Webinar Takeaways: Working With Data

Last week Eric Haase hosted the webinar: Working with data. Missed it? No worries, here is the lowdown on how to make the most out of your organization’s data.

We talk a lot about technological evolution and the fast changes that come with it. Business models change year by year, and maybe even quarter by quarter, to keep up with the changes ánd of course to stay competitive. And thus relevant.

What rapid changes of technological advancement brings with it, is an ever increasing amount of data. That is available for every organization and their employees.

Next generation skills

Looking at the macro-economic environment we see lots of data out there: big data, ai, robotics. In short, innovation accelerating at great speed. Volatile even. We’re looking at an ever changing landscape.

This, for instance, is what happens in just one minute online:

Visual Capitalist 2018

To handle this uncertain, volatile and ambiguous environment we train powerskills.

ERIC HAASETweet

The future-proofness of your organization depends on how many next generation skills you’re incorporating into your day to day business, now. When your employees are working within the rapidly changing landscape, they will need to learn skills that will still be relevant in ten to twenty years time. A combination of hard and soft skill: Power skills. Tools that will help them navigate an ever changing landscape.

Data is a crucial element in this. Because future-proof skills, skills that will still prove relevant in ten/twenty years time, are closely knit to data literacy.

The importance of data

Why do we need to focus on data at all? Participants from the webinar noted:

‘It is important for making decisions’ and even more nuanced: ‘data is important to make objective decisions’.

In addition: ‘It’s the only way of objective analysis, free of prejudice’ and ‘factual information’.

‘By using data what gets measured gets managed’ and ‘measuring is knowing for sure’.

It seems pretty obvious that data can be used to base decisions on, and being data literate can mean that your decisions are made more objectively than decisions that are intuition based.

Data really powers everything that we do. That thinking may have been what led LinkedIn to become a global professional-engagement juggernaut. I believe when decisions are based on data, future is no longer left to chance, but becomes a reflection of choice.

FORMER LINKEDIN CEO JEFF WEINERTweet

When you minimize the part of ‘chance’ in your business decisions, you minimize risk. Subsequently also unnecessary costs, i.e. it will be less likely that you make a bad decision.

Use of data: what’s in it for you

If data can minimize risk taking in a significant way, of course organizations will use data to their advantage. This is definitely not a new trend. Organizations will try to incorporate data analysis within their infrastructure, their value chain and their business models to decrease costs and improve profits.

A good example of an adaptive company is Tomtom. Going from route planning, personal digital assistant, software for the consumer market to becoming the main mapping data provider and primary supplier of data for Apple’s map app.

What we see here is a business that has changed it’s model from product driven to data driven. TomTom played to their strength! With the amount of data the already possessed it was a smart choice to become a supplier of one of the biggest tech companies worldwide.

Being adaptive in the fast changing environment is necessary. But what we in addition see in the history of data use is that organizations are making a shift from using data based on the ‘what’ − what data are we looking at? What is happening at the moment? − to a forecasting ‘why’ − why is this change happening at this moment.

Gartner predictive analytics

With the ultimate goal for organizations of moving towards the ‘how’, what is the correlation of the ‘what’ and ‘why’. Our example TomTom, as mentioned above, being not only an adaptive player but at the same time a data supplier for one of the biggest predictive companies: Apple.

Example of going from descriptive to prescriptive:

What: people are buying icecream

Why: it’s hot, so people are buying icecream

How: How can we map and make use of the warmest periods of the year to sell even more icecream

Forecasting is crucial, and the most sophisticated form of data literacy, how can we make the ‘what’ and ‘why’ of customer behaviour happen, so that we as an organization benefit from this. The use of data has gone from hindsight, descriptive, to foresight, prescriptive.

Prescriptive analyses are extremely valuable, also of course within HR. Because who would rather work in hindsight if it is also possible to work with foresight? You want to know why someone leaves. Of course. But more importantly, you’d like to know when (and why) other employees might leave too.

You can use data to know when (and why) an employee shows high risk of leaving, and how you can take action to prevent others from leaving because of that exact same issue.

In short you can use this data to gain insights, and with these insights form some type of action.

How to get started: master the basics

Take the first step and: create your own insights.

When we boil ‘working with data’ down to the basics, data literacy really means: asking the right questions.

So when you are looking at a set of data, be challenging and curious. Ask yourself critical questions. What am I looking at exactly? Is all the data I’ve collected relevant? And is the data set complete? If you have created a clear picture, then you can take action.

In order to ask the right questions and to create your insights you are going to need the right tools. If you follow three easy steps, you’re on your way to becoming data literate!

Master the basics

• You need problem-solving techniques if you want to drill down on raw data and on what’s really important; train your employees in these power skills!
• Business tools training can provide you with the basics that you need to start creating structure to your data.
• Don’t forget to present your data in the best way possible! Include others in the analyses that you’ve done, and really drive your point home.