How can organizations be data rich and analysis rich too?

#1

With hardware technology getting cheaper and software getting increasingly sophisticated every year, data creation, collection and storage has become easier than ever. According to Statista.com, forecasts suggest that by 2030 around 50 billion IoT devices will be in use around the world in the consumer electronics space.

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I wonder what the number would look like if we also account for the devices the businesses use themselves.

However, Data Rich Information Poor (DRIP) phenomenon is still a reality. Data is everywhere, yet not every business is using it effectively. With 2020 rapidly accelerating the global digitization, the future belongs to those who are able to lead and “speak data”.

As expert data practitioners, what are your suggestions? In your experience and opinion how can organizations and societies at large become more analyses rich?

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#2

Our team has been working to build a data culture to encourage users to look beyond Excel style walls of numbers and use more efficient visualizations. We’re using ‘lunch and learn’ style trainings to teach our users how to utilize dashboards to get their data in a more effective way and using their feedback to build better dashboards. It’s a big give and take on both sides to help our company become more data savvy

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#3

You’re right, it is a big give and take to build a data culture across the organization. I am happy learn about the initiative you folks at Signature Performance have started. I’m curious though - do you also train your users on “how to look at data”? If yes, what’s the first advice you give in this direction?

#4

Hi Tejas! We are working on the “how to look at data” part, too. It’s a slow process, but we’re starting with our Emerging Leaders group and teaching them WHY data is important, and why they should look at it. The “how to” part is being done with some fun examples of looking at the same data in different ways (find all the 9s on this crosstab…okay find them with highlighting, now as a bar chart…etc). We’re also showing them what to look for when looking at data to determine if there is any bias associated with it, or if more context is needed. We’re taking our county’s COVID dashboard (but making it about chocolate ice cream instead of COVID vaccines) and showing what things are missing from it – does it mean anything to know 100K like chocolate ice cream? Not if you don’t know how many total are eligible for “chocolate ice cream”, total people in the county, total chocolate ice cream bowls available, etc. Biases like who is presenting the data and why are they presenting the data is also important.

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#5

LOVE THE APPROACH! Makes it so much easier to grasp as well as brainstorm various angles to the problem statement. Thanks for sharing this with us, Rachel.

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#6

In my opinion, one of the factors impacting the analysis is the bias. The key is to remove the intentional as well as the unintentional bias. Intentional bias can include the bias to look at the data to extract a preconceived or pre-decided outcome. Unintentional bias can be introduced due to personal prejudice.

Without bias, the data can be looked at with more independence and impactful analysis can be derived.

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#7

You make an interesting point there, Ravi. How would you suggest one should remove these biases?

#8

As Rachel mentioned, we have started a number of projects to increase data literacy–from a presentation illustrating why it’s important and how to examine data critically with real-world examples that draws in people who might not normally care, to lunch & learn type sessions on how to get the most out of specific dashboards, and building an online community where we can share resources, questions, and suggestions. I think in addition, it’s important to design dashboards with this in mind, and different ways of guiding users with the design can help them get the best information out of the data we provide instead of placing the burden on them to be better at reading it. To that end, I’m always interested in ways we can be better at ‘telling the story’ with the data.

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#9

T H I S :star_struck:

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