Frictionless integration: The key to generating shared expertise
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Frictionless integration: The key to generating shared expertise

Every organization that has successfully created data science or artificial intelligence solutions has done so by building an internal culture of data. You should start with culture and people, and let technology be a facilitator.

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This article is the second of a two-part series discussing the value of shared expertise. You can find part one here.

“Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

“Data is the new oil” is a phrase that has been thrown around the last few years as an axiom of the modern world. It headlines articles in high-profile magazines like The Economist and Forbes. It is a simple, powerful idea that has led companies to invest millions of dollars into big data technologies. Still, it didn’t take long for people like Tim Harford from the Financial Times to realize that “Big data has arrived, but big insights have not.”

Tell me if any of these scenarios are familiar to you:

  • We are collecting a lot of data on our customers, but we still can’t answer the questions we're interested in.

  • We hired a group of business analysts/data scientists to generate insights. They have built some very interesting dashboards but we’re always missing data and tools to make better decisions.

  • We have heard about some exciting AI use cases and we are definitely getting into AI but we don’t know where to start.

  • We have the best software and large databases, but we feel lost. We need a better data strategy but we simply don’t have the time right now.

  • [Try filling in your own data related headache here. There are many.]

None of the above scenarios are invented. I have heard analysts to company owners share headaches like this over and over again. These are real problems in and of themselves, but what makes it even more difficult is that there are so many misconceptions out there with respect to data.


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How much data do you need?

Imagine for a moment that you could collect all of the data you ever desired to better understand your clients. How much data would you need? Less than you imagine. A great deal of real world analytic jobs are actually still in the megabyte to gigabyte range, even at large companies like Facebook, Microsoft and Yahoo

Odds are you don’t really need huge amounts of data to better understand your clients (although more is generally better). Why then are companies still pouring time and money into gathering more data when they still haven't seen the expected return on investment? This is an example of the potential danger of misunderstanding an analogy. Clive Humby is credited with coming up with the “data is the new oil” analogy back in 2006. His original comment was:

“Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

Every organization that has successfully created and deployed data science or artificial intelligence solutions has done so by intentionally building an internal culture of data.

This means that before we start filling up our digital warehouses with barrels of crude data, we need a smarter approach to analytics. 

In our previous post we talked about building shared expertise. We mentioned that different business units and products view clients differently. You can think about each area as an expert in a specific set of topics and breaking down silos is a way for each area to share their expertise with the rest of the company. This opens conversations and creates opportunities to build new things.

Strategy before technology

Every organization that has successfully created and deployed data science or artificial intelligence solutions has done so by intentionally building an internal culture of data. You should start with culture and people, and let technology be a facilitator. Technology leveraged on top of an integrated data strategy can give you the insights you long for. If you are trying to generate growth, then bringing your data together is one of the most powerful things you can do to better understand your clients and respond to their needs

Monitor clients using data, and understand their needs

Hopefully, you are already thinking about how your company can start generating shared expertise. This takes us to the heart of the point: once you decide to unify data, you should think twice about the technology you’ll be using.    

Let’s look at what happened in Bayer AG when Jeff Rasp, the Director of Digital Strategy, and his team connected the data flowing through several of their systems. These integrations, along with educating other teams on the new possibilities, allowed them to reduce wasteful spending by 30% and improve engagement by more than 50%.

The team spent over a year assessing the technology landscape and looking for solutions that could be used strategically as part of a unified technology environment. One of the key discoveries was a content management system that could connect to their analytics system, which allowed the team to tailor content to specific customer segments.

Here results were driven by integrating data sources and consolidating information to see a unified view of the customer. Around the world, marketing leaders are 1.6 times more likely to integrate and share measurement metrics with other teams compared to laggards. 

This is something we pay close attention to at Modyo. The number of people using our platform grows every month, so we have to make sure that the tools we are using and building are easy to integrate. This is the only way we can amplify our vision and generate a complete view of our ecosystem to guide our decision making. It doesn’t matter how much data you're collecting if you can’t integrate your tools and unify that data. 

Remember, your data is crude, and by refining it, you avoid missing out on huge opportunities.

Photo by Josh Calabrese on Unsplash

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