07/21/2020
By
MJV Team

7 steps to implement Data Science in your company

Data Science is where information comes from, what it represents, and how to transform it into a valuable resource for creating business and IT strategies.

Data Science is about mining large amounts of structured and unstructured data to identify patterns that can help an organization control costs, increase efficiency, recognize new market opportunities, and increase the organization’s competitive advantage. 

We recently published an article here on the blog about the importance of Data Science in companies. If you haven’t read it, it’s worth a look.

Here are our seven steps to implement Data Science in your business: 

1. Know your company’s data generation, capture, and processing capacity

It is essential to understand your organization’s general objectives and the most critical actions to take to achieve these goals. Also, you must keep up to date with the industry’s general landscape, including trends, risks, and opportunities.

Conducting an initial SWOT analysis, which focuses not only on your department but also on your fundraising operations, can be useful here. This practice will help you to predict which problems and opportunities to measure through reporting.

The application of Design Thinking is perfect for this step—nothing like DT to map opportunities and gaps within the organization.

It is also important to raise the current capacity for generating and processing data in your business. What tools are available? How can data be gathered/integrated? Where is it stored? How can it be shared? These are some of the questions that need answers.

2. Hire data scientists

There is no Data Science without data scientists. They are professionals who have a combination of analytical skills and machine learning, data mining, and statistics experience. They are also going to be your key to algorithms and coding.

In addition to managing and interpreting large amounts of data, many scientists create data visualization models that illustrate the business value of digital information.

You can hire directly or through third-party data scientist services. There are specialized consultancies that provide this type of service. Decide which is best for your business.

3. Set up a Data Science department in your company

Considering that data science depends a lot on your company’s mindset, it is beneficial to set up an area dedicated to it.

It can be a committee with IT, sales, and marketing specialists, or it can be a department with scientists, analysts, and other data professionals.

4. Purchase Data Science technologies and services

It is also essential to equip your Data Science team with the necessary tools.

The advantage of hiring a consultancy is that you do not need to make this type of investment yourself, but if you choose your team, know that there is no Data Science without cutting-edge technology.

Within your core business and the objectives outlined for your Data Science strategy, there are several tools for the job. They range from Business Intelligence and CRM platforms to more sophisticated solutions to deal with Big Data, Artificial Intelligence, etc.

5. Establish metrics and share findings

Metrics must be repeatable, reliable, and timely. It is essential to assess how well your efforts are contributing to your institution’s fundamental goals, as this is one of the most significant factors to increase efficiency.

And of course, don’t forget to share this information with others! If it is important enough to measure, then you should share with other areas that benefit from the knowledge. Also, you can use analysis to confirm or refute the assumptions within your operation. Prove or disprove them with data!

6. Invest in data visualization

Another buzzword, right? Visualization is the concept of telling a story from your data, usually through graphics and images.

Data visualization is one of the most important parts of Data Science. They should clearly communicate your findings to people who are unfamiliar with the intricacies of data science or industry processes.

Please take advantage of dashboard-type analytical platforms, as they help the information be easy to digest for all audiences.

7. Work the Data Science culture

Last but not least, here is our seventh implementation tip. Remember: in the future, all professionals will be analytical.

Usually, the business decision-making process depends on the experience of data management and analysis. However, this process can change and improve by simulating a variety of potential scenarios, using the customer’s in-depth knowledge and predicting upcoming trends to lead us to the best results.

How are executives going to predict future scenarios if they don’t know how to think ahead? If they don’t ask the right questions, not even the best Data Science strategy will work. Therefore, it is essential to foster a Data-Driven culture within your business.

What did you think of our tips for implementing Data Science in your company? Deepen your reflection on this topic; download our Design Driven Data Science e-book now.

Design Driven Data Science - MJV Technology & Innovation

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