Why Data Science matters and how to approach it for your Digital Transformation

Big Data

Big Data is one of the biggest buzzwords of the past few years. Here’s a great quote about it:

“Big Data is like teenage sex. Everyone says they’re doing it, hardly anyone really is, and nobody knows what they’re doing.”

Those of you who are familiar with our Digital Transformation methodology know that we like things a little more pragmatic, so here is how you should start using data as a backbone of your business.

The why

Every industry has digital challengers that are outperforming the competition. They all use data as a core ingredient of their business strategy, and they do so from the start.

Airbnb for example hired its first Data Scientist when the team had just 7 people in an apartment. It’s important to bring data scientists in projects from the start, not later on once things turn out to be a mess.

Uber has its own #UberData blogposts, in which they explain some of their data projects in detail. This not only shows their accomplishments, but they also use these posts to attract data scientists to come work for Uber.

Uber Data Scientist

Netflix set new standards for personalization in its streaming service. They mapped out very well which aspects of the business can be optimized through data and also have a clear view on what role it will play in the future of the company.

If you want to compete with digital challengers, data needs to be treated as one of the crucial drivers of transformation in your business. The battle simply can’t be won without it. At this point however, few businesses have teams of data scientists in place that take care of all data sources and analyze them to create business opportunities. This gives companies that use data well a large competitive advantage.

The how

We see 5 main reasons why few companies are accomplishing stuff with their data :

  1. No clear data architecture
  2. A lack of a data strategy
  3. Missing clear ownership
  4. Not having enough quality data
  5. Putting data in a silo

1. No clear data architecture

As many business processes are virtualizing, more and more data sources are popping up. Think about CRM systems, client service systems, social media platforms, mobile apps, in-store data, website analytics, collaboration tools, sales platforms, etc. All of these platforms are generating new data streams, are you using these?

The very first step to use data to your advantage is setting up a clear data architecture to build an overview of all the different data sources. Define which types of data should be tracked, linked or combined in order to drive business value.

2. A lack of a data strategy

One of the biggest mistakes is monitoring and mining a plethora of data, without having a clear idea what the actual output should be. So why hire data scientists if you don’t even know what you expect from them?

You need to define clear objectives, here are the most common ones to inspire you:

Data Science Business OutputSource: Experian Data Quality

3. No clear ownership

An architecture and strategy are the fundamentals. To put things into practice you need to appoint the right people. You need a team with ownership over your data, because we all know that everyone is too busy and managing the data of a company is not a side job, it’s more than a full time job if you really want to take it serious.

Data Scientists are a new profile for most companies, and they are hard to find as this is a domain which is just starting out. Here is how Netflix is recruiting a Senior Data Scientist.

4. Not enough quality data

According to Experian Data Quality, 86% of businesses suspect their data might be inaccurate in some way. 44% says incomplete data is their biggest problem and 41% says the biggest problem is outdated information.

This doesn’t just happen overnight, there are 4 reasons why your data may be inaccurate: Human error, poor internal communications, a lack of a data strategy and a lack of resources. Find the problem and solve it at the source, or cleaning up your data will only be a temporary measure but a huge investment.

“DJ Patil, the recently appointed Chief Data Scientist of the White House, summarizes the data problem well, noting that “you have to start with a very basic idea: Data is super messy, and data cleanup will always be literally 80 percent of the work. In other words, data is the problem.” ~Source: Techcrunch

5. Putting data in a silo

Last but not least: setting up a data science team that is working in a silo, not horizontally serving the different business units, will turn the outcome into an isolated data-crunchers story. We believe that data scientists should be incorporated into the cross-departmental team of the Chief Digital Officer to make it succeed.

Digital Leadership Team

Whatever business you’re in, we hope you understand the importance of incorporating data into the core of your business activities. Don’t let the opportunity pass by! Our book on Digital Transformation might help you to get started on the strategy part.

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