How To Conduct An Effective Informational Interview
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One thing that will gain you interviews with your target companies is by talking with more people who are “in the know.” Do this by conducting more interviews of your own.
The informational interview is an effective way to build your network and gather information to move your career forward. Informational interviews can actually be quite fun. Meeting for coffee, or briefly in someone’s office, takes the pressure off both parties. The job seeker is simply asking for information, guidance, and advice. The person being interviewed is just providing that information and expertise. No one is saying, “Please give me a job!” And no one is making an offer. It’s just a chat.
That’s right—this is not about asking for a job!
Not right away, at least.
As a job seeker, you should hope to get some questions answered relevant to the industry you are in, the company where your interviewee works, and the company’s competitors. You should ask about good ways to network in the field. Getting names of other professionals to contact for further informational interviews is a great result.
Here’s how to do it:
1. Make A List Of People You'd Like To Meet
Make a detailed list of people you’d like to talk to about the next move in your career. People who have mentored you in the past, people you admire in your field, and people at your target companies are great people to meet with. Anyone who may be able to help you, or knows someone who can help, should go on your list.
2. Find Them On LinkedIn
BigstockLook up your target companies on LinkedIn and see who works there. You can find their contact information easily on their LinkedIn profile. If they don’t have a profile, Google them to get their phone number or email address.
3. Call Them Or Send Them A Message
BigstockPhone messages often go unreturned and inboxes are often full for many professionals. Be persistent, try multiple avenues of communication, or go through a contact’s assistant (but don't harass them). Be clear that you just want a brief meeting to discuss a specific set of questions regarding their job, company, and industry, and that you are not inquiring about a job opportunity.
4. Meet With Several Contacts Each Week
BigstockBe committed to holding 2-3 informational interviews consistently. Stick to the amount of time that you mentioned when setting up the meeting and don’t go beyond it, no matter how tempted you might be! You can always set up another meeting or use additional questions as a reason to stay in touch and build the relationship.
5. Bring Your Resume (Just In Case)
BigstockDon’t offer it. But, if they ask for it, you’ll be prepared. Also, if the topic comes up in conversation, you can ask for advice on how to beef it up. Are there classes you should take? Organizations you might join? Get their feedback on what might make you a stronger candidate.
6. Ask Relevant Questions About The Industry, Company, Or Position
Bigstock- How did you become interested in this field?
- What brought you to this company?
- What is a typical day like in your position/department?
- How much time do you spend doing ______ each day?
- What types of problems do you solve in your position?
- What can you tell me about the corporate culture?
- What are the biggest challenges the company faces right now and in the future?
- What skills and qualities make someone successful in this field?
Bigstock- With the little you know about me, what suggestions do you have that might help me to break into the field or a company like yours?
- If a position were to become available here, would you keep me in mind?
- What other companies would you recommend for me to explore?
BigstockAsk who they know who might be a good person for you to speak with. Get their contact information. Ask if it’s okay to tell the new connection who referred you.
9. Send A Thank-You Message
BigstockDo this within 24 hours in the format of your choice. Email is convenient and green. You might be perceived as tech-savvy. Or, you might be looked upon as impersonal. A handwritten note is perceived as more personal by some. Or, on the flipside, archaic. It’s up for debate and depends on your industry. Just pick one and thank your interviewee quickly.
10. Stay In Touch
BigstockConnect on LinkedIn and send occasional messages updating the contact on your progress. If you come across any articles that might help them, pass them along. Monitor the company and your contact using Google Alerts. When you discover they have gotten a promotion or have spoken at a conference, be sure to send a congratulatory email. Keeping in touch will help the relationship to grow.
Eventually, the informational interviews you conduct will pay off. Word will spread that you are looking for a new position. People will remember your personality and respectfulness. The relationships you are cultivating will result in a network that is keeping you in mind for when their company is ready to hire. Before long, you will be interviewing for real!
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This article was originally published at an earlier date.
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Blog Organizing Data Governance For Business Impact Anthony BrandaJuly 19, 2022
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So, following up on my previous articles, let us discuss how to organize data governance (DG) for business impact. Yes, emphasis on business impact. Powered and enabled by technology but calibrated based on business use cases of data and the expected ROI. Leverage your data strategy and data management framework and assessment of your data governance maturity to architect the organization.
Data governance is managing data as a strategic asset for business impact… We can't stress this point enough.
So, What Is The Role Of The Head Of Data Governance?
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The HO of data governance is a strategic business and data leader who can ensure alignment between the architecture and business intelligence teams and the business. Said different, the HO of data governance is not solely a hands-on role; it has a blend of hands-on knowledge of technology and a deep understanding of data analytics but all for the aim of serving as a strategic business interface to maximize the use, access, and acquisition of data for competitive business advantage. The HO of DG is a strategic business leader and has to be able to serve as a unicorn between technology and the business, framing the business problem into data analytics and technology solutions that move the business forward. So while topics like technical metadata, the configuration of tools, file formats, and setting up the data catalog are essential, they aren't the starting point of the DG leader.
The business usage of data should come first in terms of managing data as a strategic asset. The HO of data governance understands the use cases and how to put CDEs through lineage. The role is not only about setting up the data catalog and file formats for big data but also ensuring the correct data is a gold standard based on business needs and use cases warranting its inclusion in the catalog. It is a strategic and hands-on role but has its implementation teams to make DG a reality. The HO of data governance is responsible for uplifting the maturity levels of data management and the overall function, including the technology as an enabler.
Further, the HO of data governance is a leader that is the steward of the data management framework and ensures that data is uplifted across the lifecycle. I do not distinguish much between the data governance leader, the chief data officer, and the data management leader. These are variations on the same theme, and they are creating much confusion by separating all of these. Also, when the head of the data platform or data warehouse reports to the HO data governance/CDO/data management, it is a maturity multiplier as changes can be made to the data warehouse, and metadata can be more closely aligned between producers and consumers.
We have sub-optimized data governance by making it about the technology and thinking that architecture, engineering, or technology is the primary driving force behind treating data as a strategic asset. These essential functions should be plugged in and reported into the CDO/HO of data governance. The business side of what we govern should be given slightly more criticality than the technology. While I acknowledge that technology is essential, these folks should all hold significant roles. But to make data governance about configuration, engineering, or file formats is a sign of a struggle within the organization; that is why there is such a high turnover in the CDO role itself.
Teams Under The HO Data Governance
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1. Data Lifecycle Management and Uplift: Includes the data stewards, business owner management, and the data analysts and business analysts that help uplift data.
a. Data lineage and data uplifting efforts are made here.
b. Data dictionary and cataloging efforts.
c. Business glossary for getting standard business verbiage and metrics across the organization.
2. Data Quality: This team creates quality measures and KPIs, and dashboards to provide the monitoring function and works with risk to make sure the proper controls are in place to ensure quality data.
a. The team works with data stewards, data owners, and data custodians to align the data quality measures with the business and technical requirements and considerations.
b. The team works with engineering to create self-service analytics dashboards. It runs fit-for-purpose forums to ensure data stewards know quality issues in each line of business. This team serves as a squad of people who centrally monitor quality and work with data owners and stewards to document quality issues and refer any fixes to a remediation team.
I am often asked what data quality measures are anyway, and I have a start list that I recommend all firms adopt, and they are:
- The number of CDEs that are monitored
- The % improvement in the completeness, timeliness, accuracy, validity, consistency, and uniqueness of each data element.
- The degree to which data satisfies the requirements of its intended purpose
- The structure and semantics of each data element and data set as a whole
- The % reduction in data entry and data output errors
3. Remediations/Information Management Review: This team takes any issues raised by data quality and other functions and manages the fix of any problems or errors throughout the firm. This team may:
a. Make sure errors are fixed at the source.
b. The source of the issue is identified and resolved or minimized.
c. Data that is missing is extracted properly for fit-for-purpose use.
d. Data privacy rules are enforced.
e. Data risk and controls are put into place at the source, stagging, or ETL to ensure errors or issues are addressed.
f. This team reports to data risk and compliance on how issues are being addressed and closed out.
4. Data Risk and Compliance: This is a small team of data risk and protection and legal professionals who maintain a director's questionnaire with all the identified data risks and how these risks could potentially create problems within an overall enterprise risk framework. This team works with the HO of data governance to co-lead a data risk forum where the progress on addressing data risks is reported. Line of business (LOB) data owners and stewards also sit on this data risk committee.
5. Data Engineering (within DG): I believe this should be a defined separate team reporting into HO data governance/CDO/HO DMO. This squad or tribe or pod contains engineers who specialize in the technology to govern data. They are experts in Collibra, Informatica EDC/Axon. They also help stand up any stagging data areas or pipes to move and manage data. Again, this is a separate team from the team that manages the data warehouse or data operations. It can be the chapter lead or squad leader who manages this team. The expectation should not be that the head of the data governance organization or leader is the same person configuring Collibra or working on file formats or technical metadata. This team should be joined at the hip with the HO of DG to help implement governance and all its sub-activities, including lineage. The HO DG should know how the technology works and evaluate if it serves the team's needs. However, getting the data in and out of the data governance tool kit is this team's and squad leaders' primary responsibility. This is often a missing element in organizational design and talent architectures.
6. Data Management Leader (aka Data Analytics Platform Lead): This is the role where the data analytics platform and engineering team (formerly the BI team) reports to the CDAO/ HO of DG. This allows for much more effective data governance as both analytics and DG are significant clients of the platform. This allows the learning from analytics to quickly be incorporated into DG and the platform. Your data warehouse or big data "platform" should be a living, breathing “brain.” Connecting the dots across DG and platform helps keep technical metadata up to date and creates a form of rapid cycle learning.
This is my POV on what constitutes a best practice data governance function and team. There certainly can be other variations of this, but this is what I have seen get traction. Let me know what your experience has been with data governance teams. What is the best way to define and organize DG for business impact?
I am looking forward to your thoughts.
Also, I believe some of the best teachers are on LinkedIn and YouTube. I highly recommend George Firican's data governance course for a good POV on what I consider good data governance. His course can be found here.
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Part Three A: Standing up the Data Management and Governance Teams: Why CDOs Don't Have More Impact
As we previously discussed, the final step in our five steps to achieving data governance is to stand up the data management organization, including data governance.
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As of this writing, I see many industry experts discussing how the chief data officer (CDO) role is struggling to gain traction in many organizations and how the CDO and often the analytics officer have yet to prove the value of the data analytics.
I believe some reasons for this are:
1. Strategic Perspective: Many organizations view data governance (DG) as primarily a tech function, not a hybrid or business-aligned one. While having DG report to tech leadership may work, these leaders must be well-versed in bridging gaps to other functions and are open to receiving organizational business input. If DG is part of tech, it can only be well represented across the organization if tech leaders see DG as a strategic enterprise necessity and an operational activity.
2. Skillsets: In many organizations, with DG and data residing in tech as a siloed vertical, I find the function cared for by only data architects and engineers. While these are essential functions, they cannot drive change, adoption, and business alignment alone. The customers of data governance (e.g. analytics, marketing, etc.) need to be heard by tech leaders with hybrid skills in DG technology and the business applications of data. These leaders with varied skills can drive out an integrated data and data governance strategy and framework.
[There may be other reasons—please let me know if there are other reasons.]
What I am about to say may ruffle some feathers, but of all organizational configurations, at least in financial services, and from observing what works and what doesn't, what works best for any DG program is when the analytics and data functions are under the same leader. This arrangement better aligns the business use of data through analytic insights.
I have seen centralized, hybrid, and decentralized functions. When analytics and business insights are closely connected to DG, there is a continuously reinforced learning loop. I will expand on this in another post. Still, when business needs and analytics teams drive and participate in data governance, change happens faster, and maturity and adoption rapidly increase. This also assumes a mature and well-governed analytics function with business alignment and linkage.
Data Governance is a hybrid function among the business, the data, and the technology teams. Said differently, data is the raw material with value added is transformed into information when you take information and combine it with a business need that becomes knowledge that has a business impact. Data governance and business lines all influence the components.
I've written about data management organizations (DMOs) and data governance organizations. What's the difference? I view data management as the day-to-day work of following the rules and policies set forth by governance by maintaining the data infrastructure. I believe the tight alignment of these functions is achieved if they are in the same team under one head of (HO), either the DMO or HO of data governance. Ideally, have data management (DM) and DG under one HO under the CDAO or have a HO of DG or DMO and HO of data management (a.k.a. HO data analytics platform) as direct reports to the CDAO.
There is a disturbing trend in some organizations to create ever smaller and smaller departments for data quality and governance that is separate from the DMO and the engineering teams that provide the plumbing and tooling to enable data governance. With such diffuse efforts, no wonder data governance teams and CDOs (in general) are not getting the traction they need. The impact is lessened by breaking the functional components down too finely, more handoffs are created, and additional cross-department efforts are required to align and implement all aspects of data governance.
Other industry commentators agree—this doesn't work well in either large or small organizations.
In my next post, I will offer my take on a data governance organization built for impact.
Readers: I'd like to hear your thoughts on this post. Let me know about your experiences with data management and data governance.
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Now Hiring: Remote SysOps Engineer
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3 Important Career Lessons Learned On And Off The Field
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How Ex-NFL Player, Chris Gronkowski, Is Using Social Media To Change Careers
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Originally posted on: https://www.workitdaily.com/how-to-conduct-informational-interview