Database Developer Job Outlook
According to the U.S. Bureau of Labor Statistics , database administrator jobs are expected to grow 10% per annum between 2019 and 2029 due to the high demand for these professionals across a variety of industries. Information gathering and utilization is more than a growing trend. Its an essential part of business survival in the 21st century. Marketers and business leaders are now finding ways to garner much more information from service users and customers, especially from online users, and this trend for more and more data is expected to continue for the foreseeable future. Database developers will be one of the main beneficiaries of this voracity for information and its many advantages.
What Do Data Governance Managers Do
Governing data across its lifecycle is a big job and for most organizations. Its hard to know where to start despite understanding its importance and value. The first step, according to Gurevich, is having the capability to collect data from wherever it is created and stored and organize it all into meaningful buckets.
You need to organize the data in a way that it can be digested by outside individuals, not just technologists but the business community. Pulling reports with millions of rows of information, static lists of locations and entitlements isnt actionable.
Showing data in a way that is relatable to other business influencers outside of IT is how actionable plans get formed.
Another important piece of information data governance specialists should remember, Gurevich says, is all the answers dont live in one place. Figuring out who owns the data and therefore who should and should not have access isnt the decision of IT and their rows of data. Determining access, whether or not data is still needed, and other decisions must be sorted out from a wide variety of sources and people.
And entitlements change often, Gurevich notes. The obvious examples are you have summer interns, people change roles, leave the organization, enter the organization. You need good housekeeping to stay on top of how an organization and its data evolves.
Identify The Data Governance Priorities
Step 2 in developing a new data governance program is to identify the data governance opportunities that overlap with the organizational priorities step 1 identifies. These organizational priorities may even have been developed into improvement initiatives already.
All analytics depend on timely and reliable data, so its likely that finding overlapping data governance opportunities will be easy, especially in organizations whose data governance is still maturing. For example, if decreasing adverse drug events were one of the organizations priorities, proposing the creation of an accompanying data validation process to ensure the accuracy of the analytics would be an easy sell. Look for an implicit data governance prerequisite such as this and call it out
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Tips For Learning Data Governance
Here are three tips for you:
1. First: Set a learning schedule for yourself. I feel it’s much better than leaving your learning to chance and just learning when you have some free time. Free time gets booked fast. I find that setting an hour aside each day to learn data governance would give me better results quicker than not reserving that time.
2. Second: Set goals for yourself. Some say that goals are overrated, but I think that if we set goals and we share those goals with others makes us more accountable to keeping to a learning schedule and accomplishing our goals. For example a goal could be the get your certificate of completion from one of my courses in two weeks time. Not only you would have learned something new that you could apply at work the next day, but now you have a certificate that you can showcase around to your colleagues, employer, and even job recruiters.
3. Third: I wish I would have had a study group or at least a studying partner. I feel it makes the process easier as you can share what you’ve learned and even teach each other. Plus, it enforces that accountability. Thank you for reading this article and I look forward to seeing you become a data governance professional or just learn a bit more about data governance as it’s important.
Skills For Customer Data & Governance Specialist Resume
- Understanding of healthcare and health insurance from an operational and competitive / regulatory perspective
- Understanding of project/program management, change management, data quality, and master data management
- Establishes rules for the automated cleansing, mapping, and syncing of data
- Basic understanding of SQL, databases from a data user perspective and basic reporting / statistical software, i.e. R, Stata, etc.
- Exposure to data flow diagramming, process modeling, and metadata
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Type Conversion And Syntax Errors
Once youve tackled other inconsistencies, the content of your spreadsheet or dataset might look good to go. However, you need to check that everything is in order behind the scenes, too. Type conversion refers to the categories of data that you have in your dataset. A simple example is that numbers are numerical data, whereas currency uses a currency value. You should ensure that numbers are appropriately stored as numerical data, text as text input, dates as objects, and so on. In case you missed any part of step two, you should also remove syntax errors/white space .
Experience For Enterprise Data Governance Specialist Resume
- Support Research Alliance, Internal and Plan facing analytic environments by responding to / addressing user issues and questions or directing the issue to the appropriate responding party and following up
- Creates, organizes and maintains a library of deliverables and decisions from the Data Governance teams, council and work group including metadata, process flows, data flows and data lineage documents
- Participates in, adds subject matter expertise to and assists in the facilitation of Data Governance council and work group meetings
- Assists in the development and execution of the Data Governance Strategy
- Collaborates with the Data stewards, data custodians and report developers to ensure consistent use of data definitions, authorized data sources, and calculations. Additionally, enlists business unit support to take ownership and responsibility for consistent definitions and quality of their data
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Experience For Model Risk & Data Governance Specialist Resume
- Assumes ownership and is responsible for the quality of one or more data elements
- Lead the development, management, communication and enforcement of Acceptable Data Use governance program to define and manage risks and opportunities in the use of data across multiple business units in the U.S and Internationally
- Direct and manage meetings or discussions required for administration of program which includes gaining consensus across a variety of stakeholders and documenting acceptable data use decisions
- Maintain and develop central repository for loading, monitoring and closing Acceptable Data Use requests
- Identifies key terms, documents their definitions, business processes, & associated rules
- Profiles data to see where business rules are violated and corrects where needed
Experience For Data Governance Specialist Resume
- Collaborates with the Data Stewards, Architecture, and Information Technology to prioritize and resolve: data sourcing, business rules, data aggregation, data distribution, and data consumption
- Ensures operational data stewards define and maintain: data definitions, descriptions, set values, quality rules, and calculations in the business glossary
- Establishes and maintains programs to monitor and measure data governance adoption and achievements
- Document various governance roles and responsibilities then educate data stewards, data owners and data custodians
- Serve as a liaison between Business and Functional areas and technology to ensure that data-related business requirements are clearly defined, communicated and well understood and considered as part of operational prioritization and planning
- Produce data governance dashboards on a recurring basis
- Enroll data owners, data stewards and data custodians around active management of critical data and key issues
- Manage documentation as well as remediation of key data issues
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Subject Matter Expert Responsibilities
Being a subject matter expert is all about balancing priorities. Not only are you responsible for your primary job functions, but you also contribute to other areas of the business. As you might imagine, subject matter expert responsibilities vary from business to business and role to role. Some expert knowledge may be required by only one department, but expertise from another may be required by many. In most cases, domain experts interact with the product development, marketing and sales departments most often.
Consult with and advise product development
The insights you possess are valuable. As a subject matter expert, your body of knowledge can have a huge impact on the growth and vision of the company. Working with the product development team, SMEs contribute their unique perspective to strategic initiatives and projects.
For example, a software company may have an SME in IT who is a data security analyst. Before building a new release, the companys development team would strategize with the data security analyst. Together they would ensure that the planned enhancements dont impact compliance. Alternately, the SME may recommend strategies to avoid risk.
Educate and strategize with marketing
Create proposal content with sales
For more about how the proposal team and subject matter experts can create winning responses, check out this blog.
Linking To The Real World Continued
Earlier, we spoke about how the data discovery phase takes more than 5-6 weeks. When Azure Purview was presented as a solution, it was looked upon with scepticism. With the reincarnation of Azure Purview, Microsoft has injected a lot of features into a genuinely stable productthat is here to stay. The scan of the entire Data Lake, which was over 1PB, took less than two days to complete. Customers can speed up this operation if the integration runtime is scaled from one to four. These scans can be run on a schedule and as frequently as required.
After the scan and tagging operation, one of our customers was able to complete the data-discovery journey for various projects in 1-3 weeks rather than 5-6 weeks per dataset. This is a marked improvement, as the catalogue setup itself is easy and is a PaaS service.
This product is just starting its journey outside private preview and has an exciting vision and roadmap. So please give us your feedback to help us shape the product in future.
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Key Data Governance Activities
Your data governance team makes official decisions related to data. Here are some common examples:
- A business unit wants to start collecting customer data in one of its processes.
- A new product or service is being launched, and it will require new business data and processes.
- A recent audit or risk assessment identifies business data risks that need to be mitigated.
In the above cases, the data steward who owns those areas must present or champion this work to the data governance team. In turn, the team will discuss and approve its usage. This includes items like ensuring no duplication across units, establishing the metadata and usage of the data and its fields, and outlining the key risks and mitigations that will be needed. Once data governance has approved the decision, only then will IT make application or system changes related to the data.
Linking To The Real World
Many of our customers have a petabyte-scale Data Lake, and it doesnt take long for data governance to become extremely difficult. The purpose of the Data Lake is to accelerate data projects by having the entire organisations information in one place. The average time taken to identify and retrieve the relevant data from the Data Lake is somewhere between 5-6 weeks. We might have heard of the old adage time is money, and its true for our customers. The time taken at the beginning of the project to discover relevant information is unacceptable because the project team is to be paid during this phase, and the business gets frustrated as no progress is made on the project. In the meantime, the competitor would have shipped the latest product when the project team was searching for relevant information to kick start the project.
The user journey for data discovery and mapping can be summarised as shown below:
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Skills For Data Governance I Wish I Acquired Sooner
Ok, so now you’ve accumulated some practical knowledge. Here are some of the skills I wish I would have acquired earlier on in my career.
Public speaking: There are a lot of presentations that data governance professionals need to deliver, a lot of meetings and workshops they need to lead, a lot of conversations to be had, so mastering your public speaking skills comes in handy.
Toastmaster clubs are great for that as they teach you how to speak publicly and also provide you with a platform where you can practice. I also recommend the book “TED Talks: The official TED guide to public speaking”. But in the end you need to practice. Just like working out regularly to get in good physical shape you should practice in front of a mirror a couple times a week and jump on any opportunity you can get to deliver a presentation.
Change management: Change management is a large contributing factor to the success of a data governance program. So learn about it. It might be that your organization has a change management model already so you can reach out to that team to give you a good intro and the toolkits that you can start using. Otherwise I recommend looking into the ADKAR Prosci model. It’s not the only one out there, but it’s a very well-known one.
What about at work? What can you do there? It really depends if you’re working in a place where data governance exists or not.
Sell The Need For Data Governance
Once youve gained executive commitment, youre done, right? That sounds good, but it takes a lot more than that to drive the necessary changes.
Instead, use organizational change management practices to sell the entire organizational body on data governance. Dont underestimate the need to bring the rest of the organization along with you. Since for some , their roles may grow or expand to support data governance, youll need them to be committed and not merely compliant.
Start by communicating the data governance vision from the top of the company, and then cascade it throughout the organization.
Next, hold smaller discussion forums and Q& As with key staff and stakeholders. There, youll hear their feedback and concerns and position yourself to address them from the outset.
Last, identify those staff that are enthusiastic, and enlist their services as your champions. In particular, find those staff that have key process or data ownership responsibility already, and actively recruit them to be data stewards, which well cover in detail below.
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Enable Early Adopters To Become Enterprise Data Governance Leaders And Mentors
As the first couple of teams achieve their data governance objectives, it may be time for them to generalize their processes for broader adoption. This shift in perspective from local to organizational naturally coincides with each early adopters transition from team leader to enterprise data governance leader. With new success under their belts, they are well-positioned to champion data governance generally and to recruit and mentor others their message is not just conceptualits based in their own hard-won experience, which gives them instant credibility and a host of concrete examples to drive home the how and why of data governance.
Deal With Missing Data
When data is missing, what do you do? There are three common approaches to this problem. The first is to remove the entries associated with the missing data. The second is to impute the missing data, based on other, similar data. In most cases, however, both of these options negatively impact your dataset in other ways. Removing data often means losing other important information. Guessing data might reinforce existing patterns, which could be wrong. The third option is to flag the data as missing. To do this, ensure that empty fields have the same value, e.g. missing or 0 . Then, when you carry out your analysis, youll at least be taking into account that data is missing, which in itself can be informative.
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How I Would Learn Data Governance
As you might know, I’m putting together a lot of content and creating online courses on data governance drawn from my own practical experience as a data governance professional. Knowing what I know now, I want to share with you how I would learn data governance if I had to start over.
Before we get into it I want to mention that the way people learn is different for everyone so your view of this might differ. I think I can still provide you with a roadmap that is independent of someone’s learning style, but please share in the comments your tips and tricks for learning something new.
A lot of people fall into data governance and they learn it on the job. This is not a bad thing, but it is very time consuming, and why wouldn’t you want to be in a data governance role sooner? After all, data governance jobs are highly paid with the majority of salaries around $124,500 a year .
So if I had to start over, I would want to acquire the knowledge and skills required for me to become a data governance professional.
Data Governance Goals And Benefits
A key goal of data governance is to break down data silos in an organization. Such silos commonly build up when individual business units deploy separate transaction processing systems without centralized coordination or an enterprise data architecture. Data governance aims to harmonize the data in those systems through a collaborative process, with stakeholders from the various business units participating.
Another data governance goal is to ensure that data is used properly, both to avoid introducing data errors into systems and to block potential misuse of personal data about customers and other sensitive information. That can be accomplished by creating uniform policies on the use of data, along with procedures to monitor usage and enforce the policies on an ongoing basis. In addition, data governance can help to strike a balance between data collection practices and privacy mandates.
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