The challenge in many early-stage companies is that the executives running them often don’t have as much deep experience in critical areas needed for growth. Usually, the founding team is still learning and evolving their skills and depth of knowledge in the domain.
This is good in the beginning when things are moving quickly, as you need flexible leaders who can quickly learn in new environments. But as you scale, you begin needing expertise and depth as well.
Here are three key questions I ask leaders facing the challenge of how to evolve and plan their professional development. These will not only help the business create the best leadership team; it will help keep people engaged and motivated throughout the growth process.
1. What drives engagement?
The first question to ask yourself is: what do you really enjoy doing that keeps you engaged and continuously challenges you? It’s more than just liking something. You need to really be compelled and driven to get better at it over time to be able to maintain your focus over the long term.
Write down all of the tasks and work that you do. Now think about when and how you engage in that work. Find the three to five things that you notice a high degree of engagement in.
Look for periods where you lose track of time or tend to push off other tasks, or even things like eating, to spend more time doing. Find those activities where you’re totally engrossed in the work and forget about everything else.
If you can’t find any obvious activities, find the ones that you have the most curiosity about and start carving out a little more time and focus to get into them and notice what happens. Does your curiosity increase or do you get bored quickly and want to move on?
2. What are you really good at?
It’s not enough to just enjoy something. You need to be good at it too, in order to create value. Something you love doing that you’re not proficient at is a hobby, not a profession. Look for things where you get lots of positive feedback around and things that people ask you to do frequently.
If you can, get more feedback from colleagues and bosses about what they see as valuable skills and contributions. You don’t need to be a world expert on something, but you want to be seen as having a high degree of skill and performance. Focus on what other people think you’re really good at, not just your own assessment.
Sometimes, we know too much and are too self-critical. You may feel like you don’t really know what you’re doing, or know that there is so much more to learn, but someone not educated in the field may see you as brilliant. It’s more about what others think, not just what you think.
3. What can nobody else do?
Finally, you need to look for the things that nobody else can do like you can. If everyone else is also going at something, there is little room for differentiation or to be seen as a unique resource. You want to find something that you enjoy, that you’re good at, AND that nobody else can do.
If you can’t find anything truly unique off-hand, start looking for ways you can add or combine skills and experiences to create a valuable and unique capability.
Maybe you’re really good at contract law, minored in environmental studies in college, and are a hobbyist rock collector. Can you combine them to focus on contracts involving public land use for mining and forestry?
Developing a niche is an excellent way to become highly sought after and highly compensated. Don’t be afraid to really carve out a unique domain; just make sure there are at least a handful of people and companies who really need that expertise.
Becoming a high-achieving executive is about creating unique and desirable value in your market. Focusing on these three questions will help you find something you’re not just passionate about, but something that you can create a real niche around. As they say, the riches are in the niches.
By: BRUCE ECKFELDT, FOUNDER AND CEO, E&A, GAZELLES/SCALING UP BUSINESS COACH, @beckfeldt
With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:
How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
What prevents people from more deeply exploring and using data?
In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?
With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.
Current realities and barriers to scale smarter decision-making with AI
With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.
Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.
At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.
Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.
The opportunity to solve data science challenges
This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:
Reducing data exploration and prep work
Empowering analyst experts to deliver data science outputs at lower costs
Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
Extending, automating, and accelerating analysis for business groups and domain experts
Reducing time and costs spent on deploying and integrating models
Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias
Business scenarios that benefit from predictive analytics
There are several business scenarios where predictive capabilities can be immensely useful.
Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.
A solution to consider: Tableau Business Science
We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.
This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.
When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.
Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.
In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.
The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.
Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.
Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.