Thinking about implementing Data Science? Here are 5 steps to guide you

If you are wondering whether you should invest in data science, you should know that managing large amounts of data to identify patterns may help an organization control its costs, increase its efficiency, find out new market opportunities and increase the competitive advantage of the business.

Here we will present you important steps you should take when implementing data science in your business. But before that, the concept of data science may be confusing for those who are not in tech. Let us start with that.

What is data science?

Data science can hardly be described as a single meaning, but rather as a combination of multiple concepts that focus on data analysis and the insights proposed based on this analysis. It was previously developed by math or statistic experts, yet data specialists began to use Machine Learning and Artificial Intelligence, generating data analysis as we know today.

Essentially, data science can be defined as the use of multiple technologies and methodologies to capture, store and process information, generating high-value business insights. This new way of analysing data is much faster, effective, and extremely popular thanks to its incredible capacity of unifying huge data matrices of structured and non-structured data and turning them into readable and simpler formats.

How can I implement data science in my business?

Understanding and acknowledging the importance and potential of data science for your business is the first step towards implementing it on your company. Here are 5 steps you should follow to implement it on your organization:

Mapping the potential of you company

It is important to run an internal analysis to determine the final goals of your company with data science and what activities will help you get there more efficiently. You should keep up with the landscape of the market, research how your competitors are using their data and map the trends. The market itself may already have great examples of data analysis that you could adapt to your reality.

A SWOT analysis that will focus on the data capturing and processing operation may be useful. This will help you map where are the greatest opportunities and threats and prepare to them by preparing regular reports, for example.

It is also important to take in consideration the current data processing capacity of your business. Some of the questions you should be asking at this point are which tools are available, how can data capturing and integration work and where will data be stored.

Find Data Scientists

These are multiskilled professionals with a combination of analytics, machine learning, data mining and statistics skills. Plus, they are experienced with algorithms and coding. Besides managing and interpreting huge amounts of data, these scientists must also create data previewing models to illustrate the business value, generating business insights.

Can you imagine how and rare these professionals are? But at the end of the day, there is no data science without data scientists.

You can either directly hire a data scientist, which may consume a lot of time and resources, or hire specialized services that will save you money, time and speed up your data science implementation. To find the best data scientists for your project, click here.

Acquire – or develop – Data Science technologies

You must equip your team with the best tools. Depending on your business model, you may find solutions that are already available, or you may feel the need to develop a personalized solution. One of the advantages of hiring a consulting and outsourcing company is that they can determine what is the best course of action in your case and even develop a unique solution for you.

Either way, you will have to internally understand and determine the goals of your data science strategy. This will be a determinant factor to choose the best-fit tools. These tools may be Business Intelligence or CRM tools or more sophisticated solutions that are able to handle Big Data, Artificial Intelligence, etc.

Determine metrics and indicators. And share them!

Metrics must be objectives, replicable and trustworthy. It is essential to constantly evaluate how and if your efforts are contributing for the goals of the organization. Shallow or non-relevant metrics will make you question whether you should have invested in data science at all.

And of course, share this information with everyone else! If it is important to measure, it is important to share with other areas that may benefit from the same information. Analysis of metrics can confirm or dispute suppositions within the operation. Whenever you are in doubt, use data to prove it.

Invest in data view

There is no point in investing in data science if you cannot showcase your discoveries. Here, graphics and pictures will be your best friends, you must tell a story from all the information gathered. You must provide relevant and clear conclusions even for people not familiarized with data science.

Dashboards and other analytical platforms will help you provide an easy-on-the-eye experience for all types of crowds and make the information shared much more interesting and relevant.

Now that you know what steps to take when implementing data science in your organization, start looking for the best partners to help you in this journey. Madiff is an Innovation and IT Consulting Company, leader in providing the most cost-efficient Remote Agile Teams for projects in Data Science, Cybersecurity and much more. Click here and get a quote for your project in 48 hours!

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