Make Everyone Data Savvy, Forget Data Science.<!-- --> | <!-- -->Assume Wisely
Make Everyone Data Savvy, Forget Data Science.

Make Everyone Data Savvy, Forget Data Science.

Posted: May 1, 2018

Data savvy managers use data for execution, putting insight into context to make things happen. Mckinsey predicts a shortfall in meeting the demand for 1.5 million data savvy managers. According to the American Management Association 75% of companies report they are ill-equipped to meet current analytics needs. Here are six skills tech startups are looking for in a data savvy manager: listen, ask great questions, understand data science, evaluate alternatives, acknowledge and mitigate bias, and catalyze change.

A major hurdle to iterate and improve strategic data driven decision making is people. Data analytics is pretty straight forward; i.e. math is just that, math. It's people (humans) that can be challenging. Which means people (could that be you?) are the solution. Data science relies heavily on statistical computing. Scripts and math. Algorithms. If (1) you start with good data and (2) you have a competent data scientist conduct and interpret the analysis, you still need (3) to put those results into context; make something happen. Someone has to do! Teams (doers) need to execute on insights.

The good news is that you don’t have to know how to code or do advanced maths to become data-savvy. In fact, you don’t have to be particularly tech savvy at all. What you do have to do is adopt a data-friendly mindset. Whether you are looking to lead the way as a data-savvy employee, or lead the charge for culture change as a manager or C-level executive, here are some suggestions for encouraging everyone in your organization to become data savvy.

Six skills tech startups are looking for in data savvy managers

Listen. Understand the problems your team, senior, & mid-level managers are facing.

Ask great questions. Focus on how to ask the right questions rather than the method of arriving at the answers. Frame the problem into a set of questions that, if answered, direct action. Understand (& communicate) that decisions must be made once these questions are answered. Help other managers to do the same. A data-savvy manager structures a strategic problem and facilitates discussion to reach an actionable resolution or decision. Strategic questions and sub-questions will drive the analytic process and must always be clearly tied back to the business need and desired outcome.

Understand data science. Some basic understanding of what data is and how it is collected is all that is needed to start to see the bigger picture. If you can store it, you can analyse it; so what data do you already store in your job? What could you learn from it?Take a survey level course on data science. LinkedIn Learning offers a course that you can get through in an afternoon. When you understand the process you can ask actionable questions that lend themselves to be answered with a data model.

In the planning phase, data-savvy managers must consider how to strategically advance the data decision process. By understanding the data platform and clear objectives for the data needs, the manager will be able to communicate effectively with the scientists or engineers and drive the needed data collection, modeling and analysis, based on the desired outcome.

Having an understanding of the platform and the vision of what can be achieved, a data-savvy manager can identify opportunities to continue building out the desired analytical platform and the resources to further develop their data decision.

Acknowledge and mitigate bias. Team members have (and use) inherent bias. Teams that manage group-think will naturally make better evaluations. Recognize and mitigate the inherent biases team members have. Involve the project team early in workshops for critical thinking and creative problem solving. Have them offer insights into biases and identify required resources upfront. Involving the whole team allows discussion to focus around the strategic problem and ultimately get its buy in on the plan moving forward. Early buy-in is important for establishing accountability and responsibility throughout the process.

Catalyze change. Communicate and empower decisions throughout the organization. The most common problem is the role the organizational culture plays in utilizing analytics. Senior managers, mid-level managers and technical personnel all share responsibility for developing a culture to implement these decisions quickly. Employees, whether technically skilled or not, should recognize that data has the potential to impact every aspect of the business. Champion this message throughout the organization and begin shifting the organizational culture to one that is data decision-oriented. Achieving early small wins can lead to big victories toward ultimately changing the organizational culture through the demonstration of value created for the firm and others.

Data Savvy Managers Drive Processes

These six skills are crucial to developing processes. If you're in finance, you need analytics for risk; supply chain, then you need optimization; for marketing, customer segmentation. You can see the demand and the need. While each vertical has it's own nuance, each process has these four components in common:

  • generate meaningful questions
  • pose those questions effectively
  • build understanding around data driven decisions
  • create a culture that can implement those decisions

People in a variety of roles are starting to understand the value of being data-savvy. Data is already becoming ubiquitous in business as well as in daily life. It used to be that the IT department could be contained to its own office or floor, but today, it’s becoming harder and harder to segregate the realm of data from any other aspect of business. That means that data — and the application and analysis of said data — is going to become more and more important in every department, from sales to HR and from R&D to marketing:

  • Predictive analytics: Using past and current data to answer questions about the future
  • Scorecarding: Tracking business metrics against strategic and operational outcomes for better decision making
  • Dashboards: Real-time presentations and aggregations of relevant data
  • Social analytics: Metrics from social media and networks
  • Content analytics: Assessment of available content and how it is used
  • Web analytics: Tracking traffic and key words used to find particular sites

Different kinds of data and analyses can be used to influence behavior, improve organizational policies and practices, or make better decisions. Processes within a business are generally places where you generate a lot of data and can manage data more efficiently. These can be great places to get staff on board with a new data-centric culture.

There is no such thing as "perfect data"

Data newbies need to be taught and reminded that there are always anomalies in data sets, and that data needs to go through quality control and be cleaned before it is useful. Building these concepts and processes in from the start will eliminate big headaches later. Just accepting this fact will reduce headaches. Before you can analyse data it needs to be cleaned, (wrangled or munged). Data wrangling is 80% of the work.

A data savvy manager doesn't have to code or be a math expert

Data scientists are “deep analytical talent”: people with solid backgrounds in applied math, economics, or life science. They are skeptical, communicative, curious, and creative. They know more statistics than engineers and more engineering than statisticians. Data savvy professionals, on the other hand, have these qualities but not the same level of technical training. They can think of problems as data-driven or analytical. They know how to think about data, how to ask the right questions and challenge the answers they receive. In short, data science requires rare (specialist) qualities:

  • an ability to take unstructured data and find order, meaning, and value.

  • Deep analytical talent.

Data Savvy doesn't. a data-savvy manager incorporate all of the information into the decision-making process. Whether it is information marketing will use for customer segmentation, or research and development for knowledge discovery, there must be an avenue for the information to be incorporated into the decision making process. A data-savvy manager must structure the process with the end in mind.

Data savvy doesn't require you to code or be a math expert, check out my learner's guide to becoming a data savvy manager.

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