Transform data into insights – boost your innovation capabilities

Generate the insights that are relevant to you

I get a lot of questions on how to handle all data (or Big Data) and particularly how to use the insights for something that creates business value. Not only in the distant future but here and now.

Very often I sense great frustration and nervousness related to this question. I have previously identified 5 crucial steps to develop a customer feedback system to drive innovation. There is definitely reason to delve deeper into these steps. Let´s start with the first one:

“Understand the difference between data and consumer insights”

I would say that all of us know that we have a lot of data. We also know that we could benefit from using it more often and more targeted. Most of us also know that it’s one of many requirements of future business success. The problem is that for many managers the task feels unsurpassable.

Questions in abundance stacks up:

Where should I start?
Whom should I listen to?
What platforms do I use?
What BI tool should be employed?
Should I have a cloud solution etc, etc?

The more questions you try to answer, the more questions pop up.

A popular response is to do something because it’s better to do something than nothing, right? Well, maybe… but you don’t delve into the technical issues until you have a clear understanding of what you want to achieve – that there are infinite possibilities as to what you can do, but a lot less that you should do.

How can business leaders transform big data into something resembling insights?

In today’s world of information technology and exploding social media interaction, data is abundant. According to IBM we create 2.5 quintillion bytes of data (that is a lot of zeros, in fact it’s 2 500 000 000 000 000 000 if you were to write it out in numbers, or 1018) per DAY. (https://www-01.ibm.com/software/data/bigdata/what-is-big-data.html)

“We create 2.5 quintillion bytes of data per day”

This is so much that 90% of the data in the world today has been created in the last two years alone. That’s BIG DATA … and if you think that the amount of data will decrease, think again. With IoT and 5G, this is just the beginning.

Only convert data into insights your business can use

In that abundant wealth of data, an overall problem is that there are even more insights than data points that can be harvested. I would argue that the number of insights available is already on the brink of infinity. The trick is to generate the insights that are relevant to you, your business and your market – today and in the future.

A mistake many data scientists and analysts make, is to try to solve many problems at once. Having the skills and finding the ultimate machine that can answer everything, every time, everywhere is like finding a pot of gold at the end of the rainbow. It just can’t be done. What you must do is to focus and to quickly convert data into insights your business can use. I have successfully used a use case approach to reach those goals.

A use case helps you solve a specific business question by using data and technology solutions. A good use case provides insights that are relevant and on time to the relevant stakeholders to support them in their decision making.

This is the playbook I use: A use case approach in 8 steps

Identify business driven use cases, questions or challenges that business managers need to handle now and in the near future. In terms of innovation, identify which parts of the innovation process that needs insights – what, when, and why decisions that need to be supported. Is it innovation or is it about product development?
Get your business people to prioritize between use cases from a business perspective, i.e. which use cases will give the most significant impact on your business?
Understand what data is available and where. It is a simple enough statement, but a task that can be daunting. Often there are many different sources such as data warehouses, providers, etc. If the task feels overwhelming, let the prioritized use cases guide you to what data should be investigated.
Do a feasibility analysis, i.e. soberly evaluate resources (time, money, etc.) needed to solve each use case. Don’t go into details yet, do a high-level analysis.
Create a priority matrix – map business value versus implementation cost. It´s not uncommon for a second or third priority, from a business perspective, to be the first use case that makes sense to implement based on a trade-off between feasibility and business value.
Create a roadmap based on the priority matrix.
Build the first use case solution, start with a MVP (Minimum viable product). A MVP:
1. has enough value that people can understand, use, and buy in
2. demonstrates enough future benefit to retain early adopters
3. provides a feedback loop to guide future development
Show business value to get traction within your organisation, then move on to next use case, show business value, iterate, iterate, iterate…

Never underestimate the need for communication – learn from a Nobel prize winner

A word of caution: As data driven, logical, and analytical people we tend to think that data is king; in some sense, that’s true. But a human mind works differently. As Nobel prize winner Kahneman (https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow) identified, we use two different modes of thought:

System 1, which is fast, instinctive and emotional (The Hare)
System 2, which is slower, more deliberative, and more logical (The Tortoise)

Research has shown that the Hare is a lot more dominant than the Tortoise in human decision making. That is why humans (and yes, managers are humans) are very reluctant to believe insights that contradicts their gut feeling. That’s why it’s important to include hypothesis sessions, i.e. what do the managers believe the data will tell them, when working with customer insights in general, and with Big Data specifically. Because, when you know the Hare, it’s easier to be the Tortoise.

Furthermore, you need to set up a structure with communication plans, steering groups, and all that management mumbo jumbo. In the end, if you don’t get the HiPPO (Highest Paid Person’s Opinion) to sponsor you, then you will be another frustrated fish swimming around in the Data lake.

Even though this post only touches on surface of all the implications of turning data into insights, I hope it has helped you navigate in the choppy waters of Data Lakes and Big Data. If you have any questions or comments, please don’t hesitate to contact me.

Erik Enecker
Chief Product Officer at Nepa USA & COO Global Product at Nepa

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