The Digital BA Series: 5 Killer Questions Types For Digital Transformation

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Jun 02, 2019
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A recent article concludes that for an organization to get the desired results from their digital initiatives, such as data analytics, predictive analytics, machine learning, AI, etc., data scientists have to ask the right questions.[i] The article was written as a guide for data scientists to help them ask questions to get at the business decisions needed to be made when developing predictive models for these applications.

However, the article’s questions are stated in a way that might cause even the most informed business stakeholders to scratch their heads. If most decision-makers can’t answer them knowledgably, what can the organization do? Get BAs involved, of course! Having a BA participate in the question and answer sessions can alleviate a great deal of misunderstanding and help ensure success with digital projects.

This article imagines that for each of the 5 question types, there is a three-way conversation with a data scientist, a business decision-maker, and a BA. The questions the data scientist asks are from the article, which the BA rephrases to be more easily answered.

Type 1 – Alignment with the organization’s goals and strategic direction

Data scientist to business stakeholder – First things first. What exactly do you want to find out with this digital effort?

Business stakeholder to data scientist – I’m trying to predict sales of a new product we’re thinking of launching.

Business analyst to business stakeholder:

I’m sure this project can help with that effort. But before we talk about specifics of the types of information you’re looking for, what is the business need for this effort? That is, what problems are you trying to solve? Let’s make sure this initiative, which is not going to be an easy undertaking, will address your need. Perhaps there is a quicker, less costly way to achieve your goals. And I have some related questions that will give us more context:

  • How does this effort align with the strategic direction of the organization?
  • What are does the organization do well that will help ensure the project’s success and minimize risks?
  • How will this project help overcome some of the things we don’t do so well?
  • What opportunities are out there and how can the organization take advantage of them?
  • What should we be worried about? How are competitors, for example, doing with their digital initiatives?

Type 2 – Scope of input needed to create and train models

Data scientist to business stakeholder – Where will your data come from?

Business stakeholder to data scientist – I’m sorry, I don’t know the names of the specific databases. I thought I was here to make business decisions, not answer questions best answered by the IT folks.

Business analyst to business stakeholder - At this point we don’t need to know the names of the specific databases. What we mean by where the information will come from are things like:

  • Which business areas will be involved in this project?
  • Which stakeholders will have input into the decisions affecting the creation of the models?
  • Given that this effort will affect divisions in different parts of the world, who will establish the business rules?
  • What types of information will come from other sources, like social media and Google analytics?

Type 3 – Data presentation

Data scientist to business stakeholder – What data visualizations do you want us to choose?

Business stakeholder to data scientist - I’m sorry, I don’t understand what you mean. Do you mean like how I want to see the data? If so, I don’t know. What are the possibilities?

Business analyst to business stakeholder – There are a lot of tools that will take the data and interpret the results for you. They help you make sense of the tons of data you’ll be presented with. They can help you analyze data, point out anomalies, and send out alerts that you specify. They can be in the form of charts, dashboards, or whatever, but keep in mind that if they are hard to read, they will be meaningless to you. I can show you some examples and the pros and cons of such things as animation and use of images, but first let’s talk about the information itself.

  • What results are you hoping to get?
  • What type of predictions about your customers would be helpful? Your products?
  • What types of trends would be helpful to you in making business decisions?
  • What types of exceptions do you want to be alerted about?
  • What information do you want that’s actionable vs. historical?

Type 4 - Statistical analysis leading to the desired outcomes

Data scientist to business stakeholder – Which statistical analysis techniques do you want to apply?

Business stakeholder to data scientist – Well, statistics is not my strong suit. What are my choices?

Data scientist to business stakeholder – Regression, predictive, prescriptive, and cohort, and there are others, like descriptive, cluster.

Business stakeholder to data scientist – blank stare

Business analyst to business stakeholder – Maybe I can help here. These types of statistical analyses have a number of similarities. They include use of historical data, algorithms, models to train the machines, and business rules. Not to oversimplify and at a very high level, all predictive models make use of historical data and algorithms to predict future outcomes.

Here are questions based on examples of different outcomes using different statistical analysis:

  • What groups of customers do you want to target? Cluster analysis classifies data into different groups and can help you target certain customer groups.
  • What types of trends do you want to track? Cohort analysis allows you to compare how groups of customers behave over time.
  • What kinds of recommendations might you want as a result of the analysis? Prescriptive analysis not only predicts future outcomes, but it will “prescribe” or recommend the best course of action.

So to answer the question we need to understand what you’re trying to accomplish. We’ll let her (nodding to the data scientist) figure out the most appropriate analysis method and tool.

Type 5 – Creating a data-driven culture

Data scientist to business stakeholder – How can you create a data-driven culture?

Business stakeholder to data scientist – We already have a data-driven culture. Everyone in this organization understands how important data is to our ability to survive as an organization.

Business analyst to business stakeholder – This might be more complex than it first appears. In order to use historical data, which we need to do regardless of the chosen algorithms, it needs to be cleansed. Cleansing is needed to make the data predictive, and cleansing data takes lots of time and money. And it’s the last thing anyone wants to do. So I have some questions for you:

  • What’s the organizational commitment to cleansing dirty data?
  • Who will decide how clean the data needs to be? How clean is clean enough?
  • Who will decide who owns the data when the same data exists in multiple databases? In order to get the outcomes we want, there needs to be one single source. If the same data exists in multiple databases, someone needs to be its sole owner.

In sum, we’ve provided questions within 5 question types. However, to be effective, we BAs need to learn as much as we can about the digital world—about the world of digital transformation and what it means for the organization. We need to immerse ourselves in research and journal articles and think of how to make sense of it for our organizations. We need to think of digital projects from both the data scientist and business perspectives. And we can do that. After all, we’re BAs and that’s what we do best.


Author:Elizabeth Larson, CBAP, CSM, PMP, PMI-PBA

Elizabeth Larson, CBAP, CSM, PMP, PMI-PBAis a consultant and advisor for Watermark Learning/PMA. She has over 30 years of experience in project management and business analysis. Elizabeth has co-authored five books and chapters published in four additional books, as well as articles that appear regularly in industry journals. Elizabeth was a lead author/expert reviewer on all editions of the BABOK® Guide , as well as the several of the PMI standards.


References/footnotes:
  1. Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions, By Sandra Durcevic in Data Analysis, Jan 8th 2019, https://www.datapine.com/blog/data-analysis-questions/

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Featured
Jun 02, 2019
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6 Comments
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COMMENTS

abeal posted on Monday, June 3, 2019 9:28 PM
@Elizabeth -- Wow, the only good thing about the article you used as a reference is that it offers a great contrast with the questions you suggest BAs ask :-). To be clear, if a data scientist on my team started asking business stakeholders, "What data visualizations do you want us to choose?" and "Which statistical analysis techniques do you want to apply?", I'd make sure the person was summarily fired, heh.

Seriously, I've written here about how BAs can be instrumental in data science projects (https://www.modernanalyst.com/Resources/Articles/tabid/115/ID/5305/The-Critical-Role-BAs-Can-Play-in-Companies-Using-Advanced-Analytics.aspx), but any data scientist worth the title will also communicate with stakeholders using the questions you listed under "business analyst to business stakeholder", never the ones under "data scientist to business stakeholder".

Having said that, both DS and BA folks should pay attention to the examples of good questions to ask stakeholders that you offered here ;-).

-- Adriana Beal, Data Scientist at Carnegie Technologies
elarson0315 posted on Tuesday, June 4, 2019 1:40 PM
Thanks for the comment, Adriana. That was exactly the point of my article. Almost every article I've read lately (HBR and Forbes) has reconfirmed what I've been saying for about 2 years now --translators between the data scientist and the business are needed. What a great role for the business analyst--if they learn the digital language. (see my many articles and presentations on this subject for the last 2 years). I disagree with you about the data scientist being able to communicate and ask good questions, but that's a subject for a different day.
abeal posted on Tuesday, June 4, 2019 1:57 PM
Heh. Elizabeth, I'm sure there are plenty of data analysts and data scientists asking the wrong questions. But so there are BAs too, as I've had the opportunity to see over and over :-).

It's great that you're helping educate folks on such an important area, but maybe I need to introduce you to the data scientists in my network -- they reason they're so successful is precisely because they ask the kinds of questions you recommend.
elarson0315 posted on Tuesday, June 4, 2019 2:03 PM
Such a great discussion, The next place I'd take our conversation is--if the DS is doing translation, are they really doing a DS role? I have no idea what the answer is, but thank you for triggering my mind to go in a bunch of new and interesting directions. It would be great to carry on the conversation live sometime.
abeal posted on Thursday, June 13, 2019 6:56 PM
Elizabeth, I agree, great discussion.

In my view, your question could be asked of many roles, not just DS :-).

For example, my husband is a computer science researcher working in a business environment. He is always asking stakeholders who request a proof of concept about technology X, "What problem are you trying to solve here?"

If he is doing the translation himself, is he really doing the researcher role? In my opinion, the answer is a solid yes. Even though the approach he uses to make sure he's solving the right problem is the same a skilled BA would adopt, he's still doing his role as a researcher. It's part of his job to make sure the business problem is well understood and formulated before jumping into execution mode.

Likewise, that's how the successful data scientists in my network operate.

Granted, not all researchers who work with my husband put on their "BA hat" when presented with a solution idea disguised as a problem. I'm sure it's the same with data scientists (probably more often with PhDs who spent most of their lives in an academic environment, with little exposure to the "real business world"). This is where I agree with you that business analysts have a great opportunity to shine, doing the "three-way conversation" you describe to make sure every initiative is fully aligned with the goal of optimizing business outcomes.

However, I think it's important for BAs to keep in mind that, in a data science initiative, when they're doing this kind of translation, they're there to address a deficiency on the part of the data scientists involved. If you read any good book in data science or advanced analytics, you'll see a big portion dedicated to the tasks of formulating the problem. Understanding the problem is seen as one the most critical portions of any DS project. This is where DS/research initiatives in business environments diverge from most software engineering projects. In the latter is much more common for projects to require a dedicated BA in charge of translating business needs into requirements that the software team can implement.
elarson0315 posted on Friday, June 14, 2019 9:20 AM
The great discussion continues! My perspective is, as it has always been, a business perspective. All the articles I read tend to come from business sources, not DS research—HBR, Forbes, and occasionally CIO and Fast Company. And in article after article, the need for some form of translator to communicate between the business leaders and data scientists is mentioned. Two recent articles talked about the need for business leaders to either be a translator or interpret AI results. I’ve been talking about how a strategic BA, true trusted advisor to execs, is perfect for this role. Here are just two of the many articles coming out lately on this subject. https://www.cio.com/article/3356818/8-key-roles-of-successful-ai-projects.html?nsdr=true; https://hbr.org/2019/03/the-ai-roles-some-companies-forget-to-fill
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