Why Data Science Matters and How To Form Your Own Team

With 11,501 stores across the globe, Walmart is known to be a powerhouse in retail. Starting out as a discount store in the United States in 1950, the department store chain has evolved with the decades and has managed to stay relevant. How did this happen?  In recent years, the company collects about 2.5 petabytes of data every hour and uses it to deliver a unique customer experience per store. During rush hours, some branches have difficulty managing long lines at counters. With the help of predictive analytics, there are branches that implement self-checkout to keep these lines short. As they analyse purchase patterns, they can also put out products that are higher in demand to prevent items from selling out. Once the inventory is dwindling, their inventory management tools allow them to restock in a timely manner. Data-driven strategies are not only applied in their physical stores. To make people's e-shopping experience better, Walmart also tracks individual customer preferences to provide product recommendations and discounts after a purchase.

Data Science Matters

As eventual digitisation is inevitable, companies like Walmart, Netflix and Mastodan C have welcomed change and have adapted and innovated. As data science and analytics continues to evolve, it’s not too late to become an early adapter. From SMEs and startups to enterprises and conglomerates, all businesses may benefit from monitoring, studying and analysing data. Here are five ways you can benefit from data science:

1. Make smarter decisions

You may have heard of or studied the scientific method when you were little. Once a problem is identified, you do some research and pose a hypothesis or an educated guess. Afterwards, you test that guess by conducting experiments. Once you are able to collect your observations, you can finally determine whether your hypothesis was correct or not. This method is still applied when making decisions for your business and is made more efficient with the help of data analytics. While Traditional Business Intelligence already uses different tools and methods to interpret existing data, adding data science allows companies to process large volumes of data and uncover trends. The elementary scientific method you have learned as a child is now equipped with high-tech, automated tools that can quantify data and record patterns. Because its insights augment your own understanding of the business, you can give evidence-based recommendations to your stakeholders.

2. Launch better products

Data science allows you to study market trends and customer behaviour to create a product that your target market will want to buy. By using tools to record queries on search engines and posts on social media, you will find out the problems and challenges the market face. Thus, you can be proactive and provide the answers they are looking for. This may also apply to new business models, app or website layouts and customer service processes that will benefit your market. Studying why your customers follow certain brands will help you create strategies that speak to them on a personal level. If you already have an existing product, analysing reviews on your product and on your competitors' will help you improve yours. As an example, Apple uses Big Data to analyse preferences and change their application designs to encourage Apple customers to download and use those apps.

3. Manage data more efficiently

As we have seen from Walmart, many companies are lying on a gold mine of data without even knowing it. To maximise this potential, artificial intelligence and machine learning tools unearth patterns that we might overlook and help us gain proper insights. Software like Python or R can support your data cleaning efforts and pointing out discrepancies in your raw files. With the help of these tools, you can analyse the health of your business and predict if your strategies will succeed.  Data science also helps you set up metrics for your business performance, helping you assess whether your business is on the right track. You won’t have to gather all the data you need for a report. Instead, you can set up tools to monitor all your online activity and compile them all on a dashboard.

4. Made data-driven predictions

Using statistics, you can segment your contacts, asses the risks your business wants to make, forecast sales and closed deals and analyse the market. By studying the trends, your business can prepare for an event that is yet to take place.

5. Assess decisions post-launch

Once a strategy is launched, data scientists can give daily, weekly, monthly or quarterly reports. This allows you to leverage campaigns that work and eliminate those that don't.

How to Form Your Own Team

Now that we know the benefits of data science, it's time to build the team that will help you clean, prepare and present your data. When looking for the person with the right data science skills, it's essential to know the core members of the team:

• Data Analyst: This person acts as a generalist and a bridge between the engineer and the scientist. Their main role is to gather and organise data based on the questions given to them. Based on your business' strategy, they will find important patterns and trends that can be turned into action items and insights. This is then communicated to stakeholders or customers. When looking for an analyst, you will need someone who knows how to use programming software and languages and wrangles with data and visualize them.
• Data Engineer: As the engineer, they are responsible for building and optimising the systems that your analyst and scientist will use. By maintaining the system, they make sure that the data is handled, received, transformed and stored properly. When looking for an engineer, someone who is knowledgeable in software development, various programming language, SWL databases and frameworks is preferred.

Data Scientist: Your data scientist will act as your researcher. They are responsible for finding, cleaning and presenting data with an understanding for the industry you are in and what processes your company uses. Just like the previous people, they will also need to be experts in programming. However, they also need strong foundations in statistics, mathematics, machine learning, SQL and Hadoop.