When optimizing your data and analytics tech landscape, how do you balance business outcomes that you need vs those that will differentiate your business? For example, fixing core challenges in the architecture that hold you back vs adding new offerings that make your business stand out?

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Chief Data and Analytics Officer in Banking, 501 - 1,000 employees
This can be a challenge, as the two are often not mutually exclusive.  One has to focus on fixing core challenges in the architecture that hold you back and can optimize your data and analytics tech landscape to help your business succeed. These could be on things that could free up man hours, improve efficiency or reduce risk.  
Prioritization is key. Some challenges will have a greater impact on your business than others.  Its important to communicate to business stakeholders on the consequence of not addressing these core challenges.

Director of Data, Self-employed
A complex question. The simple guidance on "How do you eat an elephant?" comes to mind here. The first step is to create a cross-functional collaboration between business and engineering teams to ensure business and "IT" goals are aligned. Evaluate the investment required against the potential ROI. Choose a candidate that can support the Proof of Technology and Proof Of Concept and then agree mutually a way forward(Agile approach). Embrace change management! Regularly assess against your objectives , and adapt to changing needs.
Head of Data Strategy in Software, 51 - 200 employees
Finding the right balance between growing the business vs. running the business can often be a challenge.  In my experience your cost model here will have a big influence.  IT organizations that are directly allocated $$ from lines of business to operate can have far more flexibility vs. those who are only allocated a block of money on an annual basis. In either scenario it's critical to have full alignment between IT and the business on your roadmap and the priority of all the top initiatives throughout the year.  IT leaders need to establish a baseline for the minimum spend required to keep the lights on and maintain all existing services.  The resources left over can then be allocated across any business-led initiatives based on priority (which should be based on a combination of strategic imperative and expected ROI). Optimally, a PMO organization would be heavily involved in managing this process on behalf of IT/Data leaders.  
Practice Head, Data Science & Cognitive Analytics in Banking, 10,001+ employees
This is a global problem and to me the 80/20 rule worked well. 80% efforts to fox core challenges as it is your bread and butter. Also this will help strengthen your core capabilities for future technologies. 20% efforts on new value adding features as a delighted for the consumer. This means not just research but providing all resources to successfully implement the new offerings as well.
President, CEO, & CDAO in Services (non-Government), Self-employed
The best way to start is to prioritize different projects using a clearly defined rubric. Some of the areas to score are as follows:

How quickly can you get it done

The feasibility around making it happen (based on capabilities and costs)

Alignment with your strategic values and your mission

Expected revenue/margin

Advanced nature of the project (is it a true differentiator?)

Customer interest/requests

Number of competitors in the same space

Looking at those different things, you can score the different projects and then plot them in a quadrant priority matrix (like a Gartner magic quadrant) to understand what things you want to go after. You might need to have a portfolio of scoring dimensions on the tech side vs those on the business differentiation side, and make sure that you have at least some in each bucket reflected. So maybe you have two separate prioritization matrix charts. Or maybe you weigh things differently to make sure that you have somewhat of a balance between the two.

What it often boils down to is resources. If your revenue is the issue, it's like the chicken or the egg scenario: the things that we want to do to increase revenue we can't do because we need to fix the tech stack, but we can't afford to fix the tech stack until we get the revenue. If you don't have the revenue, then obviously there needs to be a conversation about how you get that money.

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