TKSPD

Join the 1% of data experts who know how to win with data. They're here: https://lnkd.in/g7wZzePe #cdo #data #business

Business-savvy data experts: 1. Speak business as a 1st language and data as a 2nd language. 2. Earn the trust and respect of business stakeholders. 3. Get promoted faster. Ready to become a strategic partner? Only 1% of data experts are business-savvy. Join them today👉🏿https://lnkd.in/gy9gFzv3

Thoughts? #data #datascience #cdo

#data #cdo

Data leaders are set up to fail. 🚫 Job descriptions for data leaders emphasize technical skills. This is problematic! Instead, JDs should focus on business, interpersonal and conceptual skills. In these days where the CDO is tasked with delivering tangible value, technical skills won't cut it anymore. What's needed is the ability to: *Understand business goals/objectives. *Help formulate business strategy. *Ability to translate business strategies into data & AI initiatives. *Communication/interpersonal skills to build consensus and influence. *Transformational leadership to motivate the data/AI team to contribute to business goals. *Big picture mindset to understand how data/AI can aid in threat mitigation and market opportunity exploitation. *Adequate knowledge of crucial data/AI technologies (& products) to achieve business goals. A data leader doesn't have any business with python, scala, and R. You have scientists, engineers, and analysts getting paid to do that. Your job is to provide strategic leadership for them. #data #business #cdo #skills --------------------- Looking to become a business-savvy data expert, to earn the trust of your business stakeholders? Check out the link in the comment section.

Agree?

Is your data team structured to deliver value? There are 3 popular structures: a) Central b) Hub & Spoke c) Fully distributed Option (a) keeps the data team away from the various business units. Would make sense if business users are data literate & self-served analytics is fully operationalized and data experts are focused on data management initiatives (compliance, privacy, governance, security, infrastructure) and on achieving a single source of truth (SSOT). Option (b) moves scientists and analysts closer to the business units to help them with analytical initiatives, leaving the rest of the data team to focus on data management initiatives. Apart from SSOT, it would make sense to create multiple versions of truth (MVOTs) in each domain so they can focus only on the data related to their department. A case for Data Mesh? Data Fabric? Options (c) moves all data experts into each department. With the current spate of poor-quality data, lack of business literacy amongst data teams, and inefficient infrastructure, I wonder if any company is successfully doing this right now. I think option B makes the most sense. But companies may go for other structures that best suit their current circumstance. More importantly, for data teams to add value, we must focus on the following: a) The business goals and objectives. b) The strategy chosen to achieve the goals. c) The constraints facing their domain. d) The business model. Overall, we must strive to acquire some degree of business acumen and communication skills to better understand and collaborate with business stakeholders. What do you think? #data #business #analytics #cdo ------------------------- There are short courses for those ambitious data experts looking to improve their business literacy & to acquire the needed skills to align data and AI initiatives with business goals. They're all here 👉🏿https://lnkd.in/g46jFtEW

Companies have goals. They want MORE of these: Customer satisfaction, employee engagement, productivity, sales, profits. And LESS of these: Turnover, absenteeism, risks, costs. To get value from AI (both traditional & generative), they must have a clear strategy on how it will help them achieve more of what they want and less of what they don't want. Companies don't need stronger models; they need a STRATEGY on how to get existing models to help them achieve their crucial goals and objectives. The model below makes it possible.

Attention data leaders (& experts)!!!

Data science is not just about crunching numbers, it is also about providing useful insights to add value to the business. Take a look at how descriptive, diagnostic, predictive and prescriptive analytics can be deployed to achieve business outcomes in the retail world. #data #analytics #datascience #ai #business ________________________________________________ Join thousands of data professionals who are becoming more strategic in their roles at https://lnkd.in/gXDkgPvk