20 May 2019
Data Science and Analytics: Key Factors For a Good Data Strategy - TP&P Technology Vietnam


New technologies such as Artificial Intelligence and Machine Learning, as well as Data Science and Analytics are becoming key to competitive advantage - allowing businesses to innovate and compete in the new era of digital economy. Data is becoming more and more relevant and critical to success for companies across multiple industries – being able to successfully apply analytics and integrate new technologies across business will provide more business value and enhance productivity, helping businesses staying ahead of competition compared to their competitors who don’t.

However, even though Data Science and Analytics can provide many amazing benefits for businesses organizations, it’s still a challenge to successfully adopt and apply Data Science and Analytics across businesses. Without a good data strategy and competent team of data scientist to extract useful information and insights from data, businesses would waste valuable resources and the big data collected lose all element of its usefulness. Thus, it’s critical that businesses have a solid data strategy in place and have a team of skilled data scientists to analyze and manage data and find insights, discover useful intelligence for businesses.

In this article, we explore the importance of having a good data strategy and the important factors that businesses need to consider to fully take advantage of Data Science.

Why it’s critical to have a good data strategy?

In order to successfully apply data science and analytics for businesses, organizations clearly need to have the business data (and probably a lot in many cases) first and foremost. Thanks to digital transformation, in this day and age, data is being generated rapidly and at a volume that is bigger than ever. However, many businesses areas seen making the mistake of collecting as much data as possible, without taking into account what they will do with all that data.


So, before starting to collect the data needed, every business is required to start with a clear data strategy which helps them answer the question about what long-term goals business wants to achieve, and how data can help achieving such objectives. It doesn’t matter what kind of data business already collecting, or how they do it - without a good data strategy, businesses might waste their time and resources in collecting the “not suitable” kind of data for their needs.

Thus, if business organizations want to effectively and efficiently manage their data, they need to have a strategy in place to focus on the data required to accomplish their business goal. Here, the data must be able to address specific business challenges, help the organizations generated and add value, achieving long-term goals. This means organizations must clearly identify the key business challenges and/or questions that need solutions, and then collect and analyze the data for business insights that help address those challenges.

For the time being, many businesses still consider data science and analytics is simply an IT related matter. And this is not the case, especially when the in-house IT is more or less responsible for technical matters such as database storage and maintenance rather than analyzing and manipulating such data to reach businesses goals. Data science and analytics for businesses require buy-in and participations from everyone, including management and leadership teams. Otherwise, the goal of growing business with data-driven strategy won’t be feasible. Data science shouldn’t be applied within just a few specific areas of business such as IT, sales and marketing, etc. but should be adopted across businesses for long-term strategic goals.

Additionally, it’s worth noting that different businesses would require different type of data, so no one kind of data (e.g. text) is better than any other kind (e.g. pictures). Employing data science strategically should be more about collecting the best suitable kind of data for your organizations, which could be completely different compared to what’s best for other organizations. As mentioned previously, data is being generated at a rapid rate with gigantic volume on a daily basis, the top priority should be to focus on collecting the specific, right kinds of data that is required of your business.

Key factors to consider for a good Data Science and Analytics Strategy

In order to successfully create a data science and analytics strategy, businesses need to consider many factors that are critical, as follow

  • Specific data needs: To find the best suitable data for business, companies must clearly define how they are going to use the data. Certain businesses and projects would need specific kind of data, while others might require a different kind.
  • Data collecting method - after having identified what objectives your organizations want to achieve with data, the next step is to think about how to collect the best suitable data for those needs. Basically, there are several ways to source and collect data, including using the internal data that is already available in-house and putting in-place new methods of collecting - otherwise, businesses can also consider purchasing data from external source.
  • How to leverage data and derive insights out of those data - For a robust and successful data strategy, businesses need to plan on how they can apply data science and analytics to extract business insights out of those data which can help improve decision making, enhance operations and add business values.
  • IT infrastructure and technology requirements – Next, businesses need to decide on the hardware infrastructure and software including the necessary technologies needed to analyze data and turn them into insights.
  • Competencies of In-house team vs. Hiring Data Scientists from Data Science Consulting Company: In order to deliver or exceed the required rate of return on investment (ROI) for data analytics project, it is critical that businesses hire the right team of data scientists who possess all the necessary skills and talents required of the project. There are two main options for organizations to develop their competencies on data science by developing in-house or outsource the project (or in part) to an external data science consulting company. 
  • Data governance - As you may already know, collecting and storing data, especially personal data, involves serious legal and regulatory obligations. Thus, it’s important that any organization considers privacy laws and security standards as well as other requirements into their data strategy. Failing to properly taking care of these matters may result in potential liabilities with serious consequences in the future.


In the new digital economy era, it’s become easier than ever to have access to data that help businesses improve their decisions making, discover hidden useful information to improve overall operations. Those organizations that understand and treat data as an assets will be able to keep up with the market trends and thrive in the new environment. And in contrary, the ones who don’t might find themselves in trouble in the coming years. The increase in availability of methods for collecting and analyzing data, together with the use of AI and Machine Learning, will enable companies to develop their data and analytics strategy become successful in the new era of data-driven businesses.


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