Web2GoTech Always Strives to Meet Its Global B2B Initiatives

Data-Driven Approach

Web2GoTech Inc. data-driven approaches and decisions are made based on data coupled with intuition. Following a data-driven approach offers measurable advantages. That’s because a data-driven strategy uses facts, collaboration, creativities and information.

Data is a powerful tool. It empowers Web2GoTech B2B clients and customers to recognize trends, conduct analyses, make informed decisions, and set realistic goals. And yet, a company can have all of the data it needs to be successful and still fail if no one knows how to understand or leverage it.

Datasets can seem like an alien language to many in an organization outside of the analytics team. This is where data visualization comes into play. Using data visualization, professionals can take raw data and turn it into something easy to interpret.

BI refers to the process of extracting insights from data to make informed decisions regarding a business question or challenge. Across industries, organizations need skilled professionals who can make sense of the vast amounts of data they generate and put it to good use.

No matter your role, understanding how to collect, interpret, and communicate with data can be beneficial. Whether you aspire to work as a business analyst or wish to apply business analytics in a different position, developing your understanding of business analytics can empower you to make informed decisions and address your organization’s needs.

Data Collection

The first step in leveraging analytics to drive business decisions is to collect a data sample from which conclusions can be drawn. It’s important to understand the basics of data collection to avoid biased or faulty data.

In some cases, a dataset already exists, and it’s up to the business analyst to pull relevant information. For example, if you’re interested in discovering a retail store’s most profitable products, you might start by pulling historical sales data for transactions that took place over a specific period. Depending on an analytics team’s structure and sophistication, a data scientist may be responsible for cleaning or wrangling raw data.

Some business questions may require generating new data using several methods. A customer survey, for instance, can be a powerful means of collecting both quantitative and qualitative data. Care should be taken throughout this process to ensure the sample is representative and avoids bias. Simple mistakes, such as asking overly ambiguous or complex survey questions, can be detrimental to your data’s quality and lead to flawed decision-making.

Other forms of data collection include interviews, questionnaires, and focus groups.

Statistical Analysis

Once armed with a dataset that’s relevant to the business question you wish to address, you need analytical skills to make use of it. Being able to analyze and interpret data is the driving force behind making well-informed business decisions.

Several statistical methods can be helpful when it comes to analysis, including:

• Hypothesis testing, which is a statistical means of testing an assumption.
• Linear regression analysis, which can be used to evaluate the relationship between two variables.
• Multiple regression analysis, which is used to evaluate the relationship between three or more variables.

Through these forms of analysis, you can draw insights and conclusions that answer your business question or identify opportunities worth pursuing and risks in need of mitigation.

Communication

While insights derived from reliable data are key to making informed business decisions, it’s likely that other stakeholders need to be involved in the decision-making process. For this reason, effectively communicating your findings is essential.

Without strong communication skills, the value of your analyses can go unrealized. You need to use data and your findings as tools to convince others of a solution to the business question in focus. This can include oral communication and presentation skills, along with written communication in the form of reports.

Data Visualization

Data visualization goes hand in hand with strong communication, as it allows you to present findings in an easily digestible format for those who may not be as data literate as you are.

When communicating with stakeholders, it’s typically unnecessary to include every bit of data you’ve captured. Rather, it’s more effective to distill findings to key takeaways and present them in a manner that’s easy to understand. Data visualization allows you to do just that.

When developing your data visualization skills, there are two components to keep in mind: Knowing what visualization techniques are most effective for the type of data or business question you’re addressing and proficiency in the tools needed to bring those visualizations to life.

Making Informed Decisions

Data-driven decision-making (sometimes abbreviated as DDDM) is the process of using data to inform your decision-making process and validate a course of action before committing to it.

In business, this is seen in many forms. For example, a company might:

• Collect survey responses to identify products, services, and features their customers would like
• Conduct user testing to observe how customers are inclined to use their product or services and to identify potential issues that should be resolved prior to a full release
• Launch a new product or service in a test market in order to test the waters and understand how a product might perform in the market
• Analyze shifts in demographic data to determine business opportunities or threats

How exactly data can be incorporated into the decision-making process will depend on a number of factors, such as your business goals and the types and quality of data you have access to.

The collection and analysis of data have long played an important role in enterprise-level corporations and organizations. But as humanity generates more than 2.5 quintillion bytes of data each day, it’s never been easier for businesses of all sizes to collect, analyze, and interpret data into real, actionable insights. Though data-driven decision-making has existed in business in one form or another for centuries, it’s a truly modern phenomenon.