Why is it important for all leaders to be data fluent?

Data like any language is effective when others around you understand it and make decisions based on its meaning. But, data fluent doesn't mean being a data scientist. Instead it means "the ability to understand and use data effectively to inform decisions" according to Mandinach and Gummer. [1] One addition to this definition would be ability to communicate with data.

Leaders with data fluency whether team leaders, department directors, or senior executives benefit. These data fluent leaders ask questions like those below but more importantly are able to make data informed decisions.

  • What are key metrics that help me understand my customer's experience?
  • Am I hiring, rewarding, promoting and training my team members to be data fluent?
  • What data can I share with others to empower them to make the organization better?
  • Am I being a good data steward and ensuring proper data privacy and ethics are being utilized?
  • How can I use data to make our operations more efficient and effective?
  • Am I communicating with data appropriately to show the value our organization
  • What new data could I seek out or capture to bring more organizational value?
  • What percentage of employees have access to self service business intelligence and analytics and have been trained on it?
  • What metrics do we track to measure our employee experience?
  • What percentage of data we capture are we using to inform decisions?
  • I understand my NPS is in the top quartile, but what is driving this metric and what other metrics should I be monitoring to understand my customer satisfaction?
  • How are we developing new products and services based upon data from our customers?

Further, data fluent leaders are able to help their organizations have a data driven or data informed culture.  Doing so will not only lead to more fulfilling environment and to great success.

Are you a leader interested in being data fluent? Check out our Data Fluency Accelerator starting fall 2018.

 

[1] McAuley, D., Rahemtulla, H., Goulding, J., & Souch, C. (2014). How Open Data, data literacy and Linked Data will revolutionise higher education. Retrieved from: http://pearsonblueskies.com/ 2011/how-open-data-data-literacy-and-linked-data-will-revolutionise-higher-education/

How business and tech partners can better work together

The Twin Cities Data Fluency Group had its second meeting in May. This month involved an engaging discussion on “How the business can better work with analytics and tech partners.” Tricia Duncan and myself (Dave Mathias) moderated three great panelists – Nate Hallquist from Syngenta, Serena Roberts from Capella, and Jack Vishneski from ThreeBridge and consulting with Cargill.

There was a lively discussion on several fronts, but key takeaways were as follows:

  • Building relationships is key. Most information work takes teams and that means working with people. The more you build relationships the better chance to succeed as Nate mentioned.
  • Bring everything back to problem being solved. Data and analytics only serve a purpose if they solve problems. As Jack succinctly mentioned it is all about solving problems and bringing conversations back to those problems will help ensure success.
  • Trust is key. As Serena mentioned being a trusted advisor as an analyst and business partner alike is a must. Serena has the unique experience playing both roles in sales and sales enablement and building trust with both these hats has been essential to her success.
  • Rapid prototyping should be norm. Rapid prototyping is a must for dashboards and both to help ensure customer satisfaction and efficiency. These rapid prototypes can be done in a dashboard tool if a similar dataset available but just as nice it can be hand drawn on a whiteboard or paper.

In addition to these takeaways, there was a good discussion on the role of self-service business intelligence (BI) and how much autonomy the business should have and how much of it stays in the analyst, data science, or technology hands. There was mixed feeling here both on panel and in audience. Some companies have shown more success than others in distributing data fluency and technology into the business. However, there was agreement that tools are making it more able for end users to do more challenging problems.

One metaphor that seemed to resonate is treating self-service BI as a grocery store and not a treasure chest can help. As Nate described this the analyst, technology, or data science groups ensure that often used data has been made available with appropriate cleaning, integrity, and trust to business users. However, organizations need to ensure end users have proper training, tools, and help available so they can focus on conversations and insights while reducing the risk of invalid data models or technical debt.

There was a lot of overall agreement that data fluency is critical for organizations broadly and the language of data will be more easily picked up by some than others. But, to have a data-driven or data-informed culture at an organization requires your people to be data fluent.

This is a short summary of the great discussion that occurred, and all are welcome to attend the next TC Data Fluency MeetUp will be in July (date TBD). If you are an analyst or data scientist, then this is a great opportunity to bring one or more of your business partners to help further your relationship.

Thank you to Nate, Serena, Jack, and everyone that attended, and Tricia, Nate, and I hope to see you in July.

She Talks Data Perspective From a He

Earlier this month I had the opportunity to attend the She Talks Data MeetUp in the Twin Cities. This group’s goal is “[o]ur goal is to build a close-knit community of women (and men in support of women) who can come together to grow professionally and personally.” It was started as an offshoot of the She Talks Data group in Silicon Valley a few months ago locally by Serena Roberts and Laura Madsen.

Serena has said several times that this is not just for women. Plus, one of my friends, Karla Hillier, was presenting, so I thought great to support her and at same time attend this new group and learn.

As I told Serena beforehand, I was afraid attending and how I would be received and feel. But my fear quickly dissipated from the moment I walked in. Right from the start it was an engaging and welcoming environment, but I did feel something different.

As the first speaker Emma Denny, an employment law attorney, kicked off right from the start the room was riveted. There were questions related to workplace discrimination and sexual harassment. Emma talked about rights that people had in Minnesota. But, she also talked about the high thresholds that people face in these cases and difficulty in proving these cases. There were great tips such as telling people in writing when they felt harassed and literally spelling it out that you think it is because of gender or other protected class.

At one point, Emma asked how many people in the room had felt discriminated or harassed at work and nearly everyone’s hand was raised. I can say I felt bummed and really more angry. I felt angry that so many talented amazing people in our community have felt discrimination and harassment. I felt angry that so many of amazing people will likely face this more as their career continues. I felt angry that people often time creating those environments are oblivious that it is even occurring until it is too late or worse don’t care.

After the group I reflected what I could do. Yes, as a product person at heart my nature is when I see a problem I want to help find a solution. Of course, there is no single solution, but we can all help one action at a time whether in groups or at work to provide a more inclusive environment.

I encourage other men to respectfully participate in She Talks Data and other groups like these where appropriate and where welcomed. Not only as a sign of support, but also to be in a better position ourselves to be supportive when challenging situations with bosses, colleagues, employees, and clients will inevitably occur. After all we are people on this journey of life together with a finite amount of time, so let’s make the most of it and support each other through it.

Shout out to all the great people I met and good conversations I had. Special shout out to: Jen Roberts and Tricia Duncan that I had pleasure of meeting and sitting with; Serena Roberts and Laura Madsen for organizing group locally and continued leadership in community; and Emma Denny and Karla Hillier for sharing their knowledge and inspiring others.

Interested in learning more? Go to the April 4th She Talk Data MeetUp and catch April Seifert who will be one of the presenters. In fact, April and I were just talking this morning on all things CX, analytics, and podcasting and sure she will have a lot of great wisdom to share.

#shetalksdata #community #data #analytics

Data Informed Organizations Require Empowered, Data Fluent People

Per my discussion on Stages of the Data Transformation Journey from data-ignorant to data-informed, it is not a “one-size-fits-all” and is ongoing. However, it is a misconception that a data-informed organization only means having executive buy-in backed by an awesome data science team. Yes, both are important. But, you can be a data-informed organization without the most advanced data science team, or even without a data science team at all.

Instead, data-informed organizations require data-fluent people making decisions, and an empowered, data-informed culture behind it. Data-fluent people meaning people that can apply data, gain insights from data, and tell stories with and around data. Data informed and empowered culture meaning an organization that encourages and empowers data-informed decisions.

For example, a data-fluent project manager may seek out new data he typically does not receive to better ensure his project meets targets. Or a data-fluent product manager may utilize customer data insights to develop the next "big thing" for her organization.

Most decisions are not made by people with VP or CXO in their title. Instead, decisions are made by analysts, associates, engineers, customer service managers, generalists, architects, scientists, and dozens of other titles. It is the culmination of thousands of decisions each day that is the fate of organizations. Ideally, each of these decisions is made by a data-fluent person or persons in the context of customer value. (Look for more to come on the subject of being customer-focused).

Most decisions are not made by people with VP or CXO titles

The real value of data-fluent people, though, is not what they do alone. Instead, data-fluent people help drive improvements for the entire organization. And that makes the organization run better. Think what companies like Toyota did on the factory floor — but now it is on the office floor.

In addition to encouraging specific improvements to processes and systems, data-fluent people help drive a data-fluent culture. This is where data-fluent people provide the highest value.

The important question then is, does your organization hire for, train towards, and reward data fluency? If the answer is no, then your organization will not be data informed.

So, the next time an executive asks how do we become a data-informed organization? Respond, “We need to start attracting, retaining, developing, and empowering data-fluent people."

 

#datafluent #datatransformation #datainformed #data #strategy #analytics #bigdata

Shameless Plug: If you are in or around the Twin Cities then a new Critical Data Thinking & Communication Certificate in partnership with Minnesota State Mankato is being offered in Edina starting February 19th. This is a program I helped design with others and is geared at helping business professionals increase data fluency. The inaugural class will receive 25% off and learn more here.

Stages of the Data Transformation Journey

The data transformation journey moves companies from data ignorant to data reactive to data driven to data informed and, in fact, eventually to real-time data informed. The data transformation journey is a process. This journey isn’t straight and it will take some organizations longer than others to progress. Organizations also generally don’t move in lockstep in this journey. But to be clear, companies that don’t progress along this journey will eventually not exist.

That is a lot for one paragraph so what are these data maturity stages:

Data Ignorant: Data ignorant means your organization doesn’t meaningfully use data. The organization may be getting data but doesn’t understand quality, meaning, and/or context. The organization may have great people, but their true north is their gut or their manager or executives' gut. There are still many successful companies doing this. Leaders with experience and intuition can often add enough value to overcome the influence of data for some time. Just remember that yes ignorance may seem bliss but data ignorance is only bliss until you have competitors that are not.

Data Reactive: Data reactive is just what it sounds like - organizations capture data but don’t strategically use data in their decisioning but instead use data to react. Further, they don’t strategically define the data to capture but more rely on what others do and copy it. For example, a T-shirt company realizes they lost 34% market share in a market they had once dominated. Now they react and make a salesperson change and a social media ad buy. Data reactive organizations or departments are still very prevalent. They often have founders or leaders that rely heavily on gut still but they have also realized that complete data ignorance can bring and they have moved forward to data reactive.

Data Driven: Data driven organizations understand that data is valuable and almost always good data is better than any one or several person’s experiences. Data driven organizations understand that they must be thoughtful on capturing data and then having processes in place where people use this data in their jobs to make decisions. Many data driven organizations are extremely data sophisticated by most standards. The challenge with data driven organizations is they let the data drive them often without full understanding or context of the data.

Data Informed: Data informed means data is thoughtfully understood and processes aligned to maximize data informed decisions in an organization. Further, data captured and other data sought is strategically determined to add organization and customer value. Data informed organizations not only make decisions based on data but they understand the context of data in those decisions. This context is provided from a combination of organizational experience, competitive landscape, industry expertise, and decision impact on various stakeholders. 

Moving along this data transformation journey is not simple and is not one-size fits all. It is not something you can just ask how much will it cost to get me to end of this journey. This journey is constant and doesn't have an end. Needs and technologies continue to change around data. Things like artificial intelligence, blockchain, information security, and quantum computing all are currently and will continue to change the data transformation space. Further and most difficult, data transformation is a culture change for organizations.

Look for future postings where we will dive into further discussion on how to move along this data maturity journey. For now I wish you Godspeed along this data transformation journey no matter where you are.

#datatransformation #datafluency #datainformed #data #strategy #bigdata #analytics #blockchain #quantumcomputing

Watch out for data science shiny objects

There are three main ways that organizations use data and analytics in their organization:

  1. Enhancing customer / product experience
  2. Enhancing employee experience
  3. Increasing operational efficiencies

Individual data science efforts will often cross more than one of these areas.

Why I am writing this post is, I see organizations making decisions more often based on increasing operational efficiencies than enhancing customer / product or employee experience. The shiny object that never goes wrong on an earnings call is cutting costs. But, does it add to the long term value of your product, your organization, and your most valuable assets, your employees? Often the answer is a short-term yes but a long-term no.

Be a leader that delivers customer and employee value from the power of data and analytics first. Along the way some operational efficiencies will come along as part of your efforts.