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Data Governance

Season 3, episode 10 of the DataTalks.Club podcast with Jessi Ashdown, Uri Gilad

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Alexey: This week we will talk about data governance. We have two special guests. This is the first time I have two guests in this podcast, so far it was only one. It will be a lot of fun. So we have two guests — Jessi and Uri. They both work at Google and they both are co-authors of the data governance book. (2:31)

Alexey: Jessi is a senior user experience researcher at Google Cloud. She conducts user studies with customers and then she uses these findings to shape Google’s data governance program and products. Uri is a product manager at Google and he is leading data governance efforts there. Welcome.

Uri: Thank you. (3:27)

Jessi: Hello! (3:28)

Jessi’s background

Alexey: Before we go into our main topic of data governance, let’s start with your background. We’ll start with Jessi. Jessi, can you tell us about your career journey so far? (3:29)

Jessi: It’s been a long one. I have done a lot of different things. I started out as a psychology major. Looking at research, I loved research back in the early 2000s, there were not a whole lot of jobs for somebody who had a degree in psychology. So I bounced around and did a lot of different things — real estate, coaching, athletics. Until I finally found this thing called “UX research”. (3:41)

Jessi: I am like “what is this UX you speak of”. It was this perfect marriage of psychology and useful stuff that you could do for people that was pretty immediate. I started in that seven or eight years ago. I worked at T-Mobile on their enterprise products for about four years until I moved to Google. I have been working with Uri pretty much the entire time I have been here at Google cloud. I’ve worked on data governance for the last three years.

Uri’s background

Alexey: Thanks! Uri? (4:46)

Uri: My background is much more banal for a product manager. Jessi, I never knew you were in real estate. Every day you learn something new. I started off as an engineer in a security company, then I moved to product. People were tired of me criticizing the product. They were like “How about you do something about prioritizing those criticisms?”. I have spent several years in a security company. I was an early employee in ForeScout and Check Point. Most recently before Google I was the vice president of product management in a security company for endpoints devices called MobileIron. (4:50)

Uri: It’s funny how I got to Google. Throughout my career, I cared for data on endpoint devices. Google's offer was about crossing the mirror, crossing to the looking glass, and getting to care about data at the source — at the server side, at the cloud provider side.

Uri: Data governance has been around for a long time. It’s something Google has been doing throughout its history. The challenge was to bring data governance to Google cloud customers. I am sure we will talk more about that.

Alexey: Now you also care about security but from a different angle. (6:24)

Uri: Yeah. (6:38)

Data governance

Alexey: We mentioned data governance, and the talk today is about it. So what is it? To be honest, I still have a really vague idea, what data governance is and why we need it. (6:40)

Jessi: I will start. I should start with saying what data governance is not only. Often people talk about data governance, depending on who you talk to, there are a myriad of different definitions. From our perspective, what it's not only — it‘s not only securing your data, so it’s not only about how we govern — what I like to call the scary stuff — PII, credit card numbers, things like that. It’s not just about securing those and monitoring access to those. (6:57)

Alexey: What is PII? (7:40)

Uri: Personally identifiable data. (7:45)

Jessi: Thank you. Things like a name, a social security number, date of birth. Recently with GDPR, a lot of companies and folks are worried about data governance — governing their sensitive data. We talk a lot about this in the book. It’s more than that. It’s also about knowing what data do I have. Period. If you don’t know what data you have, you cannot really use it. You cannot run analytics. You also cannot secure it. You cannot decide where it should go. What should you do with it? whether or not it is a duplicate? Should you retain it? (7:47)

Jessi: So, data governance encompasses the entire story of what data I have and how I can use it best. Whether that means securing it or using it for business analytics things like that. Uri, did I miss anything?

Uri: No. But I would like to offer a different perspective that adds on to what Jessi said. First of all, data governance is not new. Data governance, broadly speaking, is a practice of people, processes and tools — or the combinations of those three — in order to understand, care, and extract insights from your data. The reason I am saying it’s not new — the term became more popular recently. But it always was the case where any company would have a customer list and certain insights of that list. That has been ever since companies were born. The normal insights were like “This customer is behind on his payments” or something like that. (8:57)

Uri: Now something has been happening surprisingly slowly and yet had implications that made data governance a very popular term. Cloud did not happen in one day. The cloud transformation transition is still happening — slower than expected — but cloud got to the game unlimited infrastructure. That gave the ability for companies to say, “I would not only save the payments table, I will save the customer behaviors table as well. I will look at all the events that are happening as people interact with the product. I will know if people are spending time in my e-commerce website in the T-shirts department, maybe I need more choice in T-shirts. If people are buying a lot of T-shirts towards the 4th of July, maybe it’s a good time to make sure my supply chain is ready for the 4th of July.”

Uri: This trend of saving more data for insight which… I don’t actually know what these insights are right now. I will just collect all the data. This has made people uncomfortable and caused several things.

Uri: One — the regulator stepped in and said, “You are collecting a lot of information about people. This information may identify them. Those people have not signed up to be identified.” I don’t want the 4th of July T-shirt website to know my buying habits. That is highly personal. It may indicate where I live, it may indicate how much money I have. I’m not comfortable with that.

Uri: The European regulation says: A) you must explicitly ask people to share the information and B) you should separate the people with processes and tools that collect PII — personally identify information — from other systems. That will allow me, the customer of the T-shirt website, to say “I’m done with your website. I want to be taken off.”

Uri: Accumulation of highly personal data that can identify you led to some mishaps. The most notorious one is the Cambridge analytical scandal. There are others. Companies who were making money from data were forced to be more prescriptive and cautious about using the data. That chain of thought led to this practice.

Uri: But as I said, it was always there. If you look at the banking industry, they must keep — depending on the country — seven years of data. That will allow us to go back on transactions and detect fraud. That’s usually proven by a chain of transactions or a chain of money movements. Banking and the financial services community in general always had rules about how you can collect the data, how long to keep it, and who should be able to access it.

Uri: Data governance is taking that practice and making it more accessible. Hopefully, as Jessi said, it will become not just a security mechanism but rather an enabler that allows people to access more data safely. It defines ground rails that prevent them from falling into inadvertently sharing data. It helps with making more useful insights and bringing us more useful products. There are a lot of products — not just T-shirts or things like that — that are possible because we can understand what people want.

Alexey: I will try to summarize and you correct me if I am wrong. We have a lot of data. We start collecting data. In order for an analyst to understand the data, they need to know what we actually have. We need to have a way of knowing, we have this, this, this data. This is one part of that — having the data itself and having the tools to access the data. Then there are tools to catalog the data. The third component, you said, is processes — how people go about accessing the data, collecting the data, and storing the data. (14:04)

Alexey: You also mentioned that in banks, the rule of keeping the data for seven years has been around for quite a while. This is one of the data governance rules, right? They say “we must keep the data for seven years”. This is a rule they have to follow. And this is how they define data governance. Right?

Uri: That is a good example of retention — which is how long you retain the data. It’s an example of a data governance policy, yes. (15:14)

Alexey: So we have a bunch of policies like this and then collectively they are called “data governance”. Right? (15:23)

Uri: Very good. (15:31)

Implementing data governance: policies and processes

Alexey: This sounds excellent! As a company I really want to have it. I want our analysts to know what data we have and where this data lies, how to access this data. How do I implement this? (15:33)

Jessi: I wanted to add a little bit. You were talking about these tools and the processes. You were really hitting on the process that the tools have. I’m an analyst. What tool do I use to find my data, to access it, to run analytics? But as Uri was hitting on, there are policies — this is also a part of the process. The policies say what we do with this kind of data. He was giving the example with the banks: this is the kind of data we need to keep for seven years. But there is also the process around how we ingest the data. What do we do once it’s ingested? That’s a big piece that often doesn't get done or doesn’t get done well. (15:51)

Jessi: Uri was talking about collecting all this data. A lot of companies just collect it and dump it, right? Let’s just dump it somewhere in this big storage and then later we are going to figure out what to do with it. I’m not saying it’s wrong. But you need a process that tells you how to go through that. How to find out what it is. classify this.

Jessi: We are going to talk about data catalogs later, so I am hitting on it a little bit early. But if we are talking about implementing data governance, first you have to start with classifying the data. What are the different data classes that I want to identify? This is sensitive stuff. This is business critical.

Jessi: Let’s say that you have revenue data. Depending on what you call it, there might be four different kinds — ROI, sales, and so on — different labels that you want to clump together in a class. So, it’s about defining what are these different data classes that you want to have. Then you go through your data to map it to these different classes. At least it’s a first starting point. There is a lot more. Uri did you want to add on to this classification part?

Uri: A little bit. Do not start from a technocratic point of view. You want to have data governance. Great! Why? Are you subject to regulation? Are you afraid of data exfiltration? Are you in a business that requires you to have a certain liability or certain accountability to customers? What’s the core reasoning? It may be multiples of those things. As a business, you need to understand what kinds of data you hold. Then Jessi went into classification which is the way to go about it. So, answer, why am I concerned about data governance? What kinds of data do I want to do something about? Then the third part is about policies — the policies I want to put in place to answer that “why” with confidence. (18:24)

Reasons not to have data governance

Alexey: Can I ask something? You mentioned that, like we should ask ourselves why we need data governance. Before we implement this, we need to ask this “why” question. I am wondering if there are reasons why we don’t need it. Maybe it is just overkill for us? Maybe we are a small company, we don’t have that many analysts? Or maybe we do not store sensitive data? What kind of reasons can we have not to implement it? (19:40)

Uri: Jessi mentioned that data is valuable — because otherwise you wouldn’t be collecting it. If data is valuable, you want to make sure that you know what you have, where it is and how to access it. This is the act of cataloging, an act of data governance. I know that I am speaking from a very clear interest, but I’d argue that cataloging and understanding what you have is a step that clearly is in the path to getting insights. We can agree — and if not, I will try to convince you — that data insights are valuable. You will make your business more efficient, more streamlined. You will identify new sources of revenues. That will be one answer. Jessi seems to have more. (20:09)

Jessi: We talked in the beginning of how broad the definition of data governance is. Different companies are going to have different levels of intricacy into their data governance program. But we would vehemently argue that even if you have five employees, you still need some way of knowing what data you have, so that you can have insight from it. Maybe your processes are not going to be as complicated as in a bank that is collecting a lot of sensitive information. (21:06)

Jessi: We have been doing research on companies. There have been some that are small, cloud native. They don’t have a lot of employees, they don’t collect a lot of sensitive data and their data governance program is a lot less complicated. There are very big financial institutions that have many moving parts. They have to think about many employees. You are right, it can be a bit less complicated.

Jessi: But we’d argue that no matter what, if you have any data at all, you have to think about how you are going to structure a governance program for that data.

Uri: You collect data, you store it somewhere, you query it with some system. That costs money. You pay money for storage to retain the data and for compute to process this data. How do you know if this money is actually doing something for you? That’s data governance. (22:34)

Start with “why”

Alexey: So, it’s not a question whether we need data governance or not. We do need it. But the “why” — the question we should ask ourselves — is what’s the reason we need it. It’s clear we need it. But why do we need it? It’s not a decision between “yes” or “no”. It’s a decision between what are the problems we want to solve first — before starting to implement it. (23:00)

Jessi: Exactly. That’s going to help you figure out how extensive the data governance program should be. And Uri is completely right. Yes, you are going to need it. But figure out that “why”. Is it because I am collecting a bunch of sensitive data? Am I going to need to show regulators a certain something? For our T-shirts website, is business asking us for differences in Fourth of July versus other times? Do we need to increase our supply chain? Answering those kinds of questions will help you figure out what parts of a data governance program to prioritize first. (23:29)

Cataloguing and classifying our data

Alexey: I guess most of the time the answer is data catalog. You need to have this catalog of data. You need to know what kind of data you have. You said that we need to classify the data — first of all, we need to know what kind of data sources we have. Then for each data source, we need to understand each field in this data source. Is it personally identifiable or not? Is it business critical? What kind of classes can we have there in addition to these two? (24:14)

Uri: There’s no preset dictionary of classes that you use for every purpose in every business. The classes of data are really defined by you. There are examples of classes of data which we will go over, but they are defined by you. For each group of data, I want to assign a specific policy or a specific governing method. Here are common examples from the industry. (24:59)

Uri: We already mentioned one — PII, or personally identifiable information. We mentioned PII because the European regulators made the point that you must separate PII from other kinds of data. It’s a phone number which can clearly identify you, a phone hardware number, an address, or name. It depends on the business you are in. In some businesses you may be collecting names and addresses, but not in an identifiable manner. Google office is an address, but it does not identify people. There are thousands of people working there.

Uri: There is special health information which has been around for a long time. It’s the demographics of the patient, the treatments that patient is having, the success of those treatments. Especially in medical research, by the way, since we all went through an interesting year. The sensitivity of treatments on a specific person which can identify them. Maybe there are things they don’t want a processor of medical information to know. But these things are useful in determining the possible success of a treatment.

Uri: So, we have patient health information, personally identified formation, fiscal information, list of transactions and amounts and so on. There are other classes of data.

Uri: As we are potentially talking to people who want to start a governance program, try not to subscribe to outside defined classes of data. Think about your business. Think about what data you possess. Think about how you would like to control, make accessible and delete that data. That is how you should think about it. There are examples. We literally wrote a book about it. But think about yourself, not about others.

Let data work for you

Alexey: When it comes to implementing this data catalog, I imagine that I can start with creating an Excel spreadsheet or Google spreadsheet. Then I put all the data sources there — all the fields, all the classes. That’s probably a reasonably good start, but that’s not always the best tool. There are better ones. Do you know if there is something purpose-built that can help us? (27:48)

Jessi: =Yes. There are many tools. Uri, I am sure you have a lot of opinions on this. There are many tools and people create a lot of their own tools. But one of the important things is to really think about the future. How scalable is what you are doing? Is it going to have to scale enormously? One of the problems we have seen with data governance — it’s manual. Especially if we are talking about cataloging and classifying, doing this as an excel spreadsheet is so manual. (28:23)

Jessi: We have talked to companies where data governance often fails. It’s because it’s so manual. There’s no one to do that. In fact we talked to a company a couple years ago. They had a full-time data steward — a person whose sole job was to catalog, classify, do all this kind of stuff. That person quit because they said the job was — and I quote — “soul sucking”. That’s a true story, I’m not lying. Most other companies don't have a single person who is doing this job.

Jessi: The takeaway from that is to think about the process you are going to start. Think about how to keep this going. Who is going to be doing this work? Am I setting up a process that is just so time consuming. It’s going to be really difficult to keep it going.

Uri: I want to double down on a lot of what Jessi said. Alexey, what you discussed is really okay. I need to get data governance, I will open this spreadsheet, I will start putting it data, and I will work for my data. But if you work for your data, something is wrong there from the start. The data should work for you, not the other way around. That’s not my metaphor, one of the analysts who I worked with said that. Make data work for you. This is what data governance is about. (30:20)

Uri: Think about it this way. You need the data governance program. That probably means you have data somewhere. You process this data and you store it. Where is the data right now? This is something you should know because you pay for that storage every day. What systems do I already have that tell me something about my data? Take those systems, and then, as Jessi said, extend your timeline into the future five years, ten years. See if this is the place where I store the data right now. Am I happy about it? Is it performant? Is it scalable? Is it future proof? What systems do I have that integrate into this system today?

Uri: I am going to do a little bit of a shameless plug here. When you use a platform such as Google Cloud, we have tools already built into the platform. They support a lot of the basic workflows and basic needs. Google Cloud has a Google Cloud catalog. As you drop data in a file storage or columnar storage, it automatically assesses the data and indexes it — we are also known for being a search company. This allows you to find what you need. That’s a good starting point you can have today. To be fair, other clouds have similar systems. So, assess your situation right now. Then, if you need to buy a specialized tool, don’t work for your data.

Uri: If you’re not satisfied with your existing infrastructure, go back to the “why” — why do I need data governance? Do I need to comply with regulations? Or I need to extract more insights? Or I need to be wary about exfiltration? There are tools that are built around those concepts. Evaluate them in light of your current and future goals. Then decide. Did you want a concrete tool? Like “Do this kind of thing”? That conversation is very different for each particular company, in each particular vertical. I am sorry if that is disappointing.

The human component

Alexey: What I understood is, starting with an excel spreadsheet is not ideal. At the end, the person maintains it will quit because it’s a soul sucking job. You need to make data work for you. There are tools that look at your data and organize it. But I guess this human component will not go away. At the end, you still need to make a decision if this particular field is personal or not personal, is this field is business critical or not. There still needs to be a person who looks at this and makes this classification, right? (33:03)

Uri: Even more importantly, if this particular field is personal, what do you do with it? That can only come from a human. Do you delete it? Do you encrypt it? Do you allow it to be accessible only by certain people? That is most definitely a human component. That’s the human that the data works for. (34:02)

Alexey: And then data does not stand still, it evolves. We add new fields, we remove fields, and we add new data sources. Somebody still needs to keep track of it. Even though a system could be automatically synchronizing with the data sources and see a new field, then send an email saying “Hey, data steward, please take a look at this field”. At the end, somebody needs to go there and do a manual action. (34:25)

Data quality

Uri: I want to add to that. We talked a lot about binary concepts, like is this data A or B. Is this column protected or not? Should this be accessible to this person or not? There are also some fuzzy concepts in data governance. One of which is data quality. Data quality is more elusive. (34:59)

Uri: This data source contains PII. It’s entering through this system and going to that system. Do I trust it? Is it high quality enough to be used in a machine learning model? Is it high quality enough to make a business decision? This is a tricky concept. This is also part of data governance. We can talk about that as well.

Alexey: This is an interesting discussion. How do you know if this data is high quality? Is it because the analyst working in that team said so? (35:49)

Uri: There are mechanical ways to understand quality and more elusive ways. First of all, what is the source of the data? I’m working with an insurance company. They have proudly mentioned that they are at “World 3.0” now. I am like “What does that mean?” This said “We have processed most of the handwritten data. We are now reading data written in Microsoft Word 3.0. That’s probably 1990s or slightly later. We are making our way through the backlog.” So, some of their data is potentially handwritten. Can that be used today in making a decision about a mortgage? For an insurance company, that’s an interesting question. Only somebody who is familiar with it knows the answer. (36:02)

Uri: Then there’s data coming from an oil rig. Transmission there is iffy. There is a lot of noise on the communication channel. Should I be using that in order to determine gas leaks?

Uri: Then there’s a data source that is a combination of merging data sources A and B. I have a quality signal for both. What is the derivative quality signal? Those are technical. You can detect out of bound values, missing fields, errors in rows. Those are more technical practices that you can implement fairly easily and also give you a quality signal.

Alexey: So, it’s up to the producer of the data to say if the data can be trusted or not? They have a domain expert, a business expert — somebody from insurance or somebody from this oil ring. They can say that. So we still need to trust the producer who created the data. (37:45)

Uri: You can implement a lot of tools that give you a leg up. But I’d argue that the final decision of trust should be handled by a human. We are still useful. (38:07)

Defining policies

Alexey: So, we have the data sources. We have the data storage, where we put data. It can be a data lake or a data warehouse — some place where we keep the data. Then we have this data catalog. It can also communicate with our data storage and get all this information from there. We have a data steward who maintains the data catalog and knows what is going on there. What else do we need to have to implement data governance? (38:25)

Jessi: We talked about it a bit. The next step is having the policies on these classifications. Once you have your data classes, you have to define what to do with that. Are there certain ones that need to stay in this storage? For example, some need to be kept for seven years. For the business critical ones, what’s the freshness? (39:00)

Jessi: The policies can be varying. I just listed straightforward ones. We have done more research into policies and they are a lot more complicated than we had originally thought. For example, here is a simple policy — allow access of this data for these folks only.

Jessi: But depending on the purpose of accessing the data, it might change who can have access. We heard an example from a company that sells furniture. They had said, “We have somebody's email address. They bought a couch. We are going to ship them the couch. So the person handling these shipping addresses can have access to that data. Now we have a sale on couch covers. We would like to market this to folks who have recently bought a couch. But we — even if it’s the same analyst as previously — can’t access these emails for marketing purposes unless that customer explicitly gave permission.” How do I create a policy that captures that nuance for this particular use case of this data, that it cannot be used for that?

Jessi: So once you have your classes, you are going to have to think about what I do with those. What policies I am going to put on this? As Uri said at the very beginning, this all goes back to your “why”. This is how I figure out the key things that I really need to care about. If regulations are top of mind and that’s the most important, then my policy should fall in line with that. As you can see, you can go down a million rabbit holes of “govern this”, “govern that”, “classify this”, “classify that” policies. So, be able to map back to “What do I have to do?” and “What’s the most important?”. Start there.

Uri: You have organized your data. You have understood your data. You have attached tags to further understand and index your data. Now you describe a couple of policies — what is allowed to do with my data. The way I like to think about this — policies are not “thou shalt not” or “thou shalt”. They should not be thought of as prescriptive. (42:04)

Uri: Policies should answer the question “How do I make sure the CIO, the CISO, the CEO feel comfortable about everybody in the company accessing this data?”. The way to make those C-suit people feel comfortable is saying “Anybody can access the data, but only this specific group can access personally identifiable data. Or we make sure to scrub away any location information. It never even enters our system and therefore the data is safe.” Those guardrails enable democratization of the data and enable people to extract insights.

Uri: The tricky thing about insight — you don’t know what you don’t know. You may not know that if you collect the playback events of a music app, then suddenly a year later you can predict trends in music. That’s valuable. Then you can replenish your catalog and you can make more attractive music.

Uri: Now, since we are talking about tooling, there are mechanisms that allow you to do it. Jessi mentioned how you can make email available for everyone but make sure that when somebody accesses data, they can easily mass-mail about the new couches. But the system makes sure that only those people who opted in for email communications will actually get the notification.

Uri: There are other things like training machine learning models. Many machine learning models don’t need personal emails. They do need a way to identify this is person A, this is person B. You can hash the emails. Hash in a one-way function which creates a unique identifier, but makes sure that you do not expose any personal information. You still can differentiate between humans to do things like count distinct and aggregate by and so on. This will create a hopefully useful machine learning model to predict shopping plans.

Implementing policies

Alexey: So, these policies are “it can be accessed”, “it cannot be accessed”, “which team can access it”, “this should be hashed”, “this should not be hashed”. Is it something that is implemented on top of the catalog? Or is it built into the catalog? How does it usually look in practice? (45:04)

Uri: There are variations. There are systems that say “you should access all data through the catalog”. The catalog will normalize everything. The catalog will be your workbench. The catalog also cares for how the data is shaped before you see it. It maintains those guidelines. That’s one approach. The thing I like about this approach is it’s a central point for governance. It equalizes all the data. The downside is, it puts a constraint about the tools you can use. You can only use the catalog as an interface or tools that interface with the catalog. (45:28)

Uri: I’m a little bit biased here, but I’d recommend a system that makes sure the actual storage system implements the governance — with a control plane and the dashboard and so on.

Uri: By doing it in storage, implementing governance means you can use any tool that can access the storage and still be safe. That opens up compatibility with much more tooling. At the end of the day nobody should be working to figure out the guidelines. They should be invisible. And they should enable you to use whatever tool you want to do to generate the business purpose you came to the data for.

Shopping-card experience for requesting data

Alexey: But there should be some central place where I as an analyst or I as a data scientist can go and say “I need this piece of data”. Then somebody, a lawyer or data protection officer, or data producer, can evaluate my request and say “You don’t really need this data, use this instead” or “Your use case is valid, the access is granted”. We need a tool that allows that. Should it be on top of the catalog or elsewhere? (47:02)

Uri: It can be on top of the catalog. It can be on top of the storage. It can be agnostic to both. We recently launched a product called Dataplex. I’m biased, but it’s a good example. Dataplex sits beside all the storage systems in Google and tells you, “These users have access to this data. Are you happy about this?” and “This data is accessible for these users. Are you happy about that?”. (47:35)

Uri: There are other systems. Just to give a shout out to Collibra for example, which is a data catalog data governance system. They implement a shopping cart experience, which is as you described. I need the financial information for the last two years in order to perform the analysis. I searched for financial information in the last two years. I am presented with certain data mods. Now I can request access to that. You get a shopping-cart-like experience with authorizing and auditing. This is another concept of data governance, which records this transaction, which is also important.

Jessi: Alexey, a lot of what you were describing, it’s manual too. As Uri said, you have to have people involved. That’s important. But think about how much of this can I automate or make easier for myself. I have classified my data. I have added tags. These tags do certain things with the data. Then it’s not on us, the data producer. (48:50)

Jessi: We have talked to a lot of data producers or data engineers who spend so much time dealing with requests of analysts and data scientists, wanting this data set. Why do you need it? They give it to them and then they don’t use it. I am sure many people are familiar with that.

Jessi: When implementing this process, make some of that not so manual. If that is a tool that you use or just the way that you have classified it and tagged it. That will help you also.

Proving the value of data catalog

Alexey: Thank you. We have a question. It’s also about the data catalog. How can we systematically evaluate data governance initiatives such as having a data catalog for data access? How can we prove their value to the business? (50:19)

Uri: I would go back to the business objectives. Start at the highest level. You are storing a lot of data. You are spending effort and compute resources — it costs money to process this data. You can use the catalog to answer this question: “What’s the relationship between the money you spend on storing the data and the usage of that data?”. The catalog can answer it and show you the most used tables. Then you can make decisions such as “There’s a lot of data that nobody is touching. Let’s move it to cold storage, which is cheaper.” That could be one way. (50:36)

Uri: There is another way. I am subject to a regulation. This regulation can take a bite out of my revenues, if I do not comply. How do I comply easily, cheaply, without a lot of audit and manual labeling?

Jessi: If you go back to “what governance do I need?”. A lot of times folks think they have to have this entire governance program. They need all these tools and this storage and all this head count. It’s super complicated and super expensive. And then your C-suite says “Hell, no. That’s way too much. We are not going to do that.” Oftentimes that’s where this question comes from. But if you can look at what is the minimum. We wrote a book on this. So, we don’t think data governance should be a bare minimum thing. (51:53)

Jessi: But if you are trying to implement something, you need to start somewhere. Look at what is that minimum that doesn’t require a huge amount of head count, that doesn’t require a lot of manual stuff and a ton of expensive tools. That’s the place to start.

Jessi: As Uri said, once you can start doing some of these things, once you can start showing the value of it. We have all this data. Now we are able to get more insights from it. Then it will start to sell itself. Then you can see how I expand from there.

Alexey: How to find this minimum? (53:08)

Uri: Again, look at your business objectives. I need data governance. Fine. Why? Because regulation. Because insights. Because things. Determine that from your objective. What you need to solve for successfully will be that minimum. The maximum will be how do I solve this in a way which is future-proof, caters for buying other business units or accumulating 10x data from YX sources. That will be the maximum. That’s the difference between planning for now and planning for the future. As a general approach, in the world of lean startups, you need to plan for “now” plus 1x. Because you do not want to be surprised. (53:21)

Alexey: That makes sense. For the first iteration, you plan as much as possible for now, but allow yourself to also plan for the future a bit. Don’t be so ambitious. Then prove that this thing is valuable and only then expand. Right? (54:15)

Uri: Yes. (54:34)

Using data catalog

Alexey: We have a question from Danish. Could you explain with examples what a data catalog could contain and how we can leverage it? We talked a bit about it. (54:37)

Uri: Here is what I want data catalog to do for me. I want data catalog because it has a lot of technical metadata information associated with data — table names, data set names, column names. Let’s begin with that. Collect all that, index all of that, do it without effort or with little effort. If I have a new table and I need to go to the data catalog and register that table — that becomes the manual labor you, Alexey, mentioned earlier. I don’t think anybody wants a data catalog that creates more work for you. (54:56)

Uri: The better data catalog has a logical layer or business layer on top of this technical information. A lot of my data contains information about customers. Here is a customer entity, it includes these kinds of things. Customer entities can be found in these systems, in these datasets, in these tables.

Uri: Catalogs also usually care for lineage of the data. That’s another kind of metadata which says where the data came from, where the data is going next. They do this by both analyzing your SQL and data warehouse as well as analyzing other flows of data in and out of your organization. Obviously we want to be able to search rather than list all of this data. Those are like the core things, the features I see in many partners.

Uri: Democratizing data quality is an interesting trend. People can five star certain data sets, if they are happy with them or not. Shopping-cart experience which we mentioned earlier. Policy management is sometimes a part of a data catalog, sometimes not because you already have a view of your data. Many data catalogs also include additional business metadata. For example, I don’t only want to know if there is a customer entity. I also want to add if that’s an internal customer or external customer information. Those are things that are normally found in a catalog.

Uri: Again, think about the catalog as the dictionary that leads you to all of your data — where it is and what it means. So, you go from a table in a dataset to what does this table mean, where did it come from, who uses it, how often can you use it. That will be the kind of information in a data catalog.

Data governance = data catalog?

Alexey: I think most of the time today we spend talking about data catalog. I am wondering, is data governance all about having this data catalog? Or are there other things that are important? (57:46)

Uri: We talked about even more basic concepts as well. Data is stored somewhere. That’s something the catalog often scrapes. Data is processed somewhere either in a data warehouse or data analytics engine. Those are things that are part of your strategy. Then, there’s a policy engine which is either part of the storage system or a data catalog. Data catalog became synonymous with data governance because it’s a tool used by two groups — data analysts and data stewards. Each of them has a different purpose in mind. One understands the data in order to query it. The other understands the data in order to govern it. That’s why a lot of data governance tools exist in the catalog or have catalog as a part of it. (58:05)

Wrapping up

Alexey: We should be wrapping up. Anything else you want to mention before we finish? (59:04)

Uri: Thank you for having us. This was great! (59:12)

Jessi: Yeah. This is fun! (59:14)

Alexey: Any tips? (59:17)

Uri: Read the book. (59:20)

Alexey: How can people find you, if they have questions? (59:24)

Jessi: I know we are both on LinkedIn. That is probably, at least for me, the easiest way. (59:34)

Uri: LinkedIn, Twitter, and the normal communications channels we all have. (59:43)

Alexey: Thanks a lot for joining us today, for sharing your knowledge. Thanks everyone else for joining and listening, for asking questions. (59:54)

Jessi: Thank you too. (1:00:40)

Alexey: Bye. (1:00:40)

Uri: Bye. (1:00:40)

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