 00:06:39,040 --> 00:06:40,560 sometimes one-to-many isn't enough? 180 00:06:40,560 --> 00:06:41,680 Take a car dealership. 181 00:06:41,680 --> 00:06:43,600 A single customer might own multiple vehicles. 182 00:06:43,600 --> 00:06:44,320 That's easy. 183 00:06:44,320 --> 00:06:45,920 One customer, many vehicles. 184 00:06:45,920 --> 00:06:46,960 But what about a household? 185 00:06:46,960 --> 00:06:49,200 A single vehicle might be owned by multiple people. 186 00:06:49,200 --> 00:06:50,800 A husband and wife, for example. 187 00:06:50,800 --> 00:06:53,440 So you've got a situation where one customer can own many vehicles 188 00:06:53,440 --> 00:06:55,360 and one vehicle can be owned by many customers. 189 00:06:55,360 --> 00:06:57,040 That's a many-to-many relationship. 190 00:06:57,040 --> 00:06:59,200 Dataverse supports this natively. 191 00:06:59,200 --> 00:07:00,800 You don't need to create an extra table. 192 00:07:00,800 --> 00:07:02,800 You just define a many-to-many relationship 193 00:07:02,800 --> 00:07:05,040 between your contact table and your vehicle table 194 00:07:05,040 --> 00:07:06,880 and Dataverse handles the rest. 195 00:07:06,880 --> 00:07:08,960 Behind the scenes, it creates a hidden table 196 00:07:08,960 --> 00:07:10,160 that stores the connections. 197 00:07:10,160 --> 00:07:11,040 But you never see it. 198 00:07:11,040 --> 00:07:13,360 You just see that a contact can have multiple vehicles 199 00:07:13,360 --> 00:07:15,360 and a vehicle can have multiple contacts. 200 00:07:15,360 --> 00:07:16,240 But here's the catch. 201 00:07:16,240 --> 00:07:18,880 That out-of-box many-to-many table is invisible. 202 00:07:18,880 --> 00:07:20,560 You can't see it in your list of tables. 203 00:07:20,560 --> 00:07:21,760 You can't add columns to it. 204 00:07:21,760 --> 00:07:23,680 You can't store extra data on it. 205 00:07:23,680 --> 00:07:26,160 So if you want to track the purchase price of that vehicle 206 00:07:26,160 --> 00:07:28,320 or the data was bought or the financing terms, 207 00:07:28,320 --> 00:07:28,960 you can't. 208 00:07:28,960 --> 00:07:31,440 The hidden table only stores the relationship itself. 209 00:07:31,440 --> 00:07:32,800 Nothing else. 210 00:07:32,800 --> 00:07:34,640 If you need to track additional information, 211 00:07:34,640 --> 00:07:36,800 you create a custom intersection table. 212 00:07:36,800 --> 00:07:39,760 Let's say you create a table called "Purchase". 213 00:07:39,760 --> 00:07:42,560 This table has a lookup to the contact who bought it. 214 00:07:42,560 --> 00:07:43,600 A lookup to the vehicle. 215 00:07:43,600 --> 00:07:44,400 What they bought. 216 00:07:44,400 --> 00:07:46,400 And then you can add whatever columns you need. 217 00:07:46,400 --> 00:07:48,480 Purchase price, purchase date, 218 00:07:48,480 --> 00:07:50,400 warranty expiration, payment status. 219 00:07:50,400 --> 00:07:51,680 This gives you full control. 220 00:07:51,680 --> 00:07:52,560 You can add columns. 221 00:07:52,560 --> 00:07:55,200 Trigger power automate flows when a purchase is created. 222 00:07:55,200 --> 00:07:56,480 Import data from Excel. 223 00:07:56,480 --> 00:07:58,080 Everything you'd expect from a proper table. 224 00:07:58,080 --> 00:07:59,200 So when do you use which? 225 00:07:59,200 --> 00:08:02,000 The out-of-box many-to-many is great for simple associations. 226 00:08:02,000 --> 00:08:04,240 Tags on a blog post, categories on a product, 227 00:08:04,240 --> 00:08:05,600 quick lightweight connections 228 00:08:05,600 --> 00:08:07,680 where you just need to know what's linked to what. 229 00:08:07,680 --> 00:08:10,640 The custom intersection table is for business transactions. 230 00:08:10,640 --> 00:08:14,080 Things that have their own data, their own process, their own life cycle. 231 00:08:14,080 --> 00:08:14,960 Think of it this way. 232 00:08:14,960 --> 00:08:16,800 The out-of-box version is like a sticky note. 233 00:08:16,800 --> 00:08:18,800 Quick, easy, gets the job done. 234 00:08:18,800 --> 00:08:20,320 But you can't write much on it. 235 00:08:20,320 --> 00:08:23,120 The custom version is a proper filing cabinet with folders. 236 00:08:23,120 --> 00:08:24,400 Takes a bit more setup, 237 00:08:24,400 --> 00:08:26,480 but you can store everything you need. 238 00:08:26,480 --> 00:08:27,760 Security and access. 239 00:08:27,760 --> 00:08:30,160 Storing and connecting data is only half the story. 240 00:08:30,160 --> 00:08:33,280 The other half is making sure the right people see the right data. 241 00:08:33,280 --> 00:08:35,360 And this is where dataverse really separates itself 242 00:08:35,360 --> 00:08:37,040 from a simple spreadsheet or list. 243 00:08:37,040 --> 00:08:39,280 Dataverse uses role-based security. 244 00:08:39,280 --> 00:08:42,160 You don't assign permissions to individual users one at a time. 245 00:08:42,160 --> 00:08:43,280 You create roles. 246 00:08:43,280 --> 00:08:45,040 Think of them as job descriptions. 247 00:08:45,040 --> 00:08:47,360 And you assign permissions to those roles. 248 00:08:47,360 --> 00:08:48,960 Then you add users to the roles. 249 00:08:48,960 --> 00:08:50,720 A salesperson gets the sales role. 250 00:08:50,720 --> 00:08:52,640 A support agent gets the support role. 251 00:08:52,640 --> 00:08:54,720 A manager gets a role with broader access. 252 00:08:54,720 --> 00:08:56,480 Clean, scalable, and easy to audit. 253 00:08:56,480 --> 00:08:58,160 Now there are three levels of control. 254 00:08:58,160 --> 00:08:59,280 First, table level. 255 00:08:59,280 --> 00:09:01,280 Can this person even see the asset table? 256 00:09:01,280 --> 00:09:02,720 Second, record level? 257 00:09:02,720 --> 00:09:04,560 Which specific rows can they see? 258 00:09:04,560 --> 00:09:07,600 And third, column level can they see every field in a record 259 00:09:07,600 --> 00:09:08,960 or are some fields hidden? 260 00:09:08,960 --> 00:09:09,840 Let's break that down. 261 00:09:09,840 --> 00:09:11,200 Table level is straightforward. 262 00:09:11,200 --> 00:09:13,120 You decide which tables a role can access. 263 00:09:13,120 --> 00:09:16,160 Maybe your support team doesn't need to see the financial tables at all. 264 00:09:16,160 --> 00:09:16,640 Done. 265 00:09:16,640 --> 00:09:18,400 Record level is where it gets interesting. 266 00:09:18,400 --> 00:09:21,360 You control this through something called access levels. 267 00:09:21,360 --> 00:09:22,800 The most common ones are user. 268 00:09:22,800 --> 00:09:24,400 You can only see records you own. 269 00:09:24,400 --> 00:09:27,280 Business unit, you can see records owned by anyone in your department. 270 00:09:27,280 --> 00:09:30,000 And organization, you can see everything in the environment. 271 00:09:30,000 --> 00:09:31,440 Here's something important to understand. 272 00:09:31,440 --> 00:09:34,240 Security and dataverse is additive, not subtractive. 273 00:09:34,240 --> 00:09:38,080 If a user has two roles, they get the most permissive access from either role. 274 00:09:38,080 --> 00:09:40,560 You can't give someone a role that says read only. 275 00:09:40,560 --> 00:09:43,200 And then another role that says, can't see this table 276 00:09:43,200 --> 00:09:45,360 and expect the second one to override the first. 277 00:09:45,360 --> 00:09:46,560 It doesn't work that way. 278 00:09:46,560 --> 00:09:48,720 The system always gives the highest level of access. 279 00:09:48,720 --> 00:09:51,840 So you need to be intentional about which roles you assign to which users. 280 00:09:51,840 --> 00:09:54,000 Business units create natural boundaries. 281 00:09:54,000 --> 00:09:55,840 You set up a business unit for sales, 282 00:09:55,840 --> 00:09:58,160 another for support, another for finance. 283 00:09:58,160 --> 00:10:00,320 Users in the sales unit see sales data. 284 00:10:00,320 --> 00:10:02,720 Users in the support unit see support data. 285 00:10:02,720 --> 00:10:06,240 It creates a clean separation without needing complex role configurations. 286 00:10:06,240 --> 00:10:07,120 And it scales. 287 00:10:07,120 --> 00:10:09,440 Add a new salesperson to the sales business unit 288 00:10:09,440 --> 00:10:11,840 and they automatically inherit the right access. 289 00:10:11,840 --> 00:10:14,080 The golden rule of dataverse security is this, 290 00:10:14,080 --> 00:10:16,720 don't assign system administrator to everyone. 291 00:10:16,720 --> 00:10:19,600 That role gives full access to everything in the environment. 292 00:10:19,600 --> 00:10:22,080 It's tempting to use it as a shortcut when someone says, 293 00:10:22,080 --> 00:10:23,920 I can't see this data. 294 00:10:23,920 --> 00:10:24,720 Don't do it. 295 00:10:24,720 --> 00:10:28,640 Instead, clone a base role, trim it down to only what that person needs 296 00:10:28,640 --> 00:10:29,360 and assign that. 297 00:10:29,360 --> 00:10:30,480 It takes a few extra minutes, 298 00:10:30,480 --> 00:10:32,880 but it saves you from security headaches down the road. 299 00:10:32,880 --> 00:10:34,960 Teams make permission management even easier. 300 00:10:34,960 --> 00:10:36,960 Instead of assigning roles to 20 individual users, 301 00:10:36,960 --> 00:10:39,280 you create a team, assign the role to the team, 302 00:10:39,280 --> 00:10:40,480 and add users to the team. 303 00:10:40,480 --> 00:10:42,960 When someone joins the team, they inherit the team's access. 304 00:10:42,960 --> 00:10:45,920 When someone leaves, you remove them from the team. 305 00:10:45,920 --> 00:10:48,000 No need to update individual role assignments. 306 00:10:48,000 --> 00:10:49,680 It scales beautifully. 307 00:10:49,680 --> 00:10:51,360 Dataverse versus SharePoint. 308 00:10:51,360 --> 00:10:53,360 So after all that, you might still be wondering, 309 00:10:53,360 --> 00:10:56,000 should I use Dataverse or can I just stick with SharePoint? 310 00:10:56,000 --> 00:10:56,880 That's a fair question. 311 00:10:56,880 --> 00:10:58,160 Let's compare them directly. 312 00:10:58,160 --> 00:10:59,440 Here's the biggest difference. 313 00:10:59,440 --> 00:11:02,880 SharePoint is a collaboration platform that happens to have lists. 314 00:11:02,880 --> 00:11:05,040 Dataverse is a proper relational database, 315 00:11:05,040 --> 00:11:06,880 built from the ground up for business apps. 316 00:11:06,880 --> 00:11:09,200 SharePoint lists work great for team sites, 317 00:11:09,200 --> 00:11:11,040 document libraries, and simple trackers, 318 00:11:11,040 --> 00:11:14,160 but they're not designed to be the backbone of a real application. 319 00:11:14,160 --> 00:11:16,320 Dataverse is, now let's talk cost. 320 00:11:16,320 --> 00:11:19,120 SharePoint lists come with your Microsoft 365 subscription. 321 00:11:19,120 --> 00:11:20,000 You already have them. 322 00:11:20,000 --> 00:11:21,360 No extra license needed. 323 00:11:21,360 --> 00:11:23,840 Dataverse requires a power app's premium license, 324 00:11:23,840 --> 00:11:26,000 which runs about $20 per user per month. 325 00:11:26,000 --> 00:11:27,920 That's real money, and it matters. 326 00:11:27,920 --> 00:11:29,280 But you get what you pay for. 327 00:11:29,280 --> 00:11:30,960 Scale is another big difference. 328 00:11:30,960 --> 00:11:32,800 SharePoint has a hard limit. 329 00:11:32,800 --> 00:11:34,960 The 5,000 item view threshold. 330 00:11:34,960 --> 00:11:37,760 If your list goes over that, certain operations just fail. 331 00:11:37,760 --> 00:11:40,320 You can work around it with indexing and careful filtering, 332 00:11:40,320 --> 00:11:41,680 but it's a constant headache. 333 00:11:41,680 --> 00:11:44,480 Dataverse can handle millions of rows with proper queries. 334 00:11:44,480 --> 00:11:46,000 No arbitrary view threshold. 335 00:11:46,000 --> 00:11:47,360 No workarounds needed. 336 00:11:47,360 --> 00:11:49,360 Relationships are where things really split. 337 00:11:49,360 --> 00:11:52,320 SharePoint has look-up columns, but they're not enforced. 338 00:11:52,320 --> 00:11:55,280 You can create a look-up to a record that doesn't exist. 339 00:11:55,280 --> 00:11:57,680 You can delete a record that other records point to, 340 00:11:57,680 --> 00:11:59,600 leaving orphaned references everywhere. 341 00:11:59,600 --> 00:12:02,000 Dataverse enforces referential integrity. 342 00:12:02,000 --> 00:12:03,280 You can't create a broken link. 343 00:12:03,280 --> 00:12:04,400 The data stays clean. 344 00:12:04,400 --> 00:12:05,840 And column-level security? 345 00:12:05,840 --> 00:12:06,880 SharePoint doesn't have it. 346 00:12:06,880 --> 00:12:10,080 You can't hide a salary field while showing the rest of an employee record. 347 00:12:10,080 --> 00:12:10,960 It's all or nothing. 348 00:12:10,960 --> 00:12:12,960 Dataverse lets you secure individual columns. 349 00:12:12,960 --> 00:12:14,880 The HR manager sees the full record. 350 00:12:14,880 --> 00:12:17,600 The team leads sees everything except the salary. 351 00:12:17,600 --> 00:12:19,520 That's not possible with SharePoint alone. 352 00:12:19,520 --> 00:12:20,880 So when do you use each one? 353 00:12:20,880 --> 00:12:22,560 Use SharePoint for simple trackers, 354 00:12:22,560 --> 00:12:24,560 document libraries, team-level collaboration, 355 00:12:24,560 --> 00:12:25,680 and low-budget solutions. 356 00:12:25,680 --> 00:12:28,080 It's fast, it's familiar, and it's already paid for. 357 00:12:28,080 --> 00:12:30,160 Use Dataverse for business-critical data, 358 00:12:30,160 --> 00:12:32,800 complex relationships, enterprise security, 359 00:12:32,800 --> 00:12:35,760 model-driven apps, and anything that needs AI integration. 360 00:12:35,760 --> 00:12:38,400 It's the right foundation when your app needs to grow. 361 00:12:38,400 --> 00:12:40,400 And here's a common pattern that works really well. 362 00:12:40,400 --> 00:12:41,360 A hybrid approach. 363 00:12:41,360 --> 00:12:43,360 Use Dataverse for your structured data, 364 00:12:43,360 --> 00:12:44,880 customers, orders, products, 365 00:12:44,880 --> 00:12:47,360 the things that need relationships and security. 366 00:12:47,360 --> 00:12:48,880 Use SharePoint for documents. 367 00:12:48,880 --> 00:12:50,800 Contracts reports attachments. 368 00:12:50,800 --> 00:12:52,640 Dataverse links to the SharePoint document, 369 00:12:52,640 --> 00:12:54,400 so you get the best of both worlds. 370 00:12:54,400 --> 00:12:56,480 Structured data in a proper database. 371 00:12:56,480 --> 00:12:59,280 Documents in a collaboration platform designed for them. 372 00:12:59,280 --> 00:13:00,640 Power Platform integration. 373 00:13:00,640 --> 00:13:02,400 So Dataverse handles the data layer. 374 00:13:02,400 --> 00:13:05,200 But how does it fit into the bigger power platform picture? 375 00:13:05,200 --> 00:13:06,880 This is where everything comes together. 376 00:13:06,880 --> 00:13:08,480 Dataverse is the shared data layer 377 00:13:08,480 --> 00:13:12,240 that power apps, power automate, and power BI all use. 378 00:13:12,240 --> 00:13:15,440 Think of it as the foundation every other tool builds on top of. 379 00:13:15,440 --> 00:13:17,280 Power Apps connects to it for app data. 380 00:13:17,280 --> 00:13:19,840 Power Automate uses it for triggers and actions. 381 00:13:19,840 --> 00:13:21,600 Power BI queries it for dashboards. 382 00:13:21,600 --> 00:13:23,760 One Data Platform, three different tools, 383 00:13:23,760 --> 00:13:25,600 all working with the same information. 384 00:13:25,600 --> 00:13:26,880 Let's start with Canvas Apps. 385 00:13:26,880 --> 00:13:28,720 You can connect the Canvas app to Dataverse 386 00:13:28,720 --> 00:13:30,960 just like you'd connected to any other data source. 387 00:13:30,960 --> 00:13:32,400 SharePoint, SQL, Excel. 388 00:13:32,400 --> 00:13:33,200 But here's the difference. 389 00:13:33,200 --> 00:13:36,080 When you connect to Dataverse, you get full delegation. 390 00:13:36,080 --> 00:13:38,000 That means your app can query millions of records 391 00:13:38,000 --> 00:13:39,120 without slowing down. 392 00:13:39,120 --> 00:13:42,160 The query runs on the server, not on the user's device. 393 00:13:42,160 --> 00:13:45,040 And you inherit Dataverse's security model automatically. 394 00:13:45,040 --> 00:13:47,440 You don't have to build security logic into the app itself. 395 00:13:47,440 --> 00:13:48,960 The platform handles it. 396 00:13:48,960 --> 00:13:50,720 Model-driven apps are a different story. 397 00:13:50,720 --> 00:13:52,720 These apps are built directly on Dataverse. 398 00:13:52,720 --> 00:13:54,080 They're not possible without it. 399 00:13:54,080 --> 00:13:55,600 When you create a model-driven app, 400 00:13:55,600 --> 00:13:57,440 you're essentially building a user interface 401 00:13:57,440 --> 00:13:58,880 on top of your Dataverse tables. 402 00:13:58,880 --> 00:14:00,880 The forms, the views, the dashboards, 403 00:14:00,880 --> 00:14:03,520 they all come from the Data model you've already defined, 404 00:14:03,520 --> 00:14:05,440 no separate Data Source configuration, 405 00:14:05,440 --> 00:14:06,720 no custom connection strings. 406 00:14:06,720 --> 00:14:08,720 The app and the data are one and the same. 407 00:14:08,720 --> 00:14:11,280 Power Automate can trigger on Dataverse events. 408 00:14:11,280 --> 00:14:13,680 When a record is created, updated, or deleted, 409 00:14:13,680 --> 00:14:15,600 you can kick off a flow automatically. 410 00:14:15,600 --> 00:14:16,880 Send an email notification. 411 00:14:16,880 --> 00:14:19,280 Update a related record, start an approval process. 412 00:14:19,280 --> 00:14:21,440 All triggered by changes in the data itself. 413 00:14:21,440 --> 00:14:23,440 This is where the real automation happens, 414 00:14:23,440 --> 00:14:24,880 not in scheduled batch jobs, 415 00:14:24,880 --> 00:14:27,520 but in real-time responses to business events. 416 00:14:27,520 --> 00:14:29,760 Power BI can query Dataverse tables directly. 417 00:14:29,760 --> 00:14:31,120 You don't need to export Data, 418 00:14:31,120 --> 00:14:32,400 build a separate Data Warehouse, 419 00:14:32,400 --> 00:14:34,560 or set up complex ETL processes. 420 00:14:34,560 --> 00:14:37,120 You point Power BI at your Dataverse environment, 421 00:14:37,120 --> 00:14:39,280 pick the tables you want, and build your dashboards. 422 00:14:39,280 --> 00:14:40,400 The data is live. 423 00:14:40,400 --> 00:14:42,560 When someone updates a record in Power Apps, 424 00:14:42,560 --> 00:14:45,200 the dashboard reflects that change in real-time. 425 00:14:45,200 --> 00:14:47,680 No stale reports, no manual refreshes. 426 00:14:47,680 --> 00:14:49,200 This creates a continuous loop. 427 00:14:49,200 --> 00:14:51,760 You analyze Data in Power BI and spotted trend. 428 00:14:51,760 --> 00:14:54,400 Maybe a spike in support tickets for a specific product. 429 00:14:54,400 --> 00:14:56,000 You act on it in Power Apps. 430 00:14:56,000 --> 00:14:58,080 Create a case, assign it to a team. 431 00:14:58,080 --> 00:15:00,320 You automate the follow-up with Power Automate. 432 00:15:00,320 --> 00:15:02,400 Send a notification, update the status. 433 00:15:02,400 --> 00:15:04,080 All of it runs on the same data. 434 00:15:04,080 --> 00:15:06,720 One platform, one source of truth, end-to-end. 435 00:15:06,720 --> 00:15:09,200 Now here's where things get interesting for the future. 436 00:15:09,200 --> 00:15:12,400 Co-pilot and AI agents use Dataverse as their memory. 437 00:15:12,400 --> 00:15:15,200 When you ask an AI assistant a question about your business data, 438 00:15:15,200 --> 00:15:16,720 it's not searching the internet. 439 00:15:16,720 --> 00:15:18,640 It's querying your Dataverse tables. 440 00:15:18,640 --> 00:15:21,680 Your customer records, your order history, your inventory levels, 441 00:15:21,680 --> 00:15:23,120 that's what feeds the AI. 442 00:15:23,120 --> 00:15:25,760 Not random web content, your actual business data. 443 00:15:25,760 --> 00:15:29,120 And it's secured by the same role-based security we talked about earlier. 444 00:15:29,120 --> 00:15:32,080 The AI can only see what the user has permission to see. 445 00:15:32,080 --> 00:15:33,120 Looking further ahead, 446 00:15:33,120 --> 00:15:35,840 fabric integration means you'll be able to analyze Dataverse data 447 00:15:35,840 --> 00:15:37,360 in one lake without copying it. 448 00:15:37,360 --> 00:15:38,640 No more moving data around. 449 00:15:38,640 --> 00:15:40,000 No more duplicate storage. 450 00:15:40,000 --> 00:15:43,040 Your operational data and your analytical data live in the same place. 451 00:15:43,040 --> 00:15:45,200 That's the direction Microsoft is heading. 452 00:15:45,200 --> 00:15:46,320 Getting started. 453 00:15:46,320 --> 00:15:48,080 So after all that, you might be wondering 454 00:15:48,080 --> 00:15:49,200 where to actually start. 455 00:15:49,200 --> 00:15:50,160 Let's make it simple. 456 00:15:50,160 --> 00:15:51,120 Start with one question. 457 00:15:51,120 --> 00:15:54,000 Do you need a system of record or just a lightweight tracker? 458 00:15:54,000 --> 00:15:56,720 A system of record holds your core business data. 459 00:15:56,720 --> 00:15:59,040 Customers, orders, products, cases. 460 00:15:59,040 --> 00:16:02,240 It needs to be secure, reliable, and connected to other systems. 461 00:16:02,240 --> 00:16:04,640 A lightweight tracker is more like a project list, 462 00:16:04,640 --> 00:16:06,800 a task board or a simple inventory sheet. 463 00:16:06,800 --> 00:16:09,360 Useful, but it doesn't need enterprise-grade infrastructure. 464 00:16:09,360 --> 00:16:10,960 If you're building a real business app 465 00:16:10,960 --> 00:16:14,240 with multiple related tables, security layers, and automation, 466 00:16:14,240 --> 00:16:16,080 Dataverse is the right foundation. 467 00:16:16,080 --> 00:16:18,480 You'll thank yourself later when you need to add a new table, 468 00:16:18,480 --> 00:16:20,960 change a relationship, or integrate with another system. 469 00:16:20,960 --> 00:16:23,120 If you're just tracking a few items for your team, 470 00:16:23,120 --> 00:16:24,640 SharePoint is probably fine. 471 00:16:24,640 --> 00:16:26,080 Use the right tool for the job. 472 00:16:26,080 --> 00:16:27,920 The easiest way to start is to go to make. 473 00:16:27,920 --> 00:16:31,360 PowerApps.com, create a solution, and add your tables. 474 00:16:31,360 --> 00:16:33,120 Don't worry about building an app yet. 475 00:16:33,120 --> 00:16:34,160 Just define your data model. 476 00:16:34,160 --> 00:16:35,040 What tables do you need? 477 00:16:35,040 --> 00:16:36,720 What columns go in each table? 478 00:16:36,720 --> 00:16:38,080 What relationships connect them? 479 00:16:38,080 --> 00:16:40,720 Get that right first, and everything else becomes easier. 480 00:16:40,720 --> 00:16:43,440 Use the standard tables before creating custom ones. 481 00:16:43,440 --> 00:16:45,360 Dataverse comes with tables for contacts, 482 00:16:45,360 --> 00:16:47,040 accounts, activities, and more. 483 00:16:47,040 --> 00:16:49,440 They're already designed for common business scenarios. 484 00:16:49,440 --> 00:16:53,680 The contact table has fields for name, email, phone, address, company, 485 00:16:53,680 --> 00:16:55,040 everything you'd expect. 486 00:16:55,040 --> 00:16:57,440 Before you create a custom/customer table, 487 00:16:57,440 --> 00:17:00,320 ask yourself if the standard account or contact table will work. 488 00:17:00,320 --> 00:17:01,360 It probably will. 489 00:17:01,360 --> 00:17:03,600 And it saves you from reinventing the wheel. 490 00:17:03,600 --> 00:17:06,000 Plan your data model on paper before you start building. 491 00:17:06,000 --> 00:17:08,000 Draw the tables, draw the relationships, 492 00:17:08,000 --> 00:17:09,840 figure out who needs to see what. 493 00:17:09,840 --> 00:17:12,800 A few hours of planning can save you weeks of rework. 494 00:17:12,800 --> 00:17:14,320 And here's a common mistake. 495 00:17:14,320 --> 00:17:16,560 Overcomplicating the model upfront. 496 00:17:16,560 --> 00:17:17,600 Start simple. 497 00:17:17,600 --> 00:17:19,040 Add complexity as you need it. 498 00:17:19,040 --> 00:17:22,080 You don't need 15 tables and 20 relationships on day one. 499 00:17:22,080 --> 00:17:23,200 Start with the core tables. 500 00:17:23,200 --> 00:17:23,920 Get them working. 501 00:17:23,920 --> 00:17:25,600 Add more as your requirements grow. 502 00:17:25,600 --> 00:17:26,880 And here's the beauty of dataverse. 503 00:17:26,880 --> 00:17:29,680 You can change the data model later without breaking your apps. 504 00:17:29,680 --> 00:17:30,560 Add a new column. 505 00:17:30,560 --> 00:17:31,680 Create a new relationship. 506 00:17:31,680 --> 00:17:32,960 Change a data type. 507 00:17:32,960 --> 00:17:33,920 The apps adapt. 508 00:17:33,920 --> 00:17:35,200 Try doing that with a spreadsheet. 509 00:17:35,200 --> 00:17:36,480 You change a column in Excel. 510 00:17:36,480 --> 00:17:38,400 And every formula, every pivot table, 511 00:17:38,400 --> 00:17:40,240 every chart that references it breaks. 512 00:17:40,240 --> 00:17:41,200 Not in dataverse. 513 00:17:41,200 --> 00:17:43,360 The platform handles the changes gracefully. 514 00:17:43,360 --> 00:17:45,120 That alone is worth the switch. 515 00:17:45,120 --> 00:17:46,240 So here's what we covered. 516 00:17:46,240 --> 00:17:48,640 Dataverse is a managed relational data platform 517 00:17:48,640 --> 00:17:51,120 that gives power platform apps a shared, secure, 518 00:17:51,120 --> 00:17:52,160 and scalable foundation. 519 00:17:52,160 --> 00:17:53,360 It's not just a database. 520 00:17:53,360 --> 00:17:55,200 It's a unified layer that handles tables, 521 00:17:55,200 --> 00:17:57,280 relationships, security, and logic. 522 00:17:57,280 --> 00:17:59,680 You don't have to build those things from scratch every time. 523 00:17:59,680 --> 00:18:01,040 The main idea is this. 524 00:18:01,040 --> 00:18:03,120 Think of dataverse as the office building. 525 00:18:03,120 --> 00:18:04,400 Not just a single room. 526 00:18:04,400 --> 00:18:06,400 The office building provides the structure, 527 00:18:06,400 --> 00:18:08,720 the security, the plumbing, the electricity. 528 00:18:08,720 --> 00:18:09,840 You just furnish the rooms. 529 00:18:09,840 --> 00:18:11,440 That's what dataverse does for your apps. 530 00:18:11,440 --> 00:18:12,960 It provides the infrastructure. 531 00:18:12,960 --> 00:18:14,160 You build the solutions. 532 00:18:14,160 --> 00:18:15,920 If you want to see dataverse in action, 533 00:18:15,920 --> 00:18:18,640 check out the video on building your first model driven app. 534 00:18:18,640 --> 00:18:21,200 It walks through the whole process from start to finish. 535 00:18:21,200 --> 00:18:23,680 And subscribe on your favorite podcast platform. 536 00:18:23,680 --> 00:18:25,920 Share this episode with someone who's still using Excel 537 00:18:25,920 --> 00:18:26,640 for everything. 538 00:18:26,640 --> 00:18:28,240 They might thank you later.